From b8d81897165452fab7a576dafc2c435d7cd35b14 Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Wed, 14 Aug 2019 14:31:49 +0930 Subject: [PATCH 01/30] Bug fix for truncating a directed walk. --- stellargraph/data/explorer.py | 45 ++----- stellargraph/mapper/link_mappers.py | 9 +- stellargraph/mapper/node_mappers.py | 7 -- tests/data/test_breadth_first_walker.py | 156 +++++++++++++++++++++--- 4 files changed, 153 insertions(+), 64 deletions(-) diff --git a/stellargraph/data/explorer.py b/stellargraph/data/explorer.py index 0830d2009..6d798275e 100644 --- a/stellargraph/data/explorer.py +++ b/stellargraph/data/explorer.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- # -# Copyright 2017-2018 Data61, CSIRO +# Copyright 2017-2019 Data61, CSIRO # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -18,8 +18,6 @@ "UniformRandomWalk", "BiasedRandomWalk", "UniformRandomMetaPathWalk", - "DepthFirstWalk", - "BreadthFirstWalk", "SampledBreadthFirstWalk", "SampledHeterogeneousBreadthFirstWalk", ] @@ -732,26 +730,6 @@ def _check_parameter_values( ) -class DepthFirstWalk(GraphWalk): - """ - Depth First Walk that generates all paths from a starting node to a given depth. - It can be used to extract, in a memory efficient way, a sub-graph starting from a node and up to a given depth. - """ - - # TODO: Implement the run method - pass - - -class BreadthFirstWalk(GraphWalk): - """ - Breadth First Walk that generates all paths from a starting node to a given depth. - It can be used to extract a sub-graph starting from a node and up to a given depth. - """ - - # TODO: Implement the run method - pass - - class SampledBreadthFirstWalk(GraphWalk): """ Breadth First Walk that generates a sampled number of paths from a starting node. @@ -777,7 +755,7 @@ def run(self, nodes=None, n=1, n_size=None, seed=None): self._check_parameter_values(nodes=nodes, n=n, n_size=n_size, seed=seed) walks = [] - d = len(n_size) # depth of search + max_hops = len(n_size) # depth of search if seed: # seed the random number generator @@ -791,24 +769,27 @@ def run(self, nodes=None, n=1, n_size=None, seed=None): q = list() # the queue of neighbours walk = list() # the list of nodes in the subgraph of node # extend() needs iterable as parameter; we use list of tuples (node id, depth) - q.extend([(node, 0)]) + q.append((node, 0)) while len(q) > 0: # remove the top element in the queue # index 0 pop the item from the front of the list - frontier = q.pop(0) - depth = frontier[1] + 1 # the depth of the neighbouring nodes - walk.extend([frontier[0]]) # add to the walk + cur_node, cur_depth = q.pop(0) + depth = cur_depth + 1 # the depth of the neighbouring nodes + walk.append(cur_node) # add to the walk # consider the subgraph up to and including depth d from root node - if depth <= d: - neighbours = self.neighbors(frontier[0]) + if depth <= max_hops: + neighbours = ( + self.neighbors(cur_node) if cur_node is not None else [] + ) if len(neighbours) == 0: - break + # Either node is unconnected or is in directed graph with no out-nodes. + neighbours = [None] * n_size[cur_depth] else: # sample with replacement neighbours = [ - rs.choice(neighbours) for _ in range(n_size[depth - 1]) + rs.choice(neighbours) for _ in range(n_size[cur_depth]) ] # add them to the back of the queue diff --git a/stellargraph/mapper/link_mappers.py b/stellargraph/mapper/link_mappers.py index 06f19b3fd..76a49ec6a 100644 --- a/stellargraph/mapper/link_mappers.py +++ b/stellargraph/mapper/link_mappers.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- # -# Copyright 2018 Data61, CSIRO +# Copyright 2018-2019 Data61, CSIRO # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -366,13 +366,6 @@ def get_levels(loc, lsize, samples_per_hop, walks): for hns in zip(*head_links): node_samples = self.sampler.run(nodes=hns, n=1, n_size=self.num_samples) - # Isolated nodes will return only themselves in the sample list - # let's correct for this by padding with None (the dummy node ID) - node_samples = [ - ns + [None] * num_full_samples if len(ns) == 1 else ns - for ns in node_samples - ] - nodes_per_hop = get_levels(0, 1, self.num_samples, node_samples) # Get features for the sampled nodes diff --git a/stellargraph/mapper/node_mappers.py b/stellargraph/mapper/node_mappers.py index 1e9518b9c..ed12e2370 100644 --- a/stellargraph/mapper/node_mappers.py +++ b/stellargraph/mapper/node_mappers.py @@ -254,13 +254,6 @@ def sample_features(self, head_nodes, sampling_schema): # The number of samples for each head node (not including itself) num_full_samples = np.sum(np.cumprod(self.num_samples)) - # Isolated nodes will return only themselves in the sample list - # let's correct for this by padding with None (the dummy node ID) - node_samples = [ - ns + [None] * num_full_samples if len(ns) == 1 else ns - for ns in node_samples - ] - # Reshape node samples to sensible format def get_levels(loc, lsize, samples_per_hop, walks): end_loc = loc + lsize diff --git a/tests/data/test_breadth_first_walker.py b/tests/data/test_breadth_first_walker.py index 4e345e61e..cf4ffc14a 100644 --- a/tests/data/test_breadth_first_walker.py +++ b/tests/data/test_breadth_first_walker.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- # -# Copyright 2017-2018 Data61, CSIRO +# Copyright 2017-2019 Data61, CSIRO # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -17,7 +17,7 @@ import pytest import networkx as nx from stellargraph.data.explorer import SampledBreadthFirstWalk -from stellargraph.core.graph import StellarGraph +from stellargraph.core.graph import StellarGraph, StellarDiGraph def create_test_graph(): @@ -25,7 +25,7 @@ def create_test_graph(): Creates a simple graph for testing the BreadthFirstWalk class. The node ids are string or integers. :return: A simple graph with 13 nodes and 24 edges (including self loops for all but two of the nodes) in - networkx format. + StellarGraph format. """ g = nx.Graph() edges = [ @@ -122,6 +122,15 @@ def test_parameter_checking(self): with pytest.raises(ValueError): bfw.run(nodes=nodes, n=n, n_size=n_size, seed="don't be random") + # If no neighbours are sampled, then just the start node should be returned, e.g.: + # subgraph = bfw.run(nodes=["0"], n=1, n_size=[]) + # assert len(subgraph) == 1 + # assert len(subgraph[0]) == 1 + # assert subgraph[0][0] == "0" + # However, by consensus this is an error: + with pytest.raises(ValueError): + bfw.run(nodes=["0"], n=1, n_size=[]) + # If no root nodes are given, an empty list is returned which is not an error but I thought this method # is the best for checking this behaviour. nodes = [] @@ -144,19 +153,25 @@ def test_walk_generation_single_root_node_loner(self): n_size = [1] subgraphs = bfw.run(nodes=nodes, n=n, n_size=n_size) assert len(subgraphs) == n - assert len(subgraphs[0]) == 1 # all elements should the same node + assert len(subgraphs[0]) == expected_bfw_size(n_size) # "loner" plus None assert subgraphs[0][0] == "loner" + assert subgraphs[0][1] is None n_size = [2, 2] subgraphs = bfw.run(nodes=nodes, n=n, n_size=n_size) assert len(subgraphs) == n - assert len(subgraphs[0]) == 1 # all elements should the same node + # "loner" plus 2 * None + 2 * 2 * None + assert len(subgraphs[0]) == expected_bfw_size(n_size) assert subgraphs[0][0] == "loner" + assert subgraphs[0][1] is None + assert subgraphs[0][2] is None + assert subgraphs[0][6] is None n_size = [3, 2] subgraphs = bfw.run(nodes=nodes, n=n, n_size=n_size) assert len(subgraphs) == n - assert len(subgraphs[0]) == 1 # all elements should the same node + # "loner" plus 3 * None + 3 * 2 * None + assert len(subgraphs[0]) == expected_bfw_size(n_size) assert subgraphs[0][0] == "loner" n = 3 @@ -172,7 +187,8 @@ def test_walk_generation_single_root_node_loner(self): subgraphs = bfw.run(nodes=nodes, n=n, n_size=n_size) assert len(subgraphs) == n for subgraph in subgraphs: - assert len(subgraph) == 1 # root node only + # "loner" plus None + assert len(subgraph) == expected_bfw_size(n_size) assert subgraph[0] == "loner" n = 99 @@ -180,7 +196,8 @@ def test_walk_generation_single_root_node_loner(self): subgraphs = bfw.run(nodes=nodes, n=n, n_size=n_size) assert len(subgraphs) == n for subgraph in subgraphs: - assert len(subgraph) == 1 # root node only + # "loner" plus 2 * None + 2 * 2 * None + assert len(subgraph) == expected_bfw_size(n_size) assert subgraph[0] == "loner" n = 17 @@ -188,9 +205,114 @@ def test_walk_generation_single_root_node_loner(self): subgraphs = bfw.run(nodes=nodes, n=n, n_size=n_size) assert len(subgraphs) == n for subgraph in subgraphs: - assert len(subgraph) == 1 # root node only + # "loner" plus 3 * None + 3 * 2 * None + assert len(subgraph) == expected_bfw_size(n_size) assert subgraph[0] == "loner" + def test_directed_walk_generation_single_root_node(self): + + g = nx.DiGraph() + edges = [ + ("root", 2), + ("root", 1), + ("root", "0"), + (2, "c2.1"), + (2, "c2.2"), + (1, "c1.1"), + ] + g.add_edges_from(edges) + g = StellarDiGraph(g) + + def _check_directed_walk(walk, n_size): + if len(n_size) > 1 and n_size[0] > 0 and n_size[1] > 0: + for child_pos in range(n_size[0]): + child = walk[child_pos + 1] + grandchildren_start = 1 + n_size[0] + child_pos * n_size[1] + grandchildren_end = grandchildren_start + n_size[1] + grandchildren = walk[grandchildren_start:grandchildren_end] + if child == "0": # node without children + for grandchild in grandchildren: + assert grandchild is None + elif child == 1: # node with single child + for grandchild in grandchildren: + assert grandchild == "c1.1" + elif child == 2: # node with two children + for grandchild in grandchildren: + assert grandchild == "c2.1" or grandchild == "c2.2" + else: + assert 1 == 0 + + bfw = SampledBreadthFirstWalk(g) + + nodes = ["root"] + n = 1 + n_size = [0] + + subgraphs = bfw.run(nodes=nodes, n=n, n_size=n_size) + assert len(subgraphs) == n + assert len(subgraphs[0]) == 1 # all elements should the same node + assert subgraphs[0][0] == "root" + + n_size = [1] + subgraphs = bfw.run(nodes=nodes, n=n, n_size=n_size) + assert len(subgraphs) == n + assert len(subgraphs[0]) == expected_bfw_size(n_size) # "root" plus child + assert subgraphs[0][0] == "root" + + n_size = [2, 2] + subgraphs = bfw.run(nodes=nodes, n=n, n_size=n_size) + assert len(subgraphs) == n + # "root" plus 2 * child + 2 * 2 * grandchild or None + assert len(subgraphs[0]) == expected_bfw_size(n_size) + assert subgraphs[0][0] == "root" + assert subgraphs[0][1] is not None + assert subgraphs[0][2] is not None + _check_directed_walk(subgraphs[0], n_size) + + n_size = [3, 2] + subgraphs = bfw.run(nodes=nodes, n=n, n_size=n_size) + assert len(subgraphs) == n + # "root" plus 3 * child + 3 * 2 * grandchild or None + assert len(subgraphs[0]) == expected_bfw_size(n_size) + assert subgraphs[0][0] == "root" + _check_directed_walk(subgraphs[0], n_size) + + n = 3 + n_size = [0] + + subgraphs = bfw.run(nodes=nodes, n=n, n_size=n_size) + assert len(subgraphs) == n + for subgraph in subgraphs: + assert len(subgraph) == 1 # root node only + assert subgraph[0] == "root" + + n_size = [1] + subgraphs = bfw.run(nodes=nodes, n=n, n_size=n_size) + assert len(subgraphs) == n + for subgraph in subgraphs: + # "root" plus child + assert len(subgraph) == expected_bfw_size(n_size) + assert subgraph[0] == "root" + + n = 99 + n_size = [2, 2] + subgraphs = bfw.run(nodes=nodes, n=n, n_size=n_size) + assert len(subgraphs) == n + for subgraph in subgraphs: + # "root" plus 2 * child + 2 * 2 * grandchild or None + assert len(subgraph) == expected_bfw_size(n_size) + assert subgraph[0] == "root" + _check_directed_walk(subgraph, n_size) + + n = 17 + n_size = [3, 2] + subgraphs = bfw.run(nodes=nodes, n=n, n_size=n_size) + assert len(subgraphs) == n + for subgraph in subgraphs: + # "root" plus 3 * child + 3 * 2 * grandchild or None + assert len(subgraph) == expected_bfw_size(n_size) + assert subgraph[0] == "root" + def test_walk_generation_single_root_node_self_loner(self): g = create_test_graph() bfw = SampledBreadthFirstWalk(g) @@ -201,25 +323,25 @@ def test_walk_generation_single_root_node_self_loner(self): n_size = [0] subgraphs = bfw.run(nodes=nodes, n=n, n_size=n_size) assert len(subgraphs[0]) == expected_bfw_size(n_size=n_size) - assert len(set(subgraphs[0])) == 1 # all elements should the same node + assert len(set(subgraphs[0])) == 1 # all elements should be same node assert nodes[0] in set(subgraphs[0]) n_size = [1] subgraphs = bfw.run(nodes=nodes, n=n, n_size=n_size) assert len(subgraphs[0]) == expected_bfw_size(n_size=n_size) - assert len(set(subgraphs[0])) == 1 # all elements should the same node + assert len(set(subgraphs[0])) == 1 # all elements should be same node assert nodes[0] in set(subgraphs[0]) n_size = [2, 2] subgraphs = bfw.run(nodes=nodes, n=n, n_size=n_size) assert len(subgraphs[0]) == expected_bfw_size(n_size=n_size) - assert len(set(subgraphs[0])) == 1 # all elements should the same node + assert len(set(subgraphs[0])) == 1 # all elements should be same node assert nodes[0] in set(subgraphs[0]) n_size = [3, 2] subgraphs = bfw.run(nodes=nodes, n=n, n_size=n_size) assert len(subgraphs[0]) == expected_bfw_size(n_size=n_size) - assert len(set(subgraphs[0])) == 1 # all elements should the same node + assert len(set(subgraphs[0])) == 1 # all elements should be same node assert nodes[0] in set(subgraphs[0]) n = 3 @@ -227,21 +349,21 @@ def test_walk_generation_single_root_node_self_loner(self): subgraphs = bfw.run(nodes=nodes, n=n, n_size=n_size) assert len(subgraphs) == n * len(nodes) assert len(subgraphs[0]) == expected_bfw_size(n_size=n_size) - assert len(set(subgraphs[0])) == 1 # all elements should the same node + assert len(set(subgraphs[0])) == 1 # all elements should be same node assert nodes[0] in set(subgraphs[0]) n_size = [1] subgraphs = bfw.run(nodes=nodes, n=n, n_size=n_size) assert len(subgraphs) == n * len(nodes) assert len(subgraphs[0]) == expected_bfw_size(n_size=n_size) - assert len(set(subgraphs[0])) == 1 # all elements should the same node + assert len(set(subgraphs[0])) == 1 # all elements should be same node assert nodes[0] in set(subgraphs[0]) n_size = [2, 2] subgraphs = bfw.run(nodes=nodes, n=n, n_size=n_size) assert len(subgraphs) == n * len(nodes) assert len(subgraphs[0]) == expected_bfw_size(n_size=n_size) - assert len(set(subgraphs[0])) == 1 # all elements should the same node + assert len(set(subgraphs[0])) == 1 # all elements should be same node assert nodes[0] in set(subgraphs[0]) n_size = [3, 2] @@ -469,7 +591,7 @@ def test_fixed_random_seed(self): assert len(w0) == len(w1) assert w0 == w1 - def test_benchmark_sampledbreadthfirstwalk(self, benchmark): + def test_benchmark_bfs_walk(self, benchmark): g = create_test_graph() bfw = SampledBreadthFirstWalk(g) From 307cc9efb28e86bca8ad7357ae9c051d628b8000 Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Wed, 14 Aug 2019 15:02:47 +0930 Subject: [PATCH 02/30] Attempt to fix build issue about deep nesting. --- stellargraph/data/explorer.py | 33 +++++++++++++++++---------------- 1 file changed, 17 insertions(+), 16 deletions(-) diff --git a/stellargraph/data/explorer.py b/stellargraph/data/explorer.py index 6d798275e..414ca7a8f 100644 --- a/stellargraph/data/explorer.py +++ b/stellargraph/data/explorer.py @@ -778,22 +778,23 @@ def run(self, nodes=None, n=1, n_size=None, seed=None): depth = cur_depth + 1 # the depth of the neighbouring nodes walk.append(cur_node) # add to the walk - # consider the subgraph up to and including depth d from root node - if depth <= max_hops: - neighbours = ( - self.neighbors(cur_node) if cur_node is not None else [] - ) - if len(neighbours) == 0: - # Either node is unconnected or is in directed graph with no out-nodes. - neighbours = [None] * n_size[cur_depth] - else: - # sample with replacement - neighbours = [ - rs.choice(neighbours) for _ in range(n_size[cur_depth]) - ] - - # add them to the back of the queue - q.extend([(sampled_node, depth) for sampled_node in neighbours]) + # consider the subgraph up to and including max_hops from root node + if depth > max_hops: + continue + neighbours = ( + self.neighbors(cur_node) if cur_node is not None else [] + ) + if len(neighbours) == 0: + # Either node is unconnected or is in directed graph with no out-nodes. + neighbours = [None] * n_size[cur_depth] + else: + # sample with replacement + neighbours = [ + rs.choice(neighbours) for _ in range(n_size[cur_depth]) + ] + + # add them to the back of the queue + q.extend([(sampled_node, depth) for sampled_node in neighbours]) # finished i-th walk from node so add it to the list of walks as a list walks.append(walk) From abb6246f895046ebcb304b222031f48b8aa2aeef Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Wed, 14 Aug 2019 15:21:20 +0930 Subject: [PATCH 03/30] Attempt to fix build issue about assert. --- stellargraph/data/explorer.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/stellargraph/data/explorer.py b/stellargraph/data/explorer.py index 414ca7a8f..40f507f46 100644 --- a/stellargraph/data/explorer.py +++ b/stellargraph/data/explorer.py @@ -243,7 +243,8 @@ def naive_weighted_choices(rs, weights): subinterval_ends = [] running_total = 0 for w in weights: - assert w >= 0 + if w < 0: + raise ValueError("Detected negative weight: {}".format(w)) running_total += w subinterval_ends.append(running_total) From 031f66bbe7bb3802948c9006a769918e740ef135 Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Wed, 14 Aug 2019 16:01:51 +0930 Subject: [PATCH 04/30] Add actual method and tests - see if complexity measures break. --- stellargraph/data/explorer.py | 206 ++++++++++++ .../test_directed_breadth_first_sampler.py | 300 ++++++++++++++++++ 2 files changed, 506 insertions(+) create mode 100644 tests/data/test_directed_breadth_first_sampler.py diff --git a/stellargraph/data/explorer.py b/stellargraph/data/explorer.py index 40f507f46..736f3dcc8 100644 --- a/stellargraph/data/explorer.py +++ b/stellargraph/data/explorer.py @@ -105,6 +105,93 @@ def run(self, **kwargs): """ raise NotImplementedError + def _raise_error(self, msg): + full = "({}) " + msg + raise ValueError(full.format(type(self).__name__)) + + def _check_parameter_values(self, nodes=None, n=0, length=0, seed=None): + """ + Checks that the parameter values are valid or raises ValueError exceptions with a message indicating the + parameter (the first one encountered in the checks) with invalid value. + + Args: + nodes: A list of root node ids such that from each node a uniform random walk of up to length l + will be generated. + n: Number of walks per node id. + length: Maximum length of walk measured as the number of edges followed from root node. + seed: Random number generator seed + Returns: + The random state as determined by the seed. + """ + self._check_nodes(nodes) + self._check_repetitions(n) + self._check_length(length) + return self._check_seed(seed) + + def _check_nodes(self, nodes): + if nodes is None: + self._raise_error("A list of root node IDs was not provided.") + if not is_real_iterable(nodes): + self._raise_error("Nodes parameter should be an iterable of node IDs.") + if ( + len(nodes) == 0 + ): # this is not an error but maybe a warning should be printed to inform the caller + print( + "({}) WARNING: No root node IDs given. An empty list will be returned as a result.".format( + type(self).__name__ + ) + ) + + def _check_repetitions(self, n): + if type(n) != int: + self._raise_error( + "The number of walks per root node, n, should be integer type." + ) + if n <= 0: + self._raise_error( + "The number of walks per root node, n, should be a positive integer." + ) + + def _check_length(self, length): + if type(length) != int: + self._raise_error("The walk length, length, should be integer type.") + if length <= 0: + # Technically, length 0 should be okay, but by consensus is invalid. + self._raise_error("The walk length, length, should be a positive integer.") + + def _check_seed(self, seed): + if seed is None: + # Restore the random state + return self._random_state + if type(seed) != int: + self._raise_error( + "The random number generator seed value, seed, should be integer type or None." + ) + if seed < 0: + self._raise_error( + "The random number generator seed value, seed, should be non-negative integer or None." + ) + # seed the random number generator + return random.Random(seed) + + # For neighbourhood sampling + def _check_sizes(self, n_size): + err_msg = "The neighbourhood size must be a list of non-negative integers." + if n_size is None: + self._raise_error(err_msg) + if type(n_size) != list: + self._raise_error(err_msg) + + if len(n_size) == 0: + # Technically, length 0 should be okay, but by consensus is invalid. + self._raise_error("The neighbourhood size should not be empty.") + + for d in n_size: + if type(d) != int: + self._raise_error(err_msg) + if d < 0: + self._raise_error(err_msg) + class UniformRandomWalk(GraphWalk): """ @@ -1094,3 +1181,122 @@ def _check_parameter_values(self, nodes, n, n_size, graph_schema, seed): type(self).__name__ ) ) + +class DirectedBreadthFirstNeighbours(GraphWalk): + """ + Breadth First sampler that generates the composite of a number of sampled paths from a starting node. + It can be used to extract a random sub-graph starting from a set of initial nodes. + """ + + def __init__(self, graph, graph_schema=None, seed=None): + super().__init__(graph, graph_schema, seed) + if not graph.is_directed(): + self._raise_error("Graph must be directed") + + def run(self, nodes=None, n=1, in_size=None, out_size=None, seed=None): + """ + Performs a sampled breadth-first walk starting from the root nodes. + + Args: + nodes: A list of root node ids such that from each node n BFWs will be generated up to the + given depth d. + n: Number of walks per node id. + in_size: The number of in-directed nodes to sample with replacement at each depth of the walk. + out_size: The number of out-directed nodes to sample with replacement at each depth of the walk. + seed: Random number generator seed; default is None + + + Returns: + A list of multi-hop neighbourhood samples. Each sample expresses multiple undirected walks, but the in-node + neighbours and out-node neighbours are sampled separately. Each sample has the format: + + [[node] + [in_1...in_n] [out_1...out_m] + [in_1.in_1...in_n.in_p] [in_1.out_1...in_n.out_q] + [out_1.in_1...out_m.in_p] [out_1.out_1...out_m.out_q] + [in_1.in_1.in_1...in_n.in_p.in_r] [in_1.in_1.out_1...in_n.in_p.out_s] ... + ...] + + where a single, undirected walk might be, for example: + + [node out_i out_i.in_j out_i.in_j.in_k ...] + """ + rs = self._check_parameter_values(nodes, n, in_size, out_size, seed) + max_hops = len(in_size) + max_slots = 2 ** (max_hops + 1) - 1 + + samples = [] + + for node in nodes: # iterate over root nodes + for _ in range(n): # do n bounded breadth first walks from each root node + q = list() # the queue of neighbours + # the list of sampled node-lists: + sample = [[] for _ in range(max_slots)] + # Add node to queue as (node, depth, slot) + q.append((node, 0, 0)) + + while len(q) > 0: + # remove the top element in the queue + # index 0 pop the item from the front of the list + cur_node, cur_depth, cur_slot = q.pop(0) + sample[cur_slot].append(cur_node) # add to the walk + depth = cur_depth + 1 # the depth of the neighbouring nodes + + # consider the subgraph up to and including depth d from root node + if depth <= max_hops: + # get in-nodes + neighbours = self._sample_neighbours( + rs, cur_node, 0, in_size[cur_depth] + ) + # add them to the back of the queue + slot = 2 * cur_slot + 1 + q.extend( + [(sampled_node, depth, slot) for sampled_node in neighbours] + ) + # get out-nodes + neighbours = self._sample_neighbours( + rs, cur_node, 1, out_size[cur_depth] + ) + # add them to the back of the queue + slot = slot + 1 + q.extend( + [(sampled_node, depth, slot) for sampled_node in neighbours] + ) + + # finished multi-hop neighbourhood sampling + samples.append(sample) + + return samples + + def _sample_neighbours(self, rs, node, idx, size): + fn = self.graph.in_edges if idx == 0 else self.graph.out_edges + neighbours = [n[idx] for n in list(fn(node))] if node is not None else [] + if len(neighbours) == 0: + # walk has ended abruptly + return [None] * size + else: + # sample with replacement + return [rs.choice(neighbours) for _ in range(size)] + + def _check_parameter_values(self, nodes, n, in_size, out_size, seed): + """ + Checks that the parameter values are valid or raises ValueError exceptions with a message indicating the + parameter (the first one encountered in the checks) with invalid value. + + Args: + nodes: A list of root node ids such that from each node n BFWs will be generated up to the + given depth d. + n: Number of walks per node id. + n_size: The number of neighbouring nodes to expand at each depth of the walk. + seed: Random number generator seed; default is None + + Returns: + The random state as determined by the seed. + """ + self._check_sizes(in_size) + self._check_sizes(out_size) + if len(in_size) != len(out_size): + self._raise_error( + "The number of hops for the in and out neighbourhoods must be the same." + ) + return super()._check_parameter_values(nodes, n, len(in_size), seed) diff --git a/tests/data/test_directed_breadth_first_sampler.py b/tests/data/test_directed_breadth_first_sampler.py new file mode 100644 index 000000000..ad901324e --- /dev/null +++ b/tests/data/test_directed_breadth_first_sampler.py @@ -0,0 +1,300 @@ +# -*- coding: utf-8 -*- +# +# Copyright 2017-2019 Data61, CSIRO +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import random +import pytest +import networkx as nx +from stellargraph.data.explorer import DirectedBreadthFirstNeighbours +from stellargraph.core.graph import StellarGraph, StellarDiGraph + + +def create_simple_graph(): + """ + Creates a simple directed graph for testing. The node ids are string or integers. + + :return: A small, directed graph with 4 nodes and 6 edges in StellarDiGraph format. + """ + + g = nx.DiGraph() + edges = [ + ("root", 2), + ("root", 1), + ("root", "0"), + (2, "c2.1"), + (2, "c2.2"), + (1, "c1.1"), + ] + g.add_edges_from(edges) + return StellarDiGraph(g) + + +def create_test_graph(): + """ + Creates a simple graph for testing. The node ids are string or integers. + + :return: A simple graph with 13 nodes and 24 edges (including self loops for all but two of the nodes) in + StellarDiGraph format. + """ + g = nx.DiGraph() + edges = [ + ("0", 1), + ("0", 2), + (1, 3), + (1, 4), + (3, 6), + (4, 7), + (4, 8), + (2, 5), + (5, 9), + (5, 10), + ("0", "0"), + (1, 1), + (3, 3), + (6, 6), + (4, 4), + (7, 7), + (8, 8), + (2, 2), + (5, 5), + (9, 9), + ( + "self loner", + "self loner", + ), # node that is not connected with any other nodes but has self loop + ] + + g.add_edges_from(edges) + g.add_node( + "loner" + ) # node that is not connected to any other nodes and not having a self loop + + return StellarDiGraph(g) + + +class TestDirectedBreadthFirstNeighbours(object): + def test_parameter_checking(self): + g = create_test_graph() + bfw = DirectedBreadthFirstNeighbours(g) + + nodes = ["0", 1] + n = 1 + in_size = [1] + out_size = [1] + + with pytest.raises(ValueError): + # nodes should be a list of node ids even for a single node + bfw.run(nodes=None, n=n, in_size=in_size, out_size=out_size) + with pytest.raises(ValueError): + bfw.run(nodes=0, n=n, in_size=in_size, out_size=out_size) + + # n has to be positive integer + with pytest.raises(ValueError): + bfw.run(nodes=nodes, n=-1, in_size=in_size, out_size=out_size) + with pytest.raises(ValueError): + bfw.run(nodes=nodes, n=10.1, in_size=in_size, out_size=out_size) + with pytest.raises(ValueError): + bfw.run(nodes=nodes, n=0, in_size=in_size, out_size=out_size) + + # sizes have to be list of non-negative integers + with pytest.raises(ValueError): + bfw.run(nodes=nodes, n=n, in_size=0, out_size=out_size) + with pytest.raises(ValueError): + bfw.run(nodes=nodes, n=n, in_size=in_size, out_size=[-5]) + with pytest.raises(ValueError): + bfw.run(nodes=nodes, n=-1, in_size=[2.4], out_size=out_size) + with pytest.raises(ValueError): + bfw.run(nodes=nodes, n=n, in_size=in_size, out_size=(1, 2)) + + # sizes have to have same length + with pytest.raises(ValueError): + bfw.run(nodes=nodes, n=n, in_size=[1, 2], out_size=[3]) + + # okay to have zero sized samples + with pytest.raises(ValueError): + bfw.run(nodes=nodes, n=n, in_size=in_size, out_size=[0, 0]) + with pytest.raises(ValueError): + bfw.run(nodes=nodes, n=n, in_size=in_size, out_size=[1, 0]) + with pytest.raises(ValueError): + bfw.run(nodes=nodes, n=n, in_size=in_size, out_size=[0, 5]) + with pytest.raises(ValueError): + bfw.run(nodes=nodes, n=n, in_size=[0, 0], out_size=out_size) + with pytest.raises(ValueError): + bfw.run(nodes=nodes, n=n, in_size=[1, 0], out_size=out_size) + with pytest.raises(ValueError): + bfw.run(nodes=nodes, n=n, in_size=[0, 5], out_size=out_size) + # Is it okay if a zero appears in the same place for both in and out sizes? For now, yes. + subgraph = bfw.run(nodes=nodes, n=n, in_size=[5, 0], out_size=[1, 0]) + assert len(subgraph) == len(nodes) + + # seed must be positive integer or 0 + with pytest.raises(ValueError): + bfw.run(nodes=nodes, n=n, in_size=in_size, out_size=out_size, seed=-1235) + with pytest.raises(ValueError): + bfw.run( + nodes=nodes, n=n, in_size=in_size, out_size=out_size, seed=10.987665 + ) + with pytest.raises(ValueError): + bfw.run( + nodes=nodes, n=n, in_size=in_size, out_size=out_size, seed=-982.4746 + ) + with pytest.raises(ValueError): + bfw.run( + nodes=nodes, + n=n, + in_size=in_size, + out_size=out_size, + seed="don't be random", + ) + + # If no root nodes are given, an empty list is returned, which is not an error. + nodes = [] + subgraph = bfw.run(nodes=nodes, n=n, in_size=in_size, out_size=out_size) + assert len(subgraph) == 0 + + def test_zero_hops(self): + g = create_simple_graph() + bfw = DirectedBreadthFirstNeighbours(g) + # By consensus, a zero-length walk raises an error. + with pytest.raises(ValueError): + subgraph = bfw.run(nodes=["root"], n=1, in_size=[], out_size=[]) + # Otherwise, the case should be [[["root"]]]. + # assert len(subgraph) == 1 + # assert len(subgraph[0]) == 1 + + def test_one_hop(self): + g = create_simple_graph() + bfw = DirectedBreadthFirstNeighbours(g) + # - The following case should be [[["root"], [None], [child]]] + subgraph = bfw.run(nodes=["root"], n=1, in_size=[1], out_size=[1]) + assert len(subgraph) == 1 + assert len(subgraph[0]) == 3 + assert len(subgraph[0][0]) == 1 + assert subgraph[0][0][0] == "root" + assert len(subgraph[0][1]) == 1 + assert subgraph[0][1][0] is None + assert len(subgraph[0][2]) == 1 + assert subgraph[0][2][0] in ["0", 1, 2] + # - The following case should be [[["root"], [None], []]] + subgraph = bfw.run(nodes=["root"], n=1, in_size=[1], out_size=[0]) + assert len(subgraph) == 1 + assert len(subgraph[0]) == 3 + assert len(subgraph[0][2]) == 0 + # - The following case should be [[["root"], [], []]] + subgraph = bfw.run(nodes=["root"], n=1, in_size=[0], out_size=[0]) + assert len(subgraph) == 1 + assert len(subgraph[0]) == 3 + assert len(subgraph[0][1]) == 0 + # - The following case should be [[["root"], [None, None, None], [child, child, child, child]]] + subgraph = bfw.run(nodes=["root"], n=1, in_size=[3], out_size=[4]) + assert len(subgraph) == 1 + assert len(subgraph[0]) == 3 + assert len(subgraph[0][1]) == 3 + for child in subgraph[0][1]: + assert child is None + assert len(subgraph[0][2]) == 4 + for child in subgraph[0][2]: + assert child in ["0", 1, 2] + + def test_two_hops(self): + g = create_simple_graph() + bfw = DirectedBreadthFirstNeighbours(g) + # - The following case should be [[["root"], [None], [child*2], [None], [None*2], ["root"*2], [grandchild*4]]] + in_size = [1, 1] + out_size = [2, 2] + subgraph = bfw.run(nodes=["root"], n=1, in_size=in_size, out_size=out_size) + assert len(subgraph) == 1 + assert len(subgraph[0]) == 7 + assert len(subgraph[0][1]) == in_size[0] + assert subgraph[0][1][0] is None + assert len(subgraph[0][2]) == out_size[0] + for child in subgraph[0][2]: + assert child in ["0", 1, 2] + assert len(subgraph[0][3]) == in_size[0] * in_size[1] + assert subgraph[0][3][0] is None + assert len(subgraph[0][4]) == in_size[0] * out_size[1] + for child in subgraph[0][4]: + assert child is None + assert len(subgraph[0][5]) == out_size[0] * in_size[1] + for parent in subgraph[0][5]: + assert parent == "root" + assert len(subgraph[0][6]) == out_size[0] * out_size[1] + for grandchild in subgraph[0][6]: + assert grandchild in [None, "c1.1", "c2.1", "c2.2"] + for idx, child in enumerate(subgraph[0][2]): + grandchildren = subgraph[0][6][(2 * idx) : (2 * idx + 2)] + if child == "0": + for grandchild in grandchildren: + assert grandchild is None + elif child == 1: + for grandchild in grandchildren: + assert grandchild == "c1.1" + else: # child == 2 + for grandchild in grandchildren: + assert grandchild in ["c2.1", "c2.2"] + # - Check structure size for multiple start nodes + # - For each start node, should be [[[node], [in], [out], [in.in], [in.out], [out.in], [out.out]]] + nodes = list(g.nodes()) + in_size = [2, 3] + out_size = [4, 5] + subgraph = bfw.run(nodes=nodes, n=1, in_size=in_size, out_size=out_size) + assert len(subgraph) == len(nodes) + for node_graph in subgraph: + assert len(node_graph) == 7 + assert len(node_graph[0]) == 1 # 1 start node + assert len(node_graph[1]) == in_size[0] + assert len(node_graph[2]) == out_size[0] + assert len(node_graph[3]) == in_size[0] * in_size[1] + assert len(node_graph[4]) == in_size[0] * out_size[1] + assert len(node_graph[5]) == out_size[0] * in_size[1] + assert len(node_graph[6]) == out_size[0] * out_size[1] + + def test_three_hops(self): + g = create_test_graph() + bfw = DirectedBreadthFirstNeighbours(g) + graph_nodes = list(g.nodes()) + for _ in range(50): + node = random.choice(graph_nodes) + in_size = [random.randint(0, 2) for _ in range(3)] + out_size = [random.randint(0, 2) for _ in range(3)] + subgraph = bfw.run(nodes=[node], n=1, in_size=in_size, out_size=out_size) + assert len(subgraph) == 1 + assert len(subgraph[0]) == 15 # 2**(num_hops+1) - 1 = 15 + assert len(subgraph[0][0]) == 1 + assert len(subgraph[0][1]) == in_size[0] + assert len(subgraph[0][2]) == out_size[0] + assert len(subgraph[0][3]) == in_size[0] * in_size[1] + assert len(subgraph[0][4]) == in_size[0] * out_size[1] + assert len(subgraph[0][5]) == out_size[0] * in_size[1] + assert len(subgraph[0][6]) == out_size[0] * out_size[1] + assert len(subgraph[0][7]) == in_size[0] * in_size[1] * in_size[2] + assert len(subgraph[0][8]) == in_size[0] * in_size[1] * out_size[2] + assert len(subgraph[0][9]) == in_size[0] * out_size[1] * in_size[2] + assert len(subgraph[0][10]) == in_size[0] * out_size[1] * out_size[2] + assert len(subgraph[0][11]) == out_size[0] * in_size[1] * in_size[2] + assert len(subgraph[0][12]) == out_size[0] * in_size[1] * out_size[2] + assert len(subgraph[0][13]) == out_size[0] * out_size[1] * in_size[2] + assert len(subgraph[0][14]) == out_size[0] * out_size[1] * out_size[2] + + def test_benchmark_bfs_walk(self, benchmark): + g = create_test_graph() + bfw = DirectedBreadthFirstNeighbours(g) + + nodes = ["0"] + n = 5 + in_size = [5, 5] + out_size = [5, 5] + + benchmark(lambda: bfw.run(nodes=nodes, n=n, in_size=in_size, out_size=out_size)) From b03461f9c5b0d0b1b93d0b1fb90d6953eafc6c88 Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Wed, 14 Aug 2019 16:18:31 +0930 Subject: [PATCH 05/30] Attempt to fix complexity issues - these are always too strict. --- stellargraph/data/explorer.py | 65 +++++++++++++++++------------------ 1 file changed, 31 insertions(+), 34 deletions(-) diff --git a/stellargraph/data/explorer.py b/stellargraph/data/explorer.py index 736f3dcc8..b9ca7b5e2 100644 --- a/stellargraph/data/explorer.py +++ b/stellargraph/data/explorer.py @@ -177,19 +177,13 @@ def _check_seed(self, seed): # For neighbourhood sampling def _check_sizes(self, n_size): err_msg = "The neighbourhood size must be a list of non-negative integers." - if n_size is None: - self._raise_error(err_msg) - if type(n_size) != list: + if n_size is None or type(n_size) != list: self._raise_error(err_msg) - if len(n_size) == 0: - # Technically, length 0 should be okay, but by consensus is invalid. + # Technically, length 0 should be okay, but by consensus it is invalid. self._raise_error("The neighbourhood size should not be empty.") - for d in n_size: - if type(d) != int: - self._raise_error(err_msg) - if d < 0: + if type(d) != int or d < 0: self._raise_error(err_msg) @@ -1242,26 +1236,27 @@ def run(self, nodes=None, n=1, in_size=None, out_size=None, seed=None): sample[cur_slot].append(cur_node) # add to the walk depth = cur_depth + 1 # the depth of the neighbouring nodes - # consider the subgraph up to and including depth d from root node - if depth <= max_hops: - # get in-nodes - neighbours = self._sample_neighbours( - rs, cur_node, 0, in_size[cur_depth] - ) - # add them to the back of the queue - slot = 2 * cur_slot + 1 - q.extend( - [(sampled_node, depth, slot) for sampled_node in neighbours] - ) - # get out-nodes - neighbours = self._sample_neighbours( - rs, cur_node, 1, out_size[cur_depth] - ) - # add them to the back of the queue - slot = slot + 1 - q.extend( - [(sampled_node, depth, slot) for sampled_node in neighbours] - ) + # consider the subgraph up to and including max_hops from root node + if depth > max_hops: + continue + # get in-nodes + neighbours = self._sample_neighbours( + rs, cur_node, 0, in_size[cur_depth] + ) + # add them to the back of the queue + slot = 2 * cur_slot + 1 + q.extend( + [(sampled_node, depth, slot) for sampled_node in neighbours] + ) + # get out-nodes + neighbours = self._sample_neighbours( + rs, cur_node, 1, out_size[cur_depth] + ) + # add them to the back of the queue + slot = slot + 1 + q.extend( + [(sampled_node, depth, slot) for sampled_node in neighbours] + ) # finished multi-hop neighbourhood sampling samples.append(sample) @@ -1269,14 +1264,16 @@ def run(self, nodes=None, n=1, in_size=None, out_size=None, seed=None): return samples def _sample_neighbours(self, rs, node, idx, size): + if node is None: + # Non-node, e.g. previously sampled from empty neighbourhood + return [None] * size fn = self.graph.in_edges if idx == 0 else self.graph.out_edges - neighbours = [n[idx] for n in list(fn(node))] if node is not None else [] + neighbours = [n[idx] for n in list(fn(node))] if len(neighbours) == 0: - # walk has ended abruptly + # Sampling from empty neighbourhood return [None] * size - else: - # sample with replacement - return [rs.choice(neighbours) for _ in range(size)] + # Sample with replacement + return [rs.choice(neighbours) for _ in range(size)] def _check_parameter_values(self, nodes, n, in_size, out_size, seed): """ From a701117d623f40e61ed88428e7e0102502b48041 Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Thu, 15 Aug 2019 09:10:51 +0930 Subject: [PATCH 06/30] Missed one black check - fixed. --- stellargraph/data/explorer.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/stellargraph/data/explorer.py b/stellargraph/data/explorer.py index b9ca7b5e2..72c356d20 100644 --- a/stellargraph/data/explorer.py +++ b/stellargraph/data/explorer.py @@ -134,7 +134,7 @@ def _check_nodes(self, nodes): if not is_real_iterable(nodes): self._raise_error("Nodes parameter should be an iterable of node IDs.") if ( - len(nodes) == 0 + len(nodes) == 0 ): # this is not an error but maybe a warning should be printed to inform the caller print( "({}) WARNING: No root node IDs given. An empty list will be returned as a result.".format( @@ -1176,6 +1176,7 @@ def _check_parameter_values(self, nodes, n, n_size, graph_schema, seed): ) ) + class DirectedBreadthFirstNeighbours(GraphWalk): """ Breadth First sampler that generates the composite of a number of sampled paths from a starting node. From 256b233eb88aa2fe3d87b55923b4692abdd81f7c Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Thu, 15 Aug 2019 10:07:10 +0930 Subject: [PATCH 07/30] Fixed code smell (again) due to repeated code. --- stellargraph/data/explorer.py | 564 +++++----------------------------- 1 file changed, 78 insertions(+), 486 deletions(-) diff --git a/stellargraph/data/explorer.py b/stellargraph/data/explorer.py index 72c356d20..a45bcaaf2 100644 --- a/stellargraph/data/explorer.py +++ b/stellargraph/data/explorer.py @@ -49,42 +49,18 @@ def __init__(self, graph, graph_schema=None, seed=None): # We require a StellarGraph for this if not isinstance(graph, StellarGraphBase): - raise TypeError( - "Graph must be a StellarGraph or StellarDiGraph to use heterogeneous sampling." - ) + raise TypeError("Graph must be a StellarGraph or StellarDiGraph.") if not graph_schema: self.graph_schema = self.graph.create_graph_schema(create_type_maps=True) else: self.graph_schema = graph_schema - if self.graph_schema is not None and type(self.graph_schema) is not GraphSchema: - raise ValueError( - "({}) The parameter graph_schema should be either None or of type GraphSchema.".format( - type(self).__name__ - ) + if type(self.graph_schema) is not GraphSchema: + self._raise_error( + "The parameter graph_schema should be either None or of type GraphSchema." ) - # Create a dict of adjacency lists per edge type, for faster neighbour sampling from graph in SampledHeteroBFS: - # TODO: this could be better placed inside StellarGraph class - edge_types = self.graph_schema.edge_types - self.adj = dict() - for et in edge_types: - self.adj.update({et: defaultdict(lambda: [None])}) - - for n1, nbrdict in graph.adjacency(): - for et in edge_types: - neigh_et = [ - n2 - for n2, nkeys in nbrdict.items() - for k in iter(nkeys) - if self.graph_schema.is_of_edge_type((n1, n2, k), et) - ] - # Create adjacency list in lexographical order - # Otherwise sampling methods will not be deterministic - # even when the seed is set. - self.adj[et][n1] = sorted(neigh_et, key=str) - def neighbors(self, node): if node not in self.graph: print("node {} not in graph".format(node)) @@ -187,6 +163,33 @@ def _check_sizes(self, n_size): self._raise_error(err_msg) +class HeterogeneousGraphWalk(GraphWalk): + """ + Base class for exploring graphs with heterogeneous edge types. + """ + + def __init__(self, graph, graph_schema=None, seed=None): + super().__init__(graph, graph_schema, seed) + + # Create a dict of adjacency lists per edge type, for faster neighbour sampling from graph in SampledHeteroBFS: + # TODO: this could be better placed inside StellarGraph class + edge_types = self.graph_schema.edge_types + self.adj = {et: defaultdict(lambda: [None]) for et in edge_types} + + for n1, nbrdict in graph.adjacency(): + for et in edge_types: + neigh_et = [ + n2 + for n2, nkeys in nbrdict.items() + for k in iter(nkeys) + if self.graph_schema.is_of_edge_type((n1, n2, k), et) + ] + # Create adjacency list in lexographical order + # Otherwise sampling methods will not be deterministic + # even when the seed is set. + self.adj[et][n1] = sorted(neigh_et, key=str) + + class UniformRandomWalk(GraphWalk): """ Performs uniform random walks on the given graph @@ -206,14 +209,7 @@ def run(self, nodes=None, n=None, length=None, seed=None): List of lists of nodes ids for each of the random walks """ - self._check_parameter_values(nodes=nodes, n=n, length=length, seed=seed) - - if seed: - # seed the random number generator - rs = random.Random(seed) - else: - # Restore the random state - rs = self._random_state + rs = self._check_parameter_values(nodes, n, length, seed) walks = [] for node in nodes: # iterate over root nodes @@ -235,76 +231,6 @@ def run(self, nodes=None, n=None, length=None, seed=None): return walks - def _check_parameter_values(self, nodes, n, length, seed): - """ - Checks that the parameter values are valid or raises ValueError exceptions with a message indicating the - parameter (the first one encountered in the checks) with invalid value. - - Args: - nodes: A list of root node ids such that from each node a uniform random walk of up to length l - will be generated. - n: Number of walks per node id. - length: Maximum length of walk measured as the number of edges followed from root node. - seed: Random number generator seed - - """ - if nodes is None: - raise ValueError( - "({}) A list of root node IDs was not provided.".format( - type(self).__name__ - ) - ) - if not is_real_iterable(nodes): - raise ValueError("nodes parameter should be an iterable of node IDs.") - if ( - len(nodes) == 0 - ): # this is not an error but maybe a warning should be printed to inform the caller - print( - "WARNING: ({}) No root node IDs given. An empty list will be returned as a result.".format( - type(self).__name__ - ) - ) - - if type(n) != int: - raise ValueError( - "({}) The number of walks per root node, n, should be integer type.".format( - type(self).__name__ - ) - ) - if n <= 0: - raise ValueError( - "({}) The number of walks per root node, n, should be a positive integer.".format( - type(self).__name__ - ) - ) - - if type(length) != int: - raise ValueError( - "({}) The walk length, length, should be integer type.".format( - type(self).__name__ - ) - ) - if length <= 0: - raise ValueError( - "({}) The walk length, length, should be positive integer.".format( - type(self).__name__ - ) - ) - - if seed is not None: - if type(seed) != int: - raise ValueError( - "({}) The random number generator seed value, seed, should be integer type or None.".format( - type(self).__name__ - ) - ) - if seed < 0: - raise ValueError( - "({}) The random number generator seed value, seed, should be positive integer or None.".format( - type(self).__name__ - ) - ) - def naive_weighted_choices(rs, weights): """ @@ -376,24 +302,10 @@ def run( List of lists of nodes ids for each of the random walks """ - self._check_parameter_values( - nodes=nodes, - n=n, - p=p, - q=q, - length=length, - seed=seed, - weighted=weighted, - edge_weight_label=edge_weight_label, + rs = self._check_parameter_values( + nodes, n, p, q, length, seed, weighted, edge_weight_label ) - if seed: - # seed a new random number generator - rs = random.Random(seed) - else: - # Restore the random state - rs = self._random_state - if weighted: # Check that all edge weights are greater than or equal to 0. # Also, if the given graph is a MultiGraph, then check that there are no two edges between @@ -405,19 +317,19 @@ def run( for k, v in self.graph[node][neighbor].items(): weight = v.get(edge_weight_label) if weight is None or np.isnan(weight) or weight == np.inf: - raise ValueError( + self._raise_error( "Missing or invalid edge weight ({}) between ({}) and ({}).".format( weight, node, neighbor ) ) if not isinstance(weight, (int, float)): - raise ValueError( + self._raise_error( "Edge weight between nodes ({}) and ({}) is not numeric ({}).".format( node, neighbor, weight ) ) if weight < 0: # check if edge has a negative weight - raise ValueError( + self._raise_error( "An edge weight between nodes ({}) and ({}) is negative ({}).".format( node, neighbor, weight ) @@ -427,7 +339,7 @@ def run( if ( len(wts) > 1 ): # multigraph with different weights on edges between same pair of nodes - raise ValueError( + self._raise_error( "({}) and ({}) have multiple edges with weights ({}). Ambiguous to choose an edge for the random walk.".format( node, neighbor, list(wts) ) @@ -514,92 +426,29 @@ def _check_parameter_values( weighted: Indicates whether the walk is unweighted or weighted. edge_weight_label: Label of the edge weight property. - """ - if nodes is None: - raise ValueError( - "({}) A list of root node IDs was not provided.".format( - type(self).__name__ - ) - ) - if not is_real_iterable(nodes): - raise ValueError("nodes parameter should be an iterableof node IDs.") - if ( - len(nodes) == 0 - ): # this is not an error but maybe a warning should be printed to inform the caller - print( - "WARNING: ({}) No root node IDs given. An empty list will be returned as a result.".format( - type(self).__name__ - ) - ) - - if type(n) != int: - raise ValueError( - "({}) The number of walks per root node, n, should be integer type.".format( - type(self).__name__ - ) - ) - - if n <= 0: - raise ValueError( - "({}) The number of walks per root node, n, should be a positive integer.".format( - type(self).__name__ - ) - ) + Returns: + The random state as determined by the seed. + """ + rs = super()._check_parameter_values(nodes, n, length, seed) if p <= 0.0: - raise ValueError( - "({}) Parameter p should be greater than 0.".format(type(self).__name__) - ) + self._raise_error("Parameter p should be greater than 0.") if q <= 0.0: - raise ValueError( - "({}) Parameter q should be greater than 0.".format(type(self).__name__) - ) - - if type(length) != int: - raise ValueError( - "({}) The walk length, length, should be integer type.".format( - type(self).__name__ - ) - ) - - if length <= 0: - raise ValueError( - "({}) The walk length, length, should be positive integer.".format( - type(self).__name__ - ) - ) - - if seed is not None: - if seed < 0: - raise ValueError( - "({}) The random number generator seed value, seed, should be positive integer or None.".format( - type(self).__name__ - ) - ) - if type(seed) != int: - raise ValueError( - "({}) The random number generator seed value, seed, should be integer type or None.".format( - type(self).__name__ - ) - ) + self._raise_error("Parameter q should be greater than 0.") if type(weighted) != bool: - raise ValueError( - "({}) Parameter weighted has to be either False (unweighted random walks) or True (weighted random walks).".format( - type(self).__name__ - ) + self._raise_error( + "Parameter weighted has to be either False (unweighted random walks) or True (weighted random walks)." ) if not isinstance(edge_weight_label, str): - raise ValueError( - "({}) The edge weight property label has to be of type string".format( - type(self).__name__ - ) - ) + self._raise_error("The edge weight property label has to be of type string") + + return rs -class UniformRandomMetaPathWalk(GraphWalk): +class UniformRandomMetaPathWalk(HeterogeneousGraphWalk): """ For heterogeneous graphs, it performs uniform random walks based on given metapaths. """ @@ -629,22 +478,10 @@ def run( Returns: List of lists of nodes ids for each of the random walks generated """ - self._check_parameter_values( - nodes=nodes, - n=n, - length=length, - metapaths=metapaths, - node_type_attribute=node_type_attribute, - seed=seed, + rs = self._check_parameter_values( + nodes, n, length, metapaths, node_type_attribute, seed ) - if seed: - # seed the random number generator - rs = random.Random(seed) - else: - # Restore the random state - rs = self._random_state - walks = [] for node in nodes: @@ -709,107 +546,35 @@ def _check_parameter_values( node_type_attribute: The node attribute name that stores the node's type seed: Random number generator seed + Returns: + The random state as determined by the seed. """ - if nodes is None: - raise ValueError( - "({}) A list of starting node IDs was not provided (parameter nodes is None).".format( - type(self).__name__ - ) - ) - if not is_real_iterable(nodes): - raise ValueError( - "({}) The nodes parameter should be an iterable of node IDs.".format( - type(self).__name__ - ) - ) - if ( - len(nodes) == 0 - ): # this is not an error but maybe a warning should be printed to inform the caller - print( - "WARNING: ({}) No starting node IDs given. An empty list will be returned as a result.".format( - type(self).__name__ - ) - ) - if n <= 0: - raise ValueError( - "({}) The number of walks per starting node, n, should be a positive integer.".format( - type(self).__name__ - ) - ) - if type(n) != int: - raise ValueError( - "({}) The number of walks per starting node, n, should be integer type.".format( - type(self).__name__ - ) - ) - - if length <= 0: - raise ValueError( - "({}) The walk length parameter, length, should be positive integer.".format( - type(self).__name__ - ) - ) - if type(length) != int: - raise ValueError( - "({}) The walk length parameter, length, should be integer type.".format( - type(self).__name__ - ) - ) + rs = super()._check_parameter_values(nodes, n, length, seed) if type(metapaths) != list: - raise ValueError( - "({}) The metapaths parameter must be a list of lists.".format( - type(self).__name__ - ) - ) + self._raise_error("The metapaths parameter must be a list of lists.") for metapath in metapaths: if type(metapath) != list: - raise ValueError( - "({}) Each metapath must be list type of node labels".format( - type(self).__name__ - ) - ) + self._raise_error("Each metapath must be list type of node labels") if len(metapath) < 2: - raise ValueError( - "({}) Each metapath must specify at least two node types".format( - type(self).__name__ - ) - ) + self._raise_error("Each metapath must specify at least two node types") for node_label in metapath: if type(node_label) != str: - raise ValueError( - "({}) Node labels in metapaths must be string type.".format( - type(self).__name__ - ) - ) + self._raise_error("Node labels in metapaths must be string type.") if metapath[0] != metapath[-1]: - raise ValueError( - "({} The first and last node type in a metapath should be the same.".format( - type(self).__name__ - ) + self._raise_error( + "The first and last node type in a metapath should be the same." ) if type(node_type_attribute) != str: - raise ValueError( - "({}) The parameter label should be string type not {} as given".format( - type(self).__name__, type(node_type_attribute).__name__ + self._raise_error( + "The parameter label should be string type not {} as given".format( + type(node_type_attribute).__name__ ) ) - if seed is not None: - if seed < 0: - raise ValueError( - "({}) The random number generator seed value, seed, should be positive integer or None.".format( - type(self).__name__ - ) - ) - if type(seed) != int: - raise ValueError( - "({}) The random number generator seed value, seed, should be integer type or None.".format( - type(self).__name__ - ) - ) + return rs class SampledBreadthFirstWalk(GraphWalk): @@ -834,18 +599,11 @@ def run(self, nodes=None, n=1, n_size=None, seed=None): Returns: A list of lists such that each list element is a sequence of ids corresponding to a BFW. """ - self._check_parameter_values(nodes=nodes, n=n, n_size=n_size, seed=seed) + rs = self._check_parameter_values(nodes, n, n_size, seed) walks = [] max_hops = len(n_size) # depth of search - if seed: - # seed the random number generator - rs = random.Random(seed) - else: - # Restore the random state - rs = self._random_state - for node in nodes: # iterate over root nodes for _ in range(n): # do n bounded breadth first walks from each root node q = list() # the queue of neighbours @@ -895,92 +653,14 @@ def _check_parameter_values(self, nodes, n, n_size, seed): n_size: The number of neighbouring nodes to expand at each depth of the walk. seed: Random number generator seed; default is None + Returns: + The random state as determined by the seed. """ - if nodes is None: - raise ValueError( - "({}) A list of root node IDs was not provided (nodes parameter is None).".format( - type(self).__name__ - ) - ) - if not is_real_iterable(nodes): - raise ValueError( - "({}) The nodes parameter should be an iterable of node IDs.".format( - type(self).__name__ - ) - ) - if ( - len(nodes) == 0 - ): # this is not an error but maybe a warning should be printed to inform the caller - print( - "WARNING: ({}) No root node IDs given. An empty list will be returned as a result.".format( - type(self).__name__ - ) - ) - - if type(n) != int: - raise ValueError( - "({}) The number of walks per root node, n, should be integer type.".format( - type(self).__name__ - ) - ) - - if n <= 0: - raise ValueError( - "({}) The number of walks per root node, n, should be a positive integer.".format( - type(self).__name__ - ) - ) - - if n_size is None: - raise ValueError( - "({}) The neighbourhood size, n_size, must be a list of integers not None.".format( - type(self).__name__ - ) - ) - if type(n_size) != list: - raise ValueError( - "({}) The neighbourhood size, n_size, must be a list of integers.".format( - type(self).__name__ - ) - ) - - if len(n_size) == 0: - raise ValueError( - "({}) The neighbourhood size, n_size, should not be empty list.".format( - type(self).__name__ - ) - ) + self._check_sizes(n_size) + return super()._check_parameter_values(nodes, n, len(n_size), seed) - for d in n_size: - if type(d) != int: - raise ValueError( - "({}) The neighbourhood size, n_size, must be list of positive integers or 0.".format( - type(self).__name__ - ) - ) - if d < 0: - raise ValueError( - "({}) The neighbourhood size, n_size, must be list of positive integers or 0.".format( - type(self).__name__ - ) - ) - - if seed is not None: - if type(seed) != int: - raise ValueError( - "({}) The random number generator seed value, seed, should be integer type or None.".format( - type(self).__name__ - ) - ) - if seed < 0: - raise ValueError( - "({}) The random number generator seed value, seed, should be positive integer or None.".format( - type(self).__name__ - ) - ) - -class SampledHeterogeneousBreadthFirstWalk(GraphWalk): +class SampledHeterogeneousBreadthFirstWalk(HeterogeneousGraphWalk): """ Breadth First Walk for heterogeneous graphs that generates a sampled number of paths from a starting node. It can be used to extract a random sub-graph starting from a set of initial nodes. @@ -1004,20 +684,11 @@ def run(self, nodes=None, n=1, n_size=None, seed=None): A list of lists such that each list element is a sequence of ids corresponding to a sampled Heterogeneous BFW. """ - self._check_parameter_values( - nodes=nodes, n=n, n_size=n_size, graph_schema=self.graph_schema, seed=seed - ) + rs = self._check_parameter_values(nodes, n, n_size, self.graph_schema, seed) walks = [] d = len(n_size) # depth of search - if seed: - # seed the random number generator - rs = random.Random(seed) - else: - # Restore the random state - rs = self._random_state - for node in nodes: # iterate over root nodes for _ in range(n): # do n bounded breadth first walks from each root node q = list() # the queue of neighbours @@ -1085,96 +756,17 @@ def _check_parameter_values(self, nodes, n, n_size, graph_schema, seed): graph_schema: None or a stellargraph graph schema object seed: Random number generator seed; default is None + Returns: + The random state as determined by the seed. """ - if nodes is None: - raise ValueError( - "({}) A list of root node IDs was not provided (nodes parameter is None).".format( - type(self).__name__ - ) - ) - if not is_real_iterable(nodes): - raise ValueError( - "({}) The nodes parameter should be an iterable of node IDs.".format( - type(self).__name__ - ) - ) - if ( - len(nodes) == 0 - ): # this is not an error but maybe a warning should be printed to inform the caller - print( - "WARNING: ({}) No root node IDs given. An empty list will be returned as a result.".format( - type(self).__name__ - ) - ) - - if type(n) != int: - raise ValueError( - "({}) The number of walks per root node, n, should be integer type.".format( - type(self).__name__ - ) - ) - - if n <= 0: - raise ValueError( - "({}) The number of walks per root node, n, should be a positive integer.".format( - type(self).__name__ - ) - ) - - if n_size is None: - raise ValueError( - "({}) The neighbourhood size, n_size, must be a list of integers not None.".format( - type(self).__name__ - ) - ) - if type(n_size) != list: - raise ValueError( - "({}) The neighbourhood size, n_size, must be a list of integers.".format( - type(self).__name__ - ) - ) - - if len(n_size) == 0: - raise ValueError( - "({}) The neighbourhood size, n_size, should not be empty list.".format( - type(self).__name__ - ) - ) - - for d in n_size: - if type(d) != int: - raise ValueError( - "({}) The neighbourhood size, n_size, must be list of integers.".format( - type(self).__name__ - ) - ) - if d < 0: - raise ValueError( - "({}) n_sie should be positive integer or 0.".format( - type(self).__name__ - ) - ) + self._check_sizes(n_size) if graph_schema is not None and type(graph_schema) is not GraphSchema: - raise ValueError( - "({}) The parameter graph_schema should be either None or of type GraphSchema.".format( - type(self).__name__ - ) + self._raise_error( + "The parameter graph_schema should be either None or of type GraphSchema." ) - if seed is not None: - if type(seed) != int: - raise ValueError( - "({}) The random number generator seed value, seed, should be integer type or None.".format( - type(self).__name__ - ) - ) - if seed < 0: - raise ValueError( - "({}) The random number generator seed value, seed, should be positive integer or None.".format( - type(self).__name__ - ) - ) + return super()._check_parameter_values(nodes, n, len(n_size), seed) class DirectedBreadthFirstNeighbours(GraphWalk): From c2ea10c96beb2f99cca2a47a6148a9915892c218 Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Fri, 16 Aug 2019 10:46:06 +0930 Subject: [PATCH 08/30] Keep partially written code - broken --- stellargraph/layer/graphsage.py | 264 ++++++++++++++++++++++++++++ stellargraph/mapper/node_mappers.py | 174 +++++++++++++++++- 2 files changed, 436 insertions(+), 2 deletions(-) diff --git a/stellargraph/layer/graphsage.py b/stellargraph/layer/graphsage.py index dae068b57..972900a24 100644 --- a/stellargraph/layer/graphsage.py +++ b/stellargraph/layer/graphsage.py @@ -25,6 +25,7 @@ "MaxPoolingAggregator", "MeanPoolingAggregator", "AttentionalAggregator", + "DirectedGraphSAGE", ] import numpy as np @@ -747,3 +748,266 @@ def default_model(self, flatten_output=True): PendingDeprecationWarning, ) return self.build() + + +class DirectedGraphSAGE: + """ + Implementation of a directed version of the GraphSAGE algorithm of Hamilton et al. with Keras layers. + see: http://snap.stanford.edu/graphsage/ + + The model minimally requires specification of the layer sizes as a list of ints + corresponding to the feature dimensions for each hidden layer and a generator object. + + Different neighbour node aggregators can also be specified with the ``aggregator`` + argument, which should be the aggregator class, + either :class:`MeanAggregator`, :class:`MeanPoolingAggregator`, + :class:`MaxPoolingAggregator`, or :class:`AttentionalAggregator`. + + Args: + layer_sizes (list): Hidden feature dimensions for each layer + + Either: + generator (Sequence): A NodeSequence or LinkSequence. + Or: + in_samples (list): The number of in-node samples per layer in the model. + out_samples (list): The number of out-node samples per layer in the model. + input_dim (int): The dimensions of the node features used as input to the model. + aggregator (class): The GraphSAGE aggregator to use. Defaults to the `MeanAggregator`. + bias (bool): If True a bias vector is learnt for each layer in the GraphSAGE model + dropout (float): The dropout supplied to each layer in the GraphSAGE model. + normalize (str or None): The normalization used after each layer, defaults to L2 normalization. + + """ + + def __init__( + self, + layer_sizes, + generator=None, + in_samples=None, + out_samples=None, + input_dim=None, + aggregator=None, + bias=True, + dropout=0.0, + normalize="l2", + ): + # Set the aggregator layer used in the model + if aggregator is None: + self._aggregator = MeanAggregator + elif issubclass(aggregator, Layer): + self._aggregator = aggregator + else: + raise TypeError("Aggregator should be a subclass of Keras Layer") + + # Set the normalization layer used in the model + if normalize == "l2": + self._normalization = Lambda(lambda x: K.l2_normalize(x, axis=-1)) + elif normalize is None or normalize == "none" or normalize == "None": + self._normalization = Lambda(lambda x: x) + else: + raise ValueError( + "Normalization should be either 'l2' or 'none'; received '{}'".format( + normalize + ) + ) + + # Get the input_dim and num_samples from the generator if it is given + # Use both the schema and head node type from the generator + # TODO: Refactor the horror of generator.generator.graph... + self.generator = generator + if generator is not None: + self.in_samples = generator.generator.in_samples + self.out_samples = generator.generator.out_samples + feature_sizes = generator.generator.graph.node_feature_sizes() + if len(feature_sizes) > 1: + raise RuntimeError( + "GraphSAGE called on graph with more than one node type." + ) + self.input_feature_size = feature_sizes.popitem()[1] + + elif in_samples is not None and out_samples is not None and input_dim is not None: + self.in_samples = in_samples + self.out_samples = out_samples + self.input_feature_size = input_dim + + else: + raise RuntimeError( + "If generator is not provided, n_samples and input_dim must be specified." + ) + + self.max_hops = max_hops = len(layer_sizes) + if len(self.in_samples) != max_hops: + raise ValueError("Mismatched lengths: in-node sample sizes {} versus layer sizes {}".format(self.in_samples, layer_sizes)) + if len(self.out_samples) != max_hops: + raise ValueError("Mismatched lengths: out-node sample sizes {} versus layer sizes {}".format(self.out_samples, layer_sizes)) + + # Model parameters + self.max_slots = 2 ** (max_hops + 1) - 1 + self.bias = bias + self.dropout = dropout + + # Feature dimensions for each layer + self.dims = [self.input_feature_size] + layer_sizes + + # Aggregator functions for each layer + self._aggs = [ + self._aggregator( + output_dim=self.dims[i + 1], + bias=self.bias, + act="relu" if i < max_hops - 1 else "linear", + ) + for i in range(max_hops) + ] + + def __call__(self, xin: List): + """ + Apply aggregator layers + + Args: + x (list of Tensor): Batch input features + + Returns: + Output tensor + """ + def aggregate_neighbours(tree: List, stage: int): + # compute the number of slots with children in the binary tree + num_slots = (len(tree) - 1) // 2 + new_tree = [None] * num_slots + for slot in range(num_slots): + # get parent nodes + num_head_nodes = K.int_shape(tree[slot])[1] + parent = Dropout(self.dropout)(tree[slot]) + # find in-nodes + child_slot = 2 * slot + 1 + size = self.neighbourhood_sizes[child_slot] // num_head_nodes + in_child = Dropout(self.dropout)( + Reshape((num_head_nodes, size, self.dims[stage]))(tree[child_slot]) + ) + # find out-nodes + child_slot = child_slot + 1 + size = self.neighbourhood_sizes[child_slot] // num_head_nodes + out_child = Dropout(self.dropout)( + Reshape((num_head_nodes, size, self.dims[stage]))(tree[child_slot]) + ) + # aggregate neighbourhoods + new_tree[slot] = self._aggs[stage]([parent, in_child, out_child]) + return new_tree + + if not isinstance(xin, list): + raise TypeError("Input features to GraphSAGE must be a list") + + if len(xin) != self.max_slots: + raise ValueError( + "Number of input tensors does not match number of GraphSAGE layers" + ) + + # Combine GraphSAGE layers in stages + stage_tree = xin + for stage in range(self.max_hops + 1): + stage_tree = aggregate_neighbours(stage_tree, stage) + print("DEBUG: number of outputs = {}".format(len(stage_tree))) + out_layer = stage_tree[0] + + # Remove neighbourhood dimension from output tensors of the stack + print("DEBUG: final shape = {}".format(K.int_shape(out_layer))) + out_layer = Reshape(K.int_shape(out_layer)[2:])(out_layer) + return self._normalization(out_layer) + + def _compute_input_sizes(self) -> List[int]: + # Each hop has to sample separately from both the in-nodes + # and the out-nodes. This gives rise to a binary tree of 'slots'. + # Storage for the total (cumulative product) number of nodes sampled + # at the corresponding neighbourhood for each slot: + num_nodes = [0] * self.max_slots + num_nodes[0] = 1 + # Storage for the number of hops to reach + # the corresponding neighbourhood for each slot: + num_hops = [0] * self.max_slots + for slot in range(1, self.max_slots): + parent_slot = (slot + 1) // 2 - 1 + i = num_hops[parent_slot] + num_hops[slot] = i + 1 + num_nodes[slot] = ( + self.in_samples[i] if slot % 2 == 1 + else self.out_samples[i] + ) * num_nodes[parent_slot] + return num_nodes + + def node_model(self): + """ + Builds a GraphSAGE model for node prediction + + Returns: + tuple: (x_inp, x_out) where ``x_inp`` is a list of Keras input tensors + for the specified GraphSAGE model and ``x_out`` is the Keras tensor + for the GraphSAGE model output. + + """ + # Create tensor inputs for neighbourhood sampling; + x_inp = [ + Input(shape=(s, self.input_feature_size)) + for s in self.neighbourhood_sizes + ] + + # Output from GraphSAGE model + x_out = self(x_inp) + + return x_inp, x_out + + def link_model(self): + """ + Builds a GraphSAGE model for link or node pair prediction + + Returns: + tuple: (x_inp, x_out) where ``x_inp`` is a list of Keras input tensors for (src, dst) node pairs + (where (src, dst) node inputs alternate), + and ``x_out`` is a list of output tensors for (src, dst) nodes in the node pairs + + """ + # Expose input and output sockets of the model, for source and destination nodes: + x_inp_src, x_out_src = self.node_model() + x_inp_dst, x_out_dst = self.node_model() + # re-pack into a list where (source, target) inputs alternate, for link inputs: + x_inp = [x for ab in zip(x_inp_src, x_inp_dst) for x in ab] + # same for outputs: + x_out = [x_out_src, x_out_dst] + return x_inp, x_out + + def build(self): + """ + Builds a GraphSAGE model for node or link/node pair prediction, depending on the generator used to construct + the model (whether it is a node or link/node pair generator). + + Returns: + tuple: (x_inp, x_out), where ``x_inp`` is a list of Keras input tensors + for the specified GraphSAGE model (either node or link/node pair model) and ``x_out`` contains + model output tensor(s) of shape (batch_size, layer_sizes[-1]) + + """ + self.neighbourhood_sizes = self._compute_input_sizes() + + if self.generator is not None and hasattr(self.generator, "_sampling_schema"): + if len(self.generator._sampling_schema) == 1: + return self.node_model() + elif len(self.generator._sampling_schema) == 2: + return self.link_model() + else: + raise RuntimeError( + "The generator used for model creation is neither a node nor a link generator, " + "unable to figure out how to build the model. Consider using node_model or " + "link_model method explicitly to build node or link prediction model, respectively." + ) + else: + raise RuntimeError( + "Suitable generator is not provided at model creation time, unable to figure out how to build the model. " + "Consider either providing a generator, or using node_model or link_model method explicitly to build node or " + "link prediction model, respectively." + ) + + def default_model(self, flatten_output=True): + warnings.warn( + "The .default_model() method will be deprecated in future versions. " + "Please use .build() method instead.", + PendingDeprecationWarning, + ) + return self.build() diff --git a/stellargraph/mapper/node_mappers.py b/stellargraph/mapper/node_mappers.py index ed12e2370..dc9197c20 100644 --- a/stellargraph/mapper/node_mappers.py +++ b/stellargraph/mapper/node_mappers.py @@ -25,6 +25,7 @@ "FullBatchNodeGenerator", "FullBatchNodeSequence", "SparseFullBatchNodeSequence", + "DirectedGraphSAGENodeGenerator", ] import warnings @@ -41,8 +42,9 @@ from ..data.explorer import ( SampledBreadthFirstWalk, SampledHeterogeneousBreadthFirstWalk, + DirectedBreadthFirstNeighbours, ) -from ..core.graph import StellarGraphBase, GraphSchema +from ..core.graph import StellarGraphBase, GraphSchema, StellarDiGraph from ..core.utils import is_real_iterable from ..core.utils import GCN_Aadj_feats_op @@ -124,8 +126,10 @@ def __init__(self, generator, ids, targets=None, shuffle=True): # Save head node type and generate sampling schema self.head_node_types = [head_node_type] + ### Experimental; for directed sampling + num_samples = getattr(generator, "num_samples", []) self._sampling_schema = generator.schema.sampling_layout( - self.head_node_types, generator.num_samples + self.head_node_types, num_samples ) def __len__(self): @@ -814,3 +818,169 @@ def flow(self, node_ids, targets=None): return FullBatchNodeSequence( self.features, self.Aadj, targets, node_indices ) + + +class DirectedGraphSAGENodeGenerator: + """ + A data generator for node prediction with homogeneous GraphSAGE models + on directed graphs. + + At minimum, supply the StellarDiGraph, the batch size, and the number of + node samples (separately for in-nodes and out-nodes) + for each layer of the GraphSAGE model. + + The supplied graph should be a StellarDiGraph object that is ready for + machine learning. Currently the model requires node features for all + nodes in the graph. + + Use the :meth:`flow` method supplying the nodes and (optionally) targets + to get an object that can be used as a Keras data generator. + + Example:: + + G_generator = DirectedGraphSAGENodeGenerator(G, 50, [10,5], [5,1]) + train_data_gen = G_generator.flow(train_node_ids, train_node_labels) + test_data_gen = G_generator.flow(test_node_ids) + + Args: + G (StellarDiGraph): The machine-learning ready graph. + batch_size (int): Size of batch to return. + in_samples (list): The number of in-node samples per layer (hop) to take. + out_samples (list): The number of out-node samples per layer (hop) to take. + schema (GraphSchema): [Optional] Graph schema for G. + seed (int): [Optional] Random seed for the node sampler. + name (str or None): Name of the generator (optional) + """ + + def __init__( + self, G, batch_size, in_samples, out_samples, schema=None, seed=None, name=None + ): + if not isinstance(G, StellarDiGraph): + raise TypeError("Graph must be a StellarDiGraph object.") + # TODO Add checks for in- and out-nodes sizes + + self.graph = G + self.in_samples = in_samples + self.out_samples = out_samples + self.batch_size = batch_size + self.name = name + + # Check if the graph has features + G.check_graph_for_ml() + + # We need a schema for compatibility with HinSAGE + if schema is None: + self.schema = G.create_graph_schema(create_type_maps=True) + elif isinstance(schema, GraphSchema): + self.schema = schema + else: + raise TypeError("Schema must be a GraphSchema object") + + # Check that there is only a single node type for GraphSAGE + if len(self.schema.node_types) > 1: + print( + "Warning: running homogeneous GraphSAGE on a graph with multiple node types" + ) + + # Create sampler for GraphSAGE + self.sampler = DirectedBreadthFirstNeighbours( + G, graph_schema=self.schema, seed=seed + ) + + def sample_features(self, head_nodes, sampling_schema): + """ + Sample neighbours recursively from the head nodes, collect the features of the + sampled nodes, and return these as a list of feature arrays for the GraphSAGE + algorithm. + + Args: + head_nodes: An iterable of head nodes to perform sampling on. + sampling_schema: The sampling schema for the model + + Returns: + A list of feature tensors from the sampled nodes at each layer, each of shape: + ``(len(head_nodes), num_sampled_at_layer, feature_size)`` + where num_sampled_at_layer is the total number (cumulative product) + of nodes sampled at the given number of hops from each head node, + given the sequence of in/out directions. + """ + node_samples = self.sampler.run( + nodes=head_nodes, n=1, in_size=self.in_samples, out_size=self.out_samples + ) + + # Reshape node samples to sensible format + # Each 'slot' represents the list of nodes sampled from some neighbourhood, and will have a corresponding + # NN input layer. Every hop potentially generates both in-nodes and out-nodes, held separately, + # and thus the slot (or directed hop sequence) structure forms a binary tree. + + node_type = sampling_schema[0][0][0] + + max_hops = len(self.in_samples) + max_slots = 2 ** (max_hops + 1) - 1 + features = [None] * max_slots # flattened binary tree + + for slot in range(max_slots): + nodes_in_slot = list(it.chain(*[sample[slot] for sample in node_samples])) + features_for_slot = self.graph.get_feature_for_nodes( + nodes_in_slot, node_type + ) + resize = - 1 if np.size(features_for_slot) > 0 else 0 + features[slot] = np.reshape( + features_for_slot, (len(head_nodes), resize, features_for_slot.shape[1]) + ) + + return features + + def flow(self, node_ids, targets=None, shuffle=False): + """ + Creates a generator/sequence object for training or evaluation + with the supplied node ids and numeric targets. + + The node IDs are the nodes to train or inference on: the embeddings + calculated for these nodes are passed to the downstream task. These + are a subset of the nodes in the graph. + + The targets are an array of numeric targets corresponding to the + supplied node_ids to be used by the downstream task. They should + be given in the same order as the list of node IDs. + If they are not specified (for example, for use in prediction), + the targets will not be available to the downsteam task. + + Note that the shuffle argument should be True for training and + False for prediction. + + Args: + node_ids: an iterable of node IDs + targets: a 2D array of numeric targets with shape + `(len(node_ids), target_size)` + shuffle (bool): If True the node_ids will be shuffled at each + epoch, if False the node_ids will be processed in order. + + Returns: + A NodeSequence object to use with the GraphSAGE model + in Keras methods ``fit_generator``, ``evaluate_generator``, + and ``predict_generator`` + + """ + return NodeSequence(self, node_ids, targets, shuffle=shuffle) + + def flow_from_dataframe(self, node_targets, shuffle=False): + """ + Creates a generator/sequence object for training or evaluation + with the supplied node ids and numeric targets. + + Args: + node_targets: a Pandas DataFrame of numeric targets indexed + by the node ID for that target. + shuffle (bool): If True the node_ids will be shuffled at each + epoch, if False the node_ids will be processed in order. + + Returns: + A NodeSequence object to use with the GraphSAGE model + in Keras methods ``fit_generator``, ``evaluate_generator``, + and ``predict_generator`` + + """ + return NodeSequence( + self, node_targets.index, node_targets.values, shuffle=shuffle + ) From fab6a551606d342b49733f4c9934c0ecd1dedfc5 Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Fri, 16 Aug 2019 16:54:07 +0930 Subject: [PATCH 09/30] Experimental version with debugging - works end to end. --- stellargraph/layer/graphsage.py | 108 +++++++++++++++++++------------- 1 file changed, 65 insertions(+), 43 deletions(-) diff --git a/stellargraph/layer/graphsage.py b/stellargraph/layer/graphsage.py index 972900a24..49f29ac02 100644 --- a/stellargraph/layer/graphsage.py +++ b/stellargraph/layer/graphsage.py @@ -45,10 +45,14 @@ class GraphSAGEAggregator(Layer): Args: output_dim (int): Output dimension - bias (bool): Optional bias + bias (bool): Optional flag indicating whether (True) or not (False; default) + a bias term should be included. act (Callable or str): name of the activation function to use (must be a Keras activation function), or alternatively, a TensorFlow operation. - + share_weights (bool): Optional flag indicating whether (True; default) or not + (False) to use the same weight tensor for all multi-dimensional neighbourhoods. + neigh_dim (int): Optional value indicating the maximum number of multi-dimensional + neighbourhoods; defaults to 1. """ def __init__( @@ -56,6 +60,8 @@ def __init__( output_dim: int = 0, bias: bool = False, act: Callable or AnyStr = "relu", + share_weights: bool = True, + neigh_dim: int = 1, **kwargs ): # Ensure the output dimension is divisible by 2 @@ -69,6 +75,8 @@ def __init__( self.w_self = None self.bias = None self._initializer = "glorot_uniform" + self.share_weights = share_weights + self.neigh_dim = neigh_dim super().__init__(**kwargs) def get_config(self): @@ -130,29 +138,16 @@ def build(self, input_shape): super().build(input_shape) - def apply_mlp(self, x, **kwargs): + def aggregate_neighbours(self, x_neigh, neigh_idx: int = 0): """ - Create MLP on input self tensor, x + Override with a method to aggregate tensors over neighbourhood. Args: - x (List[Tensor]): Tensor giving the node features - shape: (batch_size, head size, feature_size) + x_neigh: The input tensor representing the sampled neighbour nodes. + neigh_idx: Optional neighbourhood index used for multi-dimensional hops. Returns: - Keras Tensor representing the aggregated embeddings in the input. - - """ - h_out = K.dot(x, self.w_self) - - if self.has_bias: - h_out = self.act(h_out + self.bias) - else: - h_out = self.act(h_out) - return h_out - - def aggregate_neighbours(self, x_neigh): - """ - Override with a method to aggregate tensors over neighbourhood. + A tensor aggregation of the input nodes features. """ raise NotImplementedError( "The GraphSAGEAggregator base class should not be directly instantiated" @@ -170,27 +165,27 @@ def call(self, x, **kwargs): """ # x[0]: self vector (batch_size, head size, feature_size) - # x[1]: neighbour vector (batch_size, head size, neighbours, feature_size) - x_self, x_neigh = x - - if self._build_mlp_only: - return self.apply_mlp(x_self, **kwargs) + # x[1...n]: optional neighbour vectors (batch_size, head size, neighbours, feature_size) + x_self = x[0] # Weight maxtrix multiplied by self features from_self = K.dot(x_self, self.w_self) # If there are neighbours aggregate over them - from_neigh = self.aggregate_neighbours(x_neigh) - - h_out = K.concatenate([from_self, from_neigh], axis=2) + if not self._build_mlp_only and len(x) > 1: + sources = [from_self] + for i in range(1, len(x)): + sources.append(self.aggregate_neighbours(x[i], neigh_idx=i-1)) + h_out = K.concatenate(sources, axis=2) + else: + h_out = from_self - # Finally, add bias and apply activation + # Optionally add bias if self.has_bias: - h_out = self.act(h_out + self.bias) - else: - h_out = self.act(h_out) + h_out = h_out + self.bias - return h_out + # Finally, apply activation + return self.act(h_out) def compute_output_shape(self, input_shape): """ @@ -233,7 +228,7 @@ def build(self, input_shape): if self._build_mlp_only: self.w_neigh = None - else: + elif self.share_weights: self.w_neigh = self.add_weight( name="w_neigh", shape=(input_shape[1][3], self.weight_output_size()), @@ -241,9 +236,24 @@ def build(self, input_shape): trainable=True, ) - def aggregate_neighbours(self, x_neigh): - from_neigh = K.dot(K.mean(x_neigh, axis=2), self.w_neigh) - return from_neigh + else: + in_size = input_shape[1][3] + out_size = self.weight_output_size() + self.w_neigh = [] + for i in range(self.neigh_dim): + self.w_neigh.append( + self.add_weight( + name="w_neigh" + str(i), + shape=(in_size, out_size), + initializer=self._initializer, + trainable=True, + ) + ) + + def aggregate_neighbours(self, x_neigh, neigh_idx=0): + if self.share_weights: + return K.dot(K.mean(x_neigh, axis=2), self.w_neigh) + return K.dot(K.mean(x_neigh, axis=2), self.w_neigh[neigh_idx]) class MaxPoolingAggregator(GraphSAGEAggregator): @@ -303,7 +313,7 @@ def build(self, input_shape): trainable=True, ) - def aggregate_neighbours(self, x_neigh): + def aggregate_neighbours(self, x_neigh, **kwargs): """ Aggregates the neighbour tensors by max-pooling of neighbours @@ -381,7 +391,7 @@ def build(self, input_shape): trainable=True, ) - def aggregate_neighbours(self, x_neigh): + def aggregate_neighbours(self, x_neigh, **kwargs): """ Aggregates the neighbour tensors by mean-pooling of neighbours @@ -847,14 +857,17 @@ def __init__( self.dropout = dropout # Feature dimensions for each layer + self.layer_sizes = layer_sizes self.dims = [self.input_feature_size] + layer_sizes # Aggregator functions for each layer self._aggs = [ self._aggregator( - output_dim=self.dims[i + 1], + output_dim=layer_sizes[i], bias=self.bias, act="relu" if i < max_hops - 1 else "linear", + share_weights=False, + neigh_dim=2, ) for i in range(max_hops) ] @@ -872,6 +885,7 @@ def __call__(self, xin: List): def aggregate_neighbours(tree: List, stage: int): # compute the number of slots with children in the binary tree num_slots = (len(tree) - 1) // 2 + print("DEBUG: input tree length={}, output tree length={}".format(len(tree), num_slots)) new_tree = [None] * num_slots for slot in range(num_slots): # get parent nodes @@ -879,13 +893,19 @@ def aggregate_neighbours(tree: List, stage: int): parent = Dropout(self.dropout)(tree[slot]) # find in-nodes child_slot = 2 * slot + 1 - size = self.neighbourhood_sizes[child_slot] // num_head_nodes + size = ( + self.neighbourhood_sizes[child_slot] // num_head_nodes + if num_head_nodes > 0 else 0 + ) in_child = Dropout(self.dropout)( Reshape((num_head_nodes, size, self.dims[stage]))(tree[child_slot]) ) # find out-nodes child_slot = child_slot + 1 - size = self.neighbourhood_sizes[child_slot] // num_head_nodes + size = ( + self.neighbourhood_sizes[child_slot] // num_head_nodes + if num_head_nodes > 0 else 0 + ) out_child = Dropout(self.dropout)( Reshape((num_head_nodes, size, self.dims[stage]))(tree[child_slot]) ) @@ -903,8 +923,10 @@ def aggregate_neighbours(tree: List, stage: int): # Combine GraphSAGE layers in stages stage_tree = xin - for stage in range(self.max_hops + 1): + for stage in range(self.max_hops): + print("DEBUG: stage={}, input tree size={}".format(stage, len(stage_tree))) stage_tree = aggregate_neighbours(stage_tree, stage) + print("DEBUG: stage={}, output tree size={}".format(stage, len(stage_tree))) print("DEBUG: number of outputs = {}".format(len(stage_tree))) out_layer = stage_tree[0] From 4199831b5151c626218c9763a09b92210b1ad71e Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Mon, 19 Aug 2019 10:52:48 +0930 Subject: [PATCH 10/30] Added back apply-mlp, fixed some tests and added others. --- stellargraph/layer/graphsage.py | 44 ++++++++++++++++++------ tests/layer/test_graphsage.py | 59 +++++++++++++++++++++++---------- 2 files changed, 75 insertions(+), 28 deletions(-) diff --git a/stellargraph/layer/graphsage.py b/stellargraph/layer/graphsage.py index 49f29ac02..11e6fc0da 100644 --- a/stellargraph/layer/graphsage.py +++ b/stellargraph/layer/graphsage.py @@ -64,19 +64,16 @@ def __init__( neigh_dim: int = 1, **kwargs ): - # Ensure the output dimension is divisible by 2 - if output_dim % 2 != 0: - raise ValueError("Output dimension must be divisible by two in aggregator") - + self.share_weights = share_weights + self.neigh_dim = neigh_dim self.output_dim = output_dim - self.half_output_dim = output_dim // 2 + self.other_output_dim = output_dim // (neigh_dim + 1) + self.self_output_dim = output_dim - neigh_dim * self.other_output_dim self.has_bias = bias self.act = activations.get(act) self.w_self = None self.bias = None self._initializer = "glorot_uniform" - self.share_weights = share_weights - self.neigh_dim = neigh_dim super().__init__(**kwargs) def get_config(self): @@ -105,7 +102,7 @@ def weight_output_size(self): if self._build_mlp_only: weight_dim = self.output_dim else: - weight_dim = self.half_output_dim + weight_dim = self.self_output_dim return weight_dim @@ -138,6 +135,26 @@ def build(self, input_shape): super().build(input_shape) + def apply_mlp(self, x, **kwargs): + """ + Create MLP on input self tensor, x + + Args: + x (List[Tensor]): Tensor giving the node features + shape: (batch_size, head size, feature_size) + + Returns: + Keras Tensor representing the aggregated embeddings in the input. + + """ + # Weight maxtrix multiplied by self features + h_out = K.dot(x, self.w_self) + # Optionally add bias + if self.has_bias: + h_out = h_out + self.bias + # Finally, apply activation + return self.act(h_out) + def aggregate_neighbours(self, x_neigh, neigh_idx: int = 0): """ Override with a method to aggregate tensors over neighbourhood. @@ -168,11 +185,14 @@ def call(self, x, **kwargs): # x[1...n]: optional neighbour vectors (batch_size, head size, neighbours, feature_size) x_self = x[0] + if self._build_mlp_only: + return self.apply_mlp(x_self, **kwargs) + # Weight maxtrix multiplied by self features from_self = K.dot(x_self, self.w_self) # If there are neighbours aggregate over them - if not self._build_mlp_only and len(x) > 1: + if len(x) > 1: sources = [from_self] for i in range(1, len(x)): sources.append(self.aggregate_neighbours(x[i], neigh_idx=i-1)) @@ -231,14 +251,14 @@ def build(self, input_shape): elif self.share_weights: self.w_neigh = self.add_weight( name="w_neigh", - shape=(input_shape[1][3], self.weight_output_size()), + shape=(input_shape[1][3], self.other_output_dim), initializer=self._initializer, trainable=True, ) else: in_size = input_shape[1][3] - out_size = self.weight_output_size() + out_size = self.other_output_dim self.w_neigh = [] for i in range(self.neigh_dim): self.w_neigh.append( @@ -927,6 +947,8 @@ def aggregate_neighbours(tree: List, stage: int): print("DEBUG: stage={}, input tree size={}".format(stage, len(stage_tree))) stage_tree = aggregate_neighbours(stage_tree, stage) print("DEBUG: stage={}, output tree size={}".format(stage, len(stage_tree))) + for out_layer in stage_tree: + print("DEBUG: intermediate shape = {}".format(K.int_shape(out_layer))) print("DEBUG: number of outputs = {}".format(len(stage_tree))) out_layer = stage_tree[0] diff --git a/tests/layer/test_graphsage.py b/tests/layer/test_graphsage.py index 1b963367e..67299a98d 100644 --- a/tests/layer/test_graphsage.py +++ b/tests/layer/test_graphsage.py @@ -53,7 +53,8 @@ def example_graph_1(feature_size=None): def test_maxpool_agg_constructor(): agg = MaxPoolingAggregator(2, bias=False) assert agg.output_dim == 2 - assert agg.half_output_dim == 1 + assert agg.self_output_dim == 1 + assert agg.other_output_dim == 1 assert agg.hidden_dim == 2 assert not agg.has_bias assert agg.act.__name__ == "relu" @@ -69,15 +70,12 @@ def test_maxpool_agg_constructor(): def test_maxpool_agg_constructor_1(): agg = MaxPoolingAggregator(output_dim=4, bias=True, act=lambda x: x + 1) assert agg.output_dim == 4 - assert agg.half_output_dim == 2 + assert agg.self_output_dim == 2 + assert agg.other_output_dim == 2 assert agg.hidden_dim == 4 assert agg.has_bias assert agg.act(2) == 3 - # Test for output dim not divisible by 2 - with pytest.raises(ValueError): - MaxPoolingAggregator(output_dim=3) - def test_maxpool_agg_apply(): agg = MaxPoolingAggregator(2, bias=True, act="linear") @@ -129,7 +127,8 @@ def test_maxpool_agg_zero_neighbours(): def test_meanpool_agg_constructor(): agg = MeanPoolingAggregator(2, bias=False) assert agg.output_dim == 2 - assert agg.half_output_dim == 1 + assert agg.self_output_dim == 1 + assert agg.other_output_dim == 1 assert agg.hidden_dim == 2 assert not agg.has_bias assert agg.act.__name__ == "relu" @@ -145,15 +144,12 @@ def test_meanpool_agg_constructor(): def test_meanpool_agg_constructor_1(): agg = MeanPoolingAggregator(output_dim=4, bias=True, act=lambda x: x + 1) assert agg.output_dim == 4 - assert agg.half_output_dim == 2 + assert agg.self_output_dim == 2 + assert agg.other_output_dim == 2 assert agg.hidden_dim == 4 assert agg.has_bias assert agg.act(2) == 3 - # Test for output dim not divisible by 2 - with pytest.raises(ValueError): - MeanPoolingAggregator(output_dim=3) - def test_meanpool_agg_apply(): agg = MeanPoolingAggregator(2, bias=True, act="linear") @@ -203,7 +199,8 @@ def test_meanpool_agg_zero_neighbours(): def test_mean_agg_constructor(): agg = MeanAggregator(2) assert agg.output_dim == 2 - assert agg.half_output_dim == 1 + assert agg.self_output_dim == 1 + assert agg.other_output_dim == 1 assert not agg.has_bias # Check config @@ -216,13 +213,41 @@ def test_mean_agg_constructor(): def test_mean_agg_constructor_1(): agg = MeanAggregator(output_dim=4, bias=True, act=lambda x: x + 1) assert agg.output_dim == 4 - assert agg.half_output_dim == 2 + assert agg.self_output_dim == 2 + assert agg.other_output_dim == 2 assert agg.has_bias assert agg.act(2) == 3 - # Test for output dim not divisible by 2 - with pytest.raises(ValueError): - MeanAggregator(output_dim=3) + +def test_mean_agg_constructor_2(): + # Since we can now aggregate over more than one set of neighbours + # (e.g. in directed GraphSAGE), we must handle odd dimensions. + agg = MeanAggregator(11) + assert agg.output_dim == 11 + assert agg.self_output_dim == 6 + assert agg.other_output_dim == 5 + assert not agg.has_bias + + # Check config + config = agg.get_config() + assert config["output_dim"] == 11 + assert config["bias"] == False + assert config["act"] == "relu" + + +def test_mean_agg_constructor_3(): + # Ditto above, re multiple neighbour sets. + agg = MeanAggregator(12, neigh_dim=3) + assert agg.output_dim == 12 + assert agg.self_output_dim == 3 + assert agg.other_output_dim == 3 + assert not agg.has_bias + + # Check config + config = agg.get_config() + assert config["output_dim"] == 12 + assert config["bias"] == False + assert config["act"] == "relu" def test_mean_agg_apply(): From def3937f875f0f83c84329d3c09f0e8674ffab14 Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Tue, 20 Aug 2019 11:31:37 +0930 Subject: [PATCH 11/30] Removed debugging and experimental sharing feature; added demo notebooks. --- ...ndirected-graph-undirected-graphsage.ipynb | 1003 ++++++++++++++++ ...-directed-graph-undirected-graphsage.ipynb | 1005 ++++++++++++++++ ...rected-graph-half-directed-graphsage.ipynb | 1005 ++++++++++++++++ ...ected-graph-fully-directed-graphsage.ipynb | 1043 +++++++++++++++++ stellargraph/layer/graphsage.py | 23 - 5 files changed, 4056 insertions(+), 23 deletions(-) create mode 100644 demos/node-classification/directed-graphsage/example1-undirected-graph-undirected-graphsage.ipynb create mode 100644 demos/node-classification/directed-graphsage/example2-directed-graph-undirected-graphsage.ipynb create mode 100644 demos/node-classification/directed-graphsage/example3-directed-graph-half-directed-graphsage.ipynb create mode 100644 demos/node-classification/directed-graphsage/example4-directed-graph-fully-directed-graphsage.ipynb diff --git a/demos/node-classification/directed-graphsage/example1-undirected-graph-undirected-graphsage.ipynb b/demos/node-classification/directed-graphsage/example1-undirected-graph-undirected-graphsage.ipynb new file mode 100644 index 000000000..057d76d2f --- /dev/null +++ b/demos/node-classification/directed-graphsage/example1-undirected-graph-undirected-graphsage.ipynb @@ -0,0 +1,1003 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Stellargraph example 1: undirected GraphSAGE on an undirected CORA citation network\n", + "\n", + "This example shows the straight-forward application of undirected GraphSAGE to an undirected graph." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import NetworkX and stellar:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import networkx as nx\n", + "import pandas as pd\n", + "import os\n", + "\n", + "import stellargraph as sg\n", + "from stellargraph.mapper import GraphSAGENodeGenerator\n", + "from stellargraph.layer import GraphSAGE\n", + "\n", + "from keras import layers, optimizers, losses, metrics, Model\n", + "from sklearn import preprocessing, feature_extraction, model_selection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading the CORA network" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Downloading the CORA dataset:**\n", + " \n", + "The dataset used in this demo can be downloaded from [here](https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz).\n", + "\n", + "The following is the description of the dataset:\n", + "> The Cora dataset consists of 2708 scientific publications classified into one of seven classes.\n", + "> The citation network consists of 5429 links. Each publication in the dataset is described by a\n", + "> 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary.\n", + "> The dictionary consists of 1433 unique words. The README file in the dataset provides more details.\n", + "\n", + "Download and unzip the cora.tgz file to a location on your computer and set the `data_dir` variable to\n", + "point to the location of the dataset (the directory containing \"cora.cites\" and \"cora.content\")." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_dir = os.path.expanduser(\"~/data/cora\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the graph from edgelist (in `cited-paper` <- `citing-paper` order)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "edgelist = pd.read_csv(os.path.join(data_dir, \"cora.cites\"), sep='\\t', header=None, names=[\"target\", \"source\"])\n", + "edgelist[\"label\"] = \"cites\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Convert the graph to undirected format" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "Gnx = nx.from_pandas_edgelist(edgelist, edge_attr=\"label\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "nx.set_node_attributes(Gnx, \"paper\", \"label\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the features and subject for the nodes" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "feature_names = [\"w_{}\".format(ii) for ii in range(1433)]\n", + "column_names = feature_names + [\"subject\"]\n", + "node_data = pd.read_csv(os.path.join(data_dir, \"cora.content\"), sep='\\t', header=None, names=column_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We aim to train a graph-ML model that will predict the \"subject\" attribute on the nodes. These subjects are one of 7 categories:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Case_Based',\n", + " 'Genetic_Algorithms',\n", + " 'Neural_Networks',\n", + " 'Probabilistic_Methods',\n", + " 'Reinforcement_Learning',\n", + " 'Rule_Learning',\n", + " 'Theory'}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set(node_data[\"subject\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Splitting the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For machine learning we want to take a subset of the nodes for training, and use the rest for testing. We'll use scikit-learn again to do this" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "train_data, test_data = model_selection.train_test_split(\n", + " node_data, train_size=0.1, test_size=None, stratify=node_data['subject']\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note using stratified sampling gives the following counts:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Counter({'Neural_Networks': 81,\n", + " 'Case_Based': 30,\n", + " 'Theory': 35,\n", + " 'Reinforcement_Learning': 22,\n", + " 'Genetic_Algorithms': 42,\n", + " 'Probabilistic_Methods': 42,\n", + " 'Rule_Learning': 18})" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from collections import Counter\n", + "Counter(train_data['subject'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set has class imbalance that might need to be compensated, e.g., via using a weighted cross-entropy loss in model training, with class weights inversely proportional to class support. However, we will ignore the class imbalance in this example, for simplicity." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Converting to numeric arrays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For our categorical target, we will use one-hot vectors that will be fed into a soft-max Keras layer during training. To do this conversion ..." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "target_encoding = feature_extraction.DictVectorizer(sparse=False)\n", + "\n", + "train_targets = target_encoding.fit_transform(train_data[[\"subject\"]].to_dict('records'))\n", + "test_targets = target_encoding.transform(test_data[[\"subject\"]].to_dict('records'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now do the same for the node attributes we want to use to predict the subject. These are the feature vectors that the Keras model will use as input. The CORA dataset contains attributes 'w_x' that correspond to words found in that publication. If a word occurs more than once in a publication the relevant attribute will be set to one, otherwise it will be zero." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "node_features = node_data[feature_names]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating the GraphSAGE model in Keras" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create a StellarGraph object from the NetworkX graph and the node features and targets. It is StellarGraph objects that we use in this library to perform machine learning tasks on." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "G = sg.StellarGraph(Gnx, node_features=node_features)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "StellarGraph: Undirected multigraph\n", + " Nodes: 2708, Edges: 5278\n", + "\n", + " Node types:\n", + " paper: [2708]\n", + " Edge types: paper-cites->paper\n", + "\n", + " Edge types:\n", + " paper-cites->paper: [5278]\n", + "\n" + ] + } + ], + "source": [ + "print(G.info())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To feed data from the graph to the Keras model we need a data generator that feeds data from the graph to the model. The generators are specialized to the model and the learning task so we choose the `GraphSAGENodeGenerator` as we are predicting node attributes with a GraphSAGE model.\n", + "\n", + "We need two other parameters, the `batch_size` to use for training and the number of nodes to sample at each level of the model. Here we choose a two-level model with 10 nodes sampled in the first layer, and 4 in the second." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 50; num_samples = [10, 4]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A `GraphSAGENodeGenerator` object is required to send the node features in sampled subgraphs to Keras" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "generator = GraphSAGENodeGenerator(G, batch_size, num_samples)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the `generator.flow()` method, we can create iterators over nodes that should be used to train, validate, or evaluate the model. For training we use only the training nodes returned from our splitter and the target values. The `shuffle=True` argument is given to the `flow` method to improve training." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "train_gen = generator.flow(train_data.index, train_targets, shuffle=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can specify our machine learning model, we need a few more parameters for this:\n", + "\n", + " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use 32-dimensional hidden node features at each layer.\n", + " * The `bias` and `dropout` are internal parameters of the model. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: Logging before flag parsing goes to stderr.\n", + "W0819 15:17:31.165925 4583318976 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", + "\n" + ] + } + ], + "source": [ + "graphsage_model = GraphSAGE(\n", + " layer_sizes=[32, 32],\n", + " generator=train_gen,\n", + " bias=True,\n", + " dropout=0.5,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we create a model to predict the 7 categories using Keras softmax layers." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0819 15:17:31.181749 4583318976 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", + "\n", + "W0819 15:17:31.192147 4583318976 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", + "\n", + "W0819 15:17:31.199890 4583318976 deprecation.py:506] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", + "W0819 15:17:31.228713 4583318976 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", + "\n" + ] + } + ], + "source": [ + "x_inp, x_out = graphsage_model.build()\n", + "prediction = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's create the actual Keras model with the graph inputs `x_inp` provided by the `graph_model` and outputs being the predictions from the softmax layer" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0819 15:17:31.395158 4583318976 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", + "\n", + "W0819 15:17:31.401324 4583318976 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", + "\n" + ] + } + ], + "source": [ + "model = Model(inputs=x_inp, outputs=prediction)\n", + "model.compile(\n", + " optimizer=optimizers.Adam(lr=0.005),\n", + " loss=losses.categorical_crossentropy,\n", + " metrics=[\"acc\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Train the model, keeping track of its loss and accuracy on the training set, and its generalisation performance on the test set (we need to create another generator over the test data for this)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "test_gen = generator.flow(test_data.index, test_targets)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0819 15:17:31.487915 4583318976 deprecation.py:323] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n", + " - 2s - loss: 1.8596 - acc: 0.2474 - val_loss: 1.7286 - val_acc: 0.3043\n", + "Epoch 2/20\n", + " - 2s - loss: 1.6507 - acc: 0.3628 - val_loss: 1.5946 - val_acc: 0.4286\n", + "Epoch 3/20\n", + " - 2s - loss: 1.4967 - acc: 0.6068 - val_loss: 1.4402 - val_acc: 0.6173\n", + "Epoch 4/20\n", + " - 2s - loss: 1.3392 - acc: 0.8068 - val_loss: 1.3155 - val_acc: 0.7112\n", + "Epoch 5/20\n", + " - 2s - loss: 1.1888 - acc: 0.8941 - val_loss: 1.2048 - val_acc: 0.7514\n", + "Epoch 6/20\n", + " - 2s - loss: 1.0651 - acc: 0.9246 - val_loss: 1.1160 - val_acc: 0.7535\n", + "Epoch 7/20\n", + " - 2s - loss: 0.9226 - acc: 0.9628 - val_loss: 1.0428 - val_acc: 0.7740\n", + "Epoch 8/20\n", + " - 2s - loss: 0.8495 - acc: 0.9754 - val_loss: 0.9774 - val_acc: 0.7797\n", + "Epoch 9/20\n", + " - 2s - loss: 0.7556 - acc: 0.9560 - val_loss: 0.9289 - val_acc: 0.7875\n", + "Epoch 10/20\n", + " - 2s - loss: 0.6583 - acc: 0.9754 - val_loss: 0.8976 - val_acc: 0.7867\n", + "Epoch 11/20\n", + " - 2s - loss: 0.6153 - acc: 0.9788 - val_loss: 0.8492 - val_acc: 0.7879\n", + "Epoch 12/20\n", + " - 2s - loss: 0.5498 - acc: 0.9788 - val_loss: 0.8198 - val_acc: 0.7888\n", + "Epoch 13/20\n", + " - 2s - loss: 0.5072 - acc: 0.9754 - val_loss: 0.7897 - val_acc: 0.8007\n", + "Epoch 14/20\n", + " - 2s - loss: 0.4472 - acc: 0.9932 - val_loss: 0.7813 - val_acc: 0.7888\n", + "Epoch 15/20\n", + " - 2s - loss: 0.4149 - acc: 0.9932 - val_loss: 0.7586 - val_acc: 0.7830\n", + "Epoch 16/20\n", + " - 2s - loss: 0.3863 - acc: 0.9865 - val_loss: 0.7402 - val_acc: 0.7892\n", + "Epoch 17/20\n", + " - 2s - loss: 0.3516 - acc: 0.9898 - val_loss: 0.7368 - val_acc: 0.7892\n", + "Epoch 18/20\n", + " - 2s - loss: 0.3064 - acc: 1.0000 - val_loss: 0.7239 - val_acc: 0.7920\n", + "Epoch 19/20\n", + " - 2s - loss: 0.2936 - acc: 0.9889 - val_loss: 0.7167 - val_acc: 0.7875\n", + "Epoch 20/20\n", + " - 2s - loss: 0.2756 - acc: 0.9966 - val_loss: 0.7248 - val_acc: 0.7863\n" + ] + } + ], + "source": [ + "history = model.fit_generator(\n", + " train_gen,\n", + " epochs=20,\n", + " validation_data=test_gen,\n", + " verbose=2,\n", + " shuffle=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "def plot_history(history):\n", + " metrics = sorted(history.history.keys())\n", + " metrics = metrics[:len(metrics)//2]\n", + " for m in metrics:\n", + " # summarize history for metric m\n", + " plt.plot(history.history[m])\n", + " plt.plot(history.history['val_' + m])\n", + " plt.title(m)\n", + " plt.ylabel(m)\n", + " plt.xlabel('epoch')\n", + " plt.legend(['train', 'test'], loc='best')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_history(history)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have trained the model we can evaluate on the test set." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Test Set Metrics:\n", + "\tloss: 0.7112\n", + "\tacc: 0.7896\n" + ] + } + ], + "source": [ + "test_metrics = model.evaluate_generator(test_gen)\n", + "print(\"\\nTest Set Metrics:\")\n", + "for name, val in zip(model.metrics_names, test_metrics):\n", + " print(\"\\t{}: {:0.4f}\".format(name, val))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Making predictions with the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's get the predictions themselves for all nodes using another node iterator:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "all_nodes = node_data.index\n", + "all_mapper = generator.flow(all_nodes)\n", + "all_predictions = model.predict_generator(all_mapper)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These predictions will be the output of the softmax layer, so to get final categories we'll use the `inverse_transform` method of our target attribute specifcation to turn these values back to the original categories" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "node_predictions = target_encoding.inverse_transform(all_predictions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's have a look at a few:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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31336subject=Neural_NetworksNeural_Networks
1061127subject=Rule_LearningRule_Learning
1106406subject=Reinforcement_LearningReinforcement_Learning
13195subject=Reinforcement_LearningReinforcement_Learning
37879subject=Probabilistic_MethodsProbabilistic_Methods
1126012subject=Probabilistic_MethodsProbabilistic_Methods
1107140subject=TheoryTheory
1102850subject=Neural_NetworksNeural_Networks
31349subject=Neural_NetworksNeural_Networks
1106418subject=TheoryTheory
\n", + "
" + ], + "text/plain": [ + " Predicted True\n", + "31336 subject=Neural_Networks Neural_Networks\n", + "1061127 subject=Rule_Learning Rule_Learning\n", + "1106406 subject=Reinforcement_Learning Reinforcement_Learning\n", + "13195 subject=Reinforcement_Learning Reinforcement_Learning\n", + "37879 subject=Probabilistic_Methods Probabilistic_Methods\n", + "1126012 subject=Probabilistic_Methods Probabilistic_Methods\n", + "1107140 subject=Theory Theory\n", + "1102850 subject=Neural_Networks Neural_Networks\n", + "31349 subject=Neural_Networks Neural_Networks\n", + "1106418 subject=Theory Theory" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results = pd.DataFrame(node_predictions, index=all_nodes).idxmax(axis=1)\n", + "df = pd.DataFrame({\"Predicted\": results, \"True\": node_data['subject']})\n", + "df.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Add the predictions to the graph, and save as graphml, e.g. for visualisation in [Gephi](https://gephi.org)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "for nid, pred, true in zip(df.index, df[\"Predicted\"], df[\"True\"]):\n", + " Gnx.node[nid][\"subject\"] = true\n", + " Gnx.node[nid][\"PREDICTED_subject\"] = pred.split(\"=\")[-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Also add isTrain and isCorrect node attributes:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "for nid in train_data.index:\n", + " Gnx.node[nid][\"isTrain\"] = True\n", + " \n", + "for nid in test_data.index:\n", + " Gnx.node[nid][\"isTrain\"] = False" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "for nid in Gnx.nodes():\n", + " Gnx.node[nid][\"isCorrect\"] = Gnx.node[nid][\"subject\"] == Gnx.node[nid][\"PREDICTED_subject\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Node embeddings\n", + "Evaluate node embeddings as activations of the output of graphsage layer stack, and visualise them, coloring nodes by their subject label.\n", + "\n", + "The GraphSAGE embeddings are the output of the GraphSAGE layers, namely the `x_out` variable. Let's create a new model with the same inputs as we used previously `x_inp` but now the output is the embeddings rather than the predicted class. Additionally note that the weights trained previously are kept in the new model." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "embedding_model = Model(inputs=x_inp, outputs=x_out)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2708, 32)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "emb = embedding_model.predict_generator(all_mapper)\n", + "emb.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Project the embeddings to 2d using either TSNE or PCA transform, and visualise, coloring nodes by their subject label" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import TSNE\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "X = emb\n", + "y = np.argmax(target_encoding.transform(node_data[[\"subject\"]].to_dict('records')), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "if X.shape[1] > 2:\n", + " transform = TSNE #PCA \n", + "\n", + " trans = transform(n_components=2)\n", + " emb_transformed = pd.DataFrame(trans.fit_transform(X), index=node_data.index)\n", + " emb_transformed['label'] = y\n", + "else:\n", + " emb_transformed = pd.DataFrame(X, index=node_data.index)\n", + " emb_transformed = emb_transformed.rename(columns = {'0':0, '1':1})\n", + " emb_transformed['label'] = y" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "alpha = 0.7\n", + "\n", + "fig, ax = plt.subplots(figsize=(7,7))\n", + "ax.scatter(emb_transformed[0], emb_transformed[1], c=emb_transformed['label'].astype(\"category\"), \n", + " cmap=\"jet\", alpha=alpha)\n", + "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n", + "plt.title('{} visualization of GraphSAGE embeddings for cora dataset'.format(transform.__name__))\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/demos/node-classification/directed-graphsage/example2-directed-graph-undirected-graphsage.ipynb b/demos/node-classification/directed-graphsage/example2-directed-graph-undirected-graphsage.ipynb new file mode 100644 index 000000000..328eb98c0 --- /dev/null +++ b/demos/node-classification/directed-graphsage/example2-directed-graph-undirected-graphsage.ipynb @@ -0,0 +1,1005 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Stellargraph example 2: undirected GraphSAGE on a directed CORA citation network\n", + "\n", + "This example shows the application of undirected GraphSAGE to a *directed* graph. In order for this to work, we wrap the directed NetworkX graph with an *undirected* StellarGraph. This means that the `neighbors()` call returns only out-nodes, i.e. the nodes reachable on outward directed edges." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import NetworkX and stellar:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import networkx as nx\n", + "import pandas as pd\n", + "import os\n", + "\n", + "import stellargraph as sg\n", + "from stellargraph.mapper import GraphSAGENodeGenerator\n", + "from stellargraph.layer import GraphSAGE\n", + "\n", + "from keras import layers, optimizers, losses, metrics, Model\n", + "from sklearn import preprocessing, feature_extraction, model_selection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading the CORA network" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Downloading the CORA dataset:**\n", + " \n", + "The dataset used in this demo can be downloaded from [here](https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz).\n", + "\n", + "The following is the description of the dataset:\n", + "> The Cora dataset consists of 2708 scientific publications classified into one of seven classes.\n", + "> The citation network consists of 5429 links. Each publication in the dataset is described by a\n", + "> 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary.\n", + "> The dictionary consists of 1433 unique words. The README file in the dataset provides more details.\n", + "\n", + "Download and unzip the cora.tgz file to a location on your computer and set the `data_dir` variable to\n", + "point to the location of the dataset (the directory containing \"cora.cites\" and \"cora.content\")." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_dir = os.path.expanduser(\"~/data/cora\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the graph from edgelist (in `cited-paper` <- `citing-paper` order)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "edgelist = pd.read_csv(os.path.join(data_dir, \"cora.cites\"), sep='\\t', header=None, names=[\"target\", \"source\"])\n", + "edgelist[\"label\"] = \"cites\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the graph with directed edges" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "Gnx = nx.from_pandas_edgelist(edgelist, edge_attr=\"label\", create_using=nx.DiGraph)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "nx.set_node_attributes(Gnx, \"paper\", \"label\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the features and subject for the nodes" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "feature_names = [\"w_{}\".format(ii) for ii in range(1433)]\n", + "column_names = feature_names + [\"subject\"]\n", + "node_data = pd.read_csv(os.path.join(data_dir, \"cora.content\"), sep='\\t', header=None, names=column_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We aim to train a graph-ML model that will predict the \"subject\" attribute on the nodes. These subjects are one of 7 categories:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Case_Based',\n", + " 'Genetic_Algorithms',\n", + " 'Neural_Networks',\n", + " 'Probabilistic_Methods',\n", + " 'Reinforcement_Learning',\n", + " 'Rule_Learning',\n", + " 'Theory'}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set(node_data[\"subject\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Splitting the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For machine learning we want to take a subset of the nodes for training, and use the rest for testing. We'll use scikit-learn again to do this" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "train_data, test_data = model_selection.train_test_split(\n", + " node_data, train_size=0.1, test_size=None, stratify=node_data['subject']\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note using stratified sampling gives the following counts:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Counter({'Genetic_Algorithms': 42,\n", + " 'Probabilistic_Methods': 42,\n", + " 'Case_Based': 30,\n", + " 'Theory': 35,\n", + " 'Neural_Networks': 81,\n", + " 'Reinforcement_Learning': 22,\n", + " 'Rule_Learning': 18})" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from collections import Counter\n", + "Counter(train_data['subject'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set has class imbalance that might need to be compensated, e.g., via using a weighted cross-entropy loss in model training, with class weights inversely proportional to class support. However, we will ignore the class imbalance in this example, for simplicity." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Converting to numeric arrays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For our categorical target, we will use one-hot vectors that will be fed into a soft-max Keras layer during training. To do this conversion ..." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "target_encoding = feature_extraction.DictVectorizer(sparse=False)\n", + "\n", + "train_targets = target_encoding.fit_transform(train_data[[\"subject\"]].to_dict('records'))\n", + "test_targets = target_encoding.transform(test_data[[\"subject\"]].to_dict('records'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now do the same for the node attributes we want to use to predict the subject. These are the feature vectors that the Keras model will use as input. The CORA dataset contains attributes 'w_x' that correspond to words found in that publication. If a word occurs more than once in a publication the relevant attribute will be set to one, otherwise it will be zero." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "node_features = node_data[feature_names]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating the GraphSAGE model in Keras" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create a StellarGraph object from the NetworkX graph and the node features and targets. It is StellarGraph objects that we use in this library to perform machine learning tasks on.\n", + "\n", + "**Note** that although the NetworkX graph is *directed*, we treat it here as *undirected*, which means that `neighbors()` returns only out-nodes." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "G = sg.StellarGraph(Gnx, node_features=node_features)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "StellarGraph: Undirected multigraph\n", + " Nodes: 2708, Edges: 5278\n", + "\n", + " Node types:\n", + " paper: [2708]\n", + " Edge types: paper-cites->paper\n", + "\n", + " Edge types:\n", + " paper-cites->paper: [5278]\n", + "\n" + ] + } + ], + "source": [ + "print(G.info())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To feed data from the graph to the Keras model we need a data generator that feeds data from the graph to the model. The generators are specialized to the model and the learning task so we choose the `GraphSAGENodeGenerator` as we are predicting node attributes with a GraphSAGE model.\n", + "\n", + "We need two other parameters, the `batch_size` to use for training and the number of nodes to sample at each level of the model. Here we choose a two-level model with 10 nodes sampled in the first layer, and 4 in the second." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 50; num_samples = [10, 4]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A `GraphSAGENodeGenerator` object is required to send the node features in sampled subgraphs to Keras" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "generator = GraphSAGENodeGenerator(G, batch_size, num_samples)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the `generator.flow()` method, we can create iterators over nodes that should be used to train, validate, or evaluate the model. For training we use only the training nodes returned from our splitter and the target values. The `shuffle=True` argument is given to the `flow` method to improve training." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "train_gen = generator.flow(train_data.index, train_targets, shuffle=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can specify our machine learning model, we need a few more parameters for this:\n", + "\n", + " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use 32-dimensional hidden node features at each layer.\n", + " * The `bias` and `dropout` are internal parameters of the model. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: Logging before flag parsing goes to stderr.\n", + "W0819 15:19:59.184190 4566578624 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", + "\n" + ] + } + ], + "source": [ + "graphsage_model = GraphSAGE(\n", + " layer_sizes=[32, 32],\n", + " generator=train_gen,\n", + " bias=True,\n", + " dropout=0.5,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we create a model to predict the 7 categories using Keras softmax layers." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0819 15:19:59.200346 4566578624 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", + "\n", + "W0819 15:19:59.209197 4566578624 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", + "\n", + "W0819 15:19:59.215890 4566578624 deprecation.py:506] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", + "W0819 15:19:59.242011 4566578624 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", + "\n" + ] + } + ], + "source": [ + "x_inp, x_out = graphsage_model.build()\n", + "prediction = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's create the actual Keras model with the graph inputs `x_inp` provided by the `graph_model` and outputs being the predictions from the softmax layer" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0819 15:19:59.402662 4566578624 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", + "\n", + "W0819 15:19:59.408103 4566578624 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", + "\n" + ] + } + ], + "source": [ + "model = Model(inputs=x_inp, outputs=prediction)\n", + "model.compile(\n", + " optimizer=optimizers.Adam(lr=0.005),\n", + " loss=losses.categorical_crossentropy,\n", + " metrics=[\"acc\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Train the model, keeping track of its loss and accuracy on the training set, and its generalisation performance on the test set (we need to create another generator over the test data for this)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "test_gen = generator.flow(test_data.index, test_targets)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0819 15:19:59.497935 4566578624 deprecation.py:323] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n", + " - 2s - loss: 1.8928 - acc: 0.2517 - val_loss: 1.7296 - val_acc: 0.3380\n", + "Epoch 2/20\n", + " - 2s - loss: 1.6574 - acc: 0.4221 - val_loss: 1.6054 - val_acc: 0.3683\n", + "Epoch 3/20\n", + " - 2s - loss: 1.4946 - acc: 0.5528 - val_loss: 1.4422 - val_acc: 0.6144\n", + "Epoch 4/20\n", + " - 2s - loss: 1.3341 - acc: 0.8052 - val_loss: 1.3190 - val_acc: 0.7211\n", + "Epoch 5/20\n", + " - 2s - loss: 1.1975 - acc: 0.8162 - val_loss: 1.2131 - val_acc: 0.7432\n", + "Epoch 6/20\n", + " - 2s - loss: 1.0917 - acc: 0.8253 - val_loss: 1.1185 - val_acc: 0.7691\n", + "Epoch 7/20\n", + " - 2s - loss: 0.9588 - acc: 0.8975 - val_loss: 1.0361 - val_acc: 0.8093\n", + "Epoch 8/20\n", + " - 2s - loss: 0.8650 - acc: 0.9314 - val_loss: 0.9699 - val_acc: 0.8195\n", + "Epoch 9/20\n", + " - 2s - loss: 0.7632 - acc: 0.9551 - val_loss: 0.9118 - val_acc: 0.8179\n", + "Epoch 10/20\n", + " - 2s - loss: 0.6954 - acc: 0.9551 - val_loss: 0.8643 - val_acc: 0.8269\n", + "Epoch 11/20\n", + " - 2s - loss: 0.6265 - acc: 0.9686 - val_loss: 0.8211 - val_acc: 0.8339\n", + "Epoch 12/20\n", + " - 2s - loss: 0.5531 - acc: 0.9763 - val_loss: 0.7932 - val_acc: 0.8236\n", + "Epoch 13/20\n", + " - 2s - loss: 0.5008 - acc: 0.9856 - val_loss: 0.7622 - val_acc: 0.8273\n", + "Epoch 14/20\n", + " - 2s - loss: 0.4382 - acc: 0.9856 - val_loss: 0.7404 - val_acc: 0.8281\n", + "Epoch 15/20\n", + " - 2s - loss: 0.4193 - acc: 0.9788 - val_loss: 0.7150 - val_acc: 0.8281\n", + "Epoch 16/20\n", + " - 2s - loss: 0.3718 - acc: 0.9932 - val_loss: 0.6941 - val_acc: 0.8244\n", + "Epoch 17/20\n", + " - 2s - loss: 0.3552 - acc: 0.9889 - val_loss: 0.6781 - val_acc: 0.8277\n", + "Epoch 18/20\n", + " - 2s - loss: 0.3236 - acc: 0.9856 - val_loss: 0.6848 - val_acc: 0.8199\n", + "Epoch 19/20\n", + " - 2s - loss: 0.2852 - acc: 0.9966 - val_loss: 0.6735 - val_acc: 0.8183\n", + "Epoch 20/20\n", + " - 2s - loss: 0.2699 - acc: 0.9966 - val_loss: 0.6630 - val_acc: 0.8183\n" + ] + } + ], + "source": [ + "history = model.fit_generator(\n", + " train_gen,\n", + " epochs=20,\n", + " validation_data=test_gen,\n", + " verbose=2,\n", + " shuffle=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "def plot_history(history):\n", + " metrics = sorted(history.history.keys())\n", + " metrics = metrics[:len(metrics)//2]\n", + " for m in metrics:\n", + " # summarize history for metric m\n", + " plt.plot(history.history[m])\n", + " plt.plot(history.history['val_' + m])\n", + " plt.title(m)\n", + " plt.ylabel(m)\n", + " plt.xlabel('epoch')\n", + " plt.legend(['train', 'test'], loc='best')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_history(history)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have trained the model we can evaluate on the test set." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Test Set Metrics:\n", + "\tloss: 0.6709\n", + "\tacc: 0.8117\n" + ] + } + ], + "source": [ + "test_metrics = model.evaluate_generator(test_gen)\n", + "print(\"\\nTest Set Metrics:\")\n", + "for name, val in zip(model.metrics_names, test_metrics):\n", + " print(\"\\t{}: {:0.4f}\".format(name, val))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Making predictions with the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's get the predictions themselves for all nodes using another node iterator:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "all_nodes = node_data.index\n", + "all_mapper = generator.flow(all_nodes)\n", + "all_predictions = model.predict_generator(all_mapper)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These predictions will be the output of the softmax layer, so to get final categories we'll use the `inverse_transform` method of our target attribute specifcation to turn these values back to the original categories" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "node_predictions = target_encoding.inverse_transform(all_predictions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's have a look at a few:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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31336subject=Neural_NetworksNeural_Networks
1061127subject=Rule_LearningRule_Learning
1106406subject=Reinforcement_LearningReinforcement_Learning
13195subject=Reinforcement_LearningReinforcement_Learning
37879subject=Probabilistic_MethodsProbabilistic_Methods
1126012subject=Reinforcement_LearningProbabilistic_Methods
1107140subject=Reinforcement_LearningTheory
1102850subject=Neural_NetworksNeural_Networks
31349subject=Neural_NetworksNeural_Networks
1106418subject=TheoryTheory
\n", + "
" + ], + "text/plain": [ + " Predicted True\n", + "31336 subject=Neural_Networks Neural_Networks\n", + "1061127 subject=Rule_Learning Rule_Learning\n", + "1106406 subject=Reinforcement_Learning Reinforcement_Learning\n", + "13195 subject=Reinforcement_Learning Reinforcement_Learning\n", + "37879 subject=Probabilistic_Methods Probabilistic_Methods\n", + "1126012 subject=Reinforcement_Learning Probabilistic_Methods\n", + "1107140 subject=Reinforcement_Learning Theory\n", + "1102850 subject=Neural_Networks Neural_Networks\n", + "31349 subject=Neural_Networks Neural_Networks\n", + "1106418 subject=Theory Theory" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results = pd.DataFrame(node_predictions, index=all_nodes).idxmax(axis=1)\n", + "df = pd.DataFrame({\"Predicted\": results, \"True\": node_data['subject']})\n", + "df.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Add the predictions to the graph, and save as graphml, e.g. for visualisation in [Gephi](https://gephi.org)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "for nid, pred, true in zip(df.index, df[\"Predicted\"], df[\"True\"]):\n", + " Gnx.node[nid][\"subject\"] = true\n", + " Gnx.node[nid][\"PREDICTED_subject\"] = pred.split(\"=\")[-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Also add isTrain and isCorrect node attributes:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "for nid in train_data.index:\n", + " Gnx.node[nid][\"isTrain\"] = True\n", + " \n", + "for nid in test_data.index:\n", + " Gnx.node[nid][\"isTrain\"] = False" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "for nid in Gnx.nodes():\n", + " Gnx.node[nid][\"isCorrect\"] = Gnx.node[nid][\"subject\"] == Gnx.node[nid][\"PREDICTED_subject\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Node embeddings\n", + "Evaluate node embeddings as activations of the output of graphsage layer stack, and visualise them, coloring nodes by their subject label.\n", + "\n", + "The GraphSAGE embeddings are the output of the GraphSAGE layers, namely the `x_out` variable. Let's create a new model with the same inputs as we used previously `x_inp` but now the output is the embeddings rather than the predicted class. Additionally note that the weights trained previously are kept in the new model." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "embedding_model = Model(inputs=x_inp, outputs=x_out)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2708, 32)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "emb = embedding_model.predict_generator(all_mapper)\n", + "emb.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Project the embeddings to 2d using either TSNE or PCA transform, and visualise, coloring nodes by their subject label" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import TSNE\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "X = emb\n", + "y = np.argmax(target_encoding.transform(node_data[[\"subject\"]].to_dict('records')), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "if X.shape[1] > 2:\n", + " transform = TSNE #PCA \n", + "\n", + " trans = transform(n_components=2)\n", + " emb_transformed = pd.DataFrame(trans.fit_transform(X), index=node_data.index)\n", + " emb_transformed['label'] = y\n", + "else:\n", + " emb_transformed = pd.DataFrame(X, index=node_data.index)\n", + " emb_transformed = emb_transformed.rename(columns = {'0':0, '1':1})\n", + " emb_transformed['label'] = y" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "alpha = 0.7\n", + "\n", + "fig, ax = plt.subplots(figsize=(7,7))\n", + "ax.scatter(emb_transformed[0], emb_transformed[1], c=emb_transformed['label'].astype(\"category\"), \n", + " cmap=\"jet\", alpha=alpha)\n", + "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n", + "plt.title('{} visualization of GraphSAGE embeddings for cora dataset'.format(transform.__name__))\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/demos/node-classification/directed-graphsage/example3-directed-graph-half-directed-graphsage.ipynb b/demos/node-classification/directed-graphsage/example3-directed-graph-half-directed-graphsage.ipynb new file mode 100644 index 000000000..8798d8974 --- /dev/null +++ b/demos/node-classification/directed-graphsage/example3-directed-graph-half-directed-graphsage.ipynb @@ -0,0 +1,1005 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Stellargraph example 3: partially directed GraphSAGE on a directed CORA citation network\n", + "\n", + "This example shows the application of *directed* GraphSAGE to a *directed* graph. However, in order to compare results against example 2, in this example we do not sample from the in-node neighbourhoods." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import NetworkX and stellar:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import networkx as nx\n", + "import pandas as pd\n", + "import os\n", + "\n", + "import stellargraph as sg\n", + "from stellargraph.mapper import DirectedGraphSAGENodeGenerator\n", + "from stellargraph.layer import DirectedGraphSAGE\n", + "\n", + "from keras import layers, optimizers, losses, metrics, Model\n", + "from sklearn import preprocessing, feature_extraction, model_selection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading the CORA network" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Downloading the CORA dataset:**\n", + " \n", + "The dataset used in this demo can be downloaded from [here](https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz).\n", + "\n", + "The following is the description of the dataset:\n", + "> The Cora dataset consists of 2708 scientific publications classified into one of seven classes.\n", + "> The citation network consists of 5429 links. Each publication in the dataset is described by a\n", + "> 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary.\n", + "> The dictionary consists of 1433 unique words. The README file in the dataset provides more details.\n", + "\n", + "Download and unzip the cora.tgz file to a location on your computer and set the `data_dir` variable to\n", + "point to the location of the dataset (the directory containing \"cora.cites\" and \"cora.content\")." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_dir = os.path.expanduser(\"~/data/cora\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the graph from edgelist (in `cited-paper` <- `citing-paper` order)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "edgelist = pd.read_csv(os.path.join(data_dir, \"cora.cites\"), sep='\\t', header=None, names=[\"target\", \"source\"])\n", + "edgelist[\"label\"] = \"cites\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the graph with directed edges" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "Gnx = nx.from_pandas_edgelist(edgelist, edge_attr=\"label\", create_using=nx.DiGraph)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "nx.set_node_attributes(Gnx, \"paper\", \"label\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the features and subject for the nodes" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "feature_names = [\"w_{}\".format(ii) for ii in range(1433)]\n", + "column_names = feature_names + [\"subject\"]\n", + "node_data = pd.read_csv(os.path.join(data_dir, \"cora.content\"), sep='\\t', header=None, names=column_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We aim to train a graph-ML model that will predict the \"subject\" attribute on the nodes. These subjects are one of 7 categories:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Case_Based',\n", + " 'Genetic_Algorithms',\n", + " 'Neural_Networks',\n", + " 'Probabilistic_Methods',\n", + " 'Reinforcement_Learning',\n", + " 'Rule_Learning',\n", + " 'Theory'}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set(node_data[\"subject\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Splitting the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For machine learning we want to take a subset of the nodes for training, and use the rest for testing. We'll use scikit-learn again to do this" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "train_data, test_data = model_selection.train_test_split(\n", + " node_data, train_size=0.1, test_size=None, stratify=node_data['subject']\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note using stratified sampling gives the following counts:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Counter({'Neural_Networks': 81,\n", + " 'Probabilistic_Methods': 42,\n", + " 'Theory': 35,\n", + " 'Case_Based': 30,\n", + " 'Rule_Learning': 18,\n", + " 'Genetic_Algorithms': 42,\n", + " 'Reinforcement_Learning': 22})" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from collections import Counter\n", + "Counter(train_data['subject'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set has class imbalance that might need to be compensated, e.g., via using a weighted cross-entropy loss in model training, with class weights inversely proportional to class support. However, we will ignore the class imbalance in this example, for simplicity." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Converting to numeric arrays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For our categorical target, we will use one-hot vectors that will be fed into a soft-max Keras layer during training. To do this conversion ..." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "target_encoding = feature_extraction.DictVectorizer(sparse=False)\n", + "\n", + "train_targets = target_encoding.fit_transform(train_data[[\"subject\"]].to_dict('records'))\n", + "test_targets = target_encoding.transform(test_data[[\"subject\"]].to_dict('records'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now do the same for the node attributes we want to use to predict the subject. These are the feature vectors that the Keras model will use as input. The CORA dataset contains attributes 'w_x' that correspond to words found in that publication. If a word occurs more than once in a publication the relevant attribute will be set to one, otherwise it will be zero." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "node_features = node_data[feature_names]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating the GraphSAGE model in Keras" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create a StellarGraph object from the NetworkX graph and the node features and targets. It is StellarGraph objects that we use in this library to perform machine learning tasks on.\n", + "\n", + "Note that the NetworkX graph is *directed*, so we also treat it here as *directed*." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "G = sg.StellarDiGraph(Gnx, node_features=node_features)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "StellarDiGraph: Directed multigraph\n", + " Nodes: 2708, Edges: 5429\n", + "\n", + " Node types:\n", + " paper: [2708]\n", + " Edge types: paper-cites->paper\n", + "\n", + " Edge types:\n", + " paper-cites->paper: [5429]\n", + "\n" + ] + } + ], + "source": [ + "print(G.info())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To feed data from the graph to the Keras model we need a data generator that feeds data from the graph to the model. The generators are specialized to the model and the learning task so we choose the `DirectedGraphSAGENodeGenerator` as we are predicting node attributes with a `DirectedGraphSAGE` model.\n", + "\n", + "We need two other parameters, the `batch_size` to use for training and the number of nodes to sample at each level of the model, for both the in-node and out-node neighbourhoods. In order to contrast the results with example 2, here we choose a two-level model with 10 out-nodes sampled in the first layer, and 4 out-nodes in the second - we switch off sampling from the in-node neighbourhoods." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 50; in_samples = [0, 0]; out_samples = [10, 4]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A `DirectedGraphSAGENodeGenerator` object is required to send the node features in sampled subgraphs to Keras" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "generator = DirectedGraphSAGENodeGenerator(G, batch_size, in_samples, out_samples)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the `generator.flow()` method, we can create iterators over nodes that should be used to train, validate, or evaluate the model. For training we use only the training nodes returned from our splitter and the target values. The `shuffle=True` argument is given to the `flow` method to improve training." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "train_gen = generator.flow(train_data.index, train_targets, shuffle=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can specify our machine learning model, we need a few more parameters for this:\n", + "\n", + " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use 48-dimensional hidden node features at each layer, which corresponds to 16 weights for a node, 16 for the in-nodes (unused) and 16 for the out-nodes. This corresponds to the 32 dimensions used in example 1 (where we do not distinguish between in-nodes and out-nodes).\n", + " * The `bias` and `dropout` are internal parameters of the model. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: Logging before flag parsing goes to stderr.\n", + "W0819 15:22:59.292147 4432573888 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", + "\n" + ] + } + ], + "source": [ + "graphsage_model = DirectedGraphSAGE(\n", + " layer_sizes=[48, 48],\n", + " generator=train_gen,\n", + " bias=True,\n", + " dropout=0.5,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we create a model to predict the 7 categories using Keras softmax layers." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0819 15:22:59.307858 4432573888 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", + "\n", + "W0819 15:22:59.313180 4432573888 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", + "\n", + "W0819 15:22:59.320682 4432573888 deprecation.py:506] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", + "W0819 15:22:59.370659 4432573888 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", + "\n" + ] + } + ], + "source": [ + "x_inp, x_out = graphsage_model.build()\n", + "prediction = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's create the actual Keras model with the graph inputs `x_inp` provided by the `graph_model` and outputs being the predictions from the softmax layer" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0819 15:22:59.573738 4432573888 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", + "\n", + "W0819 15:22:59.579632 4432573888 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", + "\n" + ] + } + ], + "source": [ + "model = Model(inputs=x_inp, outputs=prediction)\n", + "model.compile(\n", + " optimizer=optimizers.Adam(lr=0.005),\n", + " loss=losses.categorical_crossentropy,\n", + " metrics=[\"acc\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Train the model, keeping track of its loss and accuracy on the training set, and its generalisation performance on the test set (we need to create another generator over the test data for this)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "test_gen = generator.flow(test_data.index, test_targets)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0819 15:22:59.665999 4432573888 deprecation.py:323] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n", + " - 2s - loss: 1.9548 - acc: 0.1576 - val_loss: 1.8109 - val_acc: 0.3035\n", + "Epoch 2/20\n", + " - 2s - loss: 1.8039 - acc: 0.2973 - val_loss: 1.7576 - val_acc: 0.3031\n", + "Epoch 3/20\n", + " - 2s - loss: 1.6934 - acc: 0.3492 - val_loss: 1.7003 - val_acc: 0.3105\n", + "Epoch 4/20\n", + " - 2s - loss: 1.6138 - acc: 0.3889 - val_loss: 1.6203 - val_acc: 0.3737\n", + "Epoch 5/20\n", + " - 2s - loss: 1.5234 - acc: 0.4966 - val_loss: 1.5189 - val_acc: 0.4791\n", + "Epoch 6/20\n", + " - 2s - loss: 1.4155 - acc: 0.6059 - val_loss: 1.4104 - val_acc: 0.5673\n", + "Epoch 7/20\n", + " - 2s - loss: 1.3081 - acc: 0.6874 - val_loss: 1.3269 - val_acc: 0.5923\n", + "Epoch 8/20\n", + " - 2s - loss: 1.1641 - acc: 0.7569 - val_loss: 1.2653 - val_acc: 0.6050\n", + "Epoch 9/20\n", + " - 2s - loss: 1.0262 - acc: 0.7982 - val_loss: 1.2255 - val_acc: 0.6124\n", + "Epoch 10/20\n", + " - 2s - loss: 0.9155 - acc: 0.8086 - val_loss: 1.1813 - val_acc: 0.6272\n", + "Epoch 11/20\n", + " - 2s - loss: 0.8801 - acc: 0.8271 - val_loss: 1.1599 - val_acc: 0.6210\n", + "Epoch 12/20\n", + " - 2s - loss: 0.8641 - acc: 0.7932 - val_loss: 1.1410 - val_acc: 0.6288\n", + "Epoch 13/20\n", + " - 2s - loss: 0.7044 - acc: 0.8745 - val_loss: 1.1228 - val_acc: 0.6325\n", + "Epoch 14/20\n", + " - 2s - loss: 0.6694 - acc: 0.8874 - val_loss: 1.1130 - val_acc: 0.6366\n", + "Epoch 15/20\n", + " - 2s - loss: 0.6119 - acc: 0.8849 - val_loss: 1.1079 - val_acc: 0.6423\n", + "Epoch 16/20\n", + " - 2s - loss: 0.5858 - acc: 0.8948 - val_loss: 1.1106 - val_acc: 0.6403\n", + "Epoch 17/20\n", + " - 2s - loss: 0.5372 - acc: 0.8907 - val_loss: 1.1156 - val_acc: 0.6395\n", + "Epoch 18/20\n", + " - 2s - loss: 0.5024 - acc: 0.9000 - val_loss: 1.1208 - val_acc: 0.6358\n", + "Epoch 19/20\n", + " - 2s - loss: 0.5256 - acc: 0.8729 - val_loss: 1.1160 - val_acc: 0.6415\n", + "Epoch 20/20\n", + " - 2s - loss: 0.4661 - acc: 0.8975 - val_loss: 1.1261 - val_acc: 0.6382\n" + ] + } + ], + "source": [ + "history = model.fit_generator(\n", + " train_gen,\n", + " epochs=20,\n", + " validation_data=test_gen,\n", + " verbose=2,\n", + " shuffle=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "def plot_history(history):\n", + " metrics = sorted(history.history.keys())\n", + " metrics = metrics[:len(metrics)//2]\n", + " for m in metrics:\n", + " # summarize history for metric m\n", + " plt.plot(history.history[m])\n", + " plt.plot(history.history['val_' + m])\n", + " plt.title(m)\n", + " plt.ylabel(m)\n", + " plt.xlabel('epoch')\n", + " plt.legend(['train', 'test'], loc='best')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_history(history)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have trained the model we can evaluate on the test set." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Test Set Metrics:\n", + "\tloss: 1.1261\n", + "\tacc: 0.6382\n" + ] + } + ], + "source": [ + "test_metrics = model.evaluate_generator(test_gen)\n", + "print(\"\\nTest Set Metrics:\")\n", + "for name, val in zip(model.metrics_names, test_metrics):\n", + " print(\"\\t{}: {:0.4f}\".format(name, val))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Making predictions with the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's get the predictions themselves for all nodes using another node iterator:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "all_nodes = node_data.index\n", + "all_mapper = generator.flow(all_nodes)\n", + "all_predictions = model.predict_generator(all_mapper)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These predictions will be the output of the softmax layer, so to get final categories we'll use the `inverse_transform` method of our target attribute specifcation to turn these values back to the original categories" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "node_predictions = target_encoding.inverse_transform(all_predictions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's have a look at a few:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PredictedTrue
31336subject=Neural_NetworksNeural_Networks
1061127subject=Genetic_AlgorithmsRule_Learning
1106406subject=Reinforcement_LearningReinforcement_Learning
13195subject=Probabilistic_MethodsReinforcement_Learning
37879subject=Probabilistic_MethodsProbabilistic_Methods
1126012subject=TheoryProbabilistic_Methods
1107140subject=Case_BasedTheory
1102850subject=Genetic_AlgorithmsNeural_Networks
31349subject=Neural_NetworksNeural_Networks
1106418subject=TheoryTheory
\n", + "
" + ], + "text/plain": [ + " Predicted True\n", + "31336 subject=Neural_Networks Neural_Networks\n", + "1061127 subject=Genetic_Algorithms Rule_Learning\n", + "1106406 subject=Reinforcement_Learning Reinforcement_Learning\n", + "13195 subject=Probabilistic_Methods Reinforcement_Learning\n", + "37879 subject=Probabilistic_Methods Probabilistic_Methods\n", + "1126012 subject=Theory Probabilistic_Methods\n", + "1107140 subject=Case_Based Theory\n", + "1102850 subject=Genetic_Algorithms Neural_Networks\n", + "31349 subject=Neural_Networks Neural_Networks\n", + "1106418 subject=Theory Theory" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results = pd.DataFrame(node_predictions, index=all_nodes).idxmax(axis=1)\n", + "df = pd.DataFrame({\"Predicted\": results, \"True\": node_data['subject']})\n", + "df.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Add the predictions to the graph, and save as graphml, e.g. for visualisation in [Gephi](https://gephi.org)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "for nid, pred, true in zip(df.index, df[\"Predicted\"], df[\"True\"]):\n", + " Gnx.node[nid][\"subject\"] = true\n", + " Gnx.node[nid][\"PREDICTED_subject\"] = pred.split(\"=\")[-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Also add isTrain and isCorrect node attributes:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "for nid in train_data.index:\n", + " Gnx.node[nid][\"isTrain\"] = True\n", + " \n", + "for nid in test_data.index:\n", + " Gnx.node[nid][\"isTrain\"] = False" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "for nid in Gnx.nodes():\n", + " Gnx.node[nid][\"isCorrect\"] = Gnx.node[nid][\"subject\"] == Gnx.node[nid][\"PREDICTED_subject\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Node embeddings\n", + "Evaluate node embeddings as activations of the output of graphsage layer stack, and visualise them, coloring nodes by their subject label.\n", + "\n", + "The GraphSAGE embeddings are the output of the GraphSAGE layers, namely the `x_out` variable. Let's create a new model with the same inputs as we used previously `x_inp` but now the output is the embeddings rather than the predicted class. Additionally note that the weights trained previously are kept in the new model." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "embedding_model = Model(inputs=x_inp, outputs=x_out)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2708, 48)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "emb = embedding_model.predict_generator(all_mapper)\n", + "emb.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Project the embeddings to 2d using either TSNE or PCA transform, and visualise, coloring nodes by their subject label" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import TSNE\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "X = emb\n", + "y = np.argmax(target_encoding.transform(node_data[[\"subject\"]].to_dict('records')), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "if X.shape[1] > 2:\n", + " transform = TSNE #PCA \n", + "\n", + " trans = transform(n_components=2)\n", + " emb_transformed = pd.DataFrame(trans.fit_transform(X), index=node_data.index)\n", + " emb_transformed['label'] = y\n", + "else:\n", + " emb_transformed = pd.DataFrame(X, index=node_data.index)\n", + " emb_transformed = emb_transformed.rename(columns = {'0':0, '1':1})\n", + " emb_transformed['label'] = y" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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AgICDmkAIAwICAgIOagIhDAgICAg4qAmEMCAgICDgoCYQwoCAgICAg5pACAMCAgICDmoCIQwICAgIOKj5h9xiLeAApJiHFx6CZY9DVT288zKYMmd/hyogICAgEMKAt4BSAX78Mdj8Kk44Tq5cxnj+IcLv/TLGSZcTkv0dwICAgIOZQAgD3nwW/hU2r6SjagxrbEFFQTklwnf8kE/WnUtzKsHlVXBRChqCEhkQEPAWE8wRBrz5vPw0RSvC6rJgAR6QMyOYyiXUtoYncvCZdjhtE9zSA0rt7wAHBAQcTARCGPDmU91ApmwzqG85DzxPoTyP3mgVJhASyLowrx8eye7PwAYEBBxsBEIY8OZz/CUoIFIukPHAU4rGbCcvj57FurpJ2IAJFBTEDbgzs5/DGxAQcFARzMgEvPmMP4SB938Tfvs9aktZ8FxeaZ7Nly/4PohgA7aCOhMsgQFvfwd4H2OXYeljsPRxSFTDcRfBxEP3d6gCAgJ8AiEMeEuYcvy5fKLhFPJtSyHp0lrXQpgidU4vabsWhTA2BGkP3lu1v0O7D3Fs+NnnYNXzEIqA68Czf4IrroeT372/QxcQEEAwNBrwFiECc+o72TJ2Ej314wmLiYFJwkwTM/O0WJDxYFII3le9v0O7D1n+JKx6AerG6PWTtU0wHSh9EjpPhvRnwX51f4cyIOCgJugRBrxlNKUWEE6fChjEDRtRBmknydT4Ns4LTeOYOJydhMTbrHnWTReb2ISBwWQmU03N9ovLnwTL0i0BQI1vZ/XRHi+5FxLLNnIyS6mzPwI1vwZrxn6KQUDAwU0ghAFvCQpFPNzJuU1/4+HuE+hzoiiEifGtXNq4iOusafs7iK+L55zneDq/gLLtEg4ZpJILOMU4lUOYpW9I1YLnAqBE8YMjz+f3xiU4gFe2CKcdbkr8O6fkf0Gp/CUG2tqoamkhVle33+IUEHCwEQhhgF64564BVdS9Eons8vbFr8CDT0KxBGccBycfDaa5ay8E3SManVjPP8VbSdvVhIwyEStNE5P3VUzeUtalW7mn6+8MdCiUC2YUwvEC2emP02RNpM6Iw9wLYP7voFxg4bhDuDN0MQ1OB4YoylacvIpxY/Zz/Kb1SuZ/+inEMFCex6wrruC4L3wBY3cJGxAQ8IYJhPBgx90CmevA2aSH75RA5AywpkP4BLCm7nD7r/8E/30HGAaYBvz1KTjzePju5/W5nVGihEJhYIK4VIf7UCjAGBLJN0KHA3/MwMslmB6Gd1dBS+gNO7tTMpkSP7nvbzhHOcSqQkRaSqiwQnmKznKZj6dX8c/lyRyVh6orvkTo3h/yaHwmolwMQ2HHonjiEZUc/cUUz6wfh1UfIWJGKTsuT97+O34bbca+7GrOTcGFKQgHW9EFBLwpBEJ4MKNcSH8avA4wGvV/dxXkluExAYwYRuoTEP9nADp74Gd3QkMthPyS43nw9wWwcAXMPQxcBT0uVBkQrRBGFxcLi2aayZKlTIkwEWJEMd6gzdbGMvzTNsh6EBVYWIC7M/CLZpi5687tXqOUYt68Jfz854twDt3KkYc6lDwXU4CyAIIVcZmUv4eHrniUNYYiahrMueYqOO5iSpRxrH48PFCCoMD1WP1ygolmERQstk2y8VrKd93Ob8+4ml+lYZwF94+Hd0T3bXwCAgICq9GDG2c5uG1gNAA2uGsp5y3aVthseX4DrS+sJb3yaxS7XwRg2SpQQMhSuF6JmjW/ZdpDFzD34eO444LzuPGzX+HExR2ctbnAiZuK3Nzj4vjbycSIUU89Dg611NLEaGqpxcZhKlPJk6ebbsqU9zoaP+mFvAejLagyPRosF0cpftiz75JqkCef3MRPfvIi1dVROhaEEUOwix7pjfq6GAopO4QHevHmtpCpbSRSVcXCX9zK9A1rcMKj6Db8+T8F3W49W9yx3D/942wYNZpNNgy4ULbCRAf6CaFf0lYHLt2ixT4gIGDfcsAJoYiYIrJYRB7wv08SkedFZK2I/F5Ewvs7jG9XlNLbmm0/kQXRi9nXlfrpL3u0vpyllPewIiaGFaGcHWDFbz6NUop4DBzKbGMr0WXfpH7xdynnuyh5FqH8KrqemU9/bh22u5mC0cnN6T7+s3cA0HOEp3IaBgZZsuTIkSVLLbX00c9vuI17+CPz1K95wl1M0dvzDUefK0CN6dFNDy873Swo5lnj5HgkZ2PvY+G4447lxGIWoZBJwqli8c9NorWCYSrEVLqHtyaNMi2kJkxZgRkKEa2uxv7FLVxd49DBaBa4c3nGOYFVagbRrnb6q1q487Lr2RSvxQFi6V5a55wAbH9J2x14LNh+LiBgn3PACSHwWaByYdVNwI+UUlOBPuAj+yVUBzpeBlSR/u5V/PZX3+HG677I//3iR/R2t+KUSrxwyy3MO+UUfjF3Lg9dey1969eDNQtHwRM5my5HsAccPEdBSMiqECKCEbIY2NZJx7JlTJ+dplzVQaEzw7h1D1GMNlAOJ1CxCPkJowkXs0x+9G94uSIhBQmzwK2ZPHnfarKRUVzJVRzPCRzCLE7nDEYzhnWsJaZipO0YK4oGf3Ke4d0d6/hFH+yJHtYY0KH6WF0K0VmuoeBF6HeibLYNzt9cotvZd8nc21skFNIGLBMnVtP2aJzHvmTxwi0m7ctMutYmMPI2SgycTSXqfFsXMxKh0N3NNVURTqx/lpA41Ie7aY5uJdwSJ1oYwFYmm6YfRryrHTsaZ9FV177G/037MC4BAQGaA0oIRWQscAHwS/+7AKcDf/RvuQ24ZP+E7gDFfhX63gvdh1NuHc8Dv7+Kn/9yK48/6XLL/2ziPZf9gN9/7noWz5tHKBYj2dTEtoULue8jHyHX43KbfS3V0kMIG0oO4YiLg0UZwTbLKCW0vVJFrrOT9eGVvOfGJ2mULTiuhe2EwIPG8R04iRBeOETN6jWAoFyXsN/b3OB1DwU3QYLDmcPJnMIkJrOaVSRI0Obl6TG2kAy3E7d6mFL3F/6nr8jt6d0nweU1Rdptg7yjJ9BKnoWHgYnLUwWTK1qh1903yX3qqRPIZEoAWJbJO6a1EC5FwTGJT68lMasOd1wN9mMdVHeUh4Sw0NfH2OOOY342Rl9xInGzRMTwUCqCSsRw5owjVlOLN+twVl/6Ie7+zzvpHzsJABe99VyNATOD8ZCAgH3OgWYsczNwPZDyv9cD/UqpwXZwK9CyPwJ2QOJ2QvpDYK8CFOs3Rjjy0M3c8KksP/3N2SAWbVs9bv3jVj4ydzTim3UmGhvJtrez+v77+daJH6ZBzebi0H1Mbk1wYnwBkRhEKIASnniogd5ORf20aaxnNaMnDvChH/ez5Zx+nFgR0xhAogYFO45TdhiYMAFQiCGUPJOEWaJqJ0sAbGw8PIoUyUg3KAMXbXAStXoYW/13bu4/nquqq7Fk58sIzq/KcEt6gG67ClfpOBp4hA0XBbTa8Mc0fGwfLM276qp38PDD69i6dYBY0qTlyl5OO6nE6PExivEiS/pnsLhwBY3hFzly+hNYr7xE5KUFKGXQPfHdPP6/eVY3HkH5hDzhSAlTPDxlkTXGUtcU44OHjmNF4Sye7QMHEPSG5HGBo2JwUvyNxyEgIGBHDhghFJELgU6l1CIROfV1PP8x4GMA48eP38ehO0ApPeAvewCI0N0bI2S5TJ3Uw5jGDtq6W6iO2azLN4K07vCoGQ7TvWoVqZPhpfKRLHePZNz093HVd97LhFAbkorQvTaC01YidtF4khPH0kyWdawhNCZFzaWzyd63GDvsojyLui1baG9oYeXpF0EoRo4YZU84a9RTPC8GPUxlFrMJs71LEyNGihRbacVTgkIvvDfEw/UsplctJevGeVGlOF6OHnpOoeihm21sJUSY0TRjexaCQvD8xRhC0QsTFUVS9Dzix/ZBktfWxvjNby7jgQdWszj2PIkTPMYkm3GjEZYXXSYn10LDZJ464UxePuoUqto209jVRuoel46Xx1Da2E1m1FjcvioGTk8hEQ8DA4VwaBwuScGHauCjtfCdblhU1CJ4RRV8vG5HS9yAgIB9wwEjhMA7gYtE5HwgClQB/wXUiIjl9wrHAltHelgp9XPg5wBHH330wfHTru5GUDmQGCBYpsJTBp4n1FQP0NYNhAxCUkZ5HmJur0WdcplRs2dzXR18pF3/WO74ula2/ecH6bpjCYlHlzM6GaLuxrlw8Qw66WAa01nGUvrpJ3HjuXiNMbK/W4BsGaBuvEfdN1rYNLOLVqeKVGgz48vrGJvO059weE6eZS1ruJhLCaEX+AnCSZzCb7gNEw9XCYbhYeKAgCWKw2pe4gkpMpMp1FKLQvEMT7OC5Sg8BMFWFknrDEKGg+sNLh5UeIAhBiGBsftwTWFVVYT3XHUIOZ4lzChMTJYXATHBCxOLLQGmYJVLlOsaSdtR1l/TyPQfvYIa6MVJNNDzfByVEax3m9gGJAWub4Aav+M7Mwq/GbvvwhwQELBzDhghVEp9GfgygN8j/KJS6moR+QNwBXAn8EHgz/stkAca1hx/D0sFIjSNLrO5NYoSoa0zhed6tPcmmHFoC0u3lZlZ30YkbJDr7iZWW8v0Cy/k8Dp4LAd3DICNgUpFSH/0DKZcewajffHIk8PAIEyYS7iMZSxlfXgdNddeivrUpXQVX8Qwc4QkxCecB8msjvLT/3kPz/eexGPbtuB0r2L0hCzvuGQzk96/kqOq3jEUhfGMZw5zWCIrKHgKz/XAAIWJi1Blerji8ixPcwHvopVWVrCMKDF/2NCi0ytzdP3jKPu9LC2alJWJAZgINYYgAlfu41+0cHBwcYfWQA54egiz5FlEzCyulCBlUFJhqnL9hEslcjNTJF8sEi7miVbFcZZCw8kwfpJeLP+rPjg3MbQt6RBKwa198P0e6HCh0YTP18H7q+GBLCwrwqSw7k2OeRM3EQjYjvKb2sPzKuDtyQEjhLvgBuBOEfk2sBj41X4Oz4FD5Gwwp4CzioxUExpv0hIqcPdfZvHMpqnk26ogdgiRMeexMd3Po33reF/NvzP+2rNoftcpdNRlGEc1/zfO5Htl+FV2OoReYWLMwxBdwRcpEiVGI6MAPZx5LMdxLMexiIW8oJ4kYaQxCOMpj7XFOm7/j2sI2VmcDU8zsKkTJRE2DSTJdHex7YnH+eNtzWyNbsLGZjzjeScn0mF04NBJERulwBAHC5OwkSNJNVvYjIvLKlaSIUMvvQCYmMSkERGHCdEMtVLFqrI2jnH83zi8aRTM3scL0U0VIeI1UJR+etwoOU9vJhCzCmzOTsMRCInDiQ1PMmXyGhzHZKCmivWdzWQTCUJiY6ZMpiaFakvIuHr49hNtcFYCzk/pHykGvU7yy53asi0s0OXADZ1wSx+kDIgIPJ6H32Xgf8fAIcM2EXAUtJYVRrFMSzKEaQbjq6+XtAtf6YB7s1BWcHwMbm6Cyft444aAt5YDUgiVUvOB+f7xemDu/gzPAYuRhLq/kk5fQ7+3Ck8stk6eSfzKI5kTn80LfzmBwyZMxDQExoxiwAjR9dmLSE0r0MtaXmYdo2jkQi6mJRzma3XNLOQYFrEQ0FOPYSKcy3mYvNZYZQXLiQKGaAMXA9i8cBK9xQTjrI30bWrDiqf0WsWyQDzE+nV9fOnxn3LUedUYGLzEIsY4h2EULqTbuo+qyGZMQ/nzhS45N0yMGHFTN73XspoiJUJYKBQODgNGG0mjgRWuYKF3k+l09DDj3eO2DzfuC0rK5df9wh1pg7R3CWVzK4dULWZMpAdbbIpuhJf65uJ4Buc2P0xjpIOCE8VVJjUNvcz5XobVdx9F+h01GDWKl6pdUgWLnCsogeVFeNXt5u/eai6vtpkkE/lBzzgMDGK+fsUE+l3YUIYzE+AnDb0u3NQN8yrMyR7LwhdW5tjQlcdxFY3rOvi3FosPXD4TCboze4Wr4KLN8GIRwuiGyaM5OHEjLJsCDQdkbRqwJwRZ9zZHmaN4qe5LvKKW4KoSRXEhZdJ4Sp53nfsAVjFF3/NHkV58KLOunE9RFQmXk8i2LpytfWwb38vicYuwCPGKWopSeSbKFMZIMymqGMe4oTm94RQpEpME+LIFQj4dw1MGxUwBJXpY0sMDTDzXxDBhyxKX2ecVaaKJV3Kj+I/OKWNyZsgAACAASURBVCgVosAFzEi9wlE1LxAz84goXAUb3BKHlI+kP9ZHmTLKtzQd3KNUCTSEClweb+JPGf3jvuen4FN1ey6CjoLVZS3+08PbxWWQHDme5Rlu7YvwTN8sGi0YY9aysNhCV6me0xteIOskeD49lZyTojHSTkOki6IbwxODJAOUczHCtWVaLmol3V5DtNFG8OhyTQyE6SGYlHqZ0VVPYCt4wYNXjBXMqpvGC91nUrnaaXCfgLLSwghQa8CKkt5lp8PRlfR3Npfo2dBPxPOImAa9M8Zw/YqtJO95lcsvn7VniRMAwLN5eKkIcfQ+uwAhdC/xBz3wvab9GbqAN0IghG9jNrCe53iWjWygKEVEhBAhbIrUzyyQaatBVJnR5zxBuDZNbGwbuW0h+v77DuxnV4Np4LkOj50zn6P/dTRnOgsJqRI5ibMmcR4zo9/ZqQiCnt/bLJtJGE3gtoNYNE5vJyI2sWgVCgtPDVbZQiLuks9A3TihSIGsp7i78wgsKWOYWSxctubGkrIGmFW1DAuHiFmmq1jFnd3Hcvb4DjzD22GTbuVv3R0Wi2vrLK59HUsklhbhhg7dy1Ki5+D+vQlm+cNdLi73cy9dXpZF6UuotkrYYtNBGVs1Y6gwL3afScyAiHQyNtJKdaSXiGEDJjEzT61VQGbn6S9HaLa2kYk20ksdLgaCiyEGY6wio6uewvWiOMoi40BLRDExuZZ12Zn0l3a0hh4cKh3ERb/Q39jq8EhB6FQGHZ6B2VJDtDODOB7RbInC7Bb++9b5XHbZITvtFSoFy0u6lzkj/I8x99jj6GHliSGIvo5RgiVF3QAZPrIsAgvy+ySIAfuJQAjfpmxhM3/hIQYYGNqfU6GGjgUhMWoApzuE6xpUHbUY21NU//kpik+/gjsmjoggXhgeXEahaR1rPjSdpFeiyenksIG72GoczuTwNTsNw3EcTxttZM3RmBLBUz1MnrGJc45o55lFp5BIZsmk2yAUo6ahiFd0CCVgxrkmgseGQi2OMoibDjba+kCJyYu9x1FwokxJrsUyyqzIHEZfOUxfqZ58LI+BgYmFQi898PBwcShSpN+J8pxfKZ0Qh6adlPABBnBxwa3m0+2CKGj07+134VNt8MB4/SPBrWyhnzTiNeAqA0sUYOFiEzVcSq5FQcEoAzrsGpTlkTBLpEJZkioHKBKksFUXYpgUnDj1Xhd19JCPJSgRougmqEu+DLh+A0K/nAZCgwljYhvpKIwf2qTAAGpNXTF3ONBuQ8HziLX185OSh9Wbw53UoBs7kRC5hhSp9rRvYKRoL7i4rsKyXiuEnQ58ul0PvfqmWFxZBZ+vf3sah/Q78L6t8HRep1fCgC/UwQ0Nexefaf7KH6Ve+9yEf4CGwsFMIIRvU17kBWxsHGzE/1NsXzVihh0MJRiNPXiuoJTQvnwstU89g91ggfhbrRggdTGW3++Q/ORUREGtO4FTsguI52+HXQhhLXW8hyt5VV6hy+ykgUZmWbP54A1J/vYM3P2Xc3n26Yfp611NLOowamKY477mEW3QvcSQ6Hk+QYgSpUAB0EtA0k4NJRUBpbCdOFscuLE7x7HNBqZRRkQ/Z2ISJkyEKPcNOPygSy9EB124b2yAiyosRgfI8CiPstVrp98TeuwEOTmTRmkeuqfG1OLybB7OSmrRVHikzBIRw6Hsmf5ifUVLqMhqN4mFXgKhVJicXceY8EocN4JhlAl5NdSXbLZaDoYHW8oTSIZzejDZKGIoQQybpsQSPCnS6yRxEAoKulxoDitOjoVZaUDGg5TAp2shYcKPerXFqoHCSZfoS8Yh4mJmSpAp4NYlscoOdiyMMv2VlmWPmSkLyxrZaOb/dWoRHGXqCt9V8NsMHBaFM5N7X1b3J0rBxa3wYkGvxzTQw8nf6YZmCz5Qu+dunZ2E8SHYbOvhUYCS0r3yz9a/GaEPeKsIhPBtSh99QyKoF2RrYfD82SMRLY6WpfAExHKYMacDu2yDFYah4UWFChmoTJmEV0IBvWaSFdGpnFTq2m04kiQ5ZrgtkwUXnAInnRjjvuy5LLUzmG4/4xsiKDNCH70kSJCKtmIac4h69dQaETazCdfv5zVHtxASm4IbZ2V+MoaUGVd3H0U3QhiHkHiIoeNaTQ1Rt4YfdCWoNrUVJUDJ0xXesX7P0MPjQe6nw82wupTAUSBS5PDGB3h021XMDCWJCOQ8KHpaYAbdNzAwxOPsupX8ufswHGVjGgpLWUwPw5wIdHpwTTWkzDBLc6eyIX8SZ9avIBVbgdu9hgk9fayZEiEWztHrVBM2i+CYOCrBoVVruL/9Qk5ofJyQUcD24pSUXqbRahuE8tPYNl2HK2ZogVpS1JaiUwXsYpnFazthciNEQxixEN5AGVIuTthCgFI0jKsUTQ8s5bprjxkxP7scvYi/0dze6zFFz0P+IfP2E8KVZZ1OcbbP+0aAvIKbe3cUwi22f6/A8fHtVruDGAKPTID3ter7QI8i3NQIR8TeitgEvFkEQvg2pYFG+uhFobCw9DAfDPUM9Q4tgoQcTH/YxrbyqONbkBfakFEJQA970VPEOWcSeYkQVyUiqkxreDQxd9LrDl+3Ax/eBtucCHHj3dQnltNb2MA7ozHOs87HwMA2HGY11vL1zhg9jkJopqgyzKpaxqhoFxm7mlczhzI5sZa4USBkFHHcBizDwaFMUlk44mJjU1U4HRcZEkGAiAG2C59a3M/CDRlso8Dcw6OMrS7gJYSoAY6KEpIso2NreCV3BH0uFP2O9Vc6YVpYEY7myZKlk06aq9q4zEzzTP9M8k4tZyXCfLwWpoy4B6gJHA4czsDKX3DHpC4mb+vnY6HbWRA6ipeZTihUYkJsGbYXY0txLI93nM3pTQ+TMHO+CZLwRPeprMvWMye84zZxq0q6cq61YFPBxUMwsiW8eBgnEUHiYQhbELFQhpCvjXP4so385MOzmDynma3lMs2sQYwomJNBhNLg+rhhMbHQDYS3Gx22NoQaviOPKbq3DbrX+LM+uLVffxf08Okto+HQYctuxobgqUl6+Djj6R7iSD+YrFD00UeBPLXUESfYG+9AJhDCA5wiRbbSigLGMpYo+s2cy1w2s5ECBX/GzKKMjenPmZmYOP4g4aBxiYcH1x2LfOQhaM+iLEHZHjTEkU8eQdqMY7gOEeVgEcKK/8vrDvdt/bDNUUxLrKM2sQzLKNCZn8Tf0nP4QHN8qLcxLQGHj3f4SX4R7aqXkNnO8+lZvNhzAofXLGVu/XOIgohZIGKUKDsJivYYlORoiJRQ2BzLcbR6oysGhjVKwbqcw8tZg1jIIlSd4unyCTRu3shJ41fgVZWJW1kMsRkV3ciL6TkYfmpF0D2EMzfbfGXqY1Qb1YQIszRby/N90yjbozk8nOLCpLETEdyRbUfOQqWXEco7NDpZzg09xZRxy1GA7TXwaOeZoIQ+u467Nl9FfbQHSxw6S6NxPG21c1OP4viqLmJWhlpqqTfrh15gMx7GjYeR2jiYBqoxiTL0uKaYBilDcKvjtJ8yi4+ZENmYwfC20mx08W/Jn3NYxIOq/6DFGkezpY1kqs3t6Tig4JzX2Rt0ikXK2Syx+vq3fMnG1Ij+seayB+EKMXQUHOobQ71UhF/1616wCfR5sLYMF26Gn43R9/5hQFuHnp6A99fAKAt/Ze1rKVLkYf7KNrYNjdYcwZEcw9wdDL0CDhwCITyAeZkV/J1HUCjChLGwOI0zmMo0RjOGMzmL+cynm24sTBqooo56GmhkGUsoUcLxt7FWKEws3Ik1eH+4FOPBdbC6FzW7Ac6bTKQqCapEyUhiEuJQ5oL5+s3r5+dhetVCmlPP46kwHgYtqcVkY+vo9N5Nk7m9qd1prqIp9SJTSLGqZDIp1oGYaaLWAAN2NQqhrCxSsTRlr5eCmwQVp44ERXJMYALjYrowl/05G5RHl6PIuYpU7wBhBUbWIza6QEd8EunQSiaE2vCUIOLRURyNKQ4R8bAwiIgeTux3hSXp2ZxRu5H12Yk81nk0MSPDIaklVEXS/HcpRU4O5bz4zqpFjVXdBKGxkN0IdokicWw3TDhUBgWm2CBaym3CtBe3LwY0AEGRJcc3+tfSUa6hx84zLTRASMbT7RhEIxYyqR7leJArgWWCZYBlkDJ0j8j15xz7PJejzTWkpEy3auBTA1/hT8Z1NKQ/i9T+kW+MMri2Tf/+oYG2Rp0dgcv2cncep1Riwc03s+ree/Fcl8SoUbzzS19iwokn7p1DI+ApHb6EsV2wR2JsSBv6zEuD6+/+U0aL47cb9T1/yep4WgLryrBN7/CHo+BD2/Q900JaSG/rh0ez8NVGWFEER+D85I5b+D3BfLaxjQSJoemKRSykngamMGWP4mfn8zjFItHa2mC951tAIIQHKC+wgIf5G6CXqgt5aqnhMf5OjBhP8QRp0hgYtNDMYcxhHOMZxShKlHiZFQhlTAxcXN+wxEBhIHVJ3GsO9U1s9O4sChNPopRIMomZHMkFKBRZBjCx6KOXAQaopZZRNKFQFCjQQTsFCjTQyChGDbV4q80i9cmF2F4C/MX4thcmamXYLCtpYs5QXFezihBhBGG0Bf3RVqoj7SgU9eFubM8i66Sw3ShxK0vZC5MwM2QlxAwOYRRNSEhbAd7U6eD0dSC5DL2RFHVlg4gqUyaMVzYoZSIY9VAiikIwRFH2ImTdJIKHIQ4KoYRDmDAKRb+tFeDRvpkkjSzHjXqUmJnFVAlS4a08JquYwdlM3kUl16TG0ReqY82kWhzXogqPvPKw6EQkz+yaBSwfmEnJG1yqvR0PsPBwlMvTfbOptopEDIflRYuIV2BGOMGyIpgRC4WD0ZbGbqnRDxvib3igxcMFYjjkiVFluNSQp82t5a/2Wbzf+AM4y5gTncPd47RAtDlwdBROSYw8BAgjW1ECPHPTTaz8859JjhqFYVmUBgZ45LrruHjePBoPOWSX5X9XLMjDt7v1fKYCTktoo6iqnQjij8fAjIge/uxzYW4UvtsIR/mjlYNWuAVPi2AYHR8FZF3fYAg9ZxgTbXhzwWY9zyjAFwX+uQZuagJHimxk/ZAIAhgYhLBYztJdCmHJg2X9eZbP+zXOXbeD61A7aRInf/WrNB122OtOr4DdEwjh/sYbgNKDUF4I5jiIXsI6y+Ex/o7jV8YmJh4e/fRTTTX3cS8mFnH/ZStTZjEvMZNDhiwwj+JolrGUEBaCgcLT84aYnMt53M0f8PCopoYYMYoUSJNhIpO4gHfRTRfzeZwMafrpx8QkRQrBIEaMEkW66EKhSJAgRpxJTOJMzsbE5F3VvTylBEOZQ9uhlhTUY/FQsZWbe+cwLayHmYywMWTxGjNK1EW6Ub5Vq0KImCVChkPJjdFbGoVFlIbQAOOYwumcMVThXJZSHH/bdTxrR5B4irwZ4aax51BNPyvq3kEhHkNZBmG7RJgythem4CQImSVa4ltYl5vhb++m51nLykEwmBhrRynotJMcVrWQqJml7CWJEEEpyFHmSZ5gAhNH3IGnW3Xzg/xzFKwcsfAA4sboc+OIUcR2ExQ9k5RV4Ljap3i292Rcf3OCQQZzL+/GGBftJWHaAFRbLjFzLTNqNnKklWNJZgJtuTlsToyi39GVd+Xv+JYZbJJ4hCquGCg6XN9qRA0A2rjoQzW7LrpLinBzj17E32TCR2rh0pQWjkJfH2sefJBkUxOG/zNckVQKu1Bg+R13cPq3vrUHL8drWV+Gz7XrHt0oS/dy/57TmwjcMkbf0+vC7Wm9q07ShPdVwafr4DM7sew8KwkPZvWyGdhuKVtSYKOFfklRzwMnBHr9udIqQ8/R2h783BfZ6pBNtAaS6PBFRbujxKQopZ3G6/EsfKED+jZ2oKaeTN0Xj+bqB28ltPYVHvyXf+GK3/+eqpbgF+jeLAIh3J94vdD7LnBeBWWDRCjnf8qjdZdSskJD6wINDCJEcHHJU8DEpJHt41RhwmQZYC1rmMMRAMzhCNayBheXKFG0eUmZuRzLRCZxCZfxKA8P9ex66cHFpZtO5vErihRJkqRIERcXD488BeLE2cTGoQrfxCJPnhgx1rGOsHqSFSWL5XaI7vJYUlaWuFlGoffFzCmXNfkauh1YU4aHc/D/WmbhhFtRRElLmpB42P6En+Eb/xjiYllZmowkzWacArVEie4oPBuWMWbNc1xeMwrSwlYi/LjlNDKpaqJWnqwRww2ZCBa14Ty2PQYDSJmbOLxqKYv7jmXAqSKkPBTgYNBslWmKrqVMlIZQlupIO44KYfkbDbhAVMKUyZFBz90NopRi9eInuafpSTJV1RjlKgpekrCVQTn1RKMbMY0yMUPIOUkmJjYxIXkb9219Nxm32o+/toJ18TDwiBv2kPtjYmuZWrWUkooz1jIZV7WU8Yk1NPW8h7QbZ0lRW746SvduLD+8YUxqJQMqPLQn0JGhVdpRa/YeFd1XS/DxbRASLYIFBd/pgqwHH6iBQm8viAyJ4CChWIz0pk175MdI3JPRPeSU76wpMNqE5wt6TrfWhA9v1b9BWW1qcfp6l9416PM7EcJ3xuCiJNyRYajcOWpopNrfwF0vm4j6vcOIaBEELZw5Tw+/GiR5dzRFzCywrhzFFD3kGjZK1NrvYCCurXKXl2CMqa1wN5bh8lbwigXqtrXihSN0x5PcdvEn+Nd536S0tZWV997L3E996nWnW8CuCYRwf5L5KtiL9c8oSQyFg7gbmVRcSlfiWJT/Jnp4Q4Yv2h7UI8sAEaI77Pyi1+FpaqjhUi7jRV6glVYSJHgnJzKDmQBMYSpJkixnGS+zghAhRjMGC4tuuhkgQ4IEeQpYWAhCkeKQMOvBxggujh4+pJ+IivGtLpN12RmgBFfGY4jLFY1LmBnvZYNdoqQMegqz6XF1paIU3Nk1lQ+2bGUlr1KggIc3tPxDx1kjCEnfBt7FZSzjdkzPnm14/q8S9jqwvlTiMy/+lDtmXUZHvAFPBKtQBDNE2q1hTKQP10vgIYRNm49PvI2nOi9geXYcLh5T4xswirN5quc0plUv5LiaxXS4SWpDOVAGDrrCnB7WuRJyTLYtXYSdy9E4ezbP//jHvJRYzsB7ZuDYCeJWG4YKY7spquMrQTwswthAVShDxCyRdxJMS65mWfoYPPQw3VhTEHOADieCrUzC4mKIw6TUCmw3QVKiiIKCE0aMAbqtFXQU5jLOghYL1th62K0upNO84IXIqCYML02WJIeZqznRfBLinwRjz7bmubVf91lrfUFKiK5MftUPc2PQWT+WdMNoIqU8VmT7jtTlXI7mY0ZeurEnbHFeO0QrogWxx4UX8tDq7LgTTkLBnWm9tKVyP9CXCvA/fbC6pHebub5eb5XmoHehQcDz9PeIP1rd7+nyGPXDoCrO+aHh6e7TOGv0gxiSxVMGUdOl7NRwf9fh/FlpYSwr6PV77WHRlso1dgkR0QKbz9FfVc26cdOZ0NVJ/xtoPATsnkAI9xdKQekeIAKis0Fh4iqDmcVXWJQ4BoU1JDRagEyiGOT8P8/fWSVOnDBhWtjxB+zqaeBczt/uJYoBMri4VFNDE6OJE2cda4eGWfV9euxngAF2fMXBwcbA9MNU8pdr6PVuq/NjWJ2dRoNZGpozynguD3QfxpQJf6HXSfBc92lsLmzvNYWArGfwS3Uah8lhPMLDbGEzJUrauAe9PGJwFxkdhwHqqGMa04fccRTcE5vIO0oeXTlFESEq0JzdxHceuZHvXvY1nh1zPHHTJe/Febj9As4d9QT10S04Xpgqs0RLKMmsludZX15AtxpgIHcE3cqA8myeaZvN6QnF1alWnpH7GFAOccNielgRsbKM7q/jvqvfR667GwA7l8PO5eAnZ2NVQW0qo/NclTGsHIbh4nomluhlHGnXJGSUCEkYR5l4QNLQvREbqJZqjqpaxZpCIwlcasJ6lxhPRamz4OWS7p2JCtEca+WV9Fw+lIIfjtbp0+1qt+pMeDgr3D0wmaLTxtmhx7k8vo5Q7CcQPpqBtja2PPMMyvMYe/zxVI8b1tjwWV3S4avEFFhZhPM2Qb8XQW78FYf97fdc8fe7qIpYlNJpojU1HPre9+70tehetYpX77mHgW3bGHfCCYw7/0JWh1MMuLq3GfV7X7WmLpmDv/qBgskhuCv9WqE0RYvluvJ2IVxUgE+26TSrMmCDDa/0wVca/j975x0m11me/d972syZsrM723e16tWWLMmSLdwtN8DGGONCi00wpuUzJCQEEgiJCSSEJF8gFQgdm1BsHHAh2JYdF9lWsWVZvfftZXb6mTnl/f54z8yumm1AIdd3XfvstZd2R7vnnD0z897v8zz3fT9KL7nGVYCV0JRusxCoHqspFOh7qMWzIid8X0FljoPONO4/+i5so4DjxwikgQwiSGniAl06DAfquqSEYthrdO0YNhKk0gR7EipWFL9apevcc097z6biN48pIPxfi2pYDp38rhUEaLR7A6RkgoxwIARDA4MkDWGfDsZRoicfv06a2cRLtNKKzcnq3hxZ1vA4wwwBgjhxruSqMKOcyL4Aotjkw4+AABcXAyOcSRjBCTNPUf891eHbU5itjjbpT0po4HgNnFu5na8N2BysCuKTFlAnUHR1DUjTzFVcw/38hArD+Hh1okGtV9pGG3OZd9K0+6+OwXeiC/ns7AtZvO9ZjkXTeJqgo9zPgfRstsxeiqUHeAjSRpECJlfK60m5JVaYVXZpz3OIQ/gUGZeSsjOT0eKEUUCrDv9dFHyhrYe54lJeiDyPpIJEMk1OJ/Phb1PJZEi0KfboyJ49FIeGsPIOIiaRHgSagQgCNCmBAIGJIQJ0oZHQBcVAomkBucrsupG2KxUpY5Vl8C9tc/hWfpifjCdxqzEiwmB2RDLkKXNzAbj4ZN0UnlTZWVyDz7VN2McBvDEJb0zqqDnXE85Bux96iGe+8AUqVQ8PSdQwuOjjf8CSd7/7pNfT/Ag8W6Q+EQNUqXvchzKKYSka02x6y21Iy+a9zz/AwiuuYNn73ke87dQM20NPP82aT30KGQTolsXO517gwPfu4+m//g6H7BQaKsMd9FUfMB+o+xMAZ1kK7KebE+XNWkipwLJ2D5wAPj+sHqu55zTqoPnwQB4e6IHbehUrdJqpSqC+VBnnQgvaTPha6Ogzues3eV+Q9xvI+w31xwXqOiVwzA8t6qUS79feLgXNwu6YhtV/FM+0kFLS/PI6Ep2dzLvuulPes6k4MzEFhP9rYYE2G/xdgA5CQ5Ng4pHT0yS1NqK4FCniUuU8VrGVLYwxRoH8cZZqOjomJtvYwjgZbuWd6OgIBBYWPj4P8xAF8vXMryQd7g0eJpF/J4fMacyLDRMVBi4ufvghUCbe1fDDxiaGzZinUfRtkmYGU1PepiYmulDdxDJlBZHSohpEkIFJQsYwZe1N7xIx8oCL58eJagYlGSUuoJlm3s5NPMPT7GUPAQEx4sxkJldwFSlS9TtYoUKOLARxfpiL02YK7rn5S1y27l7O3ng/llfhv857M984/0MU9QQGHhqCnB/jDcle7mhKoTxHYszkWsYY4wiHWVceRqIRt45QqMykRjGpLViLWcJ8FjBOhig2zvYj/PxgXx0EAYxIRGXKg1kIOokGDlUZwdd1EAECjVbRiCNUhq4DhpAM5c7jbLORgg69rpqkcVcT/GELxLUon2rs4Y9SihyyTpvDfrGPUjWOjkZJVjE1OFxYQkxT2crDBcWqvPI1NIDF4WGe+sJfcSTaQCYRRQDCrTL691+h5+JLaJx+fGZ4R6MCwjEfGjWVmfV76k6Zmlr0KwgqsSRr3/Y+iu94H/dMg+Rp5vYFnsezX/gCZiyGFY9TkXAgAvrRw1i/+Cmlt9+BQJU9z7LglYoC+bSurNIAfq9f6f7uzSpAbtAUs/SoBz2mYoD+Iq8GHL9cVtfa70GHoZ7bmFBkHA+fP251+GhfhGHfIB7+fbpQz8PSKFxuw0cGlBWfhwK2gAmwmxw6qgRaezzk5CBQ+kyD2iYG8tNmoiUb0bJjXPLcL7j4+jex9PbbiSSTr/4ETsVvFFNA+L8VQkDiz5C5u3CLY3gV0C2JEU+wteEOShTrQLaSlYwyyhCDdV3SicLcSrg3PcwhvsL/DSHLZjozmMc88uSJo9xknAC2OVHQChysHODQ2FXEIoe5tuMhpJ4nCN+qFhYmJhEi6Bh4gcHukdt4rCAoUUCIgBWNL7K8cQsJkWBuYi97CwsJAkHeT5JxU0ipERUmXxxVO3JXy1IQY3TavUQ0h6RRpjs6SJ6biIdEk1bauIlbkEiK4X2oXTuo/PNlNvESG5FIHCSzG+eSy6/GNaOsueROvrnqTg65knb7MMsatrIxey4lP0pMkyxN7uOTzcmT7t9LbOQVNjM9oTPqxohHd1Mu9bB26DosoXNLgyqN1e5NG2ruTqFSOUnrFWttJXPgAKI3jzlURUZ1iDvgQjLRRZUcmubTRTcODo5waA1a2FS8iNEwY0jp8JE0/EH6+CzbEEoqcDmr0dE5auwlH4Dv2WwavoLxahu+VGVnW8AvCq8NhHseeoiDw2MMt0QxPdXX02Mxhqs+j6xZy3vueNdxP78oAl/rgn8egy0OtOiqXFlzXKkt/Fr4edRTQ4fvm6bYlCdG7tgxKrlcPVsc8VTfLIglmLHhKTa//Q4CoCDhkKtAaY51vKn6kKeA8t864S+H4dmSKmk2hhrKm44p8kxDOMy4Gihd5bAfSiaQLGvYwb2sw41UeecMg+HCuWweX46FRlyDh/IKgHdV1d8xz1Lkl0rIMq1xcUOidB3gTgxxwtdxFKs3IgR6YxPndzZx75UfJXYGZ2lOxeljCgh/ixFIVbaxwp5Fv3cVD//tlYiXHibdUqBSjCBbl3LJP97O/EgcD58IFv/FI4yTDQFQFWAmG2z79T3mxNcBRRpo4CiH6eUYPj7lkHG6r2pRlarX0WoWkLrNrkoH67MLOT+9oX6sVI6vaQAAIABJREFUWhZYW/B/OjafQ4UUCX0QIUpIqbMxcyHNpsP0xG6m2zlWNe5g7dgysm4KDR9T+CyJ6OyrqsGyF7Q9SLN9AIRED5fLIDD588whDuabSGmKtv/mJOhCkECt4IHnkT1yBMO2GewssJ51+H6MQ1WdXCCJR/dSDiyqxcsBmGFBLhDgNdNh93Oj/XPimk2b6TJLa2cpE4SNAgUe4H4Oc0iVYnWBTYUBp502+whp+yD9pbnsqahNxIl2Xa1nnYVuWbjlMqatytKmbZPo6ICXsrj5CmJIkkt3U2rvYqxaRcg4C/UenMh+QLCYxVygX8R7p+u8WFZgcnZEZTKnCwuLK7mKpuASPjJQYWc5QVRoddnEvPB3NXH6YwA42Sxr/+1rFHQLM/ARQuBVHILAxwZecATvOcXvLYvCNzthZ1VlRh/um+idwUQ5UAeadZXFPpyHO05hdG0lEkgpkUGA0BQRqRJA1HUpN7XUjyeB0YBTDgcLgJwPF8fg/zQpd5gOXWWoA65ilbrhvfEklJjI0iRgahVGAo1DFYv5URNP8/ASL2A5Or2FZTgSHszDI/mQKBQaFSyOqLJwOVAkmLmmKg8PeRP9v5NcjybdIxNoNeFtDepYCyxYaZ88E3Mq/udiCgh/C+FL+O443JNVfYWZptqpb+otUr78D7Ev/zhvffZ+upb2kV1m05v/Do3pmaxgJeOM4+AQJYKJiR+qzF4tamXRIgWaSDNAfx0EpdQY9juJCbXVLLndapKDNs6+/EIuS2+lQpUANV0hR45W2igHki35WczWAwZFFQsLV7iYosrL2cX0JHYRFVFuSvcz6kxjV1HDNsrYmoMmWmiTjfT7AbY5jMCnphx0pcmaoTdxrNSJ4wZINJ4pwwfL8IVw0OmR557j6c99jko2S0U32fyP7+NY93mUNI+ZsV4ajTLlIE6jvYvnMhcxwzQZD/tF3+xMEjV+h4y+D0cU6aSTafTUZRc5H9bIlxnXM+hCR0dXO3jh02RlKHhJZsUPMFiey66qyjKuPiG7Mm2bSz/7WZ74zJ8xmskihMAmYMENN3DpZz7D5qNP88hZx3ANAxsHN4hxcOxaXqi28s8dAefHlOgaAKHGR/0qcW40wlfbI7ztqCoJRoQq+Pa5ylzm4lMYQjvBxHw985ePUbFtKFbRqxUCK4IQOrJcRmqCwfMuOeV5sz58fEDpCCVKXqAzUSqcvI53GgoID50qPQJiLS30XHQR+55dS9Darqzu3Cq6W2X7m24FTi4tHqmq568rZMOWAkUaigr1PEWEAkFPwn53olenEf78pDeSAFrMUTLVZh4ZWcH8aS8qBrVr05p8ibKzFBA0oO7xgarKBqOoTPg8W50/F8CDPfCeXp8xWSItykh0Sr6NE0SpPdMmCpRFePLVMfizFpVtTgHgbz+mgPB/KKSEfYehXIE1jXBvQZWPOnXljDEewPTBARqdIq5ls/X95xK1q0QHi7iZUayeeazX1gFgY4cMUb0+XeLVokoVE5Mg7NcpILVxqQISXSsjNI9ydQbFcNirT0AklGuYGFTCHqHSGZbISZOYTKKFy5GBoQBXCFy/kUYaKePg4VHGIWYUsHVFJsmgQAai6AhMLUCGRJv1oxfQW+4haeSwiFP1klSk5L5ijku8vTSO5dj8l39B1DXQps3gnpvu4qjVgxgTVKMJ9hbncnnzelqtMaQWUJAujjS5NAYfSsNcCyDGDI535sj48NfD8HQJhlnATDvOpW2/xNYllUDgS52oXqbix5BBBIsAwzrGi+IQSUzmMY9mWurH67vwau7/h3mknvolRj5LZeVFfPbaC7DjOuc03sgfHHapaoPkA52xShug06LDD7IabzgDfszn2vDoDDUZYa9q26IBDVIRiS6MTZBFNpbhjwcVGAKUO8/nmpkL2X/JQuY+8F3MohLVSyHY/pFPc+3sUwu5/24UtlRU1lVFVToMqcAwF/6MhSpFxjTV71x6mh6hL2HDXXdzcPjTNG7dSCA0IkLj+d/9BH1L34AEzu7fxoee/xpLBrZxtLGHb73hA6ydcylHQnD1gc+FA1OSYf+wDbX5lCgCTyVQ4FNzjhGo601ooGkeVuBzzGmj4FkkjCoZ3yCiFxB4yDAPTWlKTpEJFHBpYcqX8+G6JDQZVRY3rmfj4DIKfhOCgKhWojt2hP2lhQRMlEubNHWP1hRh9l61RnwsDR9Og4PL/aUB1pd9egyLdydamG68DmPbqfiVYwoI/wfi0KDLH/ydy5EjJiKis/c2wfx2QaRJvRGLYf8mk2qm0ykS8R2mtx9mPNtIpywCoEkNC5NxspiYRIkSwaKC87quwcPDJkaeHCBIk6ZMmYLIg4zwcmYpdmk28/tewtcTvNKaZmnzenXuUMDv4CDQSJDkMu18njNsRn3Q9ImxT5XAZkniCCYmAo0RRpgV30+f00FEVjCEAsyBIE+HHqHVcnCEEsojYVd+ITG9FOqnKgiRIK6PUaLEZrEeQw5R/tsldH1/hOdTlzDY0klTIUulUcdEWaVtGD+Ht7Q/igxSTNNt/qMbul+lpCglfGIAthQqtI0fo5qq0OulOZJsZ1ZsAB+YPN3xSHEB57c8QXd8D64h2Axs5mUu5TLO4myGPPjkEMSmzcS5/cOAWhQ/PgyP2Orrw1UTR05TWYpQmdiAp7KpMxVzLbWwzrdUVpHU1ALf78GPcspdJefDHw6oPmMNGIeiNg/c/Hu847/v46G/+Crp3VvQfZe++UuZdd4Kfid18rmcAB4vQGuYyEaEYpDWxkR1hOcNgLShrMt6DMVYPVX8LAc/Eyk6/+pfOXT0KH0jGQ5Pm0MlFkcAywZ28NWffECVP6Mp5mYO8+Wff5y/eNPneeQsJRFqFCoDDlDZ6nhYqgQFtLpQYO0Bevi4RAFhwfVJ5QtoSZPAsNlb1VluQFSrUHSbkJOWShdF0LkspgwhdNQ5u0z1fF7Xd5hdzlyajDxShOzuQCfnpTCp4BLBRJVVs6FOMR5KM/o8NfUkKys8Vj3KAScZvhYF385k+EZHnMvs/89mYf1/EFNAeIZjr9zHu76oMToQI9Kcx03quFo3uw5qJKIaIqKrXagAvyGFf8zFtHR0fFzdxK9WsdNphK5mSthhJmdiYmOTq++1Xz1qy3iVKhEijJOhQhUfj5Q1zKytOr/3808S8aroUuP9TS2s/egiCk2NaAgCJCYmq7iAq7kGBPxpK3xsAKTXRkmMUpUGCb3MitQrNNPC5azmh/wHC5K72VuYz2ilHQtBGYEk4JPtDkOig4McCLWKGj46EXykNAikiaGVMfUC1cBEEz5+Rc3RGbq9g627lmM7ZfRyBZGIokcrRDUo+DZOYDGUuwxTCJpPIBhkyHCIg0gkM5jBSLWFrdki7YdfRkhJg7Txm3w29q2iZcYT2GYWKQLKXoxNY5dhaC7T4nso+QnaLUEc8PFYy7PMZg5PFaO4kuOIDQ3hcN8XimqRHPfVm80IS18a6nH/tercv0IMeMrhZdoJm4CkpoYMfzQNz5cVqaNx0rW2tKQZzowz7lS448Gvs/HsVQwmmrixeIw/mXfpKU2thz3l3rInvP5WHWaZynGmHKivXakIL9NNuDYO7206WXtYi5/kVeZYkrC5pQe3uQdvEljd/sK/IyVkEi3oAvJGCk83+Njaf+aRhW9CatqE00vopFOVijwjpJI5GFKVaA2h7kE1lKZ0P/coF37vy8RyowgLDt/wVkof62DM92k1AzaPXIQuBVYooxj24HdSikH6wSoccBW559OD6jxRI0YlsIgbJYKwR6hrPl5g0WRmKLodIKBZU/dKoECw9lkOVLYd1xtoNic0uQVf44+HS6zrSbxm33cqfrWYAsIzGBnGuGffS4z1ryaSzipCSNlHSB9Pg/2jJRZ3N6AhqErojNtEkkkq+Ty5cpyO6lGMSIT0HGXM61BmAQtpoonnWMsYo695DToGShIfEMOmk8XsYTcODgYGLgEdmQE+9si/UbIbcK0UhqiyqDjK/K9t4Pt330hZ9zAxmMksmkmzja200sp5djv3dAl+kouy003TET3GyoaDLDZWMp8FWFgsYhFHtMN8tHMLO4qd7Ck3EzEyzE7sIG6dzeVczjDDihUrBLNiRzhYnEFU8/D8BBFjlEpgMDexh6oYJ+gw8ONxRMbHNKpUtYCgXETsLiBWdqIJBynj7Bt7K2PVNL/frHbaEsk+9vIsTzPAIBEsbGw2sB7TW40+qimmp2HRUAioRgOGokl29i9h9fSt7Cq18vzg9fS7SZY2PYtA0KEL4vrEfZZU6KefYjCL4BSAFkjFqnzeUQtkBbXIxUQoMxCnZlD+utGgKQBwxh1kuUq0LYHQNZxAAQCojO3E4rrQDbQZs9h0050c2LGJ5bs38YfnLmD57bejnwIEPQmfGgo9NMNMZshTDM0eU2Wmsy3VN7suMWGH9mpRCidD9HoTPp8CtUA1AEsHt7O551yem3UxJdNmefZl2mb0UenRubXzHjYXVjBSOgu1uVJZmw6cbYEUsNtRBJaRkJFrCdWmEJvWs/orn6GSaCCf7sDwHM6+716cxCoGPngLH7JX0Bzv4usZpSMc81U15zvjsNGBP26GC2w49wD0+eq4mUozDjqm8NG1CrpQDjVSCgKiWGGmPtOCI94EqagWpoCcr5M2g+M1uXrAsBdw0A2YY51mRzEVv1ZMAeEZjD3swSnpSBGAUKVD3YfU9ixji1soBcqmrFmz6fchruvEliylki+RGY6zfGYWe2YXng5lCkSIspLziBNnEy+hhZMkTheqpGmFujSDVVyIR5Vd7ARUuVQimfviAXRfoqV8Ginj4VNsaKRxvMpH9l3EsQUdPMkT7GQHW9kCQAMNzGYOb4pcy2daTcAG5oWfE7GUZRziEEKrsDh5mObkC7i4xIizgx3sYTc3cwtb2MxBDvLG5m3c58yi3+miInX8QNBoFbg4/TKGMMA0KEerVBslS7Y9yoHzbyPuSvQZDWi6Sc6LMSO+jw49xf9JpXlbWHp7gefZxIuMM46GhoNDQEAb7Qwaa6lwEb5uoSMRElqHK1RiNjcMbuTtM9/PdtHMEUNgAZq00ISsZwOtOnUmiIHB+SHDr1Z+AwUW4wHsLKmFzWBCY1YOmYWWpnpKZyq0YolV//kvzDKfIAg0ntx+Hv1X34FYOZN3h+XNFfbEiCEjdDbZU4V+adIwewG5uQt4XL6LSAJWnKa8vLGsGJnnRJV0wkX13LKBIpB8u3vCeu31xjUJ+G4mlExMelxHTZT64fJ3snbmhUihEdFLNF/dSxaL7oE+LLPKJS1PcSA3ztbxi6mGGaEp1HXsqIKugRmoEm6rASVPbUyufOi7BFYEPxZHk+AZUUYaZmLec4D7r3kjhbTFh5sU2a0UKMlFIFT2Hasq7eJ7UgrAbQi9RU0qgc+4m6TZqpL3YpSCGH5gADoRoNNS91+NhZLoMtxJCQ1Pgi5O5gJICRKN6NRYpjMeU0B4BqNMma7ZWUDiuxqGqV7cyd15vEGbxncUKcomrm9QFO/nyzDkadzWk+DmhhX4+gy2hKL4TrpYzBISJBhmiAyZut/o6SIgqHuABvg8yeOAqD9W0yAmiv5Jc3NkaO2UdXr5KWvD3iL13l+eAgc4wCZeYhVvOO01dNLFlVzFc6xlmCE8PFI0kiaNAIYZ4cf8iCgRXFxazDLv73mIocJyAuccxqIv0pl8mVi44a2KKoGtgyno6nuSJf2dbF95A1rCRPoa3dFhrmx7hrl6mhtR8xMLFNjKK3X2bI0hWqWKQ5mk6bO69AJPxS8l7lUwAp+sFWfO+FHec2APMVq4NK56bbceA6cyj4jYRCXw2FUxKJvQZjlEiSoWagRuboD7cxNMScnEmysiQNMmSBugQHO+BTedKSCUkhcefx8fPn8djrTwgCtW7ORHT+xh9rx/ZeVs5TjdY8IHmuAbmfD+SpdhWaE7miHQQSNOq0zxSEHnHSmlFzxQVSOMXiwr3d5cUxFT4pqi+Q/5CiRcqQbX/uuY+v6qBFwaUwv+a8XtKXi6CBlnwi1GD8/hSfjJsltZ0rcFXQiap/UR04qMe41Um2xiIoLjm8xKbmXT+LmUpWIfzTYVMI37YfaKKof2exOzCVP9R8nbNhqqVyeAwLQwvAzzKjmeL7dw0FW/V5FqmoVA9frHQ43i98YhKaAYXrOyQpSUEYy5aTxpggQTjSoCFwXSFQm2X8WTglKljOH7yGiUIBLlutQYG8sagVSjwgCygcmiiE+3OQWEZzqmgPAMxnRmsDO5gytv28ovv7cYTZMIPcCtGJzdNsDNZz3FO62baUG9UW88adBpC6u54qTjKubnhK3ZZA3h5KiJ7B3KNJEmToIs4wRIChRIkkRHp/esHhY9vQVNCvxw3FHMM6mKEg/M3EWBap2dqjw/dXx8xsnwJGtopLFu3n2qmMd8ZjOH7/LtuikAwBBDFCiojAQXicTFo1NPYKc20Jk6xjhjDKFRooSaASERmsC0oqS+/i5WMcxy736y1XZieplWaxRPeJQo1s8/yghqiuPx5SNlHF7B0izeGz/KVc/9Iz86+20UzBi3Hn6ad235KbH3/Gn95x/Kqx5Sp0gzlL2c9tTTRGSFgQA6gijXatdhoGxJPkkf17oZ7o/NYgcxEho8WgAtfKpModiGTrig3tKgeq6vp2z4emKoby3drevJF9PhhgeqIuD21et46tmnYPlN9Z+9vUGB8/MlyaCxg9F8J7GQPZIlS1mUkXSyxdGICXhvrwKCRl1JMl521OihTrU/oTtcRbY6ysS6HAL+d8bVWKZ/7nxtSUBKh3u64ZEcfHpYySxsFCC6Evxokt6OBZw1doCWxCgEGoERwY4nWRmFA1WNXl8QNcZx/BgeSs7hy+NlEo6EiFRZrB/A8FnLaXnmUTKRCY2JWS5iNqbRmpoglJnUNJm1P8MUypi7K6rKwkkNRlzIE5a9MdABW+h0GhDXNQyhZBsHqmqj0ONkueFbX2bfklVsW/wGKqaFWS5x6+7n+Nqtl3HX2C4ey7bVyTJdpstX21p/vRfIVLxqTAHhGQopYU9hBg/krmf4HJfZdx8keDZAZgwWXzDI4gv6mGfNopnTzIJ5lShSxAgNqE8nn6gt+gZmaIYNvRyjQgVNcTQpUkBD4/CCdg4t6WHmliP4ho4RgCt9dt1wNeVUHA3vuOyz9nUNhJ/kCaJEmcHM016zjk6CBAUKuLgMMUiJkrpXyLprjYdPkQINpOijF4Goax59qvWMLkGCJA2MMELUyNFkKAJ6EBYcO+isnztGDIkkSrSeBWuh8EPR5TVmrvwI5+z6B9786KdRjTAJ19wBK95YP86LjurnAeScRRQqs7GtfjKewQdbOmmN6FDMwff+DLFrHfFUD89c9mkKrbPQG1IUpaSEhi4lEaGhC4Eplbbt060quzgTUaVKsbgOXZP1zZAGIDU0TaDLLYACwp0VRXjK+VDFo8+fToCBCJ2JVB/ZBaoktSj3ZBVw1xxcLB1i42PsHxxhcNo00km1qRtwVbnQEgpkQS32/5FToL/6dRAdbQ1uSkG7BX82AC9VlLSgSVfuL13pFrTWZty4SzL+CqZnYQgFVOO+xNADfD9BJfz78xJ2nqBb9Dje/eXJG+7gunVPERsZpJxIEa+WiLsV+n7vC6DrBKHrjC4menk1opMEtjuqJz0Y2ssZTIj2ewxo0TVKMnTDQTFLu0y40IY3f+tLHFz3JG/c/xLZx39A0U6QHu3HHxwgeOOjfL15MdsaRtlULdOl2VwRbUOfKov+j8QUEJ6h+PcMfD2jYWvd2MKhL9JMw5tz3NG9hrRucRYXsJglJ1mjvVb4+Bzj2GvqByUSHZ0qFQICRlBTECb3FeMk1EglHda8/ypmbjnMnM2H0SNJjqw6h6E5HSRI1A29T4yAgAQJTAzW8izb2UaOHNPoYSlLSXJ8insOS/lvniBH7jhgFYi6kbdALeS1AcPKbioeSjdE/bwNpDAwaKKJDBlcXGqTLxpo4EIuqh+/hVbaaGOYIZppYZQRqrihi4fFZaymIdoOd34JRvshOwztM+i3UnxjLM9TpRJNZomYaKAcNJHS1SYjkBHyzkwyvprBh+/BVz4AO9ZCIs2Xl/0OZdOmrXcrQ2YbrXaKUrmNQgCe5qFJA4ngj9JnBgRLlHh8/EEOa4eZ1biTmYM+vlQm3rXwA8mcOUor6kklnXACRdIp4OBoeQ6VO8joNk2GqjqUfJOUVuWSWJTvjE8wPYXj0PFvXyT11C+ZLzQaTY2d7/oI/Te8h7mWYF84r6+2VkcF5H34XgYujSswyfrwQlllmCujChRqISX8wyj8MCwxz7MUmN6YVPP7xnxFOMk6i7Fj2/FEiQHfphoE2EaJo8U5jHkNhNsaIvCaYqPEzFk896XvM+f+b9O6YxOVWXMYufV9jC1bxeZQ72ugyr/dOvQHSnpRDsu3EQG2VKXiKhOifVso56Z9riJNRYQCyH1VdT/vbFS2ckY0CkCqmCVVzAJQFILy2BjRxkaWmC0seRUp0FScmZgCwt80pKT36Fa+UmpDs4p4mkZMxJmnpRn2Gmku3MG7TqHDAjXmqEKFA+xjN7vDeYISkwjTmc5KVrKB9exlTyg/V3vR2tSHyeAoka/ZQ9RCaBlnHEuP0L98Eb3LFxLgh16eXl24f2qHRGggRYkiAwzWM9VtbGEfe7iJW44Dw4Us4iAHeYmN9YxVR0dDq5t7w4QxeJUqSRI0kaZKhUGGQt9TUQdzG5suuilSpECBTrq4gAuOG0ElELyJa3mKJznKERppwsRkKUtZzDnEmKRgb+6E5k5eKgf8zpFxDlajSNJI0hjCJybKRIRNo67YiP2esj7bWfJIfOdj2BseBt1AOkVeaJhJeymDJ3xio+N4MxL0RDIMVFPEjSLNwuZjTVE+cAqLsV81irk8H/v6X7PpOQmVgHMusvnEmzUoZalWG9A0gR0p4PlJVl1yM6A0bmP+BFPVwMDUAprNAtXAIOepRTlmFLm7bYSk3sAcC/YXVBlw9r9/mcSah3Fa2gl0nVl6le57v8zVy7p56JzVPFqAINRF6tGwrwY8WIQrD8MFUVVu7gutxySqT3p3K9zUADsqSu/Ypk+UUqsS/rsEX+1UZtmHqlD2Umzqu4Er2p4hYQ0hfJ3t2XPYkHlD+GpR4VOzSz/e6HpyY2HYg2TPLNZ+7POUQ32vBKolBXRxoR7LSdjvqV5gLgTBpFAG3DUtaM0xRg1SVmXkYJJu0QyzyrwfOtGcdx7DO3ceZ6jtOQ66ZZHs6vrNXiBT8SvFFBD+JuF7+N//LN9bZlOJ3kaqXCCQgpzpUPBGibgdbIymeFfq+F5VlixPsIYhBhknQ0CAjU2RYljSs6lQZj/7cHFpoIEIUfroDd055Wn7hCdGrSyossUqEln/HmrgqFOgQDsdoU5RlS69EBjVz6nfEgjGyBAlWgcUC4sCeV7hFS5mwo5LIJjPAg5zEAuLHDlKlOqZnourplaEU+5nMZNRRhEIIkRpp50B+sPBxC4FfKJEeSNvPm4KxakiRoxreQtlynh4JEicMhuXSLbJbXyfDVzc5bAqMNk2voK9hUVUAwNP8/C1EoN+gnygMpQ9FfjjwwVmdb+Zb0YfIBGSGeJumUpQQRhgeCFw6y6zYyN8YNovmaW181Zx8jgdKZUbzO6qckM5354w9z5VSCm5/R3/yAuHdSIRiRAGa+5ppn/79Xzlz3+JWSiCLzAjXTRN/2caUupeufJ427PaYGdTq7Is0c+qhsNsLjZxqDSNbw3PpC+uxg79axX0isOqNQ8y1NiGRCcaQK9h0RGLseWee2j/xGoqfVDOhX3ROMhpgKZKhDrw5bGJCQ212FmFzwwpWYN5AvMWVKm1Zj7w426VUX1lDDaU2hnO3MJuv8p2R6ck9eO2gVKdGkN4JI08ed+mEkTr75qaCD4noeBNAGUApAhHSTExZqoxgDGp2KfnhXpJibqeStgTlaissEb2qV9IeDxNKpBvNxWB59x3vIM9Dz5Irr+f4bOWMxZN0Hz0ADd+8IP1THEqfjsxBYSvM0qU8PGPX1A3/ILesQ3QfAkUNFxf4rkeVD38qEl+bBBn87PI915fn06gRiI9SIECOhpeaGU2TpZoyOysUsXADEcwuaRIhdmieFWyzOmiRlapgaIR2q/VsrTa4N8MGSRBfZIFTPQFg/Cjl2NIgpN6nRYRjnH0pHO304YIz5mkAZ8AhzIaGu2004CyZ1vMYhIkuI+f1AHSwqKJJnwJufI5HCnNZI7WzkgiQup1Ok2dajbj5NjDbp6UT1OWUUpBHEN4rGh+Hk/q7CssxAt0liT2cUuDxTdyg2hBCqc6D7+/j+FkG7saZ7Issx9D07h1x4N8a9k7SVdGqCQTBBKKQYSrm3ZR7iuz72iBI9OyTJ8+AeKehLuH4Zd51YsDmGHC17tO746zZcsgL27KklxkofsAkkgMdmxu5N92Xc/733Iuy8X5oM+HSWXSxREl2SiH7i8CQavsIC8LzE0c5LlcNzsKC+gQcXJC5+vjqvc324TR8SJ4HlLX0YRigh7zINAiGH3DfPOL0PRGyHSBLIEfB1xIRdW0+FH/+BpDbeHxUOXS72fhjtTxM/1qUcu0fpiD74+rniBAXIdGzarPC6xF7cv5ya2cn16HEAoi9xQWsW70YjRpHDcWSUz6VwLj4dc+CtRqACdRYJYKZxhaQmknPSaA70RNICc8Puirv3dvFS5pa+Py793LndtG2C9NNNPEbmkh357k8/LVN0NTcWZjCghfI/Lk+W+eDIkc0EgTV3AlrbTB8z8j35oirWeZLQ+y059OQmTRkDjY6Jak50ffoO/sTrrPU9MOeumlQIE4cXLkENSILhIfPxyUK3FxiRKpT6LPk8MMny7lGfr6o1YyncFMmmjiEAcpUMDDQyDqwOdSPam8Ohl0az9rYZ3EyPTwSHKyFiBJA8u9/yKuAAAgAElEQVRYzpM8UbeHEwhsYryVG+ni+BLQlVzF0zxFKcyOm2U764beyrPFKBqwAfjxOHy2Fd5yBqQHL/EiESL40gQp8TGo+BEWN77MvsIipVlLvsB6zaU9oVamkr+RZ/LXEJEW37joI/ztI39Kyi1z58bv0muneeScayk0ppG+xTLrMH1/t5HH1xRp0uCH/o+54opZ/OVfriYSMXgkDz/LQ2bSpILDLtx5DP6+U437SeuKXBEJb/muncPISoDQQPoT3plBIBjar5M0LwTmnvS32hp8rgX+dEhp/hR5SOfWZIrfNd7KOwsaC3Xl0FIKwAtUKXWaCWY6jdPWRTw/TiXRgAckBIxlsjTPvQonBys2wKHF0DcbijGgBMKHtShQqQHE5EVHoFid+UCdRxcTQA3hVHpNeZr+IKcs5HoMpQ3cVFaawMnTLtQ7CWbFDnFRyzOUfRtDRvBkwKLkdmSgsW7sUnXPwt+rXZfk5BJqLpgAxdr/m0JldofDaRaTt6Wn26LKSf96wD+NwkwTHtfbGZ3bzsIQXAOpbNuW5JQ+cSp+OzEFhK8SAQGP8CBZsvV5eDlyPMTPeSfvISYDGscqCAnXV36KV72SA9ElSCRJStz03A9pHumnd8OGOhCWKdXBxQhHctYyzMmQY6DjE5AiRYE8PkFIyJZht/DVxfWTQyJZzDncwNvIMEY//Sh9YYUy5fr1+OHECQ3tuP5jbUCvYnmqqfED9GMRwSZa1y0uY/kpzz/MMG5IiFGyBkGAz2P8ktt4b71MCzCXecxkFqOMYGKyvZzmiYLAC9QsupgGLRr89YjyevxN5Qd58sS1GGlN0Buou+VJk7hRwJewLPUyabNM1WsiG3pnxvQSyzrW85jzJu5f/DbePbiBy3atIVLM8le77mPRTe9gjf0iSXOMvu+PsvXRMtM6UjRrTQQBPP74AXp6Utx11/k8kFNZl084E0+oLHFNWWkYo0KBQ4uu5v9NN6GlNU40YqONVQjSkXo6JCxobbCZ+Sps3tUJ+GkEHi8q8LnQhnOj8GRRvbqEUNnKQPi3usAOB2xdsOkDn+KSv/k49mgZojaWU6LU0Mgvz3kvB55QfbVZL8OMl+H55ZCfDllAP8FUvMa8rAGLI5Wk4H196vk94irTAltTn59vVU42tlDC9UqgyskFwHEnQNaklunC0tTLVAODQBpYGiQ1DSeIs6hhBy9mLiCCiSSUZnByFle7tto8xRpoOoEa51QrhcLxswdrJdnTUdsM1N8YEcp1qNdToFojGGmhzOanU0D4W40pIDxVBFmK2S08tnOEQ4sGaYk2Imz1SrWxKVBgP/tYsup6On/8V7QN9nCk2eTqwz/Fcx/BSadID5bofv4oBSCamnhFt6IGj0okNjYGeh1cJEH96xFG8PBophmBhodLlQoGBjLMHl9P6OhYRGiiqU6N19AoUazLGWpRywYnA1ONmCMnfbi4Yc/RpUAeE5O38NbjCCu1KFJkB9uoLROOb5L3kuzJLUDIRhY3ZlhptRz3OwYG7XRw4Cj86SOway4YCTATMKzBIaE0bJscuCx+0il/pWillVFGWRixKUnI+AJLqzBSaSWmV7m0cT8tWoLRSf2rsh+lKTqAHnMpujEGEklINMK0Bfz1e7/L9wpprGI33dFeXvzBL4jFG0lrCg00DVpaYtx333buuut8RkOxtz2JbemhFmFPQldYAh724M+H4LvdcNFF0+lZOIPDr+zEKhQJWiKUChpJ3+NTV30w3GCdPrpNNfPxuPtgqEV8yFWEoGh4LQ4KDLUAji2/kCf+5vvMeuRHTB84xPazVvL0NbdSyrXCGhhzFHHEFFDdDHSDFj8ZKGogM/kVHEiVCRZDOzgP+FATvDt0bcl4MBBmv35IwtGpCdhV6dJD6RsrgGkUkFLdh6JUxw/QEFqApVVpkCYlFPvzVO+kWnZJeL1xlE9sLlD9wSC8foHawLw+K3zFZBUoTeZhVwHfieVgnYnJIFPx24kpw7oTo/xDtjx7M9e95T6+fu9ajh4b5tkNx3hl/zjepA54gQK84Xq0sy/huh9u5rx1R9A0MKnQ/XQfnV/txc0X0U2T2VddVT98mjQLWUSBPBUcGmlCRydChBixSTouBZoODkMMhiVTTipdvlqIkG3p4ZJhjCJFfsHDaGjEw17nqQgkJ7JRJ2eMNaZnmmZmMINp9NBAiuIkQfvkGA0BPcAn78YYcDpwfIsFDTspBh4f62vg6CkIqi9th/d8El4R4PtQ6YX8PnCVBzdHPfjH0YnpAr9uvIELCAjwtRLn2T4rYmV6LJeLtCW8MjvCAiuBJFDSCwF+2C+VUtBw4CA9vdt49K2rqXz0n1jzRw/wpUpaTUp3DdbnZ+AUTHJalMyk1dYwNEolFykl59lqUZ0sDytLODHRbdHVrL0hD6JRg29+92bOf+MqdNkOR23OaWrlP398F9Nnz/m17sOSiJIrHPJUH0xKtRjXDMIdCZGBXlrWP0VS+uy68kYef9sdlNKtMANYCoxDkIdKDsQRYC9oIZBrhJpAVOZWa/HWemcuClgMoc6V1uGpkiqLJoTy8RQhoNV6qZMZorUMsxwe71iph6heUQAsFRi6VCl6ccq+TU5KdKeKrHrg+epFFoYeXmOrBtfE1QirJl1l0LWhxzVArwGiFV5LDUBP196TQgF9WcL8iBpuPHoCEmcCNZh6ctSG/k7F/0xMZYSTw92Mm/kyn7j7QiqBjp6TmFaAHSnTd1SjnLBZ2WaBEErAbVrw4a9g7dvEhUd2smCwzGPf+U+KI6M4QmDaNlf//d+rSeWT4lIuo4sudrIDH58LuYg5zMXE5EnWcJADxElQpUI+LIv6kwgsrzdqAObhMc44B9iPh08Cm9HQwPtU5Jva95MzQVBifR8fAVRwEECMOFGiHGD/Ka3XYsRD4ICMm0YXagK6K03OTm3j5dLF3DOuBOb180v44jfAMECfDcKBwADpAiMgQu38EbfCA+Vezo9BN91EOM2wu1eJLrp5GzfyIhsZESMs0LtZqa+kw1QnOZvFrOUZLM3CEqALh1glg/HITq7/v99GBpDoMdn4D3fzJyOG0q+Fq2AgobxyJsG6A2S1CE0xAyE0RkdLXHLJDIQQfKhJubE4ASAmFlMDVfYdDPc9jWHJL5DQ60Kyp4kf/OhWBgeLSCnp6EjUCVm/TggB/9QBlx2Gw1W12SjLietJ9h3mrX/xAdoKGRzNoO3pX9Dx8x/w0Oe/STWeRL4DWAxsAjQQK8FYFEoQNNVzDFCZU5MGTYYqMcY11YcUKADUUOVKJDxZhH8ZVeXghIDB4PiSJExklrWNQ03o/kr2XGbF9xMzClR8i6jmIgQ8M3gFICh4AUUfpQV1A8SxDEZ7Eq29QZU2JaQMBX67q+D46jGqYdmzhnwoYNTCa9AFrIwoQk+FCaCsRZOAdDhl464maNbhQ/2qFF3LmOdaynIOlFzkb0bgJUed400J+MNmTjkRZCp+/ZgCwsnh/Ixtu1Jk8yZmk0dlEEaeD2i9GLycRzk7Tm+zwVkixnTRHG79BMxbAfNW0Ay88+3vZ3jnTgLXpfWss9Ctk+mNGhrzWcB8FuDgsIfdvMBzNNPCCCN1X9AxMqctgdYyucl6wloP70SCC8AwQxTI1/9POwHkTnV8xdyM4FJFxyBKlDIldHRKlChSJEuWJtJYpwEhVdoVeFIjkKBryjjNkzoNmkdSE7x8Ql0pV4DDvdDeAtEKuCaUKoChMg6jE5K6wwVd32abUWEEHQuLq7mGWcw+zZN7+ming+u4/pT/dxZnM8wQe7Q9JIyAilsl9twuSt/bTyndjIZH89gBfvTFr1H49GoMMUF714Qkdt0c5JMbyWwYIR8xiTel6ZrZzu///ioAOkz42zalkatIVZIsBior2FdhYrGVarTRZYeUuDsi4LKY4F86E/W5gr9pNBuqH3f3MBysTC5nSnBdnvzgZ3jn/f/CbN9hqwPpo/s4+79+zMs336lQ4Jzwk+MX/1xYYNBRpcCkrhb4qKZKmTABcI5UU9+3VNS5v5uFSjj14VSv1Mml1hoIAhS8Bv6z91bOSW2hPdpL1k2xI7eMsUo7QqqNmbQMjIqL0CAIJO6xcYx0HNdQKLM6Bs+W1fPhjUDgoiilMaAdlTZOAiQfaBCwPKIkNjV/05pUQwAZCZdbyu91fVnpJoddtemYY8ElMeXRGhWKXXpnnyIMtenqGI/kVQ/1210n2QVPxW8QU0A4OYIsgdQJ5ETT+9APPfY+rHPkkMArFTn7nDGuvOMx9GWfg/jdkPjgcYcQmkbb2We/rtPlyfGfPECJIho6e9lbB5oKFVV+PU3USC2Tw+XkGmONoOJQITLJeLt6GsH85OMDtNAc6gchSZIKTr3PWHOIyTLO5aw+5XEK5NHDkTSakCCFop5rVRy3m3IAM09QONgRlQ16PnTvgj0rQQ99q0QEbM1nZfNTJKxhNKGRRWJg8ij/xW38bp3YdCZCR+cKrmK5WMEW4yDffvhRtLs3U2xrxdYcmq0MeiyKVw5wSoNEotOoSB1LgDnYjxAFIp96A84eh9LRPKXOOFde00339Ikm3XubYHEUHsipDGR5BP5kOMzIpFrwNBRT0hYKKBwJDxcgc0xNpj9pUfRcWP8wrHtIrcAXvA3Ovxb0V3/LX5eEB/Oq11c7pFGt0DjST6Gtk00LV2IP7ac31oqRbGLmC2vYdPOdr+te1sBxwFM9PTOAEscDmA6UfAWYcyxVRhwFqu5Eplw7Vq1MqXPqPl/RT7J+7KLj2KoS5bYTvhTR/ACt7FKxTfADfNfHMHS6DZWF3ZKA74/Cka8Dt6BA0GaiDhtXx6mdoxDAk2VlwG2gsFITauMipRLY/0073JuFH4wrcwEvLPeuc9QG4LGisrR7S1ySlPtYbOTpZT4OCToMVSLfXlGvmak4MzEFhJPDupLFC9ZiRwMKJY1YLGCkT+fYYRvXl6QbS2xZE+emp6/n3//uMa676o/AmAfRU4PAa8UG1lOiSGKS7MDHY4TR0/YYJsdr2a6d+DMb2UAnnRziIP7r6DW6uPTSS5o0XXQzyABttDPCMBUq9R6jick85p/yGLvZrUBVBHRGe3F8m2pgIxHszJ6LD9x2AjvOsuDtV8OPfgHt+6Bqw6554BsQa4UFiX2c27SeQBpENIGOxMMlS5YjHGZROIXiTEYTTVyqN3Iw8x2OmCM02w66UBsGgUbXkb3oXpVpVoUBN0YhkBgjQ4xPmwU9c0gtGKdrfBitUuZhK8lZGfhIeuL4K2z1CfDzPEwzFLuwECi92v6KYkkaIYtURxFYXnRgmwNLwt/N+/DzvOTZl16kpXeMWyqwbGwf3Hs3bF8L7/9SHTV7e3P09uaZPj1FR4cyA7UEfLARfpybKPVFDFWXFb7Hpivfzh4hKfkS3w+IjQ6+6n2zCac1hN8bQlUVS1IZVKeEAoEKEz23ADXPsCtcnSZLHSYLh2qP14DUgJNe1ZNJOnWw1LT6Rm/aiwdwtw9w7B3nQzKKsBRVrBBAQwDDAeR3gcyD/jz4V4UnmmxZYxwP5GldDev1OH4clYMiyWwow8/CTU8AdR2khgLFfABdwRAzyn/EF8zd6EJHIvhZ8HGe5xYEqmS++FXv/FT8KjFFlpkc0WuIJM7lS595BVH1GRuAIwdtqoGBCHwKRQskZPMWd37iGjZva4Hi3//apzvIAWyO55ZHiCJQvqBnMgSCIYb4f+y9d5hdV3n2/Vu7nX7O9KpRL1azZNlyBYy7jRu2aXEnoZlQQxII5eONIQQS4M1LSWgJmBKwjTG4G/duuUmWLEsadY2mt9PLLmt9f6x9ZkbFxhDyxe+HnrnONdKZs8/eZ+991r2eZ93PfY8xxiq1BlNZGMqY+tvhos4QHWUUNxyCDIxQybOdJtWCqC3Aqy1EqkPfw8fnOZ7Bx8fBwREGUbOGZbg8NnoGMmjla+3a164e+4cn+PFjD9Ew7wHedMIQYxOQfBxW/BSWj8PiBjih+WEALBw8aYMSBNIhHyj2e6+dTPT7hkBw+jFXEjGjuryGNuh1AhvHc7mw8Sl8oMPURIuxuYsJnCiGDCgm0+ztmo8fjRMfH+XnuVd2p5fh6G0JPXC2Who46gN6PWxDg8hwoEuNnxiCBTvgIwMBTzgt3Df3Dbz3jZ/itkVnaim5jQ/Bnpeo1Xz+7u8e4JJLbuSjH72Hiy76OV/4wiP4IRus09aDeZ0RqWwHP9NEOZGhlm4kXcjSXsqSyY4ysXzNAbPpertB/d91WTF9/kJgEoSNQ5rEUtforC+7xQ29LiiVtn56unqoIg1MA2f99pl55evZWF13dOY2+iRLjMEsyUd6IRWBYg177xjNliBtavLONhceKEKlqJ0qgkHgV+hm1iI6nZ3x5iLc35aa1iUN0NfHCzO+eNgGY6Kzw6zUx1hniNbP23ig+Lj9t8ymlzHayNNChRSXGf/EXDYggXmvUVDiSLy2OJIRzgzhQOabnHTGI/z6qCf40q/j7PkXA6vmYVdr2I6+YyORgFrN4n9//zhu+NbWP3h3Ng4SeUC7gm5y16orcWIMM3zYtbyDe/1eSwQE7JbDPJM9inTKwjIktuGhkK9Mc0OXB3exK2SsjqJQVKVg3G3ipWwPk+VVPGTBl9tgVnSCSSZJkqRMiWooAi7DfUSFSVTUuLLzEd5vzCE9Y783PfYUjybux2gMP9e5j3PlmuM5a+55zOqAZBy2uAG3GIL9bgue1OuSvjIwhCRulvjs0FyeisM1DTq76bAOLRuO+Noi6NGyJqFckYHzkq9tzaVnzfEcdcZ5vHTfbzAiDkL5SC8gefmJLJ1f5AOex3Ml+NdJgZcdYSzZiIFCKIVrWgxkWmlVI4wGmgUYO8w+MwbsdPVgaQgNJvoOUVh1JGHaU6/RhGsHNPmkIsEJfPqSHcwvjdBSy/PVJZdx9tAGokEAfVv47u1l7r13B52dKQxDIKXi1lu3Mnt2hmuuWc1cB85JwM15GA9vDWP2IkTg07OvF+lqnZX22T3Mbk0xEeh915QiIjw86VEgioUkwKTdNNgbhGAWZn9xQwOAQq+H2eGjInU2NuHDfk+vh/6uy2IbkAjJPQ7TSi8ziTUHTCKE0M4SUYdNV54CcRsiDjQ2UVaCCNqA10MLZC9YAFuF7l+kBKwHdgEXo2mwYRjo9hgfSJsQ9fW1tNATGlNondezklpuTngHgnRKZOkyBphr7KZTPMcQnUSET1Xp1foYBivVr0gkVtNhwS9ycH9JW0BdloZTYkfWDf/QOAKEB4dwIHIWPfPP4hsfDtj4mx+waX0fRAxcQ58u34eGBpdNW1oIjGMPobq/1ljOCp5hXejCoMkrPj4xokwyGSrPiMMC3u8rs6ZQuNJkpNpEPLYF12vHjuzFlSa2IQ872NT1RQOlKPhRqlKwPreajsgACauEZfisaXyOzcIkVz6Wr1Uf5fjIDiyhhx2BIE9uys9wShhcwDJzDukZAt1DYzkejd+PYQhEWQOcMiQ7Op7hjNgyUok55MjyhHUPT2bnMD/xMr5h4EsbX5nEjBqe30xUpfjfE3BDTjPylkbgC206wwCYDDRojPgaBPcH8NnQ/+6DTYc5CQeFEIILvvh11Blz2X73XQjHxLlgBeYp81nNGo6yMyzJ6IFuQUsD5fE8xWSDZlp4HrV4grHUAgxfZxsXHORJWQjg+jHdJzkYMhXLUuJ6HlYQUFISB4ERi+IKk4uSml1ZktOqJ5YhMAKPvYk2OisTFOwYu5IdLBvrQyUaufnmzbS2JjAMfdUNQ5BoiHHDf77E1Vev5vkqbK3pMl09ozJtB2yb9kULifkuTiKB6UQIlM5cP9sCXx0vUwtquCSwCPAwkcpnTJrYmDSF2W3G1DZQL9dC94YwFaq/V1touzQQHKgWc8i3IES6soQGG9xAg1BdOFtA6OI5XcmcyhoNQUNLApm0yUe0TBsIKmqavarQx6gawT0JeILptHUifMwQRpJMZ/nbXP05c3I6y10R0ZPFstQiBusrWuvUBNJMskxsQghoMUZREuKiRFukQMlvpRwkkNi8KTbB5c1w3aBex00I/ZkeLcH7m+B9fwRB9z/FOAKErxK2bfKhK9fxng3dKE9gOYrAB6EEkQ5BJGJipD/5B7//ao4hS5YdbA/VVhQLWcgYY+xiFybGVP9gnT06rULzhzQVKTL2JIGMsz93KrOtUZSQ1GSUlFXUzIwZYWHhKZ+xWgflIErULOBLm0fHzqAtMsyJTevwpaIjvoshZZKO9lL2U3TaGtQH6EciiRIN9U0DJFq5dCGLDtjXQ70bESmJqEyzT4U0UELxyP4XWNY1m99yLxvKGXblV9AV7Sdu5aggiRg1fOnQO3EOm2sCqaAYwFIHttX0oHFrj84QbyyU2eNLGq0aECNFjLgS/Dirm7cbXsOsxrRsLj7rr9h/1jvYyQ4EgkUsojMcFYXQICzTDSwxTF6oCXzh6LUpx0FYFgsd+NoEnJ3Sx1WPx8sa1BZGoFvCuCeZ3L0TP1AcvWsTG5auJZ9IYxRc3tuT5msdJtcM6MwlX2dWmjaGAF8pqqZNIAwy4/sh1YhcejLl8s9Ip3VBsSSh14VCYCDzLifs1hnZbndaPiyCPsaKEuyykhw3o8dtLNAu9G9NufxiYg8vsJgAgwgejaJEg8hSopmj42nGwqywHK57ropq0kdVTTvSL7b1+bs2Ax8ZPrRdYipm9CZ4BkzsA9UFpqOPOy50eXdfqJDjc6i0m20IirGIJs6gMyuBLmUW1DQfRoHO/lYBD+p9cjqwgAPSVcW0q0ZG6JK/JbRykCPg+5167fCKftjthWVTQOJxoXUvL8rVKCUoBylcHJaaWwlUGtOBZcrBkhVIns7dZX3eOmco0vgG/HsWLktp9u+R+P3iyCl7tQgKXHTBb3jjjmt49DtJ/Joglg5onOXhVS2Ov24hwln6B7+9hcXqybPYfP8pPLfFZ8lsk9PPNtnb9R+0006JIhKFjU2BAvDaAVCESjUHfBxlETFq5GWcHdUM0WoPjZFBAmWSMCsYwg+zQG3Na2FTKQlktkQsUsNIK6pBlIRZYrDaxbjbRMLKEzHKzEpuJu9H2K8EnZbOnOoKJz7+lP0SSEwiB2SDAEocvjyrlABDkWWSCSZQspOairJh/DSaogO4VKn6GfYWl5FAO5M7hp6ZC6EzkBEf1pWhJ7Gbm6oFXNFCAZcCBWLEaBVtCCHY48Hq15jeCwQ94c/h4toG+OoYtKZSpG1NhffRAHlURAPXaFj+mz9jvafetA06U2rIj1POZSl2zGa0pZOTNj7G3P4dzHvyPi76p69gdZ3CfEe7IHTaer1QYkAsDW6VrBnlxMGNdKdTcM2/YMYTnHBCNy+8MEhTS4JNYeYnsmWaTlnAyzWdUdXp/nVDrrgAS2ngHPKnZcSaTXh3A7x/UOJKAwcvvPMEDaLEYmuIKnkCVnFrjyaKWAJOiWuSz1v2aeC10Gt9eQXzLU0Yqpvc1rOz6ZsC2AOkgCZ9oEEVGILo7JBMJHRZdSZZZua3wSaUs5PTDfC1MDurtz3UdxVecJgH4i9eWU905kuLCp6t6IpEp63P2eNl3TM6FujPnDF1+dtTPu3GGH8b/yYTNNEoiqQsiR9YFFwT14SqMUDSWQ3R81g3Fq6xzvi+WELPY7e6cMqRUf33jiOn7NXC34Q0LC6+vkDPgiqP/CiN9MFQBm/88xInXdX+X3r7/mG49tOQzceJRmHrS3D7fR7nfr6FRYurJEigUPSxb2qbwwHc4ePQr6staggBKSvP0U2Psiu/mn2leaQjgzQ7o+FWertkkEANFRH7R8kIEzNlkX88oLbkzZiJgAZ7El8JWp0hEALb8JFKUPGbycokDSZEQ2upBhqnlGcSJMM1UREyLvW3+dTFK3l64BGk4WNIfVtKJTEEnDxr5VQj/5zoJAYKX1mU/QyBMKlJEyGkLomFs+z2GYAmgVEZsJ0HaLNWsEc5mCHclClTVGUClaDtj9ik/K60LnP+OBcuSQlYaOvKWr+ngcVEryHNjFXRsO9M6TXEzUaciUVHE1gW2XQjLy5Zg1DQfurbacsNMxu4MqO1Q6WCRbYGRVdYNMUTnJSZxReXZeDtl02NnJ/4xEm85z23sWOwSAUTy/OxMzHSV6wly4FtCfXw0INtowmfa9Glv/k2nJ2EWwswIW06zQl2y1nYwgcEw6qBOaqfQMSJCS3tdklYon64CG/rn2ZLBmhW5ZivTWtvysOyiGJzzUfg4+BTIo7CgJLQG5TQ6aoFZMAchDYFk/MANa3EUv8mzNQAFRyo1BI3dAVhvw/lV+Fb2eG5EDMeB9s/meHzAbq0enyoHrTL02zPSX96slFU4CuHn7qXc78/xGXx21lm9fKu/A9pUBOca99DwizTK8/kXYnzOFFEaTcPbRdRYVbd+Ee8h/+U4ggQvloYEZI4NMo8x1+T4KR3FskPGySbXbxMJ4uNw7cMvJbIk+PzNxbpyyfpajWJE6chZTCeM7n331cz/ytPYGIy7FpULU29s4UWvq5MrXy8coZoY08xPac+joBACSpBlKSVZXnjE9w/fB4tsX0Yhn4fgdAi2o/vQ+bLGHPT+BMBlXuGMW7tZfm5NvanjyFt50laeaJmlYKboiqTZKwCKXuMMWnRYEYxMUiQwMSkjVaq1BgvDxERUX4TuZW0keZszqGFVrqbmzhh1xtYF38MJbTWv4FgibuMVR0LkUgiRGlxRlmT2keBSRamNqOQ+MrGYB29k2fQl1tEHJgTZlkyPD3tzgRjeJyY6eeFwnzKgUPMcJEY9Ps+FycOdEv/r4Yh9JrNVQ3wSAn+ZkgPhJUZlytp6KxlZilrsQMXp+DWHOz1oWRHkEpgSIkyTAIMYtUSI51z+GJkLqtqsCQC3+yAr4zptc6lEXhzBN7fIpgTScJBDOQFC5q48ca38+lfbOP2zeN0Lmmj/czFTCZjUJsWxf3tpyQAACAASURBVK6LTit01hgRWvXkooPWNTfXwBEmhtlFSzDBiGwmgocQkrKKUBbdXJUIUMpACEFZwt+MaEJKOsyKgnAC02pppqin4G3Or/m4eRffrr2XPjmLpWxhD/PJqya8pIFhg8iDcTd4OYjlIPVOGJs7XaKsN7PXS6z1JKqGbua30I82E5ImFGrTbRgHs3RhGuTqZeM6INVBtg6ABlqqTgoN7obQhs71c2kYYaO+AoVJgMWA7OLLxY9PiRhEqfKUPImkWQXZxJYxuCuuXVd+nNNZZUNQwzdsRpXBAkeD+ZH4/eMIEL5aWEcjzCWcVtrIHakTcGMO0bkKXyRpE0tYwco/6G372c+d3MEj68+DzCgDSBxsuphFU9pm585uBkuKu3NrKSuP45rHsY0YjVYJQwToTgU11cd3MBi+UjuEEGCGw4ErE8TMAme130FbdOSA1wX4uF0GZmcGB4PyZI3Sk3msTANz5w5RtrJUZYSI4eJLi7hdwQt8UDZCuChjnKEgQzWIMad2Ge3xPQxMPM/Y2BZUzcctSibN/fjL5nF75jau4CocHK5Yezonji3l4b0vEAifk2atYEXbfLR1sG5sv0fcxSnNj9MfVBl3G1GYdJomCcOnpeVBon4P/V6UmoQKiqKCC5OCJRF4CUWzXeSaznXcNrqSUS8JwufN6VGub/7vkfqPG1o38vpRqHjTFHkbPQheuR+enU9I1tDX6DMtmtzzqWHANFG1Gp5hImSAEga+YWEKKEdi/CwH17dpx/ObuuHH98ANv4Tf5mFzO3z0Kjj9UOU72toSfOC9a3hxUGfPQui+uaqaXk+ry4MZ6PUo09AN7p8Z1goopyc1y1OgSUgN9hwWOPupuGWKMoanLLL+HJa88ASVb36OH9oWy972NuTVH6Aqo1MGvCJsFQnC0qslICJ8zje+DQb8H/sLgC5ZPh+cwleH3sf4S03YLkT2glvV2Z0LDLfpyU9ddi0mNGjVXSbqmbiLdo2PCLgkrQ2C94dOFrNtrd5S9yCsR31yEA3LxD7Tr6lnmvXX17M9S2lVnGsb4I0xPWkZ9LXaTt0oWQJFksSoIjXHGIHCx0Iqm6wXI2boSU6fB3Md+G55A2O/+Cc6h7ZRtmI833Q885p62NzTzYIzzyTW9BqYX0diKo4A4auFsCHzTdpzH+ZdhZfYbjdRMJN02ucx137371T5P1xIJA/xACVKOKky5VwUy5HUcOlnP53+bBqdOJuzb2V/TdFgTWALgfIbGA3StDpjYOr25FfKButGu4f9SAIanQLtTo3qQZr5dVd6Dx8xVtUDoSmIr4gRnJKi8piJc1IrFWnRE8viC7CEnmubZo1a0IDrJ3AMwfOTb2SsvIhARUkZ7Zx/96/gTRBxLYQjkIFPfuMOxFqbfdG9U+SZBS2dLGiZdnGvUOFFNrCdXnw8OujgnqKFcHYSETYmBhMB+NgsjLhc1dPPi/m53FAcJy/GOSm1k5XJKj5vIE2GIkXmRCf50KxHKUiTQBS5zLjgoG7OP25MBJq0kQxbBOpKMb7Sg9tWF5bNUKgTQmd1s22oKcEOHPxAhUAIvu3gWxYDnuC2gvZmtAX8/E745g3QmIaOVsgV4ZNfg299Fk5YdehxrYnC2qjOWJXS2alEDwoxobMmV0Ha0C0KZanZpNtrcHcRmsb1mqaPBpFB32BVZDbLopI+T7FUeBz/6Q/Tsm8Hfs887lx0Dl/xl+HePMLoitl4jTpjiojp9S4JvC0FD5dyWLJAIXRr8dBlv2siT/PArNNYd/cJVLZALaqPUVRBroRitya4mECXCTFTZ94bw1s9Gc5EIgp8ocu7H2nWHoc35uH7k7qEmzb1Zy2r6Qx5Sow7XHd+pT5QODBr7Lb0ORyR8KU2LZv2VPnAjNvAQIo4KDWVvdaIYyuhgVbqUnvCAAZ2cPT3r0MaJrnGVh64ayOMrGdPqok9TR0892//xgX/9m+0HHXU73Ob/knHESD8XWEvhea7SHjPsVpVwF4NRvOrbpIjxwjDRIjQRfcBgJknT4EiZcocc+FLPPSdN+JEAgxL4kmPvokal58f5/ZaE0eZYNBMtlCkPfMwQpRx8fhDKngzM0fJtAu9iTnVnlF3vlBIRGsMMVDEMG1UxaPhDQkyD74MqeUknBomRlgaUqhwZmsaNRIqRm9+JVRX0hquVwwVKzy6ZAXHR9YjSnpFxTAtpB9QyU1SjR7exMbF5df8ijHGKJBHItktBygYs2gUCjN0X7fQg0RBKhxT0Jp5hAsyW4gTw8AkS4U7+A1ncjb3cS8D9KOEImJGOIU3TrE9/7tCKv0QMJUFHfA5DzOgHqW13ckIsIShTXgNc4pdWSd47Pfgn8fgk03w77dAUwaiIaimErrV5we/PBQIlYIbsrC+pkG6ztysD8IBGhxmh0SPhKHbTQZ83QguFGxWGrB7bL3W+XJNv99yx+C9jXDy7T9l0+5tFI9axT9ecj1ZpwkPC0yBGpNYgwbFZTozioSZ2zFRODcF5yTTZEejeEENnwizLK00Y6oRftzyS37xiRP4+QMwsk4ThI46GVLHQ15ocQGJLjl323pNtr6eVw9DaHDJSbi3CLcXdF/jbk9npV22Bt4gZJ1GhT4f3Q4MePq5lDFNbjr4Es4k6PhoU+Hbi3BDN9w5Gx4qwnVDUAogp3SmWlV6y/q2Mwk7PtodpNUCHvo5BAFGpoW9G/oYHy+RSsdRQRHZ0oKXz/Pw5z7DZf/yFURzF0T/O6d5//+II0D4WkI44Jz8O1+mUDzNU7zIhvBLJ4gT53wupAldqpi2WVKsOHM7hZE0629fAUKiApM3nTrAO96xkDtGtGizh4evJEJBgMCYQRWzsF6TLVO9j+/gY63/NjGZ6XEoEFizm5GTLqrqoWyQRZ+RahNuZDGOV2XUzdDi2MStLFIohDKIG5JxL0qusnrKzw4g47vsNRdynNiIwkchtaGTESDcGm3hrP/g2MF2cmSnfBNtbCoyQtrOosK818DRM2rhUpEWDWYjvdxLgsSUFmuMOCWKPMWTePjEw/xPIMIWj2MPEDWoxxij7GAHPj7zmEcX3a9Ydn61aLFgZRSeLDMlTa5CMke3dfh1HaEkHyjs4wdFm46WLvYqMbW+WC+n1dAya/84BkEZCiWINcG2aujsLqCxB56JKp4ZzrMyArEGXQK+MQ/fCl0vDKH99nJqWhnGVRpg6g3/VTVtSBshJHmgS4g9tgbKtTFNBvnXTlgdg/u3bcGwbW46+SpyZiNGWSJsELUAlbIIBiFWAS+ms+Ur0vDXLfCfOejzbY4xP8dZxueImaF+jCqA8mlMXsF1Nlx3OWy4VLfHtISXrwFdWt1a0+zMbnvaHcJCZ+GWmAaXlAE35vQ1ajBBiRqbawa7fU1LixkGEWFSkBA3w6Z/VbfkOtBgGADXh8EcMleBdAzDNvDnN9BhC4Z8+I9J+H/a4KwU/NyCjw7pY3XV4Q2C62uVSQO+FbquMLAdYvoe3r5jBMs2KVRrmG6NPZtfRjU00f34/ZT//l0kkhE45z1w9rVHuu1fJY4A4R8x9rGXDaw/YBCuUOa33MM7+TMEgiRJWmljgnFc4XLMFU+w9MLnyA6lSTWXWdrcTLtqJ25G2CUnsM0i81NPkA+i1GSK9sgoAj0iqUAeqGmFBj0Lixq1qWzv1Vou6n9vpx0QTDKBg4PruJhrenBHcygl2bz/KLZ+4+Mk9+RYs2wjMSdP3rOIGzaWGWAKk5Ws5JeTZ+D6SaIzcMVOJMj2ZYi8UMRdHcHzQsFvYWNVXDawnjM5+xAR8SEGkSiCsP0CwBSKQDmMVDtpiQ7iqhpSCAJl0jd5LoUWF2GIQ94LBHvZSxddU39TKAYZYC97mM+BPn6b2MgTPB7+T/ESG1nKct7EqX8QGH67A87co8GlPoBmwsEtMuNQt9Tga49swP7y54hOjHKqkBiLlzHxyX/gl7FuPDQI1QdHA7129isP3CRszYIVBSMC42fBcCogERR4x7P9zNm7lY/tfJKzPvlJflhto8GAlz1d7itxIDmkXiLd7cP5SXiirEuB0fBYjfDFFaUzOjs8Ja6CO4vaL7Jl2Qr8Bx5g25xlGJMBfsRGKL0HsyaQc2HeAARL4Qdd+nxc1Q815dMc7WOd6OIXwXf5t8hnydAP5jxIfFxXZcJoDFtllJoe57sszcxUQreoxIV+rtnUBCQ3JOc0GPpvUUP3+VWp4FrDRLxmatJhdnQcx3DJud1Y0ma3B62mPs7RQO8zEirHZCUoqWD7CFQ97eKrFP73Hqd/zSxaLz+GBgOenOa5sTYGN87Svpo/yUElOJSgI4FVDny3S0+mAJh3NPRtgVgKIaBSKOAYIAwwspPI8XH6GhoJGtvACuD2b0FTB6w97/e+b/9U4ojW6H81Ah9KeVCKrWyZ0S+nI0qMHDkmmQCgRg0Pl3qhRhLgpMu0Lx6moblGjiy/FP/JSS33UFZ1DpqgJm1swyNiVlBKoZQkED54AQQKoQTCM6mOGCjXmDoOG3sKRARiyoHi4BhmGAeHC7mIZlrw8amZLqojRq5pNuuWvQOvOU6lkOGFx49jV/8C9hV66B/toINOzudCLuAi3ppIajHhGd/mCWGzNmmT/udnUd96FuPlCYwXR4ndtp+u2UvZyU72sfeQY2qg8RBVnZhRwxYBz00ey8/2Xc2jo6fxyOgZ/GLfNfxqYg7fGG0C1CHb1ahiYx9wbTTVyGA/+6eecxVsqpV4VD5BjBhJkiRJkSDJFl5mmKHXclccEi0WHBNTNOORkB7NhmRlVDMJ6zHowYdeGiX5mQ+TKOVQLa2MN7ShdmzlDdf/JVenfWY7ur8wE0p2eeisRgF7zoRSCbIFmDgG3LTEHC2TmdxJY7XEvkWruNlp486PfJhRXxET09lRvY0BZpT0wuc+36yBYuYZtcJbyKJe0tPiBTtcLc12/Sh8+ujz8RqaMCtVlIFmvgKGYYAhEDVIhetnS2y9TcSa5KTOn7Ks+W6WNj1ES8vjfDfxEWTLY9B4M0ROOeC8znHg2Kgu2VbCAyxKaLN1CfLXs+HRefDVDg3ebabOcuMGXJ7WLvEv1XR/Y69bQymDABsDiJsBhhDYZh6Evjd8dHmy3v5QR624AEo1iFjQkoJMHDYNwGCOkZteYN2Yy2TAIZZZPbY+tveFZiQmB5a+TeADjXBCWN1UCtSpfwaRBEyO0NliI70AUygmiSFN7bGhajXWtS0Fy4FYEu6/4TXfq3+K8boBQiFEjxDiISHEy0KIzUKIj4bPNwkh7hNCbA9/vz5EhAIfded3cP/yeKofPA73b88kvXHDISAjpgBPf0t3sJ0yJeYznzjxA5RiJJIyFfoZIJZYzwe7H2Z5fIyo4dLu5OmKDoMMp6JCvzu2VtLP7VOMbFNkR31+/VGXnb+MklBJMmR0qRMLGxuJPOAYHRzixLGxqVFjAYu4kquIE9f2SdhMuu0oJRASVNzEDaL0jczlxdHjyPYbHOuvYSVHIxBcmIK3pDTxYsjXpIIuC75x8mJO+vb1xGIpUveN0mZ00PXhi7GiUQwMdrLjkFO8mCVEQ0llP5RokyKgxVT0Vzsp+Gl2FJexr7iEGFFcBXcUbLq8tZQo4uISEFCkGGo1xg7Zh0JNlUrvLMDZe+FTkwNscxXbquYUIaK+clMH7Hqf32uNb+zJsbF3Fy0bnmbOxnW0rn+S0fFJvjY+/ZpfFyD56G9x3CpBKoMAIqZgvKGV8f4B3rF3PTFDZ2Ce0tqX9RLryy6UjwF1LdAGzAerUqGFXrB14S5ZLrDppHPI797N3NIEOalZoD7TwOagS6rNphZ2PikKS6JwQVK/LheEcm5Kv66qYJ+rvRP7fVji6MG9wwIaGrnjiz/k7LFegpSJ6XsIw0CZJjICzstQnQfnJPWtvNtTzM88SMF3KPmN+DKFlAlGzW085A3x1XHBhwf12mY20BnUz7NaXuzlmtaOfbysM+R/btMEpA5Lg/glafj7tmn90DkW3JCHoUDbPgUKRvwY/bVmhFLYhj5nBgYxs0BCKAKlP/94ALNseGdal14lOks0vvkg/Go9/PZl+NFTcM9mbNvUOrODOXo9ybL0s9zEz3mEh8mSnbr2vpygXQyz3NzIXGMXCSpTYPuNCcj58KVROGYXrM5187Vr/oPxZacSa3YYOfZENmYWsjfVRc1yCKJxyk2trMtKdrugrChkR1/7zfonGK+n0qgPfEIp9YIQIgU8L4S4D7gWeEAp9WUhxKeATwF/uK7ZHylqN3+d8k++TLES6DJS3yBLdm6i7x+vobZkxRTY1KgRJUpjuEY4xCAinH+kSePiIRC41BAYWOF6XkAAzh7Oaa4wDuHA7mEUPTBBOWEDlinwxhXZ7Qo7KXASBmv+zOTpj6e4NPMmlp8V4yVeop/9lClRoTIFyvWMqL7O6FLjTm7Hx0OhaKSJUc8hW+ug6CZAStLpAD9j4Vs2QkpmNfRxu7GXO7mTTjo5U5zNF9vmc01GN123mHBcLFS+WHssqbVvPYxfoPYTPDiSJLmIi7mXu9nLXjwCHByy7iwShgajut8bhDR0CV3BGubaETbyIhUqLGABx7GWe7mbPHli4QTExQ1NkhfzYlUb0mYMSAlNbxoPdJazbIY7hqEcvjsJ/5nX+1rq6HWt1a/iDaeU4uatfcSKRUwn1FGVEn/bZu6PrsFvj2MJ2OVCfGL0gNJYneThKUFjKcsts+CcvXqi0WjChNSAMOXLtwzqLlSJ/aPYhTIBEV1XUArf1NWBy7Y8zvVtq/AKBZp9j4n22dQamhFCaKAI19Y+3qIB5pFy2FIhwMznaN30DEE8QWXxCqrJNFUBc+0DezGTBoy2dfH5q7oorIf7IzGcAFwDjF6Yvxbe2QIfa9LmuyNBhdtGT6bsJ6jJCDGzwvJULz3RIf5ysoE0mlTzRAX+14gGsLGwXzAenieptBj5ojDTLktdbo4J+NcJ3SpSkJqpW5dRKwFKakWenIwQNzziposbGMSMMoGK0G4J3h7XpdBmU/d6HhXR+/ubYfhBFuzWFOqBrdCcxA/LxX6dKdUYIWIW8JztFCkzwcvsoJdLeBsJN8sC9/ukxZXEKZASOdrNIV6WR5NXSUoSLuiDF6vT9/r15ny+8KZ/It55NZ5p0rh/N6mh/VQam2ka3ceaW37IbD9LnwctlUlSx535yjfokXj9AKFSahAYDP9dEEJsAbrRKn9vDl92A/Aw/9NA6FYp3/h/KFYCjEhUw5oCN1tk5fce4pGvzQtVUzSh5SzOnnJ176efEYanypayDnqAh4tEYoTDX5kS+9gbApYGroqI0ptbymiug7U9T5NQRQIUlucTI4moCGKdPpFmxT/88G7ec1aGgIASRRIksLDIhwzM+j7r7hYe3pTQt4/PtlILD4y8BaEiOEhGjQ4mzSYyVg5DSk6KPkxqjocyDAICBhnkZ/yUy7mCRZEFLIpo4YBneIlhhmikaUoQoJ6daTFuWPwKfoZttHMl15AjRz99+ARMGrP4pYgwqcTUwAB6vDGBubZBEytZwUoqVOhnPxNMcAZn8QiPMM4ohGXic3kLaTLcmAv7zgwou934WFhGkeEgQpf0SBo2Crgvn+K2Qp4Uae207sEHB+En3dpI9nAx3tuLLBUxIxFtmAs6MzIE3uQkIsxIV0Vh4/I19Nz2s6lFLwWoIMBB0rp0KWkbfjoLPjigy3rjM5zbDzaorTQ0kRnu083eCsqJFCdseJSgVqP89S9xedc8njnr7Qw2dbDw0ds4PgZ9b/9zBj2Y7WiFl+NicO5eDbprY1B66nFSD9zBpvMvB0NgDOzDaGzCa+2aKk0eHLaA35wCL47AQzu0RdGJV4ET1YCel/D3ozDkOzhGgpqMgpKUgjjrs8vYZCyj26nQbupMeGNFg9nMCUMJPSmyle7Tu7OgiUj/MKYBvSa1oIGptEv8wetwRUBgIpDYRkBGTdDsD5BTaTIix5+ntnBN25kYxoGkKkPoCd+xUeDS5ax/ZBuVYhWRiCB9RTxbpOnNi2iYVSAfWMSI44S2ZCVKPMczxMfvYbF4GcU1VFQcW3g4uHSxD1cswxTadzIelrKlhEkXAhNKHfPp2vY8lXQjpcYWhFKMz13M8m3PsKq0G7taYiSeIHXegQbiR+LAeN0A4cwQQswFjgHWAe0hSAIMAf81XbM/QrgjA3j5LIYzQ7FDgDRt0tsGOZ8LGaSfKDEWsJAECSSSu7hjyrFdoahSPYDNKRAE+Bg4U2BUX98LCCj7Ke4YfCv5agRbBMzxd5NwEtiVCUxLIgyBsBReFUpmHjVoTWVfRgivKzmafvrZypZDPlcdEG1sfCV4cPQ0hKiRMl0iCY+UP8Gk18Q8ZzvHp58gnS4jRB2mNegHBNzHb3k/1zHJBLdyC4m9+5j/1EvY+QLtRy9gcM0KSs70qHk8J7xqC4NA0BD+AMgILI/CYyXNnLTRpS0fLTfWFI5VvWzjIR7Ew8XFxcLibM6jgw58PBppmjq/w77ONgDGVYHxyTUc3fg0EbPECDmyCkyZZr/zACe2SyrubAZzZ9Ioogz78PMcfLb18MfvlUocs+ExnnzzJTTkJ6YIEcV0E6fu3Yz5hm5AK4b8bO3JjC1bQ/OmZ/HiSaQM6PCqrL76ctKzZgFazPmKjHapMNElypI6VA3FiyfJd83BKRcpmoK24T5W/+bHDDZ2UJqcwCiWOOf2H9FigfJ9yuPjXHtUF7V8nszs2XS1r2W3pxmTbRYYxQI9P/s2d33g89jVMpbrYylJzQ/wkg3konECNd0iUpR68K77Ta5q0w+l4NsT8NNRDSRlqTPviDDDRnsfhECqAIlFVRo40gBT21MV5XRz+wHnOXzUAviXcQ0azaZuQSkpvd1MpuehoZvZDekhhcfayAY+kbyTVgxsNQjVIYhfc8hWI74m0Rhzmljx9+ex+duPk+vPEZgGXW9ZTtufH89Wt4QtBKXAniL2RInykjfJWcHTVMwm3h35Bf9R+zOKoU9oVNRYYikG/PD7FZ7XfAlCFUKCWJyJWYtpGNyJ6boIFLXmVlrOezNjfU/yUtcqsm94F5/v+O9tEfq/PV53QCiESAK3AB9TSuXFDMqvUkoJIQ57Dwsh3ge8D2D27Nn/rccoY2k8VW9KmJ4h2kIyrFKsOIwY8zBDjKsxUnmXaLHIeMagEtVfblPU2Z1gYVPXEo0QJUoEECRJ8Ex2OUUrQ9ocQwWSvtwsVrZvomxliEb0e1npgN1321QmApa9aZqJkSBBmTLLWM5SltPPfipUpvoL6+VRicTFxQviSGUSNWu4aBeIhOWgZBnfiNIcq+IZAiWYOl4N3BYT6IWvZ1hH59MvsPY/7wUF0jbp3rid0hPbMD78XVzHpIMOkq9gQqyU4sUXh1m/fYJIc4KLTuomGbMwBHynU9PmXwizA0fAJxrhf4WdGAXyPMQDlMOferPILdzEFVzFXOYdsK9T4rChBrFQjNv02nl86GJSkXHOb1nHsCpR9VKUpIUhFBlnLx3phxjInUfcgO2H1y8AoOWoo3jjU3/N0Nwl7Jmjm5yFUnTs28FHZ0+nkQ0m/GiOxQ/++Rtsuv0O2h69m4WpKOe/81Lmn37a1Os8BTcWdFl2fW06S6jWXOY+8yBz1j2Em0zRe/pFZBcspWlskAsfvJk5g7t54sqPkH7kXhrKFapSK6osUNCBYnLXTn77oQ9iBVWEbdO8cg3Hfu9HSDJIBakNz7B35QlI08AMwrYdwyBWylEpF1mQiTMaWkcZQgP01zum3TX2eXod9IECbHS19mrU0FJrLqHAOhZiSmIPpDIwhIGrYih0BvnKQEZ4D+uJjYte89zo6zaRg9VfDrnf9N7xpUUfs7i1eiHvjz5Mm10D0QSVn6Bi17CpF7bu0j2bp6zRQL+lpidgDau6Oek772D9WI2abeHFLZ73FYFM0GwX+fHQCaxJ9XFxyyZ84VH1uymRIYrLqc7zzBU72SSPwiRgtbmZ6/0b6bYhH95ffqCvdX0ioAwotrZTam7F8ioElkU0EuHJRe9ng3g/Q4G2fjoSrx6vKyAUQthoEPyZUupX4dPDQohOpdSgEKITGDnctkqp7wHfAzjuuON+DxrD7x/R5hb2Nq9h3ug6Kr6JFwhSEfCERJ5z5WG3KagCjPVT6x+j6DvEhyWNkTK59gbs5tlURZUq1dCqSBNaUqQwMLGxiBJjd2kubaYNyQZqXpnJyW721wSdXbuwmgvIwGPwrgybf17DtCWnvW9aFLKet2noNkmRpoFGihQpkD/gWCUS06gyN76bkepsHKFJNpawMEyHmCNRUZOZcsN130SFRybM3AaqO3nLTffjJuJIJ1w8UpLE7l00rd+KecLFr3iOXTfgY3/3ILe2NpNfPgtRgo/cO8Lnjy1zWk+FVruN3/S0sdPV/XWLQzx5qqLJI9FoHxWzSpkyJuZU1urhcTd38j6uO6B38LK0djx4saYYl234nkEiO8ry0mZ2nRTFtl1MoQd4gSDnJ2mK7MY0ypT9OEdHDvcpdNjxOKd/4q8wrr+ega75jHf0kB4Z4MTWJKuu/eYBr2234DOzInDdZfpxmMgFOotqt7RzxU4XbN/j9K98jI6N6whsByPwWfHAr9jxnnfz3DlXc9uVH8bHoSjh/IF9mFs2YKczmEqXd+19O/ELedJxD9M0UW6Jscd+y/7r/5q1n/o+z1UgIyVeNIYp65O28JKis7z3N8KbE/BCWMZ7Q1yzWwGeK8PV/dpjsRDKsuUDXW61jZlOEwaBjCJEQKAUCUMQEyauEtpR5JVP8/T5Ru93vwcvuTrTt4TW/jxc83s9RLhtmQg+Jn2yk7fl/p73Rm7kuuTzGHKIT35V8eDTgpqnvXxbGuFjn4Hr0Zlh0oCiFPQ0RrmuGb44CotsQcwuURQ5DGXxQqGHWdFRBr0IZnUVz1U/w+ein8EUDrPtLO3yCTIMcUvwId7bCEdH4OI+zYoNJYEPpcL8FwAAIABJREFU+QxKGHjRBBj6/p/0oGjCGQltk7WtpsFzoXOknfBw8boBQqFTv38Htiilvj7jT7cB1wBfDn//5n/g8A6JO1v/nOYndnFK+wQZx2friMOTlcV86PLrDvv6xv4xBvNVhkanNQAjVpyeVJliaozqQQOpQjHJJGb4M8kkUbOK43XQYjbrruYIuGo5L476fNXcx69/2ku1N88551q0XtNH+9zpy+uFpcEWWjUTLmzr8PEOa/xrCJ8VDRu4o/8omqwIBQoESiAxeWNq7JDexHpmKZEcz/EAtPfnEDKYBkFACYF0HIwXH4VXAcJbb93CLW2tVFf3kChWMR2JMc/nS8UYo9Vn6YpOMo+5nLk9jvnoLWwwM3x87V9STreBYVIVczmzK0JjRLNHpwlCBjVcRhmlbhzcQAMtZisfaBS8d8jAzk3QumM7Mhrl4dga3lx+ksZ4gGFr0kVFKUDhKsVkUCNuxHnX75AqXXzBBTQtWkTv7bdTmZhgziVXM++00yg7VYbYg4NDN7Nek2xfxtQN7FWpGblJA9S6R+jeuI5cSwem0EbLrucy90c/4/lT3kYpKTENm0DB42+4gHf84vtE9hapdc9GegGF/X1kYgZmNBaeJ4gLn95bbuKLX/4mfyOjbFl+HF133sr2taehSfqAVASWQ0smxTvSkDIPXStVCj40pBVsImI6o8tK3W6xxIEYUKAuWi1AWUi0MLYK+wD7vbAMzKFl0XoIYLalz08Nplwo7PBvMx0oDrdtRYGomqDAKEsibS7/Wr2Ck+3NNGWP5eZ7BfmSJtcIAZM5+Om34Udf0GbQW104IQbXNOistNnUZWVFAyaKgshTlYKfDp1Ih2mSEXH2BG/gytIP+X78gzQYkyghWafezsdnXU3GCtioNnJ8g8NT+TnUVJTANrFdgVWAWhyko8+RQGfAcUOX2S/J6M/71j6YDBnG3TZ8pW2aTHQkdLxugBA4BbgK2CSE2BA+92k0AN4khPgLYC/wjv+h45uKrbuzfO/RImLWJ5id3UxqMks+PZegYS4rnhrh0ksPlWDbcOMWtkdNOk5VBHlAgspY7BpP0TJL8UpzXZ3B6bW3VemX2TC6GN8Iy2FKN/ZemrI4pXU+s6+fz28KsNeTlKIvMhY8S8wMJc0wOIuzsbDYTi+TTDDJ5Cs32wtodMZpiYzRX2tHiBhKwamNLzMvNsoABnHiSBRlSiHJxyBOguPRKs+LoqtRUqKURAhjSr0m4UcQqVcXBb7p/t2ULzmRWKGKAKLdNYQtKbkOGyaPYlHnRuzf/gz39g0Qa+Pj534dOd5PW3YA5q+miM3OcifLrVEqMk7eTyOVIG5WMJ0KD4sHyJKbWlObRQ8/yb2FOUoysWMLWAIhK0ii7K4spMV+CmFkME0XSyoMXAIUp7Q+yNXW+XTbr0IbDaNlyRJaliwB9ETnOZ7lBZ6buvYRIlzAhbTQSj6A70zCHQV9fG9JwQcbNQjaAt7bAF8bhzRalaX5+Ud1Jii06ACAtB2ULNK6cysTq1aREDXE7r1UgoC+pccy78l7SQ7up7BoOclElMaDybyGgZKSTb1bKDQdg8o00nruBazY+iw7Fx8DMkAaBsmWVr4yN0bqFSyARnxdOrTQmWy9jiDQ/X9LIzA/ooHORjNha+jXm0Kv/24Mt2+xph3oZ4JhXVdCAQkTdtf0v+vXtxa+zhG6vUIw7RZR/+YpgBFQNQM2SYLHTPbP76Tx2knujpzA7h9fwPgkRKNgWLpEOZ6FJzdAugBfCtkLvoJNVZ2F1ftpBYJGmkirBvb50GQK5lqCiQBihqDfX8i7K7ey1t7NkngX725qosmCB7iPXrGNS9tjLIgPcsfYCoa9RmTZQXkCpwpuCUQS0t0anCtKl7w/asGl+/V5aQ1J5sO+npTc1nOgkMOferxugFAp9TivXPk44//LY/ld8e+PbaEsA5LCoq9pNaBwhUWk6PHyy6NceumhZr23POGy7/kq1l6HzDkmhg1D90HQadK4DMzDzNCms6yABho4NtXPUs/l5lxkaoA4NQ4fa4aHKzk+MiRwlSIhHCitoiW3hI91v0ibabOQRWTIsJe93Me9FCkSI0ZAMKU7amBMEV4CAmxh8p6uJxirdTMReBwdsYhYBQQxWmljkgkCgtDK10QhyZCeKkMu7DqP8a4lOIO9VDNJEJB2IyQQcNJbX/Ucu1EHITXzVtgSIyaRnsDAY9KPEymUWXHXk+QyDWzrOJ4CFo25UVwZUKu9SGzhcobLC+mJ76LgpzCEfq+atNlZjtPrNbMyFqMzUqTu+bjJrdCaLWIpk0KygWxDG57lMFxYQI+1G9uweG78ZHJeA5bhsSw+zOr2dQyIJzjq97xFBxngeZ4lfoAKUYW7uYt3qav40JDB5qoe+AFuyWnh6J9060nQn2V01vMfWQ0mPY2NONLHJ8BVmvShEDgoCrEGWo0s6WyR4fFhCq1djC0/htZyFjU+TsusLlbPj9L72DOkItM3Yrni0jO7gW8Uo4yntRD18298C+2jvXy67xZejKWY1SV4+/xZLEqeh85HDo31Va2Gc3AmptCgNORrtu49czTI7/fhrwa1pmedBAT691CoFBMX004VNTUtjK3QoOvO2EddQ7XO7hZAq6Ezp1TYl7nHA/ZB5VeAEoiqgZNxKexIU70nxvPveg/rn2/CMLWFEkCTMUFbpJ++bCdKtQDQW4OPDU2zebd7uqQ5LzytVWXgKmgxYF1FE3jq9ZKhIIGTWMGY0kozZ6VybI9sJ0kKhcHj2cXUZJSe9Ahxq4n+UoJJB5w0pDJ6fxWldWDvKcHa3fq5FeEcTQi9jjns632/6eCJz59wvG6A8P+meF74mEGACpmYIHBUQMGTdM9tOOw21Xgrhuxl4pYqE7/Wt/5Ae4SjvwDIUH3+oKHCwkIgSJOhgQbKosSHmxV/0aCFhdstSAzsZWPvS3wstkQ3JVhaNjtKjDGvnd7cWs5s0kxQgOd5FokKWaRiCvh8/KmMTYV/NzAxhWBeNEsPPgEB7+IvuJ/7GPt/2TvvKEmrau3/zpsqV1eH6jjdPT05D8MEwhCGJJKEAREQBVHBiwEvcq96L34qJgTDNaKiXlQkCsogecgZBoaZYXKe7uncXd1dud50vj9OdfcEQPTT9a2ls9eqNTTVb9Xp81btffbez34e+scG1sfXa2LjsImNzGEuQmjUXP4bvF9ejd+1FV0z0DQTLvoCTJz9jnv8gWMn8JTtKWCGUPsifYk0AkyLdhHr6lXjBYbOYE8/7uSSel4I5MgAvWvWMLxgOhvT85gZ31DOAAR5N8yjvadju3GeN2wWxdo5q2Y9IREiavWR12Kk4zX017UqJhQBmgMP9L8fMycJGAU0JCU3xrZciBdTDkb1GxzPMnR0bKk4RXtd1b9bGGS/EY9R28IWVKF2/FhuyRAZcjxT7GdzqY4GQznxHheyngIGvZhXDkwIODuuHgUffnvWmbz5pzvBdvCtEEhJaHiATO0EeibPoVVsobtH4ms6UvqYXR3YPjQlq6jt7WDGl6+hf91ahjLFMvuLoDJqYh45n1zTDGKiCF4H9X4fp9bezeymHVwRLCKED/YwZJ6H2I0HNaBeLcCX+t6+HGkCF8TgAwlF8A2QzqtSXpH9RW8p/yxRJVJXU9cHhAqgjlSjGKOsLGOAkvK1owTYAuj3ISxV9mkJNXg/8ACQVovShcAjhBOH/Ctw/idgSxBSI6BJj8vNH3K2cReer6EHPWofPhPnoi/y2R5rDGVLeQ0bSuAJxaDjS/XvhpJSydAYl72SwJ1pmBNQwf1XwwFOrm1kYSTL77qPYmOuAUP4DLkhwoakvkqRquctFfg9VBk4LKAq0MXcinXoeppMqYWR/Fw0GaFKVX0Zebsb8i9qhwLh32CRGkF8eiXZTSnMChOhC+y0gx62OOXE5re85tQzZvDdNV3E7N0IR2VgTjpIccDACL51x8Mrn2U9XDKkmUgbQYIEdYi4GZ78/H/TvmYVu398PAPObBJGDj2iejwFCki9l1tyeQJVjzCRiRzDcQwzTID9089R8u7RfuRohihxyJAhRAgdnQIFuuliJzuIEd+PGQME1VSRp8CjPEwPPcxnPsnqBvQv3IHetR0KWZgw/V2x4V9wzgzuuGkdz0ydAA6IvIUfsqgNuSyOt1PKh5C+R8i2mPzmk4hjPoVjWAS9EtKwsG0HJ++zSRxO0Wkkbg6wt1hDR76FkjSJ6zYxvcCqdAtTw/1Mi3RxfOV6Hsu8l/76ALrnIoUGQqepdw99iVrccBQTj4jmEDUz+MAr6UksrlyLr/n0OTqf6FaBa9S5zQ/CD+tV9rGvuWXBqxSD2L5Grx0j4xmYOjw65FHwISNgVVE5RXU/4CNd8MpESJaDxk5b9YD2RKZS/6mvsvTmbxHOZdGkR6ahhZWf/y66JnnVnYVothHSJ9mxg6bednJuJZmcQaQ0SGD2Ys796Q/Ye/O3SWdLVMQCJKZN4tLlP8EUDjhr6PfCdPmttLuX4ZYMZhS7+V7FHbRqfWA/Dd42MPafB/3x4CjA6GCAh0ApRFxZXZYXKluTodRE9i2hjppEOXsfJbPU4aoAUK2pWcEWQwFypBzvB+5bIh31/z6KFSeIep8jIrB9CDJB2OWPCuaq3ltCwvlxePFIuPcxeK//B87Tf0+fV4eHQXXMw1i1glUNs0hNef9YEMz4KtP0gZQLyYDSjryoU5Vq9y3NllD5tCVUu2PIU3/5QwMLWZfrYVs+WdZ7gZJvUEKjUJbNslCAmm5XIQnbwltZnHwcA0HWN6gMrMaObGZl9/nssCM0mjD3UI9wPzsUCP8GOzuzjfZPLSF611p6X+pHej7WrCRnvNdkQsNbZ4TLl8/kiSd2sWZNCM0tISWE4xb19RmyXWkGd3n0rBZYFYJJJ/vEm9RMoUAwxBACQS31DDJANTW8cOONdLz0EubSNqwACAfsUhHN0NEDFg4OJakzQfOIEGEPu0kxSC21dNGJiYmDM4aoHKVfO1DNIkMaHZ0IYeqpp48+PFx66MYps+KM0o+NAlA0dHawnR1s53TOoFm0QNPUv2qPLUvnwc8exq3rUtwxYpLTgrTUbmJBYhO+XqCnMYrTOpXExi4qC8Nc+dRP+NmJn2EEyIkwris5Ysda0kslzZEdDNnVtBcOR5aZHE3NKffTfNZkm2iObOeMyEQWN1hc3i6xbRvLLlHb30UkN4LROBEdjepiN3JnilKmiLAMnLZmok0TMS2TGwdVEKwrf6ukhNcLSk3h4wcQA9qUSJFCSJ2OUi2u7xHSbAxpsCNfS4ejBuZtOf4l9VHlwqt71VB93ldBcHtJ9dJ2HXsaOxcvI7lzIzIYojBpEgndJeeZuAgCSGLdHZS8CPeGbkDs9TBKBYTv8fS1g9z564toPeos2LMeglGGWubRtVen2ttDSWps8SdRSxcJMYxEsNeL8+mRK7iv8np0BLg7FDG2/QzYL4KoIu2cgUHbmNDvaGAatbyvQD/7BsJWS5WE2w9IB0evs1EUaxlvPLAtDsDKvFKcKPN647zFtaO/L1BBp8FQGdQDWYjNhfSL0JxUz7sSAmk4YymYOnzmYnhjE1yauo2CTGBgENRhapsOoWoKa5+BKecBAqfcJ/Skep20VGjaD3QqRpzR9Xj7rGc0cPe76uAksCh5EXblmglrw5T8GvJ+YKwf6qLAWx6KKq4oQZce86qfw/UD+NLEkWD7AWJGhmmxtbw2dDRRDSYeUrLfzw4Fwr/BLp4/j1XrtrD60gWEP6yheQ5NpQG+Veu9LTY5GDT42c/O4Pnn21m9upv6+ijTplXzmf94gAFZYKhHQzMkvguWKZh1cpDq2hBpRsbKZzvZzjBDnJU/gx2PPUakthbHlgS0ElP8LWzVpqMXcxDQy1/AAEfFtyAQRIiSIcNMZtNFF1FiZMlQooREUkMSic8gg2PD/KMD9ikGCRPmaI6hmy4GGRwrie4r5zRaao0SJUKEEkWe5zku5INjfcO/xoQQXDK/mkvKP9ssYg9JCuRJiiT1V1yI/dPPEdx9D+euu485XRu4a9r7GTYrmLDueSZ8YwbtiQoGXRNLZAkXh9BzJUpmLZXJkioDC4+SdIgTZzFLkDHJ7FiAcNHFKRWQNQnC0yeTEmFKno23rhtfSHxLxxMafvsA/updFD6ueC5rdcjmYSgHORNKAfhxCt4XGy+XDTFEO+1EiTHkOxiihGn4+FJjQ+p0WkyDjnJ/bLTvJVHD8xbwdF4FgadzKiMcVWCXgBcM0T1rIQFgYUj10Xo8lfnEQgGC+KTuCVLIJqgVb2JpJfLhZh5fG+Gr12/ihq/No6/hWO5/ErbfD631sHWmoBRK4kmwNZWKWsIhTIE9XgtvOK0s0rtBq4KRT4PzOggDpMd3rN/zA/d61okTxjLbsfuLckCf7IHbmsYHxnfbCvJ/a3mqRx5wjUAFwwCK17TJUGMzkvH5xaJ/cAY6ev3ofvooNZBR6t5pp8PGLdDVr0i7a3WIJMA+HY7apYLOqV+E1m8Ns9Vqxo3HiDRFGBYjJHK9zOteh0BiS8GQpwJgsdzjjJfLt4Pl4D3KIzr694yWbTWpDjUCQEo+KO7kce84kqIPX9tFB83s9SeWe8AKMVyvK4moHT5IPYMhbBw/osgNyq8t/QCt4Xbs/NEUfVVODx0Cy4zZoUD4N1ho+kJ+7rusfvZ2dhQ96iyDo444HuvwdwZMmKbOCSe0ccIJapj7859fiZ2BgV0mvg7oEs+RVM+26diWw6h2MHUDrVwUUVAIl7VyNUiJ0DSsvSWMQZdjE09SKAXpNCZiuBYOBsdXbGdutHvs/X0kBgbLOZdVvEoffUQIM4s5zGAmP+FHwHhQ2xdRuoDDSZJkJY/iljPV0d/ZV/DXwKQCNUtgEWCEEWzsg8qxf4tZWExln8wyBoEv/pb1XSF6XnoeSSVHDD9LIZWicGwtfnOUqUacwHCeiqt/SeTMZpxAiLpCD8GeOKFZE7Ex+HDEZtbdGn/81XKyPT1M+tBnWfPeC2ia0IyJAnpUuZDr6ycXTSLsPNI0KAVjzH/8T7z59IO8eN5lSAJs2wO9w5CvLzs6F4aC8IG98IsGmB5QQBmBIEmStdlKdpciaEBvqZ7BYi0RlNMdBZhoqOwlLMYDnoPinhwl/j5QOUICMQG7POWABYDQyFXOID/kQsJjxJxEcMTFKPkYaNzxoMdHL5N84iuCXAFME2wH7EdDpK6AfKWF8Kto1jswcNVnUgp22XkmhadQ6fYinNfokrNZ7UwhJEpM0dfwUXkdj+pL2etaY/UGHRWcDwvCLlspti8Nw42D8Pth2Po2BAUBoZxWUKi/PaapIOAw7szeDRm6QAWpklSHi4QOxQiYV4O+Fka64XNz4M9t8EpAlV5dCXeG4PHLf8qeSA2DwUrCXolkPsWSrjf4WueT/GeNxg0DqrxZkipbCwqwNJX97luaPbBcbKGCb6o86nCGcTcfs37FnnwrW+VkGvRhWrS9+Fh0+hOo1JQ005CnRlDaHcj4QfW60scr+w3N88HJ4xWrqdRV1nxolHB/OxQI/0YTM49g4cwjWPhXXieRDKOEZVetUvI/nvQRejmjMgWlEYEZAtt1COjm2HUaGhYBBsNp4s3N5Pv6CCYS1P+qi54rGjkjejeFxBSc6gYqzAFajdhYJjYa3KqoIkktp3PmQWtLkGCI1Nh7jV4HSjWjgQYyZLCwxkqoo8FwtCTaQMPYtSowGgfNxvn4dNFJihQRorTS+q7m597Ojv3293nlRz9iy4oVeI5D7dy5tF/zPnb7PeQLEPvJSmKb9nJm6CZWnPMFspEEaVcS6S1yweR6Zt5xOy/fdBOlTIbC4CAzvnUNI5s2svsjVxFsbqHeEFzXAKu//U1+N/dE+tpmEMqOsODJB2jb8BqaU+L2PSlaaODRoiIhwQTdUyTV2gCUJign/+tGJTCsEKyCvC/YnJ5PSCugay4CnT3O+NzbKCVYRBsv180LqJJb/1u0lnXGs8dBXzLbL9CNgWOa9LgC8gJf18HS8YthjJLq82qaR8EJcOOvoeRA/ShdnIQ3ehKkn3Dx36+RJ8Rq73CmalsJizw6Lju8Jn6e/ixfcq5jk7OcW4rnM6rXERIX8rXwjZwc3MI9+bmM+MrhJw01bxjUVJbU4cKTOSXhNOiOg1rKS1BrRDKvMERnZze6EFDXSE+8Ag8xpjG4OKRYfmy37NyE2suCHB3YP7g861Am53ZAt8BapAi5r0VlZ23lzFwXCgTVnpyB5tqEnDxFzaInUMHLtbN5z+TjuNKB3zbCL4eVCK9bvnejPcl9xzVG12KiSrRfTcL/pBR7ju/Dhf7vGOys5Ex3BT+s+jQDwSrQbELkSGjjRO85H+7PjqJog+zMTWVSdAuOG4GSj7a9ExnzeebrGvnwo3z4SycQ1A7VRve1Q4HwH2DFoovn+UQiFplMiZtvXs39928h0Fjk6K/YJKfoGIbGYTekeeWbIfydHjoCqet4ms7m+1wWXuGNoTg1dHw8KqnExqZBNDD/S1/i4U9/mmxvL3rKpOKqdsxjJjPhG6eRCqXpoUQ/RSpJAIIiBeppfEdOzyUsYSc79svyQIn9phgkTRoNoTQWPdiancKgXU2N1ceM6G7Cuj82T9htR3hsqI3hwiSeMXUuq4ATo2Bj8xAP0EMPnlQ5agVhzhbnECf+tmt7JzPDYY754hc56ppr8F2XF7wQv8m/ylR9G1XGIOZjr2Inw1R3b+WS336G4QnLGMm51G9dx/mf/Bgv33ILbrFIYWAAPRDAEIKlf/o1S1fezYk/+zmLzzxDzWclExx3y3fQasY5q/xSCd8MsCpQTfxNMGKQG4Wla2DaIFIQqoW1qJJdi9aKgUnKL1FlDlEf6Ke7VEMAmyEnPFa6s1DIQhcVLIJClVcXBOCUPYqZ5UB+0dHrlssUZ17zMfqH0vziym/iVSWZ1NREX32QjAlaqkh8YBzsZDsaR872WLtZjAdBVM+zM2jCegPeL3HRcTFY689HIIni8WvnOiaEOlgjJLcU3kdc68MUEBEWGS/M1/Kf4aHWMBc6SqmhVh/nIwVVEp1gwF1pFRT6/P0BLmorJdL3Gdq1i1CxoPQTJQxJQSZeMQag2VhUATZbLkFW6iqL7HXV72tiHHxkobLmEpCXah7TFGWRXcqzhw5sfh62PA/6RHBOAS9iYeV1CpaGgUshEGEgFMUxAvxmWLHrfK9O9XP/nFUUcqPq82b5fsbLKbwjVT/wiiq4tBLOjcP1/fCzH0HlmYMMedU0iW6+MXwDL5hHkK9voEVfy53+10n7Al+qQOu4qoqgC9g8dBwSyaTQVnzNxw3pPP/7Wrq7Y+gDuzFueQG+fsLf8lX7p7VDgfDvaB3pXm5evYKhWC92WmBsaKL36RC7d40w8TjB1Gu7KAV99g7rTKyqp2FygPnXDrHxdY18OI5XGUf6kjde9khM7WXJ5GIZzSmJEiVAABePBRxO3YJ6zrvzTjb/6U+MdHRQf/gC9pwXZIe1G4sAESJlQeAhkiRZxGLms2A/uP6BNo0ZRImSJ4+C9qvyZ4wYBibV1CjH5DRzZ+cR5ca9zyamsHH4SL7TmKLDfIXOnM6zN7Uz6cGfE8mXGDxiGV+6+Eo+N6uBifG1dNNNV66NR1Oz6XeiRNxh3oyu44aGY/6f6J9000Q3TX65FxLRASwtj/QBX6JpPo4oEhtwiTx5M/WOglI8980eRtrbQUp0y2KU21YzDESpyO7f/YYlZ50BwMJLL+WJR5/CHUrhxRMU8wWMzDAvXPLvrBcW2kx1ktd9COTBKoJhK2fnopysIcAgwOmcwZ08QsDIsSz5LJsz03lj6Ajc8h3SKJOAyzIPJ3BmBJbH4Oo+lcFoYjyr0FHl0yodEtKl6fvfoDgwQE1FBVeu+BkPLTiRbc4SZs6czqkXOtx7fYGsq3hTPV8nFszxna+2cMXXFZ+lWfYMG0qACyIk9jkaCWS57FbEoNpKMaXmQR5JvReQGGVFkby0iWsOfbKOde4kjg0rKrytJQWGEaisdpIJR4XV7FzK2z9bG+vp+aombBo6RsBi2Bekk42EhgcRpkk8FKbOgF2OUpkY7ZsOu2WkKSpgVOgKXRkWCjmpCwVikeWMMr9PT88F8veB/zIQATMCpWeBV8H3dHwjjH48cJIC04z2+h7OKHKAI0OwuaTmIgOivK+iHIz3CY6zTfhsmV8ipsMVHjy4DXb1LmJW02pyhRo00pxZWElbaZCGpjkstQQ5X8llXdyp5MNGfPU5cKTFs/2n8HJxEeHePvjR67i2IGaAlozy/GM7yH1xKZHIoaxw1A4Fwr+TZWSGH++9hWKyhF4wiVRLvBP30LdH0FLfQNvV3YiQj1cUCMOjx+ulvqqOJj/ClKuqWX1LEfqzGCboQY3X3JOYXcxxminZxU48XMJEWMox1FEPQEVzM0dcdRUAXXTyIiuIMl4ODRIiR473cBp170K0Q0fnGI7lJV4qZ6KCGHE8PKYwhVpqaaONGwYb8PwoVUYOr6wRaLgTeG6oma/UzuK6r3yFWY88gqiohFCY2mcfJrHuVX7+47v40Nwt9BabuK13CaFCmiX/ez11Tz5J3vX57tx5XPbl/6Zmxoy3XF9+YIA3/vd/2fn44xixMLWfPhPtuDaieoxpTBvjOO3108wL7SbvNBIwBvFPmIT25E78uhje9n6ElEp3r6GB+IQJDO3Ygee66Na4Y/Bdl1B1NYXBceXc5IzpLL3pF9z//R8R2LyOUlUtz33w02w+ebliLTWAPLgBRZAQzqg5L8tSwJnzY+OAkHrZyAflJVyR6iXtSl7JNlLY55Ci+m/jQUEAG2zoGVGw/FGUZaLsACUw1VKO9oT2TUx6/VmCDQ0AVGdSfPjZe8j+4ae853vfo/WCY7l8quB7vxymvdPjyHk2X/hUPU0NQc45Ee58BBqTkHdcCp4OeZDn7FswPyhfAAAgAElEQVTUUwsULugGxCMbEPj0UUdaxEkyUF64i6/FQWvBRWAI+FmDUp54IKt+5ewYfKpKrfu0KNybViCYIuNZoQDwPEy7SL8ZwrTCGJrAch2q+7oIx8I0VIXZ66isb7hMzJ0YhWJKqACODKtxjceyinMzWN7D+QEFtrHK/UcpVU/PGwK5CkSlwsD5m4BOIAZ+BPDAewyEBWKZytilVAE9VGYE0oWiNUvqqu8pUHqJTvnvMoQq2z6UhfeXCyLDaQgb8NTqq5jT+DEqI72UnDDRYB7HNdEjn+Xo8vhMrjw3OcGA4fKhyyi/dsELoz3eR8QR6OWeatwQSCnJ5ZxDgXAfOxQI/072VM8rlCghchYg8IvgOD6zPuxgWL3olS5Cl0ih4dlgGzBsDlNVE6Pl0sMoHpsks2EXAknosCSippLNQ9V8L2ZiY7OZTexmFxtYj4+klVZKvkKnVRvQL/oPUp8f7d8N0P+uAiHAQhbTzwDddJZLdD411HA0xyAQnCBP4XN5mwp9CIFJggRxKvB1jady8PnOPuyVK/GTdVTrA1SIPvxajeG+NNEnHyQ3K8Czw1PRhc/C73yNqtdfx66uxtV0OjZv4bZPXMR7//BrptYetl/2amezrPjoR8l0dRGorWLvZdVsr1tDqHs30QmNrGE17+E0WmllVjiNKzWEDFJwmihdeS6h9b9F7xzESaUxzSBmOEyitRVN06hobWVw61a8Ugk9EMB3lJK6FYkw4eij99ufc46cR8fPfsV1A2VYvK++RBqqH2hZqj/loIKfbkLTBDg+AleVT/2vFeDbA7DHMfBlE+tKBw+Oj44ajGZEJmqcYsAbZyIB5WhHZYY+X6PQlr1PvcprUnKgSSkpDg0BcNThFvf87GBZgk9fDD39Hg8+3M9eKwTBGJwHzB/9DTEGu5TfA7sA5pfSeJN16gI97M23slufTJAiPpAQEwmIAPPL/awKHf47Cf9VU361fWLre6OqvDey75rL/wZKBZZ/45MUaurJhSJU9exl1cf+k4ChURM0GCj3Gc3yPGFEU1ngBF0RG7hSPX9rpJePd+fZokWoCQQQkTg5KZhS3lRDqLENV4LsV3Rtvl7O4LYANTCG+zKAKMinQC5Tme0uR2W79ft41m5X3f97m+GjnapcOtr7DaJI1L8zAKdGVEY4pUXty67u6Xzvvts4bvYdNNdsZHP7DCZN/SBt5sSx145ocFIUHsvALBN2uyo4mgICewaQj23Cr4vhljeypmTT0BAlmfzLs7z/SnYoEP6drMvvxispaU8YpTsKU1U/ghV18BwD07CxYh52RsOxNWzfASFIOQ1MbKzHb6qnREmxusggWV/g4fEoD7OXjvKsn2SX3E0+fzh/6jsKt9xj+FAyghbWDoKDCQQh3v2H3sLiLN5HLz0MMUScOA00jgUlE4NKYRAhvB+hVkmqctPInj0EdI06fTMVIoWPrua6CnkW3XYtidJZ5BdVUmWOUPXGG5Rqa/EVVpxcZQXZkY2s+8PHsD7UTIv5AfTg2aDF2P7oo2S6u4k1NJA+Mo49PY6ZcrAH+whWt+CHNJ7iCT7MpVwcjXMbPrYvMYTAqa0i/7tPMfHF1ehX3UdlbTORmhpEmSsrUluLZhikOzpw8nkC8TihqirCNTUsuuJgQdMJJrQaat9fKuyP2LQsJQ5rOfCjADTMgSmxcdaUrSX4TI8KZnW66ke5ZTC8XiamdLVRbhRlowwpGsrB5Q9Yj18eGTgrClEdmDdP8YSWM18A6Su8YnL2wYw+UsIjWcVqkvbhaHETU+frZOaeCW2Qb4yP/4Gj5oDWprKkNT+Yj184DNsOYL7fZ+SwCtJmFE34xHWTrycPJhTYrwTu9UDpGb6RmoLOLEyC6Ag8xsuH9XjEu9qJdneQiMbxpKTu5afoOPokWhOVbLTBkOOD6sPlTGm3DRPN8h4WMvDTC7hJSn4y5wIemHgMRCtpq2+iHY0eVwF1nPJneXotbI2DaIGKEri7IGeAtEErB0PXBIYh6Cv0pimg+QCvWqPDo1n4P0m1hQaq9DrKODTkg+8qwu7FIYhG4LMfgu/8L6RzrWzf+0VyBZjaCh95C3GbL1TDgAtriupz5kpFADBncSX/VhtlpC9DNGhSLV1MQ3Dttacg/l96EP+EdigQ/p1sQqiejdYOpK2cz8aVBpWz0pgRn2gd6AEft2RgBFzMiI/vSDxPMMWcQquoY7sPVbpBuHxLej04IQIdtNPJ3v1Knu2OT4/+BpXmbIQfp+jDz/tauag5hGbkx9TfCxSIEKaFlndcu4fHJjaynjfJkKGGGg5nETOYedD8nxBwbgx+P6IUAYRQtFEpD66sghiN1Io0FilKBBESMlsz2AMOEbuL3B+e5rg7n2bTyWfh6VpZz1CC1LBwqRIDhHZkCTppbOfbhIp/hMQt9L35Jrqh9iZ3WBTN9tGEwBfgFPKEQlXkyDFEiiOCSXY603hT20LGDRHVNOoTDpVnLaTxtQr2PLhyzBNL36c0MsJx115L4+LFbLjrLlI7dlA3bx6zP/ABonUHZ9KzAsqJRcqlNI/xEp4BlAScXg0Xzz/oUu4YUb2oUQ7RklT1TynB97zybpfnHYTYjy7MlXBiWCEER4mjZRmif3KkHASBxoULaTn6aPY89xxWJIKUEqdQYOby5VRNnjy2ljVF+HkKHs8pJ95sQo3v0HHPXWz6j+9QGUwR9IrsllGk2Kc0aoOWAn0yWNugb2cj8bpBYjUZCvdF0FY5zPjMZqZGmrggUsui0Dt8+IpPILNfYkMpxAO5H9HKakJaPdv9ScB4X3IgVskjX/0FJ93wOUKpfqSUTH/9GTrfdxG9Uh8bTRhl9LHL/+0Ar+Sh0fA4astThAyNUDDCtXse5r93PciLgTo+u/yHVEViVAVU77WnDDzpTkDhYyDzoJlgD4G/F4yQymx9H7I5cM6FkQJUSRjQ4eW8ej5pqIcvVYAExT2qC9D3ORhY5e9PcJ+v2gWnw6RmuOcxRey9bDGccxJE3uJMW6Gr0ZzttkKcTrZGM9Iwb957LitWbOa117poaUlw3nkzmTSpkr6+HL/5zRqefXYPlZVBLr54LqeeOuVfNkAeCoR/Jzu2ajGvZt4g5RQYXK+z5vcuH/iTR7ZHxy3pJGfYOEUP3zOxQi6eZ1DbfgQnTT6BxmrBJ7oVsi0kygg2Da6shE46YXRmr+z8Ol0NSxPEzH6ypThBDUKuxRPd53JMzatEAjuwNJ9aajmRk//iaMKzPMMG1pMlg4dHN11lvtB5nMbpYzylo3ZllSozvZBXX2pXwilRJT1jVU5k2tH17HymHap8imkHe6CEEdRJtgUwwmHaSnHsRx5BSonh6ZQ0iZQak/Wd+C7EZydwtAgjBAh5e6BwN4m2Njy3rKRR9JFaec5RgmEFxpCuW9nKwzxE0SwykyhFq4iPTyutHMFRhP/D4LHuQXrWrEFoGr7nMWP5cmaeey6aYXDMF7/4F+/1NAvOiKoSV5OpaLRGT/pFqUrV33obMdSdzv7ZUSQzDLqauxTiwIReEhSizIWjHPu1Nao8uqWkekumBo0m3LhPvBaaxinf+Q7bHnqIrQ8+SEEzKbz3bIZPPIVBV63vjQL8W7dad7+nemu7HYjZBYJOCeG4FEMaQbtIsFRQgVrXcUQIowNkJdAMhRSIpMYwNUg7SiKRotAeJvfETNafVMOGvAJwfLEGzoodsBl+FrJfpt+L8Jo7HYkBwqBRdNIna8jJCix8bMCRgq5JM3nwFw+Q2LOdktBomDqFBiG4LqkOGI+kHZxMHndAUnTj6rRSJRiqAFtKvtz+IgQj++3T7dNPx8oO0ROIsdcdH7Y3UejWmROgqwu6B4DjgT9AwAWnCJkhVRJlDuhD0J4BZxIULSi6ah6wx1H7fWmZcCqmlbNTf/x+O1L1J9tMpdSR99WhZPFc9Xg3JoSSVjqQvymRCHLppYdx6aWHqf8xMkD28Xu46bsv8sJgI7Kilj17Rrj22ifZs2eET3xi0bt7w38yOxQI/05WQYJ/a/owd3gPsm5tJ0IzKAya6LpBcVhncKugotVGC4CbD7PxF8v5j8tnoCOYE1TMGnen1QzU3KACVjSYkJJhVj00mVX3zSEzFKRpyjB1H9hI6+wcnlT1mbQHmxwPPMmG7rkgZrEktosl2lwWxSpImG+/7hGG2cJmbBTTyujcn4vLdrayjiYWsv+XI6TB/9QrVpO9jirH7EvZdOJXLyX+0+1sfCBHsatIMGpQOyOGYXmAQSIUYFLIZO+MJbivvwwVAWoCeWL9nQTrgkw4OV4GSgg8Eac9/zT3H/tv6L92KfR3EHkpQG5uBNexCSUqkEBPx1acnb08/8SfSVxyLIEpDeTIEiLM+VxAeLQ8HIMzf/ELUtu2kevrI9HWRryp6a+610LAl5NwdBjuz0CnC/1lTsllYbimRjm/t7LDArCxpBwigN3fTzzkkk7U4Al9PAeSkoj0MQ1jLCtaFoJpQbi7WQE+1pdU2e+06HiGOWqdAxZrrHN47tRzeKEF9BoQg2Cm4PqkYm0xxHi2aZUHxrdbMRY2NDPtpZW8cfYlRDNDBOwSJd3EMHQiDrg65GMQfFOBWoQEXIEnQ9TTxFYBnTth0XvUXhV9+Hq/AqjM3JdXwV0DeHR5Ieq0FD4CT+poQlAtBsi4UbxSHsOXOIEgvmHg6Dq9bdMJCUgYaqzk8BBMTnXxwus76LJmUvLjCLNM1N6tIz1BfY3L7VNO5bxVG/Y7bPQFE6SNAB2uGqfQyhWOIV/do5ABk1tUdub5sKYZvKdgZDNok8E7RTnRsANDQmJ2+WgTNRwEloRBCXMNuLxMsXdaBHaUxofmQR1+jw/B5/tgTaFEAJugHuWrScERb9PVGNq5k70vPYVmGrQedyrR+vp3+siyy4bhVx9h+t1fpdCf5nwnz0U1BneLs3k1tIhw2OSWW9Zw4YVzqKj4y5Ji/2x2KBD+Ha3RaOCaSR+n31zJtuweNt5mc/iVg/iuRiFlUhiRVEyI0vXwKfz7cTNJ7sM/OdFSYIcD7cV7Z7LyjmYi8RIVyTx9HVF23rAY68s+hVgjnoQNJYlhDBI18nhSx/Z1nhuZQjD5CCs6l/PTeot5b/PZTpECoEAR/S0+DpvYdFAgHLVJlnocaGbiDI765K848kqNF28aYP0f9yJEjlJGUCpmidRGCQnJ1VdfQWrHiTx6x3dxMiUmvy/C5A81YMY0PDyCMsYaV/J4RRMDsTfRbjqb3PceI/L0akJVLv4lc7AqKujaswGtp4D8+Wv47RlSj28nefNlROdMIEuWLWxmAYePrU8IQfW0aVRPm3bw4t+laQLeE1WPv8YurFClzR5XzbjlTIuKkUGmdWxla9ssilaImlQvZj5DvK0N3TRwpILHj+rdRTRYHoflb/Met/0Zfngr5Nyy+rwBUy6AhqMVtda1/ao8W6lDyfPVEJomMAyTvBR0feI/mfL1ayg1TGDrkhMJZoZxEtUELIuGAOwaAWMEAqZ6PemqvpZASUIVHJg8cbwPGNRUz+7PmQMCIRpINbFaIbKcZL7Ao/ZxJPBxfVFWSYGJ3bvoT9QwUF1PyfdotnRaDJXJHh9Ws5Wvf/8GapKn0D4vAq5AlMWTpSGQWZ1OJ0C6YT75YoFIWXwYz+XYrjdYOXmZAjyV1yvVysaClRDqoWsK+PTvn4VPvA6lKsAA35UUIg5xN4tTsJijbaKDSTRaVRR9wZLQOMq3xlCZfLDcI/TKiN+dtoNvv8ASbRPHGi9RoTn8uO+/aGiaN9ZfHrXVv/gur//iRnwvixCCl26MsOxr32XK6ZdwoNk+XNkN6/oG+OXvr2NdIEYxLzGERVh6fIAVbJeTSZmVCAF79oww7+2cxT+xHQqE/wA7+cTJPPTgLnY8UIERlcw8fwgrrnhEZ+aW8u0r5lDxLhxosQR33ReirQrSZlEFh4oCzkCU5/9wKk2XaKCNIAyXmDmEKy08JJbmkfMsBuwqqoI7+fbADG5remsa1ChRKJcVD3xa68pT2LOV4YbdJCZOfPcboCeh4seIzLVMOraLtXfmyKc0sv0R3OJuhne3M+HII6mfP5+GBQtoev/J/Jn7qUvfSry0jbyMERNxhmyDjdEkaa0G4UeRk6MM/eTf8OwBjonMZ5F1JH/61ucwNu/CHJb0+pJSTQVuKkvpBytpvPkydE2jn753v/Z/sDWY8LsmuHkIXi5AW2WMpY/fxMRlaZzqMMawQ+SpvWhvCsxf3806R/V8zourctlfsj2dKghWVijgiGmD6cGOu6BqFoQSkHGVYzZefYEZN32T1DkfpWfSLExdQ7ZOofuwo+i/4X8xMyN4hoEdjhIPh/lMjc7ZFbDRgE/3ghOG8FzwNilH4nhQSIMRVkF3XzNQfbD9zDwcRIhGLcsWP8qF1gpqxACP2cfQJ+swXYeJnTsJOCVqh/owSiWSIZNQUzPDvsq+v5IEt1Si48UXaWxewpo54AfBL6o3FSEJL4MxSZBrqGBD9TSWdL4+lo59sLWWr2j6GKpylAJtgqE4WlNlJGrIgREB00OKdcYyIdAJhSQ4lk8priGdAFbeIU2M5eY91Jr13O+cPZatSwl/TMOCoAIlpX01JpHyYKfjoGnV7PRO4FHnZM6xHuAa65M8mb6Tj1RPGNuygc1v8trPv0GkCjRD1ZrdUpFnvnIVE446mWDlOGFG0Ycz9sBLRThr+0v4nkPaCEKggJMuYpkmmswyQ27lebkE15X/smjSQ4HwH2DLlk1k2fHNPPtcBz33BWi/L0mi3ufaf1/GhW8h2gsqFNnY6OhjPb3UiOJ6rI2HiDFhTO3BiRiIAcGC6DA7jBdpH1qAJgSer7EvDbYrJU3BPaxJTSUvdSJvEQhrSFJHPcNyGGfVblixFT9XQg7myWwaAjPOHe7DOEfOJvLVj3F4RQvzjGZ03kaOfNTMBciKFay69T0EEhoDWwbG2I2l7zPhyCPHUJsJKvkgH6Irejim/DYT7E0YQtLtZ9huLSXrjzfALAFpK8E2YzfHyRPJ3/sa8fp6MnjkpQ/FEprm4r6yhXUDGSbVCKq06nd136SU7HjsMdb+9rfkBwaYcOSRHH755VQ07y+tNXof/lZauGYTvl7uIfZIm9trw+S7s4iuDE5IZ/jDLRwXOonjDqx3vgt7cY3KMiwTcMCpgcIicKvg1TxMjqhs57Tebez95jXIYIijV97Nqyeex97Jswn1dVI3pRV7+gyezY3PwmUlfHNQ9SM/NAVuCcBIE1TMhc4nYe/T4OXglCWw+USQ+/QDZZkUYNmBPlYEIf5dkunPkfd7yfuSc817KTkJNg22EXZLFAMhCsEQmpRceM+POTGmM+P671KhKSAKgOcJhBC0Du4g+etehs5KkGuLIbI+xnNF/DVRvMOgWtdZd9kPWdLzEhRzMOkwaqobuKgbnslkOEb/M0v0VyhqzfzRPpdOdxJvbAXnTyC7FCK4+WS49VRoq4AdKQgNgVfr4wtBsSJAomKEuJbjbOsxQsLhQXE2Z5QPvR4qy2zQFbAp6Cpwy6DnY+ES1xSRhSc1VthncLz5DA32b6A4D9DBOor2p29F+g62HlUEAwKMYJBiOkvni79l8hn/RcqDp7KKqef5gnLyvfFanp50PJqAGaVNyP4MjusjdJCeR09/lhNPbKOh4cBG7r+GHQqE/wAzDI3vf/9UnntuD889104iEeS006Ywder+DtnBYSc72Cw3saPYRbboYfgWh1lzOCV2BNUJE8sE2wbLEljlgYWhPJw8X7LQXkVE9PCG0PGltg/rp40udFoiO0kYBRaaA5TEWUTegsJMIDiNMxj+1VO0/+IBMDQYLEBnBq2uAjFzOh2yD/35J+m8Oc2afz8FsWYG5tPHkcsanHQknLkM8m6e219ax6aOHmYIm/OPOZzMls1se2QzejBIoq0NMxhEMwzQNHrXrdtvHTo6zdpcqLgNvA7wB/l2Txu1/A5LT6EJD08GcbwoAokpDIQQhJNJ3GKRvZ6BSKfR4jqyaKNFTeLdG+llIjNq3/rwcaCt+93vePmHPyQQi6EHAmx7+GHan3uOc2+/nVhDAylSPMczdNOFhsZUpnM0S/86QnHHhjceR655kmwmz3NyhJGWCJkj5mK2GNRLm0QizDajl6V4f/nAcYCNqqf7EvpjkD8VFc0ykK9QQ921Bix44l7QfTqjUWShwNKHfk9dyCKYS3Pqb27ljGI9PooRZfTfEvD1Abg4Af9Tp8ZABjywToDJJyhw10cqYUUavjkA+OMK8ouCimLvILMWoVU9SIv9MttKRV7zFnGYUcH/ufJshqfMZtuUuYSKBeZte4PgxrW0XnMNUw4ox+uWRdtJJ1FY+QQV2fdh3FQgEO8nV5nEdcLQAOHJZcFe3YS5x+13/WJriNMDl9Go7aVEEJOXWBS4h5/mvs+9vzoaGQG/HpwcPPAYePPh6EaY2gK7OyXBvUW8uI5V6bI08hpNWic9fpKF+utcnxzvnxtCkWPvsdWsYrosnWQDOhpDfpyEyKALH18KdrotfNT4AQyXB2i0SnYVD6MkFcAGVH929IArGGZ1Aa7qUcC7XU5ZrBd4dsJiNlVMJFEcIpkd4MPFWxHbXfrtDl4qNXPOOdO55poD0vh/ITsUCP9BZhjafkoTB1qJEvezgl7ZQ5+TwgV8QyfbWUOvtZbO4SKXN5/Mx9+vSl0VMQgFYDgD+XyJFx56lLvu3EQpESF6/noyx8/EFxKvnKtMjW6j2hxm2G6gwRrhWfE453DuW67F60vj/moVbck5FA2bwV1vQigCKZveVAei2sKvqcRcsZ7Nk65m7d0zqDKKJM0oq9bD7Ssdth27k6weQ9TFeVDCzSu3ctFP/w8ik0ErFimmUgQTCWrnzqU4PEyoqurtN09vBr2Zkyv3sl7LY+oFkBo6eXRthLiMMVcsBeCwj3yEF268keJInuBAGr8xjCZcvEsX4O8okrv+Waxbr4a/UFZ08nlev/lmIsnkGMNMrL6ebE8P6++8k/lXf4IV/ImStLHdJIbw2GJsIs0I7+Ocg8ZM3tJcB/mzz7Klr5fukSITdrzOUcVh3EKc9eJp0jdczNaFbcwRYJCjSJEIkb/8uvvYMYfD938Du/MwuEAFRj8LCPDCChATFpDt66UmHKAppIKmEs+1yRWy9I+kyRj1OFIN6o+CdQwUi023C9MC8OcWRQ6Q92FecFxm6uy4en5FRo1lLAuroW/r7bZIi6EFTqEgocKF1jBMvviDvPbTn9CyfhW6ZVFKp6lobWXamQeTxQMcfc01DO3cyQd3fJUHvMsoDE4lkh8hd3wU43wYRgXt/+iDZwuqpDrJUnOGPdk7OcrYS7ffgIvqn4bJcCnf4vYL78ev1BR92l6wnvfJbCvxuqlROWLheIKp9bsYjsbJGzF2yplsc2fyrLuM80Mv8wVxCxQqIXACaBVcXQ0X7lXzipJRzURFfbHLb6ZGpGjWe9DwaNL2YJFHjd0Luh3489xjWKg9DI6PZiqau3TeI2xo1B1xKlf2qQx80NufYFwKjaFIJQ3DXWxbOYUvb7+OiSMd+EaA6bPjfOazEwn/a1ZFgUOB8P+bbWQDA/QzMiJ48d4j2PbULDxHo3XRbqYf0cn6pq305Y7ikrMjxCNwy33QOwATm0us7niB7tmVDCy5ADts0ucKEhtSLDwqTVYWaYnspMbIkrbrqDUEbUaEXnrIki33BPe3vvXrEYBpBDAJMOwJhGZQwkYfGcavqgcdSsUQb94zg2CiBKZNQotSEYOnN3hYtRGaJvczULAYLJj0Jmfws8M+yUV9P6Da6UQPBCiOjJDr7UX6PnM/+MF33B+JxIw8TdipoOCBrpUAMDWHZsLMK1OdzL7gAuxMho4v/BfS9xHdRQqNzTh/Bt/vwxrso9DbizXhnZGhma4upO+jWxY9pRreyCxgSDYwUV9LfPUmwmxlRzHCo/0nk3LCSASTQgO8N/kig8YANSTf8fUBeta9xOda3sfWuS34uRxSCC599secu/YetuUriV/3B4b+eA07hWR20CQo/nrQQlMdfPHj8JGbwI+DyIPhQ7DOJtrdTmBogGKikuFAGDufJ5hIjBFge7aNEIL5LQ04ZRX1UZqzUaedZTwDsYRCzr6VzQwcCIx5e0t7KovZWBp/vxmnfIRpNZPp+cOdhEdSzFt+EWd+6ANY0bcu3YVrajjvttvoev11zu3pYVfI5uKdFnJoA8Xtlch5E0iYGq6E5/JweRf8sRmeyMFC7WlsYmOAlqwPWRmjJt5HXUU3XfmmMsduL+lVK2FdmMwXlpITGlErRrs3mYLwmebupMHMAy6eX+Ke4mJOGbmehdYWyP8A4j9hcWgOM8uk4ClfkdS45QnUAHmGZCVxP0ulGGGZ+QyaCINQVYEXveNY13gscy47Gf+WFzD8IrrmoxuStv9cxkd/eDyv7ITwkeAeD0FT9TNHzdEtdmQnwQaBloQa4SNkjs2bdG789h6+9a3Wd3fD/gntUCD8/2Q72I4pLVb8z+G0r2skGC9gmQ67XplE17pWTv3Px1i9O8t7Z0dYfgosPwVWF+CC14p0ZuZh11eAlFg9fVjFPMOBSnqGTN4/7W5MQkT9JAFLweLVd0GMiekeaFZsf+cSrq4m29urfjDVl1CksvROfw9CgmYo1QhQpZeCBtZOg6Emk1Rep8rvg7SGvrSV3J3DWLEoMTuL77pke3tZdt11tB5//DvuT548GTlC00iRkb4hnIBGIFlJPB4jKgJj/TkhBIdffjnPv7yana+sgmQtQldsNrbnUadDMP6X+x7hZBLfddm0KcNgdycV8jVMq54Xqz/OhopZXJ7OcufgcehoxPQiALsK1dzVcyznNGapFsl3JAyXEj6fi7OjoorKoW68UomSZvLrY69iRtcm6vpT7B0sENrajjO3hsPkseji3ZdF93TCk69AyYajF8C0L0PHMGRjELZcojCrGVsAACAASURBVJvXIEpFnEAIHJv0E4+h5XJk/i975x0vVXnn//dzzpmZM/3O3F6498KlCUiRIs2GNfauiWtLYoymaWKSjSlrNDG6ZtWoWbNmY0uiQWNvqKiooAKCBZAOt/c+fU55fn88QxNsW37ZGD685nXhMmfmlJnzeb7t8+nowBcO4+Tz2NksB3/nO0QjYUKdyvx3B3aQoQtstSD+P3jnuKNfublXFgQaci48kRRExx/KmOsOJeHCfRb8tBeqhpTw+GXxPV3tQQml1xx8MG1tw9z81afJtSXRHYmtaXgaStB/dgJawEum0KjyYkGYIEGMatp2vo5HQM51EEKSSoVUHS2bh988h9RA68zB3SvwTKwmUxkmWlKPHGWDZmB5dTyGgS5shPTysn0k081hcAchcTXEnqDSI4jkVFrU1FSaM+ma2OjoOGjCxx2R3xAlD2LXaqLDLWGbbODao/9E2bQmRq19lQX+5QxOms2dz1yMb6uuIsQtkJkIWhm7ZI8AISXudh2f38HVdKSuo0lBmW+Yl5aaZHNg/vdtQ/8usd+j+G8ELz46GsO0rakmWJLA8DpohiRYnCI95Kf1/SpCbnTn8xMOXNEFubwDhoaQEs1xscpLELqBlkjRlY9Sak8lKHyEdIm3cHVzZIkU/uwLldOmESgtJd3Xh5SSaF0dQtcRjoOtmWjdg2B6EBcdhOsKkC5RFLkIABeEN0sqZRF3ezCwcDQDYdm4mk4qZVMyfjzR2lrmfv/7TLv4YoZdwVMJ+POgugl+WBrTkAaJ1jZ6N27AGRhA6+wj/94mUm0d+5SMO/WrF1JigGNZ5CXkHZfKgS4OPuE4fJFPtncyo1E8kSKG2jqxdT+2J4rf6mFex7VkMjZ/viFP9OEVxNu372yn9wqLDckqTtw2gkMb4bY+1am3LzRasCFQQUm6HzeXV4sYx0JIl0WTTsJqSYBwsYVJy+CRTBGfcpIaePoVOOk6uHYt/Ot6OP9GiPZA2QjwhECmBiGXIxeMkI/GOOydJRSXleIJBBh/+ukESkspmzyZY2+5hcnnn4+NukF72XWDEKgEnUfA+rTLSy+u5M7f/JY/3LWQTZt6P/W+fhhSKmGC0gIJSglb82rEoMtWA+nb8oo0Bh01c/fgEPywa+/PzA7ccMMy+vtSeErDeMsjiOIgzuZuso+/t7P5R6BcIeYG4AXni3jJoBcSlYaQlGndvOYcQUJEkXnggxZl1Oj3oRWB0Zkk+tBG/L96heDSNuycn22dE+lPzwJh4goTicDYsfgUUXC7wNnOWWH2WJJ6BAQ0QUz3Uu/1c3NNA9PiZ6uzXpDGy7oaj+aPw8bARaOpZBJLDv8Gv5x9P79NX060x09VVJVPdAfcqBJ8L5xldnaGbwXHY1BkpdClem1NA8d2dnv+Px72R4R/I0ziQBZ3rMHQC2ZHmpJYlq5AaA5tb05k5tm7UmNvZNTMVlnIQ3dSedUIKZFCR/jDmJkUlWYJ46xjGfA8TQcd7IgFPXhZwFEfWcfSDIPj77iDF666iqGWFoQQlE2aRP0RR7ApnWL9yCHkiQ1ESvIUvThAqqWU0uIwSEn35lZ8OS/ywAxBewADC0t4yYfCjH/kQRA6jiNIdXcTKi/ngNNO4/0sfLNDuSi4UkWtXwipus2ONF3f6rWwehscVoE2qKTHHByGh3qItWZwZ9iq8aaA2jmzOf0n/8wbt91GJpnA47qMOeFYDv3nH36q65EZGCDZN0TWX42ws0gpcTxhWsyDaG5z2f5oHKnFid3XRMPxr9L0jfNoycVwpIeoZhDQ4N4hNVx/Q5ncS6oq5YIWKkLa9s4bvgB8bp5eM060MU3yoKksjXyfS73aHn59oGpZt/erz0FUgy9F1SOVhG+vgL6zAU01T/S7MLwUqmqgygu59iaGYyX4sylOePURDluzFKHr6D4fIw8/nEOvvnqP9/KinCy6bfVeTqGGaEvwCZd1i+/goPJbKJuioQmX9W9V09x4C0cdM/NTnesPw5G7CLfFVo8dguPbLJWiNVCf5hZLiVuvyMCmvGScb88Tlc3avPVWCxVlIQYspcEphEBE/OSWbEL70kyqDPVaE33KeWJU8FAeSH+T0/W7FFHiMGjM5k/ZH1M7Bro6IZez1FyjF/wm0MhOjTtvYwLDgpwXpAnv5GtIuB4cdDrcGDlp4MNix4Ti0SG4qAhu6lP1VUMogq/xQLEO8/2AWKDGS6zVyEGNtnfKOafzL0yqWsv9Yy4i51NkmpMQaYHiQk55gg9WVqFy2HEAd5evo4B8nQfvNosxclcE3JcNMrVumNBnK0d/rrCfCP9GGMlI5pYP86SQ6NLAcRxAYqX85PqjXHi6iW+37rhMYfC2rCxIYGiAISkRUrX8511oqC1C13UmeHVinEwLzXTQQZAgoxmzS1nlI1BUX89ZDz9M/5YtOLkcxWPHonu9HAG0WQ6Lsu2knSTHXRXn4TuK+GCLwMmmSHV28JXqp3hNn01bSRXCskEIat58nVEvPINluZi6g2vbHP3rXxOsruHrjUp82kbdAMt1eDqhhqOPLJQwO1avJnzvNjKlETITgjiZHE4+h/a7tbz//GLaxy3k+Ntv32O+ccKZZzL2xBMZamlRotnFn25sApTFk27o6GYAr0cNXPdqI1kpLsK0BxBmCNsDA24D255x4ZguRE0lMZHlFM+TzBIvMJj38NfNh3PDeX9kdHGUKRdeSN0hh+ALh2nwgPCa9PhKKMq2YegC6bpkDD++V17H0XQaz/oy58c1vlyQ40KmQUo2N/fzpQ6DjNAoD/uxg2F+0ydos6FqG3QfBP6skkkDcHTomQ0XrYHxx8EHLe/hu/9OqqsqdjasSCmRtk1wH1qqAL8q6eL0Fg1LgkaQjKsMg6syXZxbeQfZbATX1QHJuIZ23l//Y7LZZzHNz3ZLEQKODKpaXVyHZkv1NWVR9bMddcodgXaH7SKMHhZUPM0TRpYJ1DKHuRRTvNtrKquhsV7lXJ+3lQWRpglKdUW8Y3zKqUMIuCQmeEi/mPuyZ3CQZxvzQsWM9I1gjA2bNfDXQftRFSSeFpQYLtP8GmsMSKQkSIE/UEHpUzBwLmwApAxhkKFe6+Gl/FR8SZvrgrcpZ1+9TikTlSkPxp92q4Ykv1DegjeWg08DEBB/Aj74CvL3zxHL9nMwy5mzbiXHrFzCN8/9T/oDquEslFIpcZ9XpYvrw7AJFRGGjQzKlFOQxyB2+CDFb3Yz2KUjdD/SkUS8Ga7+4ccr03zesZ8I/0YQCM4ZNZWXJlm89X4R0RDkUl4SacH4sfDVM/Z8/kH+wgJU05jRUMTKYYekrqNpghFVYXxhLxcW7ZDa0qlnJPXsu2P1I/dJCIrHfFitEKo9Ol/xFGbpQnD8L6G1E978/SP0tv2WjNfmtCeeoDk0gmSvRlHzNozWQdLSBCEpjQqOuflmaufN45lBmw9605iJAfzSJV9cTrsZICqVJ9sOIjSjUbS8pOIPHSSNND09jfg6ssj+NMEDDiDT18cLV13FWQ8/vEf0ZZjmPo/hkxCuqsLr0ygK5BlMe/F5YauYgZDKkilgqFooJBgQDRR9MIRR43Bn4HImautI5r0U2wl+XPEGW08rZvM1q9j89NMU1ddTfNwJPHbx1fT5iugLlRNKWwSwsAwvVRvXMve9F8lWl3H5SXOoKgacLkjeAPmlZAYH2balFKH9Eq1XpwdBpLqa8pEjeXxYMNMDUuwiQVCpMemBgRCcE4XUeSfx8OP3Y/f34YnHkY5DqqeH6oMP3kOEeyeyz3BQ5lpeiJZxd+Yotjh15EQxMfMQTm/5ZzTDxXJ31C8FiUSUSWM3s3njJg6cMuEzn/sripUB8NaCn55eOJ6dkQyKCP0Cop5+Bl3oy8eYYiZoo5UneJSzOJcwYUzT4LDD6liypImKihDTTBi0JZt7M5ScOpN6n7J7uiCqCOfZhBoJcSRAhDdzUxkRgDoNbq+0OK89xbuZIGZNEf5TJzP0+Hus92hUxaHTdTGLx1NSXsLXFkDHGCVdF9ENorIXU7bhSsHzuQP5VmAEZeFf7aFqcXRILQK25tUxj/R8SPRCxOCZAFJMZnM4yoA0EcCIgSbOe/uP3HnodxACzq6DxS9ASUyRodkKYjIEPC4+siC0nXIZJcVpvvjTV5my8G0+6FhA7agwsy46mHdKq3l9EKaYKkr+R9Pe3k+Ef0MIAbde5eF3C+GxxSA0OGUBXHEBe6Upaj3wlSL4wyAIodMQ0elz1Sr6YBPOiu5jYPl/cb9HVEJHfJg2MUweA48jqG5ai+zP4DYNYscjePsdwr4MgUCEcSeeiGvb3P3Ei5hFIwgmBkCCt6MVrWE8A0WlO9U9AEYeeSTLb7uNXCJBtr0drbsPqWlohoFZVITQNIZaWujbtImSceP2uZ95afFKOsfv+wNsyGlUGOocnhbZZcq6A95gkOmXXEL2lttw82GSGR85r4bQHbxBHx5PwVkCsISkpKefeb6XmaB/QD+VZNKDIIuQQjB9/Eoa/TrZYZuB5mYaH3+SSHMvY2/6A+ObXmbbijUMR0qZunkFE995DStjMbo4S1V1BUgLhr6O47SRtWP0bNxC+UgvZ4hn+Z1xKXrexm1vJ1Bagh6IUNS8jGP+80+EervomjCPTQvOJxUpQwDTC5wULC3lxP/4D5becAPda9agGQbjTjmFOVdeufdJc/sheR2ICGN9cINvMZZrI2QvRuxPrN3eTja9ZxOPLAjCZ/ySW3pV+naEB86PwrSPc54ooNyAB6qVDdQ13QXrJVeJz+9eBrSkS0pqaBJcp4iglgSCdLlJrkuuo2l4NrNNuPi789i+fZDm5iFcV6JpgtMPqeHmKyZj7taI22HBz3sgooFZOKS0Cz/slvyydjWrxNscUCaZ7Lr0942l95yp9E+vomnJNg4JuZzxhQbmzh2xcyH2zQ5V6yzSBTAKZDmaTGA4frpC91NmqE6UQUfVOV9KQZEOX4zAguCuGikUiCg9DK2b0GPlxPKCpKU6dwcCMQ7Z+CK3HPodjg7AL45ymJhxuPsxL4PDUGzD8UF4QRfkpAchBLbU0XHZalfyUtEcZny3m+9VXch7WTi/A9K9qkyhCzg6qAQfPpye/zxjPxH+jREMwPcuhu9epP79cSuxS2PKZfvFpEorHhVUg8p/q9XbyCOP5OX7bkZYOsIodJcGPGh1RfgWjKT0xR50LcrII47AME22vfQSbVkHQ7q4Hp/ab8chsH0TialxpiV7WXbT/TSufBv/iFpmfuObvHvvPeQTCVzHwWualB5wAJpeeC8hcHK5vfbLxmY5b/GSvY4OzSUQM6FvPm+lx/BOFl5IwV1Vu6xxdmDKhRcSqqrivfvup6+1B6NE57FNcbyacqlXAtUeguS4aV6YzZ6XSduCvJXBcSWu10d4sBeRzRGq0Xm36lBapszGl0oyas2b+Ldt5MimVzmz723WLOnB6h7CNTQmTR7BzLEh6NqOXdpDb6aVNxcLup5eRzYVYNn8U9h43Gkk42FSbhAjWkJgYIiKl5+n+J4bqZY+Mq5JwysPUr3qeZ79/gPUTCjh1N3ct0rGjePUe+4hn0qhezw7ZyX3grVKpdK0XYzh0QwVMuVep6j8BBJdtzCUCKAVxDl1LUW7M5of6QewpWeXLdVjw/DbSlX/3ZSHNTko0lSDyg4HjrQLt/WrmcOcW/BnLGhx7nCqBzXQ70hJyg6iCRjrywOqjrnd0knKHrpteGAYFulB7vnTmTSubqOrK8moUTEmTy7fq277WrrwXtqusY2ABl12hkcybZR4vFhZCz2fpiTyNnZPPyJYh3PZwZxYYzLvQ4vVKT4lm7cjs40IYhPEEVBTON0JB77crlLAEU2JYS9KwCQDKpfDB08BEo6dC985x0OxUM1pDV4lA9ftgM+xGQqG+Ho0y42h2/EMPsaF8/Ocf/h0Utr3CYZHIwXc0iu4uQ/y0iaDFwnUaL3kXPhh+gp+Oqyk/gQulboNwsCVGs8nlQXc0Z9RR/fvGfuJ8P8IPg2ZCQFTTfX4v4DSAw4gdvnR9N75PHnXRuYspJCI78/BOGQkRU3ryHcNMO3LXwZg+0svUW+U0Vk/FnJZXAloOtIVFKeGSF19Ac/19DPkD6FvbuKdN17mlH89h9m9Y3j1ppcZCs1jSHMpkd1Y6RSGaVIyfvxe+7Wct1jlvktbPkja1fFoeeaXvYjbGaA9W82rKbi8HdodiGlwXlSly4QQjD7mGEYfcwygtF47vt3L8jckeiaLi2pt/Nqst5h+6LnwyPWY03ro1fwIxyHStIVoTysyJnn+rB+xrPpkNNfG1QzeP/FLTOhoZzhYzMiwwfijJ9NguFR4VAvTQHMX374mTKh2LUd0t9G2JIUe1OiQ5VT+6QmKV67mxR/fQt7xkfOaNNqCEx64nZKSOKmMYMuWfhLJPKHOTUy991d8/y83Etb3/np7g5/UEaF99IdR6NSM/gotuUXAeizLQDccDMPLPWV3sj6v4RcFNRpZGGDvUv58DyVU44otVRbj7io4PAQ/6YZXUyqSGig0T1moFOnuDbg+wKMJkhJ0JGnHi5Sqo9SjO+CUEdKUfVmHDQ+lNK6YO2Lfx1FA3lUCAa0Z1ZUaEDDSCylS6NLEzKZJtgfZvGIyVtqg4oAuZN6C3lZitQ182DTrtIhykOm01THmpXKfv7BICZyDSv83W2pUpNNWoyhSwuIkeMug7HgYtwyeex0+2BZg4ZRj0N9ZhB6vYIIpGGU7uOlh5Be+zSnBf4bc66CVgtDRnPcIu5cAD4Mo4apS+Keow4VNTWyx44wwugmRQejFeLQYv+6TWE4/ZWwCxwZ0NL0WU6vm2aTYT4T7sR+fFuMv/iJLvhBCrOjAY3iwK/04Pom7sg0tD0dcey3Vs2YBal5x+svPs2bGAjLRKL50CsvjJa/pnP3m07R09zNcoho6aifnmfKNHKtr3mNb8xQWjf0z6W06+X6dMtHCGYHfcc5N390rssmT5wPW4rohHDSlqCK9uNJmXNFqWjurabfhuSSM90GLo27GzRZc+iGxG9MH991WwrMv+Hj6yTbCYpgzTw4y/4Qvs27hQrb+qY1RI/owh1rJJSXSdQmWQGOqimU1JxPs7VKcIiWWGWBNw3heq48z7YMX8Ng5on4fmpD0NnaxpH867SXVGF0Bxi8ZQgsFMUKCAbcCI2AR3LydshWraD/4VCQC72Af6ZxFayBE5/p2/I5E+A10y6R61Ru8/G+vctT1R372C+qZBXhApkAUSFPmVd7eewRoYUZMepTMwNMM9LyGMKqoqDuP57bXYbArnaYJFdU1WnDvAAyhfAkNoaKas9rgyRo13L5zfpBdYwUBoV4j5arfpSUEhEZct0FL8l4mTNR0QcuA9DKUnrjzEMIaLM988qGmpCJNExUV5iWszUKRz2WMf4DmZz28eP9xSFtDCJdNi8cjRniYc8R7TEn6wb+nSEOJAfdWw38MwNK0Ir9vx+GU3cZY386ohYJdGBPxUHCgsMEnYWAEJGqgQoPmdnjrzO8zL9EN294FTcN0XDj0LDh4Cgz/CrTKXQsXUQpOJ2SfgcCFAFR4Y7ieIkZ7EngxQQRABAkgqLJfZ6MbQwoQmg+kA842XEx0sQ8rnM8x9hPh5xn2Zkj9FvIrQXjAuwACXwPjv98hliHDS7zIBtYjKwK4J48CDHR0fJaGmY1Se3yMxldfxc5kaDjuOMaeeCLrH32Ur/35X3lt7vFsrh1PUXcrC9a/Re26lawNR/EJ8PpsJlzhsqZ6JukOD3+94Qx0j4t5QIbiDkEqOY0ldQ/yvbl7F6ByZHGReIW2x3rdlh6CxvDOSKPYUL6KftTPewaVRVL0QzPspg9OPynM6SftGXlufvZZrGycF3/ZwZxLsgTiDkJAx1r4ReYy9IYsUtMKKTeJkc2QNYOslpU8VLaACxqfwrQDuELjreGpPFDyS0w/tG42yFhBYt4cjuVBM5QVkYNO5RvL2XLAYaRLKrCiJViuy5aMjVNRRKx3WJkUY5OLl7N48Ta+9a1Zn11EWQtD+EZI/FBpswnV8k/w+2DUq+eIAP742fjjZ+/czJEfjo/UppaEAakaU3aQZLBAcHcMFDwRC7/3ix2SY4r4dLnLEikgVC18bT5AylXp2D6ZYXuqlmxqDoHdFJOyLlR9wp1NSpWOrTGgywHb3dWcMynUiHCGefKPR1JkJMhHDdAklu0h+4GfLyZfRJvTQK8NDwwpMi8zVGZhbgCu/QhTZlC1UyutRockiuxzhdBXd1VWuq8aitvBdqBpMMq87/wHtG2GwW6obIDiSsi/oRRnPhy9CwPsLXv8apRXsDYXIa7vmqfNuHCV5zfc7F5Bn4wTI4njarh4yDPAScFi9r6in1/sJ8LPK5xmGPwyyCQ47SAzYK2EzD0Q+gEEvv6x+VgpYUteeb6N9u7SkdyBxbxAG20IlBi4ROLiEieOvqqT3ise5v2UiQeDrYsWsfYvf+HkP/yBud/7Hm/deitfaLqN46QkWFbGF267jYd/3oreuwFMP9WjE2ypGo8nm2XTiilYlkG4aBgHAVVe6rRyunph9Qcwa/Ke+xUgiA8fUrMIaR6yjkq3mXqOllQDDkoabPfj8RQGrJssmPwpxVw0w8C1bVrfd3h4lZ9gLI2V1xjsdBm6woeje7BMv+IR6YKm47FyTP35pfh6WnnRMImZNmO/+TOue/R0ygKFG79ZTOtgLUmfRVm4i2LvIB1uBQGZIT1rPvmKanQ0ikvjpA47lsCzTzAUKcEyvfgSwyAlA9VzMSyNjo7kf81NwDcPPM+AtVyFKp4ZoH/M3R04PAgPD4NRaLiQcpfvnpR7N15oqJlLWyoS1cUus+Id9Tq38NBQ16jThkFH4BdBtMzhNGYPZ21WpTfnmEovPu2q+vl5UT4W+UK9bawPqlxltwSSsAtdi+tZVtzCoBUg5E3itzMIAZG+DKlcjreGZ3NkcS0XtKsaZURTPozLM/DDYtW49lE4NQL399oMOhJXGEgErgZ6GqzVkO2GYQsyWTB0qK1EfU9rxqrHDuh1gKM+W2L3zi8bPJP2eM+vxeDrHUrKLqyp6zLowESznX/13svXBy5nezKALZXm6Snao4wauARCH6MH/DnDfiL8vCLzZ2XKJruBPGh+9aVxByH9e/BMBu+8fW465MAPuuCd7C7h3nMicGWxWsEOMUg7bQQJYrsW6dQQ2uou5OvbGdJM5FMbsJr6SGcEQmhIKUn19rL2oYeY/tWvMvq44+heuxZPMEj55Mlous6siy5kwzevQJp+COtIoWE4OZKpOJpQrXQakvxulaOh5N77rqMzh7m8LF5itM9Cy3tIyhw518v64WlUGOpmYO52Y3Yl2EjYsI61G9bij8epnT8fz0eoEOdTKSJ1dTQtXYrQNFzLYbBFIKVLJhyjYdnzNM08nP7aMeQDQTy5rDKYbdzAXC2LUVMJQDKd5s3bfoM56mSyOQPTB5GKaQwGRmK3NdNZPB3vCIFneBCCYRoPOR4bjWId6jySjm9cjT/lEF26CN3KInUPrWPPIBWsxulLU1f3CWzwcdCi4DvmUz/9J6WwPK3ITRTOaViD8yNwx+AuYW9QUaIh4CBT1QYfHlKReL+t0qcu7PTc8KHcEwSwPq9I0otKYUrYWZPsdMDnqhrhtaVq3OjDsCzY1goBU2myVhrQ2wHWNgt7qB0t2MK6N0bjbArjlM8jM2RiGBapwQBoGi2+EG5QZ+HkWro7BV2WsqUCCKIiu9v64cSwyjJ8GP19aR77wy18qWErj4y4gG2+GeR1k3IMOu+CRIcae+nrgWUOHDIDZk/5iBOuV4PvRMg+AVoM5RDcr7xAfcft8dTpfri1HG7tV+nYcgOuLoVi90CKsu/zs61fZ40xkZzmZ5L2PnpPmm88UMsjj5yNx/PZ3E/+XrGfCD+vsNaq1ImbgoJ90x4rx+xjH0mEN/bCqgxUFGo3jlQpoAYrRf0Lf2XtB68w+LVSLBFiuLMD594VOK81IbwGeV1HrutC6AZGRPXP2QeVkji7gRcmrKKXaqYXzaR2/vw93nPq4Yew+Hs/oul3d9CxNEP1VQ6O36R2cgfrn+qmvHEFluEjV3EYTuG7OXH0vg99HOMJEORdbTVF5jARexQBaxo/qomiC7igXa2Io5qqP3VZLg0rl7D85h/iOg5C1/EXFXHC736315zdm7feymvXXouTz+Pk80gpFRm6OwhasvTSHyOkJN60Gcfro3vMgbimyWF3/xLDs8sGwxMIkEt0ccbkDdy3fBKREERNjS3T7qDu/WuoNd4mPCioHVXH6B9fQ01NmHs7koQ2baItmUT3+ghefBkvHX8JVQ8vo7LPIW9JhjqTnHfegRQX//+zExjthSdq4c5+eDurUoCXx2G2H97OqW5KvUCGQqouyguK4ECfUop5cAiaXCjTVUSXk7siQy+KKDVUNJOUKroJCkgX6m3XlcIUvyI3Yx+Jjtffhp//OyTTajRjQgNEiuHl1yUyayGcONJXjJP14O1uYv1Wm7yvHqtP4KmX5AMmWkbiYlI7T+O5FFR9iCN8mtqvFktFmrtDSsnjf7maY+Y8huP6OSKxgs3uaG7Rv0vzq7Nxe70QBa0T3Jw68Ka2XbZa+0Tox6A3QPYvKo1tnqxKH9reC6B5QfWwpVpkCAFY32C4+3yKvb2My0n6guX4RIZn/lhP19ZGVq5sZ+4nNBx9XrCfCD+vMMaA9T6wWxFmpzijT6mW7AMpFxanVOpwx2a6gLCdZ9nt32HdseAcXoQTlqQ2bMdYP4C2tBVZHER6NdyOLJotwXVwHQfzlAoqv1dDPpejI5WiUW5nQ+8qIlevxl3XSc2cOcz65jeJjRzJ9y46k8WnnMxjWzsJ5BYRKXqfunef5Avv3kU27UNoAqH/kTVT7uCfLphM1cfVYgp/1Llgj0/6HRVwfa9KheoCDm39gIZbf0ywfFeLfbqvOvqaAwAAIABJREFUj1d+9jNO/9OfkI5DdnCQ9tWreeUnP0HzePAGg8hAgHwyiebx4ItGSbS20jJ9PulYKYGhPoQQaPks0e5WUtX16O4+RM+F4MTpPYycAXc/Cm3dcOCkcr59w52MKe3DyecJVVQghGDk2nU8sHaAgUiMiM9C5rJ0dHZRHyvi6ANqWL6kkaIiH5dfPoMzzvjsg+3/XYz1wS2Ve//+2RGSn/bA4wl1bkd6VQpxqqmcHsb54NYK1T16x4CKWAYLXn1+VCQY1VQn5ursrm7StFRkGNbg3/phpl8955Twns4XjW3w/V+rSLA0rr4Gy9coN5extQka3RyWz0R0S+w+Bzdto0uHLx12H1v6pvJ+fgpGykIA4UOHKB4dI5BRjTZ1u/VquYU0b3wfQVRzUzszDniKdDaK46gPY/1gM3cFLuLspS8zkKgnmiwQX5FK3rR2wuur4LCPUq4TBgT+ST0+JfZYJHgm8/jzX+HxEX42187CzcLgcIjg/GZia56jr6kT9hPhfvxdw38eZBcBEly7EA3mQdSon97j9rnZjpX4hxei9R0PY15dTN5QS3DpETCrEueVRjQpkV4DN2MjGgfUjcp1qZ6R4vCrO5FaJ3gldsTHsu0hulMC56go5a0ZmpcupXn5ag6540HGHFjB0XEvR8drkVzCstX38PrdjxObFCA37CfbV4Q3l+TUoSu5/Ozn2BnpfggSSQftbGQjNjYNjKaOup0mtwf54eEaVVsyBTx25U/o7Gynu6uDQHExgdJSvOEwfRs3suquu1i3cCH5VIr+rVtxLGvnCIIQAm8ohJVOc+T117P0+uuxisuQuoZrqMhPOA7+xBBFXsgFgrg9uzRSrUwGTdepnjGdURE46YgPH8meEnFr//CfnL9xK8+e8XWaiyvB8FDRvJFTrr+e711yDPqLN8I+Rib+VhgaynL77StY+NQmOi3wzG9g0lcO5sqKAEcEYeEQ/KZfEYgLjPWqNGefrTQ3HVSN2iOgWIM2R0WLu2OHVufyjGqSsYFHh5Vu7QmF8ugzr6rOzGAhQBZCjcbkLfAkstS2bFAu746HDc4EvLrBqKqtXHnunWwYGsvV+X/Dn04Qa8iihxy2tU5nOGeQLII3slAeUtmTIReOCe5Qd9oTmeRWhAZOfs//dPIeTHsIPQPa7gF8gbDWb/0YIvyM2L59gObmIWpqIjQ0xHlsGH5adwodwo/YlEFIgd/JkZk4mvyp8zkg0PE/88Z/B/i/863Zj/9ZGGOg6Hcw/CPIv4byjylVg9Keg8HcNxHGNJWqarN3zT5pIkfVge8jcJEZiYaArIsM6shiP1gu7vZBRGdStd9pGl6vw5gFOVJaMTKn0odBv8ac3JO05k8hPyaMQy+NA8UkOjp44tS76Bt3AT//bpBjji1VQuSLeynRqgjoJfT3byXdsxGAzKDLe/fdy/SvfW2fx7CaVaxkOSDQ0NjCZhpo4CiOQStQvBBK1WPjk0/SsnQpTi6H5vGQ6u7GXbsW3edD2jZLf/UriseMIVRWRt+GDbiWhZ3LYfh8uK4kl3Nw8g5vvLIRIxBksilYadv4Mmlcw0BHEvSHMEyTo+bOoH/VEpV+FUJpuf7iF5/KHQOgf/NmyrMpvvaL8xg2/UjDRyQ9TCrrkFu5mMCUZ2HOyZ/tc/K/BMdxufzyZ3lnfS9t4SCaCfklW3hncw8/uuUM1pXoPDisCM9baKjZmFdkaGrwXlbdnE4OwRYLVqWhaR/uCAIYlGpQv7jQRJl14YZepdbi16C7TzWe7AFZyHh4zJ1ZAK9mIdwsjhZm9tQ1CCFp2VpHcmSQnngJXT02dq+OFYV8BOhRxsOpMmgNwkVRVSvdF0bUjaap30VZZ+/aGd3IM39KM5vapygh9sK5yOchEoKyj5HLTTiq2/mphDoRJ4Xhy0WqTro7cjmbn/70FV55pRFdFziOy8TZtay77Ehy9WXojYPYedWxlMSHGLYxjzmQkaP2E+F+fB7gmQrFz4HdArkXQfarOTHvXJVW2QeEgJ+WwmUdqkvPg8O40kfRTQ05bIGm4eKqkDFlwZH1uFe+isjnQVfD2K7px1+UpHSsxJI6mkcihI7tDeKzeygNDjDQFGHdJpu+niym41KceIfNbcdz2VVJHvQMMGvBWBxLNdP3bd5Mursb3edDCIGVyfDWrbcyYt48yiZO3GP/kyR5mxUECO4kPYlkG1tpp40adqV6rEyGZTfdRKS6msHGRlzbxskrxRLXspCOQ7q3l0h1NYZpEigtJZ9MYmUyYHhIDOeRro0uJX9eGWRSqoID31vJxDnb2DBqEt5cBse2seoaODkEp59/DoOHzKJt+XJ0n4/aefMIln18N+buKGkYRfNf/4jHGSaSTQES25F4vQHMaBiWP/X/jQj7+tI0Ng5SURGiunoXkdsS3s/CO8vb2bipj2Q8hC6FUvEpC5HrGEK828Id0+sp11S9KiNVU0yxZrMql+EbI57nSMNgqpjIAWI0qzKC4xLqtXc0b4EiwR3jFrHddABMDZK2Itappmo4eeZVdhINQLwIuvshVhpmoDdMbniYvBElKlsRw2twXR/vN07h3ue+SnVFOy3njsAyDTIl/sIgIOg6oIGxGEafBhF9b4/EHQiGq8B3DGEWMTgcRqLj86bRvAYLvtrA029Db4GwJRDwQ20VHP4R0aAj4VudsL53mFGvPkPovRW8VlnH+yedxnUNdbz8JvQPwYxJ8M4b77L4hc1UlRgIr4nUdF5+rQkZeRvnn2Yh64vRe1PI/jRS0zCLAqTjQQZHVfPpZev/vrGfCP8RYIwA48uf+umTTHioBp5Jwma3lYynD6npCF1JhkghEEKqb23Ii+sPouVzCMfB9fqQHh+1X4yiBXvwJjTcCh+G4VNuGQLwuPifGWBgwMaQeQxhkQqMJGi6DOQ07rxhJbMWjGXUUUex5uGFJHIDGEUBRNbFdRz0Qo1u7V/+woLrrttj3zvpZEckuAOqKRxaaNmDCPu3bMG1bSIjRpBLJhlsbFR3SylxHAehaTiWxWBjI/54nFhDA4n2duxslvRQCs1V9lC9NYcQq6vj3VY/1b5XOPm+Gxk1ZT7vT5lH6ZgxfHVUmGPD6iYcGzmS2MiR/6XLOG1KNc1/yZGxBT4f2I4gZ0nmjnLRPsqcbx944x144GnoHYT5B8EXT4Diok/eDsB1Jddcs4SFC9eiGwLdlMw7oppfX3sCLbqPKztVfa9v3TAdGYkeFjt9MQGk7SK7hkkUZiM2FpjMlg6usLClj9va5nNkfA2DkedJM8gM/0wONFUDV343Me7djzgn2SOictgVGR05Gx5aBO9vUnVCu1CqnT8d+gcFTsWBWEYP7lAPF8X+nVcjI1lWM5VFb5xNWaqHeFM/RfcOsf6EceTjPox2B8c1Ea5A6pCdBMEkvPURqnU7MGHWb2jdeA3B0BPknRxNjOD20LWskxMIfM+i/PcSI6Vj+nRK43DDdxVh7wsrM7Cpq585P7gIb3c7rs+k6O2lZJ9+iHMOvJ109iDqyjbSvGUtr9yziribQAyrMyZKRxAorqbtuXWEzpnGsO7FKA1CaRAXgeZYBLweVtsGR3+6j8XfPfYT4X7sE5Ue+GoMfpvqZxNgySCesEQkcuC4So0i6KWuOU7PuLEMbt2CW1yOG4rihCM0DQ0w29tPabyOrMdliCEEWTBMUjf3oL2t41p5/O4QjualPbYAAI8BrZ0uVjbD1lkZUqfXYD/UiS1dNNPF0+dSOm48usfDcEvLXvstUhaOJw+e4B7zwAKByZ7adL5wmHwySaq7m3Rv704TVAA0DaHruLZNpr8fAMPrpfzAA0kPDNLUPIwIBumpP4r+mkMBCJfEWaqdycL/mE+6t5dYQwP+WGyvfUx0dND02mvYuRwj5sz51G4ZpYltnHTsASxftoHugRzhgM7ssUHGlgLJAZj9ydHgwmfhpruVS4HXC/c+BouWwh9vgNgnTFtIKfna157kgQfWonvAETa6R9C3aJC2SAfvHHEJQwkvpgPxYBRNF2Rc5c24wzZQ6BqiIoq3MBcY1pXUWVaqlLshXDxC8nzfQXiFwAmtojF5IBN9Jp02tFoqgvxw21GnA3WuSpH2uKpxp6HQoOv1wp3/oqLCF99QKcczjlbR0uoPYPV6nXi0ggUHVxCM/JZzmhxWD9rww16CWpK0HsDbZ2HkHMyOLDItMEMp0loI4QBeSJlqOP9jIUxqxt9A3v0Xzm1O0yfjhDWwH32Pjj+vwHFzxEWMBUeM4dfXTicQ+OjRhUYLKh/7E56udqzyQoeSdMj0JBi/+lqO/JcDmDbqJaIrung9cyxawAYjDFIgO7cTtTsZ6Bccf9kpPPODf2OgfgxOoaPZ6zGoMMRe5/jzjP1EuB8fi+25KAQ0slYlmrcVPSoQrkQKkG6QnslgvbIAz9apZN9LkXlsCLc3T/tbeXrmjqF6QhKfIwnjQQoTGbueuZO28u47DxJ0ehn0j2V75ZfIelWK0HINRns3sMmzhXViLWVXn0HbyBByQw+yxMQbLSbw2BCJjg5qZs/euZ/5VIqlN9zA1pdfZODXU+gPm4TL6wn35MgODGDXBQn1pXAn7mpWWdPaxWZh4k30YWayezQICU3NPwK4jkN2eBg3n0cCJ997H+d+dy2xmB/D2LWVbbvE4yZF9fV7+CTuji2LFrHkmmtwC2nfFbffzrSLL2bGZZftJQq9F6IllMcMTj7zYNi+BhxLkbfjwJQFMPMLH7t5Jgu3/1lFGTu8LkMB6OiBRxfDV8742M1ZtqyFRx7ZgM/UsA0L4UI+JxnqkCxdE8Jz6hCB4RJcBB3BSvSqErSWHnKxAEKAO5DGVxdDTq0hDLi66gxNSwAXISVFsgePY4MuWdQ/HunZwqPDgwzmKnY2ziQ/FPxqqCaZdzLKmHaMD24q21MvwvTBGceox+6YeaB6gEq93tYHH9g6wbBOKNxFciiC6cmSkSZawsWp0jFFDgOJJISbB6MYbK+ydvo0eD/np931U2pAz+tbabn3ZSJlGYRXYDjDPPNyE+VVg/zLdz96jrPKgLLlr+BGooALbhLbccn5Q9R2r2FifDODiXoqVm9jfk0Hr7SNoMqTAhEmn8mRzQoaysDwGHzh+m+zdd7RrLzwe5T7DOp9gixK0P8fBR83pbIf+0GVW0vWCWNoadL5GhLZGpJuhJZ0La4M42Cjm148lWFC0yKUfLuU+kwf8+cdzLgTFiOK7sb1fZW+luPZsuyr9G4u5qBLLuHLr71M6XfuZknFL+k0JpBxTQbsGDGnibPOKme9vh4fXgzDQ/GR09GOHY0+q5bMnDhDg90ESkqYcOaZO/dzyTXXsPnZZ3HCcfp+10ty8wB95/8728/7Ld2X3c/w9Jt57oyLWXj66Qw2NfF6UnJxIspzP7qNdFExjqbh6IZKt2kalAaQxSbOtBGIUATHkVQffDAn//731M+dzQknjKG7O7mTLB3HZWgoy7nnTtr3iQSyQ0O8+vOf4wuHCVdVEa6qIlhSwrv33EPv+vWffDEOPVsRn6bDuJlQMw6ipSoS/Mbtn9gx2lSQ7fJ9KIUXMOGt9z757Z96ahNSShzh4jhyh2AOVg7ytgc0B93MoWlg+jXkBccTP2EixbaFJ5sneOwBTP/VSfy4Qiekw9RC1Kbn0oST3ZTLZgK5AXKJBFY6SUcuyqqBqaStCBUGVLb0INsG0Fzluq6j+oYNdpHhSK9qWin+jEv8DgvOblVD5+220p4NH5rElgbDVhhXagTfTuFqOiX+brJOAMdSUp+xGHy3WAm3fxrsLibe8dhy/JE0wqPjSh1NNwiXCf7011VkCvXqfWF2ALyxGFY+j3QTSGws4UXPO8S8/VhSI+zpxU+Sb854g5JAms6kh64h6Mr6iXnzfHFMN5MNm6CdY+JzC5n03F9g20b6+3v4UbGzz+7Xzyv+gQ51P/4rOD1icGHHadRHXyPq34yLZHtyNIP5EkaFXsLYkW6MB/HF4thOnvJXZ1Dnm44gSKKjlKcvfY5kZyfScUC7i5rZsznm17/m6l/MpNR9mIWLNNJOhFliOWecIJh92Xf5q/UImket00JVleimyXBbKzmPwwFfPJsZZ55PoEQJAye7umh69VX85RWszGnIlhz+y95C5hJk43GGi0cykk2ke3sZbGpi0Q9+wF2/+CNGKkHAo9M14xCMXBZch/BAB9qEONI0kP1Z7G8dRD4W4f7bZ/GdQw6nfLKaz7vyyjl0daVYsaKt0IknOeusCZx++gEfeS7b334b13EwdjPF0wwDKSVNr79O6YRPmP0bNQXO+xn89SbIJFQxbMZxcOEvPpV9STyqeNR19xzUzuWhZt9G9Xsgn7eJRHx09Q2jm3LnWKrrCMLJblwBrl6oQwmQPgPfGQfxzSOq+OLkEiorwztreA8OKzeK2kQv+TfeoH/UaDxBF+HRsV0PrW41CI0tqXHkLJPUhg7kdc/ilobghtPBkOiGBqgUnotSoFmSgg9yMCeh5kV9n3Kp//MeaLOgRIdhV4lhrzhsJtes+xmvbTyErbkGRnS3cvzSZ3n+pFMQUT+TTJgZhF+WQrnnE99iJyb71H6lXXAGetF9gh15fK8A3dBIWjZNmTbGe/ddT/YKuOCic3nyh9+n1x9GGj7KtAGy7Wmih5VRXDZMPNKErJRU9w3z0Cl/ZXHjKN5tHceIzDYmV2XYZo6mc/Va6u0UIqpR2vwYpYcsY37mFSYMFIPnB2Ce+g/h0rufCPfjY1HvhZvLQnyndwZt/VPRENSFNnNY6VJcXGxsDAwcXBIigWZoDBpJlvMWq5c/SfKCP5NqbsMbiRAbOZJASQkty5axbuFCplxwAZfeeA4X/LCfoZYWRPhi/vCXRq4/5kGqTh1kzDk5RpaUEwp78cdjEDcpIsqho89B7FYAzPT3I3SdAanhRnVCB+kYXTYyGMUYsHE0nVQoSjCfw85k2JqyGMpb+PI5bMehfeR4Kpe9SKaolGA8h2bquEN5hAARC+KOrqbhvDw33riMqVMraGiIEwp5ueOO49m2bYCuriQjR8aoqPj4kEBoH21xtCNd+4mYfRIcdDR0bodABEqqP3mbAsqK1UzaK8vV33VdKa0AnL3vaZo9cOyxDbz88nbau4aRhWBFOioqPOiMIVLWO7zHsRAAO5cjwzCBF5t58PFVLBSCb33rYM4/fzJCwFXFcEUn5Ne8y9QXHuKFS39KqlMnXOLQ5anElTrxXA9o1WQb+3E0De20qejPrsVtHUDWF2M5Ui1CdpxD1IB+1oJkAp4PwcmfYjKl34FVWaVq46CiQRvQvH5+eemPueDt+7l04++xvQFWzzqb0VW16C48XwcVn4EAd8DUFHn+oBuKZkboWZzALFMzrYaAzLAkNkIjGPl4ebODjjsaue0LvHP3bxGaB+m4RI6uonnyqZREf0M258c+wkP4rwkiuRxn1G7glJJ+urcNs9I7EzuTYNIp/Uw40cbrt4nWPI6T9zLc5SXdY+ELXKccSMxPL7X394r9RLgfn4gpfpt/qnkExynCp7l0a9sBqdzasdDRsbEwMNDQCRJALO+g/dLfI5MJhGWR7usjOzBAbPRogiUlbHjiCaZccAEA/ngcfzzOlVcu4vXXmyktDWKv9jE4q4UNuXZGj4pjeDU8eDiMBXuQIEC0thYhBHnDIfSDeozuAVgIwivwjPAQyffjasqNwrVtvFYOVwqMgJ9UZxd9dWNonnU41auXIQIuoscGIchddhi5kgbcjI/YzBSuG2Dx4m00NOwSIx41KsaoUXs3xOwL1TNnonu95FOpnUP5Tj6PEIL6ww//9BfEa0LtR0eeH4drvgE+D7zwRmGWMgzXXAUHNHzytkcd1cDhh29h3bpujEJ61fDDvH92qZohePOWYSZXa7RW2rRuX0t1YxeT7SFERRjLcrj99uUcfHA1Y8cWMyegPAn/XOTHad7MGQ/fwYa5x9GZqoFKSVXbBrKROG6VIN+TBI+Oe2A1Ws5Gcxyc/8feecfZVZXr/7vWLqefM71mkkx6D0kgCRAIJTQJKk1B+FlAEbBQRAQvKBcRREQRRSz3ig0REQhSpEMwpEFCSO/J1Ew7007fbf3+2JNJkGKoAW+ez2c+SWZO1lln7z3rXe963+d5AOUp5ECs2P1EWPidpRkXftG9b4HQ2e0Kj1+DnBL0Bed7FGRDRfz1qK+z4vivU6z5r8l4MEp/Z0FwN+ZE4KE6+OMXZvLzZQtwO1xEVNCfVSAVp38rTpV4A5mevSCE4OCLrmbSic/Q06ATLo+QqA1hF5agbIhFbLTxEnlhCJY50BFGGz6c9XIUbS+vYeL8JKOOtFCeR6IOjAAYIQsj5GDlu4E6X5f4QCA8gAPA5w0Kj7huIRCUUEwXXQO0BIU9wOYSSEwMQoRo+fEjKOVBdQzRWUA5Dp7r0r1pE6nm5sFjwO6tW2l88UXauj0WPpuhpq4EIQQqJ9l1x1DyFS2EjkxySEWEQ+pPoGJEBfm+PhoWLiSbTFI5eTLVM2ZwyMUX8/j6v6DFgD7dP3/LWOBC1OxD6+nHc10K6TTukn8SWfg4jTX1RAMmynZYd8KZbJpzAqdmf00OjfYZ0wmWxTEdhRAeyh0ga+ffgNU9AKUUba+8Qm9DA/HaWqpnzEBqe3b1ZjTKvJtv5ukrrySdTg/qlB76jW+8TtP0/UIkDDdcCt88H1IZqCoDXQdUHgrPgbPBdzYIHAfytVFE1yUXXDCDhx7aRHtHmvIJitEnK6w2wT++pCPSkvA0KFvdjNy8knFjooMJsGFoeJ7iued2MGaMz06bGITrjz+YP/80hrvtVY7o2oknJN/98k0E8zkCo8ppUUAi5IuUlkRQh9ajdWVgbQvuyAqUobH7YHF33RD8jG55HrYU/OaZt0K5BqNNvxOzdIALOCUAzQ6cHIUnMv64SvkZZ0rB1e+BXV+5DpeNHc+wexq59961NL7iUDpcY9bZYf7f2I9hsA+RViYIVl9KdfwnINLgFTC0Xj+T08YAWagx4bRS8JKI0HyOOv0ySv9yF7X1l+PkPMrHgB7wZd2UAiE9gjEHKIDX8u4/6EcABwLhAfxbmJjUUEsbbYQJEyWGjkEXnejoGBikSBEnThFFgKCwaRfEApC28Jw9wUN5Hp7nkenoYOH3vsfmhx9GuS47M0X0Nk8m4lUSra5GD5gk1++g/YkuxIPdjKjbyOPir0w6+2xefvAhOvozOK5LRNeYeOQcTrnlZja251ib2YSnunHPmYr842rI5hAFG1JpLBekZVE6dixnPXk3f540l3y8lPzQevK6yUGLn2TTFz9BxbgshXyMAoKA7VBi95B82g8KRx457A2vkZXJ8Pgll9CyfDlWOo2QkorJk/nkXXcRLNpDBht62GGc89hjNC1ZgmtZ1M6cSbRyHwp07zESMf8LAK8H+r4Ezk58h3oF2V9B4jegv/bzVlfHqKyMUFISonFnHyt+6qHrkqKiAOefX8XVV8NzzymuuuqNT4E977Utn3ogwEm3387jl11GuqMDIQQHrVzImlPOpdcIIT1gWAkYms9hNXXsogj0ZjELFsT21Ft3B8Hd7xCVvufgFf8mEAoB15X7VkVtzh6x74NDvlLMiVG4o8fPEocb8F/FvGfu7QLBaVUncPglU2miCROTEYwgxr6pDQEQPgf0MZB/AFQPmMeCtRCspSBrBsT30/4HDZ6OrgeZdnobpMGxBzjB+C9TLkgdhFYAZxXoE8Ht+Lc2XB91CPU2iLgfFRx88MHq5Zdf3t/T+I9CN938nQXkybGb0hyzIkx6PITV0k3jfJOO2jxRGUUg2H7y91HtaYxOC7spuZfgNxjRKIFYDJSi6qCDkLpOe1s/t74ygZDKEIxGMCIR7EyGNFFOGtHBnCHdOIUCGzdvI1VWhZso8TsFlSLauYszrv8unFrB0v7n6Vu1HccMwEtNaDc/73eD9BbA8ZC6jhYO41kWnm1j6ybCDCBw+efF17H90MM5csJyosUFbE+AodO/0WTnNwWfPHkiV1015w1pDstuv53Ft95Kvrd38JjOdRzGffKTnH733R/AHXoXSP8Icn8Bba+jOLcDzBmQ+MXrXv697y1kwYKNlJb6tIh02kYpxd13n0Z9fTH9/QVOPPFPhMMGoZCf1TiOR1dXht///lQmTHi9Dplr27SvXu2LjE+awuHtERptf+3OugrPdn3lot1hSoAUAl34Ygk2fo1w9ysCAiYG4OgI3LyP+4x+F57N+MFwUtB3zngjJ4vd8Dx4bB2s74QpxTBv8kB2/WGAl4L0jWA9g3+9SiD6bQgc4QfF5DHgvAI4vobEv9gaCiEBE7QRviFz0b0g36Povx8hhFihlDr4X7//YbltB/AhRwklnMVn2MoWeukl0JhnzRdvYllPCtcD/qyhvnMYmWNHIKVO4IuzsS9+CJHx/CYRGOgaBU0rkO/JoJsCIZJYmSj5rauZYhq8UhgDnkahu4+MY1BW4jC9sg/wg16/HsQLRTAHFihdCPLBMM88+AhfPfUWXlb/RIQE4ZyN/dRmvMowxIth5S7IOyhX4eZyKM9DeR56IQdWHpRiyh9vJ9jSyMYjjyc83EBqKbq1YdhyBH/6WZ7a2hjJZI6ystfbG6255x7yPT3owT3aldJ12fTQQ2STScKlH2KxqsITIP/FhFWWgfWSf2QqXkso+9a35pBIBPnrX9eRzzuMGVPKlVceTn29XyuNxwNcf/3RXHvts/T25vE8ha5Lzj9/OuPHv/GZomYY1MyYAfjdpBEJhwR9mTSpCRCSfiV8jVAFSgokAgeo06DZ3RMADQHjDL9eePgb+BK+GeKab5y7L2hLwTFPQkMccEF0wpi74KkzoHTfSsbvL2QM4jeB1+ebc8sqP+UDaFkJ6R2Qd6DUV1sUr+vL0fxsUKsGt82XaAyd+kF/ig8MBwLhAewzggSZxGSUUtx3+Zl0dUueSR7Clp4omlDpLBN/AAAgAElEQVRM+nwjl/9oKuPOPgk58wTur11H97Ztg1w7BATjEqkppCsIl3pYvavIZ4eDUsyNrqGYHjYah5BFZ6K3gfljTMKG35nRIwzCyXb09mY8M4BdPQS3sgZdKdqFTiAlGXpLK8n7H6SABx1ZGF8KPXl/+x41UbaLSvuaqYNZ6kBPf7ytiWn3/4b00qfZcPZFtI2dTGJiPQSj3HbTQtav94/uJk2q4LrrjmLo0D0M6lx3Nyg1GAS9gZqo8jxali9n9ElvTXZ/L9Ht+nJkuoCZoTfXvxyECIDK/cs3d6cIr+9cNE2Nr399FhdffAiFgkM4bLwuS543bwRTplTywgsNWJbLrFm1r2kyeit4+GcOEemXBnUFrpQDPxCDR5+7XVKKdf8YdLPtzzYm/Dre9CAc/z4lMZ9fCjvjELUGjIg92FgPX/sH/Pkz7897viPIBLAX03/zy3DnN+GMlH/xWoASIMperHIJ+oQ9JwRCgrsBT53KC1l4PO1f54/F4LDQfwa74kAgPIC3jVRrK7u2t/KHptkk5TgKJVPxMFmY3Ur/da/w9FmfRdRWcO4TT7DpoYfY8NBD7Hj6KQQF8CSuB4GYgVIFurbY6OUdNEyaiRuOUL95NfPqN6E8j10rVhC0JwIV2Lkc6SVLkVIDz0UUCgS3byaXySClQCsr5+/nn09y82bK9UqSHc24ORtWd0BVDCEkauBIDZSvxrIbAyKVQghfU9W1GfeXO+m//k5SkQT5e1fibE5SWemvquvXd3LhhY/w4IOfJhDwf4Uqp0yhv6kJpRSF/n5c20EpDyEkL/3PXYyYNw/NeBdthvuIBf2+88LuYBKQcGul79X3pgieAZnbQYb2iHV6nRA4GcSbz1nXJbr+5gKbFRURzjjj7fsi7m5e2WH73n4NHuQGWOi7dUbBX7eDA4twp+svzhHpq9UEFHw27lMV3mvkPFgsIJzfEwSkglgmzeZVC3ml0EXFpAnUzJgxeBryoYBScN8PQQvA6gTM7vAflCR+d9HufYqoA21vH0IPJUdyfSc8nN5jfvZEBs6KwxXvQePQ/saBQHgAg7CxaWAnPXRTTCnDGEZy1TpW3303qdZW6g49lElnnUVHssBd26ewUTsWFTsInQKGdHGjM1lmj2PV6iTTppYRLi1l2nnnMe288/jrGSfQuvx5pK5jRjT6W/IUUpL2IaN59Fs/IxeI+4uGlBz2118y7Ym/Ig0Dz7bJdHbS19CAymWx68eiJzvQbAuUItq4lXxpBWX/uJ+NW33lEzMaRcR1MMOQzKFrJt7A2MpVYEoo7KXvMZAZaoCrFK4ZREiNeKoXsW4do35/K2bZKFJqHEiN8vII7e1pFi9u4uijfcLzkddcw45nniHf24tjO/h1GYmtDF55cinVv/gdR1/ypff1/u20fMPhIglhPUPYbCHnSb7VUcfDdYHBzDDr+QEltHuNDn0GnHVgPe/bdaHAmAzRy9/X+b4Z9m5eyeE7Ldj4c947rLgDr814ij5XUVKwqPMcSkrC5KXk1m44OuqbL7+X6M1bJFo3Y4eCFBLlSBKU7tzCEb+4ED3Vz5/7XAxTY8Sc2XzhT7eimf9Gjfs9glLwUD/c1gPbLN/D8cT2DsxnNmLnLOYdWcPclh3oZZXQ6EJGg3EeRBSsAo4BMEArwSeK6v6GSBSxxRvNtswKhmtjsYS/IXQV3NsPp8VhxAfzEd83HAiEBwBAhgwP8QD9pHC7UhQWrMZdtAPxYguxaAnBoiJW/e53vLrgH/zVO50Gdyhe8RSEm8ZRPvcpIHI4gXIeftZl2tTXjn/wBRfw1OZFRMvDdGxK4VoelpL844qfQDxOtCOJ8hTKNHnx0xdTtWUNI3rbKR0zhhkXXsgTl15KIZWiItNLe00dKQTRthaE5xIvKsJqahjU77TTaYx4Cc7QOKQsVCqPiAXxkmmI6khPwyvk3/A6BD2H0qoyUh2dTL/3V8Qamuho7SfQuZxM0UiaJv4/FHlcu5Nk62PgngLaUKqmTuX4W2/loS99GQ8NITWU1LBiteRtybM/v+t9D4TPZf0NfnlkHVXxF+jemWDX4jpy2S5unzGcz06r4ofdsHjgFHROGK4qg0rdhPgt4GwBd6dfT9In7dczr9EBWFAHC7Pwu15YlPXNdVMwSCrIKQijaE7ZqJ4s6bY+NgnQtR4mT66kO2jS6kDde5iId27ZynlPvUqhbgLCzmFkm8kn8pRveBQKOXJ6FYEw5FzFuidf5K7vLuCLN33qvZvAW+DOHv80oNv1F/bOrgxrmi0SzzQwpmDx5JPbOIrJ/HBeC5qwoSsG/8z4KW6RhGNLfM/S0Gcg/6AfDI1p4O2iMnU2t5k9aAKe8T7Ln7wbQZgofO/Ij3og/BDl7W8OIcSJQohNQoitQoir9vd8/hOxlCX0009wl0PmnD9R+OVismt3knMz9DY04Lkusepqlm/XaN7aStH0aaiSCNQkIBrA8QTCDCA1QUf/6zsOauedRv1n59LXlSbdXsDKubTXTyQbK0HvSCJ1HTHQ6CA1Sesn/x9lEyeS3LyZvoYGIpWVKKWQyqM6uYuhPW2EUz3ojg2tTbiFwuB7ea6L096HVBrURol/bR5ld36O2M3HIY4fQVTXMQ35unVeKYUAioWH0bSD0bkeSqorsI0Ytpkg0rOVWNszeNYqlNvJiMr7oecMyD8MwIQzz6TfrCQfG0J8ejnzbgxx3q/b+NR1nYRLOt7P2wf4Lg5BrYeq+EI2PDmaB84bzeJvb2PFNSu4/vSnmHnRDhZnfQWVCg1ezMKFrT4BHfDNnAPH+dngh6DwE9NgfgxOj/vBbHYEjgr7wtoTAjA1AJe2tBBatJlw3iYU0AmYOp6n2LCx05fFfg9XOFXI8+gPv8PownaOb3iRUDaFmUmTaNrKtjlH019SR2LAltMwBCIY4+V7H6Xw5pKhrx1fga1e02C9z+h34be9/p9hCYbr4m5PImJBcnPHoYrDVFfHWNg3kmVbXFBhqHDhGAlnKTgrCrICzJkQvQJKF0HpEr/Rxl5JmBZ0LDSV5WR5B5eITyNwkbzeCPijiA/9RxBCaMAdwEnABOBsIcTbLzwcwJtCodjGFsJE6P/fF3CTabSKGCJooIb5Qa1761aUUiRVCX0zhtL1xVlQEsWrKMIbU40aVoYjTGLxEBPHvrYgtaUAn2gSXH/ag6QuOBMlBHpIoEIh8BSe5+EWCiilyIZjFIwADaEEW9ZtpP3VV3nkoovYtXIlbj6Pnc36AdFxEJ7fkappmt+ssbseoxSe4yBWt4Oh0X9cGb3jJYnppdQmLYoDJlKTr11wBmpjrmXR+tJLOLZNx5o1pDasotRrh1QXjmOTadjB4pdKaG2L8JWrZ/CjOyaQ7/oBeL0EolEyiRFUjXc48/vdDJucQwhF3bg0n7qxx+/CfB9xeBjKQtvJ9wdY8oM4uY3LEKKAHgui3Cy7HngS65FmpPCbUCp1aHFgafYdvFlnE/ztR3Dbl+DBn0Ly/XMzPy7id4P2D7hPlGv+wlVjwM4HV1O7sgFlaHjSD966oZEKmBzk5Cl+a5WyfUdvJ9Y1p3B494tcsPIurlr6Y36+5HuUeVlM5fpqN/Hh6Hs9U1IqHCXZ1fnWQysF9/XBCY0wewec2QwvZt7e9JpsyHv+YywAJ10AFMLx8IYU0ev5SjSyuJzFkWOgOARzM1Bs+38vtsHdDsZcf0AhwNsBzlrwetHwN5oOJo7SmCgXUeU+Q0TCYa9vov7I4UMfCIGZwFal1HallAX8BfjEfp7TfxwEEoUiv2gTWnFkD6nIU0hdx85m8RyHaJFL9zlHE4gUCFRYiH4BtosqjZKoLaO+Ps7Hj94zrqvgG+2+IsewhU/T9/3ncZSOnYWSta8iHQc74LfnK0BmMyA1hry0EKujHVc3MMNhPNtGCwSwcznyvb0U0mmMSMT3+xMCaZp7nFmlRLkubiYPqzvQz1xA/AtPYZ92P2MtDU+pwU5WKXwrmlBJCVoggAKKhg9Hahp2Lkeuuxsjm6So0IqXzfBy9zjKIxmmT82SiDvc80AFN9xaD/YKACZddBmHntuPZzlku8EruPT1BQmX10LmJ+/rPZwYgNlhj12bislsW4sImgjDQBd+to5m0PCn1/JrXaD97RrPNW6AH5wNL9wLzZvh2T/5/9613R9T+eLX17TDDZ2+PdK7QZkOP6+GqAYdDnS4vsvEHdUDXaJb2xm+aCNOyMCKBLCiAaLb2vmaUcB5hxnW6/DAT5A9u0hpYdqjVXQHSxjRu4Nztz5E1JQITSfQ27bn9cpDWGk6hpxKyb+xZ7qnzz/SdBVUa9DpwKXtfufvvqJSH4hduxuhdxdGdYnsTA/6QXoKiuaeiPO5g9lpT6QrXAZFNn5tsB6sx/f6DBmfPkMeiUNM9hNwc3QlS3jqn8fy4vV1HP+ywnhzsaWPDD4KNcJaYG8H1mZg1n6ay38kBIJxjGcta5DFEdzWXmRQR4RMRGsa5XkIIXyy84QERsDASRWInthHZmEMa0cA6WmUjw5yx9ehdoDA7Lkuy9ZsJtOuqAya1Pz0v/ECQVQwiEqnMPM5Trr+Ih6/+nZS5TV4QiCVR91LzzPq6QXoVoFMaQVRz0IzTexcDqlpRKqr0QMBAtEojmXRs22bXx/cveIpRS5ehBWOUppPozpSZPoKVAYlW7a2c/pp01jw0Cqam33yu2aafgaJr3QSKCpCOc4gHUK5LtIw2GBPQOqCYLobOx0jmDCoqrR58vlyvt6hqKiDL191Cu1rK2nbkUcaDo4IkKipoLK6DJxNe4L1+3EfBZwXGUazsQOsPEYsiBS7m0wUaCZ2T8/g65Xyd8Ij3m4N7f4f+3yB4ioK/f30t3UjM9vIXftZotfdyw2qnAeb+in0pRFmgHsqS/lGrcl574JfF2roovLmF2lZ18moMaVc++05DB1Szvz5o1mypInaFTupXtdCtiRKZlc/8fpibjImsmkHlEj4QjGcHX+Hl951YNXT6JVDSLStoaekCk+T9AUSHNm4iDunfZ6aolKKGruRhXY06aGUoLXsRGaf/THiUchkYckqyORg+gTo6Yc//B1yBVg8DUrHQZHZSXFkOUOMXXTbMb7SdTCXxkdyRvytif3gbxZOj8Ht3X5maEaDEA/hWS7mqiYqNchmbXRdYgYMjjm9gkK+BKXgiJnNfOfyRSQSTWDt9Ub62IFA6PcgK0+yfvsEwsEca7dOprLQw333KPoaBT/YP31V7xk+CoFwnyCEuAC4AGDo0KH7eTYfPcxkFl100njudArXPoIMSSKBOJoD6VyrTwhXijmfP5sldWX07kzSlcxgzCtQVRQhUlrGV0fCtIEW7I61a3nqyivp6kwy2REEsmnwPJziEgJNOwA/A6zespZzL/oY6489jXRFFUNXLqZ8yxo0x0EBZqqPgnLRdB3lOAjDIFpZSbisDCuVIlhSQnrXLgr9/RiRCBnNYOF532LnLD8tDaZ6OfI3NzHs5RdoiwSJ6DZuNsPk8ZXk+rNYSmKbMcxEgkJ/P3Y67VMrhPCzxgHXetf1SFKCsl0sDXqb+qmKaUh0NC1KR+9oKup8jl3d8EmUV3WSywcJBnVCQd3fXVNJf2srvTt3Equpobj+jS123g3KRTmfOqiO30VSuAUbGfQ9FgPKoOBYmONKyQ40zPZ6vvnq9LdjwOp5sG0llFSRTSbpXL/eP3LTJPqOVdxy5Xf485mXE+howxwgOvTvDHLj5KnMnx6j4h2sOEuXNjF//j3k837v6NZ17Tz76GYefPDTHHfcSBYvbuYf/9iC54HW1Is+poLkRUdj24LqAfPfW7v8btkvvqNg7AcHISQ1I+rJN+6kvaIOR0o8JSgPGvxpZiUtf7ufO29eyi7dojs8kpGlwxk3SrB0FVz1Y8gW/M1Hcxt094Gu+U26hX/CmGOSTP76A2TwyDpBYkYfB5U9zu97j2KrNZFrXi/G8zp8u9zPkO/ogZQniA5JIH76LNU72knpAtPUmXL4EXzlsoWMrZOEQxJds1i4pJZrf3gYt9/wONC+Z7MmQiAq8HMPRV8mRiSYobFjGBsaJhAJS0JxwTNLYHsTjKh76/l9mPGhl1gTQhwKXKeUOmHg31cDKKVuerP/c0Bi7Z1BodilWln569/Q9LuHMfCtXYYecQQzLriA4vp6bN1kXqNNp+rC1PIIBI4nKXhhHh4SZoIZxkqn+fMpp6BcFyOeYFkOIls3YHa1Y5eWY3S2IawC0ttDYXA1HScYGqj9uTiGiW5beLqBWcghpcQbCFBGMIiQktiQIf6RqWmiB4NkOzu5//NXsWPqbMKd7UjlYYUi2KEwp179Wcp2bgYpiIZNQiGNnl4bNA05wO9TSmGEwyjHId/nq9ko18VD4Mggq9RUVsmplETSCA3qpks8F3pSZTzx1DUkigd2AfnHIHUtyGJ/MVF5lNvF2kfHsPQX2xG6juc4DD38cI698UaM8HtfZPnJ717h2iuW4gkTQzdRjk1Fqcun7jiZ1aNrAPhkDM5J7EWj2BcoBd86BiU1WletwXNdpK6hKRdXSC4ccj4vHXcm4f40oAjrLnGtQHdRNT+KdTP/sMN894y3gdmz/4c1azqIRve0JmYyFqNGFbNq1UUopdi4sYu1azsoKgry2PjhLLE0yvcKupbyA+HTw/bdo/A1+P018PITUFKFUyiQ6uzCzfWSnH0mo75yE5om2W7B15phSYOv6iddqH4ekv+EUUN966vefj8zBIjHwNAhWQ5zz3iSscdvoy/gu5IoICRtYhKW7Po8C+o0qvcxc3cUNAw06AzVFVs2dVEoOGxpq+DSKxaRT65j6ug12I4fyENmllRKY8Fv76WmqgClL4M52R+g6zBw+8HbwLL1B/PnZ8+isaMexzXAOAS0CjqScOOlcOyh7+C6fsD4KEusvQSMFkLU4+sgnAV8mLQb/mMgENSIWmq+fB35sy6juW0DO2q76ItmWUsrUymnmBLOqFrEL9vGkHWiu2UfOaF8KV1mDJhL46JFWOk0saoqAMYGYEtFNUZ7C3pXO3Y4ihkKI/p7fdk1QHNdHOHTpYXrYjpZPE3DyPldA95A0BRC4Aw01iQ3bgQp0YJBEqNGYc45ipbZRxNrbkQpf1wzl8YKR1h/3Okc+ZubwFOkMxaZrJ/xaUIgNA3NMNADARzbxs7l0AwDd4COoRDonsV4fRPbAmPpK0SIF9skeyPYdojzzt5GVHsOz/mE7ysYOAlUBjt5G7nujeR7bbb+cxir7llL0bDhCCkHzXiX33EHh3/zm+/5vbzs89OYWJPglltXsKu1jylTyrjs0kM45JCadzewEHD0OXgP/QzXyqMFggjlYWKzXoxA7+4c1FotNi2KTRulICBsgk/+FlbeDV+7k02eyRNpv3Z8ZAQODb0532/9+k5CodcuVaGQzpYtPTiOL/o9blw5dn05O2xYkYTIv4xlCuhVPrWg+p0EwlMvg6ZN0NGA7nkUR3SYMJeyL10LmsRR8LU22NAFbi9ETXA1aDvO167e1eUHwpaOPZfRsvxAGE1D+cg2Ut0m3l5yrwXPIKRlMGWenXZknwOhLmDkoNC4GNR2/e5XACeNlIGBOXhIJAU7hCYL9PRFqKnKQv4vewKhORusF0EeSmPnBBavm0tVSYe/ydMqfJ1SBZUfcVL9hz4QKqUcIcRXgSfwM//fKqXW7edp/ccjlSjwfOJVXFx0ZbLD62Kp2sxs9xTMwDouHbqD5nwlntIZHuzG1HJso50jmYuVTg8eKYJvbROuKKI5kUD19WLYeVwhKYSjYNtotoWGIhiN4na0oYRECIERMFGe+9puB00bUD7xx1dKkUeQ376T9toxSCHQDA3HtZG7LYAKeforh7zm8ymlEJrmNwBVVVE6ahS5nh7aX33Vd8hwfE9CYZgI2wYU4ajgM6V/Z1l6Bm0loygusvniqS1E1izhf+cuQZo/ZOSJJ3Lo5ZeTap3EIxcopF6BkDFaV7yKUopwWTmBWAwhBJHycjY++CCHXn75a+ya3iscf/wIjj9+xLsex7VtVv7mN6y/7z4UMPGM05ky6xMEt/4EoQoIIdguhtAoahix7DmWn3EBXtCkWE/jeoK+cIJYppfZO/4J2/Os/ttkvjjzEpTwnSUf6Id5UbixgsF7tjd2WzjtfYk8T2EMUGDyHlzeBi/n/UyqxfH1Eg4OMahJW/B8lZnSd7rixUvh6ntg4zJItkLlcBg9Y7BTeVXe10jNdPrBDUBzQRmgDoL+hTBQdgZe+0gHctDXWEJ1dTM2xqCdlC5ccq5Gxg1Q9W5XalWgo7OX8so8O5N9dKdKKI11YLsG+YKOqUF9XS+g+1ZcuxH+KtgrgQzHz27mV484dPYNobRsGJ4LnUmYPAYmjnqX89vP+NAHQgCl1GPAY/t7Hv+XsITFeCg0L8rqAhQ8E11muU89QsnLazBbUoSGJghPG0VIlGHjEcDfaVZOmQIDR5m7F/ggHlVD6ygaexyrnn6OQrwISivQO1oRLQ1kSyooTfcMdHN6SE0jEAziahpOPr+nccXz/CNSpVBCoITAiyUoSIOWoko6o0UUl7vE2xoQykMCuUiI0RuW7flwA6uQGrCHyrS3UzJqFD3bt+NaFno4jG6aOJZF3lN0iVLCXoZspJpj5rVy1JxWRKyD4bVxXrh6E93JDNHKEShRwpZHH6V3xw60YBDXcjEiFUhdR2oanuvS17CNiomjAQ1JN26uA5V9CiJHg70Y8guAApgnQfAkEPuXqWxns/zxhBPYtXKln+lJya4VK9g8Zw4j5l5O49/vQ5TW4Ooh3HyeCjvDEbd/n5cv+TbJaBkuktL+dm544EoipPBsm+oFt3COHeTpoy4E/E7GpzPw8dwbt+LPnz+a++5bj6ZJpBR4niKXczjppFFomuS33bA0DzWan2nFJCzPwdq8b7KbUz714orSPYHxHUHTYeLhb/ij3XVXsbcGHH6W60b8fZsQUF3u1wiVAnMgw3McWPPEdIbMayQgC1ieCcLF1LKs7pnJcFOn/t0+BqlvM7X+UF6xRhKKdNDQUkyh1CVoZHBduPKS5YTDBuCBtlePhT4Ciu6G3F9J6Ov4n++s5Ad/OocVG2JICSfPhcs//6Ggnb4rfCQC4QF88NjFLsKEWWv5u+mABN2zCXdshov+gYuLkoLUxHLE7WdixuJMx3cPKBk9mvGnnsqGBx4Y1Nd0bZuxn/gEyfMvY9eVV1K+9mVUPguBIF5RCU5xKeGhtai1q7HzeYQQ5Hd3OO4WslYKuVssW0qU9LPDXYfM5enPXYErBApBR91IorsaqHllKenSKsjnOeiFBeR5zRo1CDuXw0qlyPf2YsTjFLq7sYTAC4awEYQCilWho1le90lmzbmLcUMbyOaLSG/KkenKEqsIgF6NEDrRqio6N2ygv6lpkBupB4MY0TCCThLVKbD7QWXJdksqJhez8pV7qCj+EfFwklQ2TCAgKYm/hGE9DfHb3sga4APDqt//nraVKzHD4UHdTNeyaFm2jKmf+xx1n/oC6x94AFQaPRTipB/fSuSuB5hyxdlMnVOOW7CY2rGOmqo4wjBwPY++UDEnLr6L5TNOpz9W7vMa8VVk9g6EbQWPNc0pvnndUWzb1s2qVe2DP6sdO4Ta0z/JzQ/BYxVQUr1nMY5IvwFoowU9rs83vKLUN9l9vzBpoOxZXgaNzQMHFwACKjrBCkNHt9+HVZyA/rRfRyxY/rwTE2p4rvdjzCp7kZjeS8EzWd59KNv6p/GTN7bA3Hc4W8F6ga+dkeeCWyZSO3UOmY5tdLUmCAYFt3z5j5x5yDroNSFUASVn+Flg4QlQNgTmQeRSEILhRfDL6yGX94UDPiD1uPcdH/pmmXeCA80y7x5/4HcUlMuKrIEp/F/W6M6X0Np64DOPIEoCyKDmOzucOpZZP/oOx4nj0QbcCpRSNC5axJZHH0UpxZiTT2bonDn8vFfyhx7F8IZNmLuaKNQMRQWChP78G2asW4Zs3I5nWWS6uvwjokBg0MzXyWT2rHZKoQAnFOa+Ox8lnSjG9Fzy5VUcd8tlRLduxixkiaR6iGd6mF7hsHSn3wj+OghBUX09/c3NeJY1SK5XgJKS1MFzeOFTt9L85FaihRauGPtnipq3k+2yyXYLSsZMR+zl5t62ahXZZBItEAClcPJ5hLAJlXgYQUm4WMO1XbSwzuZZ32ZN39F87wuXkewtJxA0fG6jppg2tp1g+U/BPOz9vdlvgT/Mm0fL8uUYodeKJFiZDAd94Qt87Gc/w0qnyfX0EK2sRDNNlFLsWLyM4K++SlGqgZCwEGYQXAfHU6wYMp0iK8Vdp9/CujFHAr4H4OeK4Ksl/lHnl17u4e9JF892UQpmtbbzzRFh1m3r5Y/ZWra+WIVyBUqBI2D4PBjziT2Ph6d8vuGi4e+wOeYd4O5e+EnSz/jSaVAaxHbCERvghq/C2i0+jWLGREhl4f4n/JPVs0+GLwZgWwE0oQhIG9vTKXgSTULnmAErxn2Fs522Hb/Czb9CTU0RwjwIcgtAr2RLUzW//8cxrN85lLHZ5XxO+x/GTdnmp0Q7BITq4IqLwfkTfgOA8IOhcbgfDPURH+n076PcLHMA+wEHMY2F/BPf9U3D6GhFK+TgT+sQORuxKeV3yQR1+PVKlPEU4objBp8oIQTDjjiCYUcc8ZpxxxiAUOTri8gPs0BYiFwAa/xBVAUssoWMb9BaKCCkxHUcIhUVFFIppGmiXNc/Ph3YwOWjRaSiCULdXQhdZ/wzD1Gy6mVypRV4AYMRvdvICZedPW+cDSIEQtepOeQQenfsQOg6Ukpcz8PzFNJzUcEgQw+vZMjxo6i75Fyal0CobAr5dDt9LY3okT4SdX4gdGybXE8P8bo6X5rOtpFSIk0POwOhhMnQWR6JEUW4o4cxylxE8ukRoMBDUChAUQwsR9DWpRhe9Mp+DYRGOOxvCgaOpj38EwLHU6yKVwol/okAACAASURBVDPBguHRKGZ0T7olhGDE4bNh3MNwwxmwfRXYFug6sm4syCBuvp9MuAgGxpPASQNDfGdTmge6HILpPLqUeFKwZGg1P3tlB8M/PZONV/gGCmbAV5rpt6HhKaiaAomBkmin6yvtfFBBEOCcIpgchEfi0NgNdUk4ZhbM/IKfIY79F7bM7L30eGM7fX3VHbYg6/nEk7AGQ423FwSbdm7g2qt+yvrNIWAKw4bk+N63/s64kZ2gKhhdt4sbLrjb38DeuhgSIyE5AZQDkSJweqH3ZxAfjWO5KDuJbuxE2BvBeg6Mqb4urfYum64+ZDgQCA/gDTGJyeREjmZ9FTnXI9DXDH9YDw9vReTtvTaFAqFrND71PJsPe4xxH//4W447N+wxjDXssAxKZQ7Rn2b4VZdT0dZMV8Glv7kZoWnowSD6QDZoZzI+2T0YJBiPk00mAbAdd9BXUDo2dijC0FeX4YYjeEIOZnfBgEZ/xuVfyjeD1ktyQIlGaNqAt5wHroscCLaR1S8z8qIzSc0+Cr2zjUhtDaYBxvAo2WQv3Vu2ECopQUhJuq0NIxzGsR08ywbl4SmFJkAzdTTTpHKCTfzwGnYlIxQHu2nu9LtrpVB4CmzXb7jI5ASWW8r+PH2afO65NL34or8xCQR8c9xcFhWO8vzxZ/NIC/y8Cqa/kc1TaTX811/huo/79bVEORKY0tPJS6XDWV45GeF7NXNNOYw0/aD4l6SH3p9D1/3TBekpQp7HoiGVPPuSg+PouBGfEqErCOuQErBjJVTU+Q03FTpcuR+8kKcE/S/2gfe3N06L++LiMwdqmgLodeHT+2gUDGBZLhdd9De6OoJUlAO4tLaHuPiqQ3nwtw+QSLSAVglIaGsGaYBR6zvz7kZNO14uTWfjBvK93RjJDIk1ilBaIGM7YU4O5n4dSu77SGeG/4qPgsTaAewHSCSzmM2X5Xm0vTqL9JdfIfdgByLn+IWO3ZIlBYdYRSVmOMKmBQv+7bhBZxG/Dp7Lp8z7INnByBtuYcjq5QzRm7BSvRihEJph+Ka2MOjvZ2cy2JkM7gBvMBCLEQyHqBw5ghGvLKL21aUc+sefkGhrRHgenqYT6WilLweOGjjiMf5l36fUoFO9M6Bao+2WalPKpzlIiRNL4IQjFD3wBzADVO/OeqWkaupUQmVlWJkMUtOYev4FtLr1NLZCgQgFrQikjpOHQFxHD2q0rDKwHYiF+9jUNIm1O6aQ7C+lJN4DKJSCcKAf2wkig/Pe61v7tjDhtNOYfO65aIEA+XQGkckggmEabv4N8fp6NOCmrreQMSurha/+AqLF0NcJvR2Ehk/gsG/8lF/UCH5cCX8v6qfmL7/hvjPP5P6Lv0Imm0fuNZ7SbALBXgpji7HKFe4F4J4JahLYwneiDwn4RAy+UgI3VMDfhvi1wY8KvlTku4F0upDxoN+DQ8Nw4b55GQOwfHkLHe0Zystcnw8voKTYJZXWeX7pZL/5SqV8a6WKQ0EM41+Nl1VBke1Lk+/rJSYLVC7zCGQVbtjDdRQ80QWPv+Tbdv0H4UBGeABviRGGyZ0jhvI7y8YdVo+T7sPOdftFGAV6MED58NHYuX0QRlQKUv9FidjG+S23UXllmrb1DsqFnu0Sx7IwQlGkrqObJq5l4eYH7JKkROo6uZ6ewU7UQDxOadjk+NuvobuoDCtWhJ5JE+zpQivuJ9zVTl6XZPLuANviDUQRpaR8wgQ/67Es3N3GtFIipEQCbk0dbjBMUNepzvUSEHtWJ6nrRCsrOWvBAmLV1fz3tRtw7buwtWLCoh0XgxwxQqofTQe3UCAxpJyqkiSbmobwj5dOpryoh8t+8XP++/PXUV+1lXBI0NVXxqau73PQnP1L0JK6zom33cYhF1/MZY+/jBUMkz3mYxDwO4QTEnbavkrNmwpcj5sF1z8K7Tt9Mn1ZLTowA79RacGXv0j3tm0E43Hctjbi0zfTFq/DBKLxXmoq2mmT1XRJk1AoT2abhpIS72AQCci/4HeMXngUTHoXMm77E0EJt1XCFssX0B5iwBjz7SVd3d05FEGgn72Xds+DrmQQoleD+D6goFzCuK/ChiVQVAFSg1QSuzdKul9QMrxA6AULFIi4nzE5loWWCMOSbpjfBolJ7/FV2H84EAgP4N+itLKcKccexY5nniE0fTqtK1ZgD2RA1dOmIQ0Dq6uLsaec8tYDOavAawMMFt2ew7UhEBFYWYWmC1xbYudyPs9ugOQu8OtU5RMnkmppob+lBdeySAwbRtHw4fRs24bmuRS3NkCVTc620OwCRW2+PK1t76Uoret+NrtbcFvXSdTX01Fw6U2U0X7kfHK6waSH7/aNf4UgmEgQ3LTaV68JhTB1jUIqRSAWw3Nd+ruSdJxzId9U1dg7Ycu2dmaGTNCKsN0Eht2HK4MoPYxViGHGBeM/MYNI2WE89vA8Xt0xHDNo0t4X4nM3382McU2UJmxqquu5+RsfjgMbIQRlY8eiImNpsyG+V8Bz8SkCoX+3YGsa1Ix83be3PfUUPdu3E6/ZU3M6d8l9/PikS8iHdWoq0nR4FRSUgZQKUzrImhSppjiqW6CGgaEUJ45vZ+GTTbQ3lnLEEcMwzf3XaftOIQSMCfhf7wTjx5eBLMXzOpHSF9JWykMKm0lTZ4GM7X4n/4/zfgAP3gbLH/E5HCOn0jXiWDbeey1HfKYB0YRvR6/AyoNmeCB9Ti3pMvg3YuIfJRwIhAewT5j73e8idZ3tTz9NoraWvuZm/0gwncbO5xl+1FGMnj//rQdxG0BEKWSSdGx2iJZLPFeS3OqiNIURjmJlMliZDGY0ipVOY4TDVE6dihEKERg3jqL6etpefRUjFMLubyLTthMpFcFECKcnSWiAcwggTfM1YtzC8wZJ9AiB1HU64qXskgHCXjeOppMaOork6IlUbPQJ8J7rgudRSCZBCOrHj0cC6Y4OlBA8cfnNbBg6Dn39VtxgEb3z65n6ogM5m0LROGSuAT2XRHkO4bJqTvzZzyk+yNeM//ZX4MhD4dllENEyTJ9oEIgMpboMRg378JVgzknAf3X4fnf6gNNBh+PXt4LvMGbvWrECzTDo7y/Q2NRHJm0TiXbwxY3bafjG55FYDC000pIYxXMUUyBAIFogNNqCTABbeNSOX88rf1/MCgX5YaWEXkpy8WenckqF+RqZtf90jB5dysdOnsTDf3eIBNuQIksqbXLYoZUcfMQlr/8PoSh85ho480pfWFxKyu78BtO7WuAeoM8/HbHK/V8hIwg4edDqoHT0G85BKViWg7t6IeXCcbEcY+Kv0ii3ESTIZKYwklEIPlwP9/+hx+QA3g3MSIRjb7yRw6+80s+I4nGalywh19ND+fjxVE6d6nsCvhVkDWCgBSYg5DKU5xEpBTsr6W8LojwLIxSi/phjGH3yyay6+w9kcr3kQw5gIy1FX1MT1TNmMOW0BI2LnibVqhGIaUQrNfpaPJJbfR3P3fU+5Th+x6OUIATeXtIetqazS5rIfBYtn6Vsy1qqVy0hH0vgGSaykPdFuAdUaPRQiKYXXmDSOefw8d/+loVNnbzaaRDbuQUpJcJpp8+byIbDTmDiI/fjqAgpUYwZilBeX8vnn78PMxIZvBy6DhNjG+h56SY616+nwTAY9/GPM/ySSxDiw2fydmIUGmy/qUPgZ4PHROCyd9GUEh8yhFR/js0t2YG6lqC5sY9QQwsTnzQ5Y8pmeiK1PKsFWG310q2KKCgDNIGRAJkqEF+8lsrqGJvnTaJjbA2W4/HdnRb/UzC5tdKvtf1fwXe+M5eZM2t58IENOI7D/Plj+PgnJqDpb5EhG6b/df+PMTcvpVBeS1OpxS/mfIVFVXMJujlObbifL2/7PVo2DvO/AcHI64ZRyt8o/bzbfzbA4+lChrmqneOKcmREhqd4kiRJZjH7/boE7wgHeIQH8MFBudD7WXA28ewPetj6dAfRSoEQYRxvIr2Nzcz82tc46OKv8GShjYUbFhC59HZMK4fK2ahtPWhIioYPIRBu5phvTyDb7bDop9uIlgfwnBSdWwTpdmtQiDvf1weeh9R1AkVFuJaFlUoB4BWX0V1SjrAdUuVVxJNtWCUVOFKjYuMqgoUcynEwYzE00/R1Ti2LeF0d8++8k+ufWMaTM0+kLJ8GwMnn6MiX0FM6jJm3/4Fxqx5EORY9pYdx2zNXUT2imLsbe3nklU30RBMcVBan7OKL6HBGEI2ZjDA2UOhsYfhRR3HCrbfuzzv1luhz/bpghcY+61++GVK7dvH9qceQz7uIYIS+vgIBt5+kVsXOSSfx2//9G7tC1WSNCq63v01BGRiqQEabiC40uh9aw4zn1rF9eAVbT5mO6suhK0VQKKbN8u0Qnhj2LhVl3gbynq+fWqK9sVzchw7uLsj+Eexl8MJS2D6M3swQPjXry3SbcUrTXbhKoztWxryO5/lheQGOuuoNjytW5eG4BrAHBDgMrR9N9pH3Apw/5HGqgz3k8F2gz+TT1PPu5f/eLt4zHqEQ4jjgU8AdSqlVQogLlFK/fi8meQD/4RAaJO6A9E847CuPku7op329jjBqUG4/U849l8AXLuKkRsVOJaHoNPRfzue4B39M5a2/RlSGKR82jmjIxU7v4tkbt3DW3YfQ15xn3YJWhJAI4RAqLfWPNQsFnxoBfh0zncYt+F44wjDwaoew+ZjTyFXXUfHPJ4kmO1COhaebSCmQro2Ln6XslncTQL4vxXdO/w5Pq2l0PvgsuZFhKk4agSdyJNw0wnZx4yUsrb+F0cGNzC7cTWmx4oc//x3t//tLhgoY5nm86nycNd73CQUMZBLCMsfnSu+k8YUX6GtqIlH3wfja2ApW5vwFfHKQf2uVlNBg6ntUgotWVbG87Awmdz+Olk0ScG3azBG8HD6W1LYAjz40j5PmPwVFSf4/e+cdJldZt//Pc8r0md3Z3je990AogQChN4MgCEiVIKI0EVEQ9RVQXnlRxFekN0GR0HtCr+md9Gw2m2zvszv9tOf3x9n0AEmk/XxzX9dc1+7MnHOe85w5z/d8233f6v8Vt6R/yVJnPKqtU6A6+Fp7aDIktQNKkJbjUpo5kqQQ2NJtsViVhXF7J3ix1zAk/LUTnom76g/5qstmc/S/wWYjJfRJ5gKwIgvNFvTX3Z7DfUXchreTEDPqOdM6joBscAOVY1IwJMasVVPp9ldQmq4DzQLHpqysnXdLj6Uu97/oZ9eAtmto9M2ESzUX2hImFxkQNg6w0RAEvD2oQsHC4hVe4gROYjBD9v1EvkDsS2j0+8DlwE1CiDxg3Bc7pP34j4aSC5Hf4gv/hlMfs+lcX0uytZXowIGopeWcvBnSMk1ITaOikg2ovHbclZzz1keEQxlMAEdH9ylkeyVNi2McesVAxp5dQayuAfzTmHvvZjrWrkVaFtVTplB+0EEsuPtumpcsQY9EkNksqteLWLeKtu/fQPeg4eSsW4GyciFoOh5NoSLPS0dSIG3H7QW0bRzTJFBYyMubq1irjUG34yjFkt7lMVLrl1ByyQDMkkLCsRZO+ugqosVFREv7k2xrY938RbTf/1dEfgGOptOW7cfKppMImQ2oGYmqqmT8RTzRdSmX69eQbG39SgxhrQFXNENnX02RBC6NwvTcryZHKYQgMGA4nxQPRDd6WLoyBt4glmVzZP4mxs5dQ2qljjMhn/R5V5LVxjFO1+m0ISMVer41jtbuLIrt4Ch9J2A5eMpz2WxCibZzg8CXg7s6XaX5Is1t8k848Is2eEDbNyM8M+7qCjZZUKC6+diePlokCRwZgN8V752nm3HgXz3wuw43dPm/3uuY6Yxgo/wWY7RajhCz8AaSrBkSRItJQLgSGhEvQgmjOTqbrQL6pR6AyO277N+7hfQJ1yB6HI2wAgo2irDI9P2vChUvPj7mIwYwcCsb1deJfTGEcSllDLhOCPHfwIFf8Jj24/8ChIIQCgVDh1IwdCgA7ySh17QxY3HSYRPh2Pi8Gg4KGwdPZPSm95G6BCUCwo+UPdimw6oXm1j8eC3JDpP8YauZdM31FI8e7YZH+8r8mxYuJNnWRrqri6xpukTeQnDcLT/m/at/x9qDjqHf3LcIZRMMSPUQVh2s3ACx7iRmKoknnENOv360tiZZYw8lP1dipyWlm9bQVj0YIynproNyuZkTH7wNr6oQb2pC9XqpPOQQVs6chdR1hOY+32/uGALSQcFCooN00JIt9FJJs3ArYr9sSAnXtbqtD1u8QEvCfd3u4n3g7prkvwRMnz6BW255n2g0Cl4Tw7SZVrSca4YtJJuTg5H0oLaE+WnTKNpCGhnccn5NALlBsiePJvD4HIwRpUjLxpPjJ1ASJmbDCB+M6POeli5t4W9/W8Dq1R1UVUW47LKJTJnS798ef3LjSp5rj1Cc6UEL5kBeCSFVJ+PA4zEYV7J3+3szAb9sh4gCZRp8knHp4kZ7oEh3r9vbSRgRg4v3sF2kx4ZLm9x7LCPBg8GdcjqdsgAkPGnAAOU87lemM8Szglc4FUzLVQ8uDCABG4VKPQ3mxt0e4/gQ/HeH63FmAccO41XSeFSDEm8LpoQsFkH8BAiQJEmCBDnfgPLTfTGEr275Q0r5CyHElV/gePbj/wgcy2LdK6+w9qWXkFIy9NRTaZ90PLW1GUR3itxREunY2PEUo/7xDypffgkz1U3S34lv4GC8wSEo6irize0sfqIDf06AcMVo4i1dzLr6ak65/35Kx4+HZC9kEjQvWkSitdUV9RXCZZiRkkBXO9Pu+ClGYTEjqvykNzWTiGfodBzKSnP51jH9eH9ZDx1pFWlZpEPl6AkPihAogQCBRJLylQvp1XKofnwu31bfRloWdt856n4/U371K16/6dc4mo6CW71qx20srxcHDWE7mFJBFQ5WJk3VyScQKPjy+wfXGdBoQuF2D+SacD2ol+NfjiF0bJtEczN6MIg/6q7i06YNJZ02uf/+RRQWBmjKZrh0xCe05ZdiKR6aKqq469if0uaJYBtZHI8fB7AlCFWFIcUUXHgwUjh0jyxF8WpkEEQU+GOx296xdGkLl132MpqmEol4qa/v5bJr3qDsuqPRxkQ5Ze0sTjKaGThsLIw63C0e+TTYFix71xXq7W4h1tKI/Nb/omV7INEF3S0wcBx+RWezufdzdF83hIUbYtziCfqAzZZrCIVwezafje+5IXyix73eFhAQYEmbLiePYqUVhBvLrHX68ZD2Q6YHHuJR7Ve0llRQEExiqYIOO8hR+sf0FytAHb/bYwz1ws2F8NM2cAMpOjGzgG+XzcCrGjgoSCdIoZqP08f66+NLjlnvIT7XEAohHgMulVIaAFLKF7f/XEr5v1/S2PbjPxRSSt6+8UZq33prK0flB7feSmfxC5iX/w8BjwejUeCtzDLu4b9R9cYb+HQTYUnM3gRNCxYQKCri8Bv/i1VP/5NAYTWa332q9OVAyrJYct89lB5cCIvfBASBhjV4sHCkRAiIBFV8io1lw7AxxaxuNKidU7eVYzQY9NLW1stTL7URrqjCGwmhqCpV1WU4TS7ZsxBi6/iVtEW16CRUXIw3HMa2LKxkkmNuv53ujRupHDWC/IWLaXEiOFk/RcllrPOdgKEG8QgLYRtkRBCfR3DOb773lVwHQ7KVgWR7qGKbrNAXhVYLnly8lsVvvE35ykX0q11B/ylTOOJXv8IbiXDOOaM588yRzG/Lcu3qDShPPU02GEIIwaujT8ZAEMr2EvdsS7rJvpcQgsSQUkb7QZHQYrue7aNlMLzPG7znngVomkpenmvdnZCPBkMh56HnuH/Eq+jZJGkEvR/+k0j/EXDlPbutjMS24Y4LYeFMt7nOzFKseQkaCdJ6AL9jgpGGrmbi0SqO34cc4aa+QqTtz1EXLvXaFihAdi/qHGclILcvd2dLiEkffpFGIHHcWSRfdDHTnMp14Y958MBh3NWyjA9SPvwyxXc8z3CSNouE2UDId/qnHmd6nhtd+HlHN9WhlQwKrUZXJLYTIGFFKdA8oDokSTKM4Vul275u7IlHWA/MEUKcIaWs2/KmEGIMcI2U8vtf1uD24z8T7StXUvfuu4RLS7dK+3gjETbNm8Oo515l7TnTMPGgLOlm8FMz0LIZPJqCNB1s20SoKtI0Wfrww6Q6O11lBNlOoKAAb04OnlCIrg9mgV64lTXDdNaRq5t0mxnyfTY+TeI44KAwTGwm6LN403CLYaSE3t4MKc2VdVISGcI5+fhyc0k2r2F4YZiV7UEiXhNh2/RmBB4sBhor6G1IIxSFSFkZqs/Hs6dPo8STQhcOhZYf1TKZrXyLMuNtqlIf0RA6nKxQQAfFTnPOsI/x+yfu1XzG6upYdP/9NMybR7CwkLEXXcSg44//3HaWoV7XO0g6rnQRfeeelezTAv5pmJeCKzelaWu3EROnIg48lmG1n3DqI3/ASt3ASXffDYCmKdxp+akLFaFJGwWJg2BztIqAkSKQTbK6eMRujzHS54YSGywY64Or8+Cg7dom1q7tJBze5uXVmaAFNH5mzkC1dOK5JVgS1iI5sG4FvP8UHL+bpe3Dp2HOi6B5XJYcM4tmZfnTM1dwyQX/JONY+FDozViEFDhvH6J+gz3QYLpFSapwDVinvSORQZcDZ+8FD6lfcWnb8lXYbIJEUOMMZqSyAg8GAjDQ0IUCOX+lUoc7ipKkuy/md8lLed44hZeNkxFCZ7r9MhcHuhHq7t3RA8MdnOB5xvXYpQ+wsBH41DgBzUcaGMowDuPw3W7/deBzDaGU8iYhxFzgLSHE1bhFTNcAYeCuL3l8+/EfiPbVq90KTGVbF7YQAl2Fso/m4NNLaBtcwuA3HkJPJHGEB9WrYskMQnHV61WvFyORoLe+Ht3vR6gq8eZmwqWleAN+KjwZiBZvDftU9i9i7cosBSEdkmA7AkuoBPwK6bRBdS4UhAQdSQHSdYcsy30ejzc3YySTroGrqOCSqZL5yihembmJdDJDRaSVUwe14WkQmGmBY1mkuroImTHOGGyiq4B0kI7BqtgGWoaOZb59AE4qi4qNIg36Zz9mWPo1Jp1xx17NZW9jIy9cdBFmKoU/GiXR3Mw7N95Iqq2NsRdc8JnbeoQbyvpZG/RabCUlnxKAo7ZzhrpsWJR2w6aT/NuMJri5p2UZd6Ed7+vL220Hs6+3zGpvI6enE9XjRQKrB45h9GHHo300c2uFbJsFi7PQHi7ivcFTmbr2TVrDxRTFW9mcW0k0E0PZSUhrS33MAX74Ywl9nvqu51pdnUttbTe5ua41STpQ7ulgZGErVl4UP1lMO0zCCiEDOYiFM3dvCGc95B5Q1VzPz3EAydj6xfx15k08fNDFNOthjuhdx0WTBlC+D+0lV0Th6la32yikuKHrLts19K2We/hqHS7ZCzq5s8JukUy55lK4CaCNYixLp7+6iQApmqni53ke0Ivdjcw5/DlzJa/bJ1CidKIoAkNa/DX1HSp6NnFc3u4HsFIspr9HUpcNkpKA7cEn8+jvMTlLnE6QEH6+ogT0HmJPc4QfADOBl4E24Cwp5Qdf2qj24z8a/rw8N7ezE/IKQmSNIJnljfS8uppFbVBmR/GoEDDd3j8UBce2UX0+Ei0tW/XvVFUFRaGnvp5oRRkTDijZagQBxo+rpG5DK4Zpo3n8GBkTaTuMKtdAmihKnyGM7yYmKKXbR+jYxDfVUj5hHLc/dAm/txz+8a3TEEYK3e9HFk902zOEoGneHI4fZiCEQiJju5yNwIjcFO/Uf8hK35/xe3vxpRtIOLksCp7DhsKTubj/WpAVsIcN9SuefBIzmSRU4lZkbOmfXHT//Yw488xddAR3xmFBeLYC3khCt+V6UQf5XU8E4KVe+K92N1Sq9eWm7ih2v/dsL9zR6eaxwO2du6tkR4qwdVm3glJPxnH6rocAdNPgkyETGDznTdJdXeRUVtJtg68vR3nLcb8mqfk5Yc3rnL3kKW499pcsLRtH2rNjuFIAIQFD+5y9T3OCL7tsIldd9TqqKgiFPASEwUFXtuF/RRJHoggLr96JqhgIQ3e9vd0h2eseNNULlsE2PRPJwYuf4+B170B+GVz78La+h73EoUFX0eOebpd7dIQP/rvYzRXWGW6496jg3rH5TIu47RfPxd1heXBbMwyRR42Thw1U6nDediTfGXJ5yZhEkRJD6ZtYj7AJiRRPJCo57lMIwdtoJ6x4mNCnpCGBgNBIiixevN84Iwh7oD4hhPgb8AmQAIYD7wBXiW8i9cV+/H+BqsmT8eflkeroQPZp3aU6O8krLeCMG75DTX07aSeNqjhsUIcjpUNn1ud+17ZBCDx9C7zq9RIdMMAV8LVtNJ+PEWedRVFhsG+hchGJ+Dnj2GpGHTaOkBfK8zQOH+6jsiof1aMjhSBjfXooUc/0UCB6CMoUgxrfhefuRBOScH4OtuEeRwiB5vOhqCr5uoFHg4zRZ1gFyL7FZFnmYAIijqHl0qoPJ+EpwxBBeu18fnpHMfM+/gPIPSAxxxUB3l4LEFxj6Ng2iZaWPdpHmQ4X5cJPClyF+C1GsNaAy5thg+Hm3Rosd2G+phWWpOEPHa6XUqS5r7gD17S4+bkt8Ah3IfTmRnGcbbyvjqKgZVMIIDrAbayu1F1v06eA5Qlwywm/4ZjL3+L6b91OTcEgkr4w20MBShUYoLvnMDsFHbvhVQc49NBKbr/9WHJzfTQ3xxk/OYXvgDw2DhtGOJbAQcF2VIJKD3amBw77zu53NHqKmyc0s7tKbkjbzRsGc2Hw3oW3d8ZBAXi0HD7uD4+Vu/2Ip0fg2gI4Mbz3lHaagN8UwUuVMMoHw7xwTMD14od4oFKDXxfsuN+UfiwWKqrcruJHmniEpFPuhk7I6YD4LRSkZmGay8Cuwy9sAgrYwkJFxc8302zsyXQuA4ZJKX8hpVwrpTwXmAPMFUJ8M7oh9+P/K2g+Hyffcw/RgQNJtraSbG8nt7qa4ff+jFmxeYTKAPITLgAAIABJREFUHMonKoSPKKG5aARrwofTLoqx0BCaRrikBM3vx7FtNI+HnKoqyiZOpGryZHL79aNg9FjM4y/D7u6AeDdkktDZTKiiimTRMA7pr3LoIJ28qJ9sT4yALklmJQ29u78dBIBpkM46VOer9CsLwluPwbv/ZPS552Ikky5/KSAdh2R7O9GBA7bKOW0PKWGzMpxSZy3RiIOi2Hg9BpFgGk2TeHT4/WNHIdNv7NFcRgcOxEildnjPsSy3Ijb/3xPk+1MndDtuf5hXuF5Eps8reaBP6FjEM6QaYjiGTVR1c1nLM9v2McgD5TqYhcXoPj9WNoPpOFhSMuLjWRzwwx/iDbsGLqDAtfmQr7isLH4BKW+I9nCxGxJnK130Vq/Gr4JHgZ+1wlkNMK4Wrm/pi1juhKlT+/P8/YfywZVtXHXkUqq1FG9+5xRaC/MJdXaR29pGsL2LjlGTYNLJu5+UI8/eGjrfEcKNQJQMcI1kZ9O+TvuXimoP/K3UNXhtjttK4QjXCz1tp5xj1FNFpSePuPSBzLovodKjjOCw4E73isxA7FLIvMD4TD1SGqSdeqT5CabTScppY7wzBO0byuq5JznC+3bz3h+FEEuA14BBX8bA9uM/G9H+/fn244+7XouUWKV+Zogn6aqV+PwauqLgeBXMYXm0LpVs8JRTlusjmKrFX1CAY1momkZOv35bi0LMVApLKtw302L2siyacR6nVjZy1cR6wpNPp7PyIDb+4Eckiw5gjLOOkNWDZaZpTcAH9X4s013B4wUlrJtyEvGiMspXLGD8J+9xSEmW6jxJYchC6doEqSC8fj+D7viA2MaNLPv730EIpG1TPWUKE75/EenLDkBTTaw+R0gAmtdDTraTmFZK1rAI+1OoqsSyFXTFIhJM09xZREfb+xT2m/a58zj63HPZMGsWmZ4evJEIjmmS7Ohg1He/izeyF9UUfXBwaKSRTdTRqHmI6oOx7DxA4rGyeJDE8NGZsmm++2Nq3lsPAhSvRv9LDkY7ehiZ7Wy/EG4o9YpmjfYx47E7O8nG45y0fgE/vHI61YcdtsPxz85xJYj+0gErDTdHlnagxnQrJ/MU1/vLSOiy3EKSBtMNvyp9ROB/7XY//0vpTue2eTWJn59Cpr0V7fgRBMpsCrrTvDd1NIWdCfzpLG2VOfjvb+C8s3rw5+0U+5MSXv4b6N4dog2AK2Mk++Lf7OQpfsMw0Q/PVMLMhJtzPNAPhwfc+d0eQsANxSVc2VxIq0zhE5CSQfI1hYtzd9pp5l2w1oDTSaGV5NvWBpb4h1LrqSRoxjgs284o43kI/BgCn527/jrwb3GNCiEqpZT1X+B4vhDs5xr9/w+LWcR85rH4Ecmbd/eSW+rmEG1sQj0BEi1Z/nHXgVSPHUrHqlXowSBCUXj7hhswkkkAhKozy5zCZqU/BQVBHEfS1pZkwoQS7r//VDa+8w7v3HgjwaIibNOge/E8UBSSGYdQSQmqz8cyU+W1G+7C9ngRto3UNMZ3r+WxFy4jZCbx6Jq71nl8buXgjHZQFNLd3cTq6ggWFhKpqACg9+3nSNxyHtJIo/QV+DR6Knmk+WjeLvpvurMBMtkUiiIxLZ3BlY0U5vTS3evl7XvfIVi4G8WA3aBxwQJm/8//0F1bi+73M+rss5nwgx+g6nuWpEp3dVH33ntk4j1sHp+labSFEAprM5KEVPikZTJig4M/G8cBWsIlTHp2Oc+83Ym3IIhQFUTWxOzJ0O/mk5g9rWIbzVYfTAkL0274dLR37zhKP0zCT1uheKfH9nrT9UB77R1DeoZ0PcaFA6Fqu+N0XHUs9orZmJ4gmVw/b9w4FSltlKRDryeKlaPhr0kT+vUiDrrqKsaef/6OB2ypg9+f5UYYGta6723veqoq9B/ryk3d8K9vnnzIPqLGgBk9blvHBB+cEYGC7a+FTEPn0WAuxpVJ3vpBX2NGELxHuKFjpxNyHwD96yEk+8K4RrfHN9EI7sf/n7CxkEgmTAsw96kEPc02gaiCYTjYCcHlVx3J6BMnAeygXfe911+nddkyHNtm7kYPdXcuIlwkaKEJS7HwlHpYuKyB1as7KKqowHEcpJSku2MkDdC8OpDFEwoRKi3lw1N/hHAk4e52FAGasKmvHMbzY8/g4mVPIpS+qlLLBK8fulshvxR/NLq1QRygY+1anvnpzaQ2OFSEVXTFIeaNYBdGOe+yMUwdEuHWe2H1Bh9+T5pB5U0U5fTQFotw6qHvE4yetMdzV37ggXznqacwUyk0rxdF2/PbumnRImZecw1WOk3WydKr9hI6cRx5//Vt4ktKeP+FAZjtGiIiyDsIkn6oXLCURU8vggEj6BUKwgF0HU23qHp9OaFvV+xyHF1sU4GoN2F+2q183Nm47Q6T/G4rQYflVnu22m4vXED0OWA72RsFl0JsSWZHQyjXzsfU/Agh8PdkmPznD1h05miS/fKwFZXQojj5z7WT1TS61q/fdSCpXtfzKyh3Q5+ZJFt0FhACQnkQyoELb/2PMYLghrdvLPyML6T+Dk4rW+diO4/YnYU0WDHQ8tx3Mi99bYbw0/DNDNjux/85VFHNIhbij8IRl4R58ZYYGxdmCObBT06AAzLvM/Pqf1A8dizDTjtta9hK83opn+QayH99NJuMyJAhgYKCQJAVWeIiw+qmeoYfPZ7ySZNonDt3q0G0sllUj4dgYSHdkTxSJRVEs0myGcVteUAQSMV5e/hxXLL8n30dztJdbaMlsF0ByBYse+IJXrv88q0tF2syAs0XRFq9HHnV9xhy2eVMUODUo+C+f2V5/IUOkBk6eoIcNWEJ100ftFtS48+CEGIHiac9gWNZvH3DDWSESnt+KabSiS4kyVeXsSl4JG8tOBVTN1H0DLF1eTTODiKanqK+o4OUDKEkmvCV5iDKctyF36uSbYvv9li27WAg+HW74L2UW7xhSzgtDNcX7Np2sT28CvylGI7f7HqAKu7Lp0K37bKYbKGrlNJlT8lXILjdPqWUpAzwerct13kNvRz521ewgh4W5B6DYsit81I4cuSuAykbBFtafgaNh+ZaiHeBbcLQg+CMa2Hc0btvxP+SkersJNXRQaSiYq9/B/82Mi+CUgV2I+7sb2nE2QIHnDUgDwKhgUx+tePbA+w3hPvxlSNGjCUsppkmcshhPBMopYxRjOGJFxbw2m0pvMEs/cbYiE0ZHnygCTN3FhX9C9k8ezYrn3qKaY8+Srh0xyTQ0OFREjJBDupW4U9FKkjHorffZoSYwLG3387Ce+5hxb/+hWNZaJE8PGXV2FJBzaSRikLRyNEoCiTb2pDNG7GCQcLxLELz9hk+FYqqoXKY6x1sh7aVK/n4D3/ATKcRquqqX0iJbRhkc/P4w5pWWja5ua8fROHycyOcf1qETfUtFOTGKSk+HcRn0Ht9gehYs4b27l5qIoVgQVBTkJrAUDUa/r6B0MEZdCVD1s6S7rVINsURrRmETwOh4diSbFOMgAB/RS6JRBbvCTsa8EWLmrjzzrmsWtVOIieAedp4hk8biaq46hDP9EI/D5z7OY3nCelWpvbXgT76MSFhvQkNxrb2Dgfw4hbobK9DKISgvWQC/Trm46gBpFBQFfB4BYsaVGyRQmoa6c5O/AUFDDrhhF0Hoagw8QS3UMoXhMJKCOdBxVD4yUPg++orIq1Mhg9//3tqZs1yVVJUlQMuu4wx55//+fqgXxikqyyjVoC9gd3mSGUK7M0gvOA95isa155jH3Wl92M/9g3ddPMsT7OW1WTJ0kgjL/EitWzgIOtQVt4dptgLJdkkFR0ZfF0xQDA/Oxp/opVwSQmpri4WP/jgLvs+ZGopOeWCnmYHy5AYaYdYk8OwI/1og3oAV2D40OuuY8K9L/JJ+Vm09KrUrWlg6UcriS9fzQG6RcwXRPUGkdn+dDccRMuyco5eOhvoq8gIRyG/FC64ZZcQWM3MmVsrSJU+70EIQSInj668YrxtzRRr7o13ewe8EIdwEEYNK6GkZPBXZgQBpKLSZElU3KpQxwm6gruGwHB0dK+DowrI2MQaLGTPahzFh6nqUBwGKXEcSboxRqY1jpLjJzptFJ0yRooUq1a1M/3y15hdE6M7L0xDVtLxwMc0Pr0U6GNNUV3Vhs/DBsMthImobsuGQl80UoEr8qBUc0OlJYorJXV36Y6N/wBVN9zFOrMY1UyjW2lUM81Gygld8HMUTcNMpRh00klMe/hhfDk7WebmWvjtNJj3slssE2t3WyXOvB5+8uDXYgQB5t55J+tefZVAfj7BwkK8oRDz7rqLunff/dxtM1l49X345Z/hL3+PsXH9K5D6J1i1ezcI7yngdINSCVt6BCVsi1kr7t/2JvAcDp6j9m7/XwH2e4T78ZViMYswMQnhlszr6BgYzOYjcnpLyfZCSY8bbnJQMQybgGbSYBW6ORrbxB+NsvmDXfkcooEQFz0Y5cP706x+00DzKhzz4wATzlfJY1sFYCplct11b0LZEdhlI4i0r8AyLZZ7B3HHsH7cLuDdB6B3GSCD5Cte/mz9NxWHHsjk6lY3LDbmSAiEdxmDlU67TDeKguM4W41hrLgSbypOW8Eo6m+eSXJTN75Bhfz5rPF8+6h/r81hX5EdMIRUbiGBeAw7kosjPWSzuXjYTF3JkaR7E3g8Ou1rPAgpwXa5KbVUBiNchKhSUWIpZMbEPGoY6ndH0VM5m98ZG6nUJbMeUqgxFNT8IEJC1quj5ihsfnop5aeNRvVq6MINbzZukUz6FCem+FM+U4GTwnBLMazIuM8po7y7D7UWjRmD/vBbrHr8UZJrlxMeMY7h519MbnX1Z0+UlPDojZDsxYyUMC8epMNWGNLZwPBgDmIvQqEODq2dS8is+xCPHqJw+DQ8wc9KwH06rEyGNS++SKioCKWPoEL1eNADAZY9/jj9p0791G3TGbjsv2DVBtCVLiyjhX++WM3tl/+ZKePuhMDlEPgU9ky7HUiD0pcL9h4OxruuoWsMwBtp2ITLHH64DgdqINzQ+Kqsj9daOxnoL+akEIS/fgUmYL8h3I+vGE007sI478FDkgR62MHv18l2KXjZRgiddnT6+zr6vi2wDWO36gwaGlMKDsR74xxOuTGChoZBFhOLCWxrcJ4/v5F02qSoKESGCjJh94aONcdZ+u5GLhk6jmWroH8xhFTwKhrJdAE3rb+MWT8Dz2c4bf2nTmXNCy+QU1lJbNMmHNvNSOnJXjYGhrHsYz/C04ga0OmZW8fCeZtY8fdTGTWq6N+a131BRFdZdulvKbr1NrydCUr9XdhWhuUHnUraFyU+28aTaCFJMWgBhLcAJd4MikBt7MauzEMNerD9OvLc8RQa6wjWbCQ+wE9NUFCzvolwro4tQoDAI8DwKGSTNmZPBlkYYnmf8Tq93mWmubEADt+NXZkccL2+FmubWka77RrPQwJu4/6EPSAsiQ4YwOTf3Ay4PZFPNmZ54LaFtHywgbKIzvTvjuCC04aibC8v39EAzbU0qeVc/nQ1TXENywKFIqa2vsvvZx6Hrn/+im5hsfydn1P+wrME+sjfu7U/4J/+v0RG7CYU+zkwkkmXqnAnlibV6yXV0fEpW7l46R1YWQOlBQbCWgWoJDMBbn7sCmaO+gla8i/gmQLadt1xTgfEfwPmAvr4gQAJ0gIcaNfh7yGQBuRkwBKg2ZBQiIcjrLBGEHbeYYpcwS86Z/CPngAPl+1Ugfo1YX9odD++UkSIYLKjNo2NjYJKUPfz/e+Pp1Pkk844gED4gpi24MjAMgjl4kjI9vYy+nu7V2gYzwQOxe1NS5AgSJATOZlStlWamqa9CynIFmSzFm/OgYgH8lUbb8cmWDuf4KZ5pJtbWL3abV63JKzOuhRi2++r4uCDGXzSSXijUSKVlS41m6rSddw05pSdiRLw4ckLoPp0lLwgfgH33PPltfr02nBbO5ywCaY3udp24BaPPPXgQmr/ZzFvqMfypjiWJ62zeO2Ua1gybTpT1/2RA3v/haOGsPDj00y8ShfgYDsC2ZHAs64FbzyN7JePuPkl6k99lWeObufZwxtZdl+c4BAfTjKLIkxAopsWTsogbTvMfnoZ7/99EfE1rQzRJUWa2/t3XSusze56Hh4B95XCwX6oNWGt4TKi3Fe6d+K0W2BIuKzO4tofvsSSJxazuTvD7NpefvCr9zn0lx/Ttf1P1LYBwc3vl7GpS8GTjVNo1FOaqeH1+UkeeWTpHh2zpvE1yp9/BiscxszLw4jmYekqxkM/6atA3Tv48/IIlZZixHcsUMr29FA1efJnbvvOPAj4QchY3w9YJeiLk0jZ1NYnwFoPsUtc4wd9TPTXgrEARCGIXDA/AesTUMKgFMOH9W51aO4kUKNQ5oFSiWwx2GD2wxYBUhRTJFo5Rn+PJgseie31aX8p+AbY4v34v4TxTOBVXsHEREfHxiZFkvFMREPjggvG4FUlD976NM1dKQZELaYUzieYbCfpGwhdXYy94AKGTdt9s7lAMJZxjGEsFhYa2tbCma1jGF/qEggbNh5PX7+i7SCE4NBDK2n5ABxHwqaV0NuFg03ar5JKt5P65y9ZMuAefljnYWO3q11aosF9A2FKOQhF4cjf/pah06ZRP3s2nlCIAcccw7rcMl485EH0ojCOdCsXHWBovo9ly/aMCm1v0WLC5DrXi9pSxzejF+4pgaE1zTzwwGJGFQfZ4Ch02REyHT30vJ3mtPp7GbhqLgOZyyGZx3g1Nol630Aqxwynvj5MR0ca25ZMOaSSVfW9GAvqsJo7UYMCZ0x/YkPLWFAbwdtcRDqto2R60P3ryCSaIZ52q3lnLMbWNQL6AppOHMHgKw8npAiSjltA88vdRAt1DJqzjQRtAwdYlw5xS3uUP5UE9ppy7MMkfPxuLdbmbqyiMAqgmGB26cy/dzVDlo7hqlMjXH0+5BRXEwtUMr/eQ8TqZHh6DkHbXcGrnUU8f2Mrl1769OcWpySWvkQ+ILdrb5E+H3YsRnrdh/jH7J1XKITgsF/8glnXXovZ2oru92Mkk/jz8hh74YWfuW1OGCyL7XLcNtJO4DgBgn4TKTXmZ/J4qWEOKf0UjvM3cIyxHl0tcrexO/q2FWC3gtYPWhzwOkAG9ImQUwOiHtAwrBAe77aLVMJGooorFPyzL19683Ox3xDux1eKKqo5iqnMZQ5JkigojGMCB+K2QAghOPu8cXz33DGY65ajt9cgoufTnVPG8s6FtFQlac2LUkMNAxmE8ilBDYFA/xTW44KCABdefQi/v+0jbCCsCcIKfPe7Ixk1qoisgJdnpbF6e7E1h9aqfBLJIMFIksXfV1i5/jFWNk/HYwlUBeo1OG0ZLNRgQLF7DmUTJ1I2cVs49gApGV3kp8m0SHt1cgRUeUAkTQpLQ0gp6aqpwYjHyR869Aspgf9lm2sEg9tpDmYcl45s+qvrUVUFn64wsu/9LjVFy/L1lG1+f+vKoGFwtPoWy0uLaDRsSkrCDBiQx7XXHgLAL379Hs0buxFeHeunx+MMLEbaXgw9jCkd8j7sIv5+Icl4FKLzwGiAkBf8OkJVsDIGrW+soeCQfuQdWIVXuJymNjYmJl68Wx9kbm+eTV22kFK1FxBI2cbcRClPdFcwPX/vBF4XZCC9sgVT7StocsCsAywFRRWkrW6eeTtCzSZ49DaBc+5NmPc9wtD0fEJWN6biwxI6aXT6x+aw+NnXmPidT6Fl64PiClXsCombg90HVB5yCKc9+igrZ8wgVldH2QEHMOI73yFY+Nl5xzOOhXfmgmnnoguBdNK0xQoYPWA15QVN3Je5mAeMy9EwUK0MHySjzFKv5o+RJ1FlxqVUA9xHrL6/y0LQ2gNBE4Tp6j71PfJZmoYqHQQmCItmWY0JRL8hMcn9hnA/vnIMYziDGUKKFD58uzVYQlHwDBsHw8ZhY/Nm0yM0tSzGkwihT6qmydNEM81M4Yi9OrZj2/zpqXnc+lAt2YyNkzJJDMhj+HVHcNUplQghmDgSfnDwGh54LkRnNAw9EPInueDMJ/AlM0TKmuiX3ER7bz8Agg4kdLj9Q7j3U7iahRBcNX08d9wxm8KAgsejkslYdMWzXHFeFQ8deigd69bToZag5RZw1s1XMuHsM/Z2anfA20n3Bt/eUfEpropBfcreIQ/mU6CkuIB21mEa1taVwbFtdGFx8++OZ16siueeW42qKjQ1xentzVK/oQsnYSCPHQ79iqEjAXouQjHBB8mjC/F1ahgdDkQnoHQ3oWkCadrYuoqlq2A7tH9US2RkFUnhUBTdyCO8jYVFiDCTmUyZIXgvU0ihknRL9QEhvESdLl7oDTI9fyc+tc9BsQZ6aQRpuxqUTgJXjkGTYEnUYIDcfFhUCzfNhXEjx+HkF+LZ2ENCjWALDQvVFbVVvax9/pnPNYSh0Sch33gTYVtItW+CjQya6sM36LNDmZ+FgqFDOeJXv9qrbSaNgavOh789qYMcgzRrGFy+ntum/4ZWJ8pDxnQKRRxNJIFNSCXAx+ZI5qVsDtXngbRpadN48oWxLF45iOpKlYuONhi0vBHiSfCth4YE1JuIoYJipYYWp4RCpZMup5Cl2RC9Soqr8r4ZJNz7DeF+fC1QUQmza9XlzpBS8sa9v2f9Iw+hoJARAiU3QP5fv8eqwSsZy1hyyCVWV0dXTQ3BoiKKRo/ebZiqt7GRR35wPb+qmQSqQAsKtGgAJ5Zm9nMreePIKk6NuIbjB6dlOLj5Zzx0+Bnk2AkGDKhF02yyUsVEp7po3VZDCG6L2cqGzz6Xc84ZRTZr8eijS4nFMgSDOtddXkzbnWfS0KLxYvxEep0golMy4+J3+H13hPHfP5bn4tBkuvJI08J7XmnnU3blh3Ypr+CwYwbyyds1OI7cahDTWUnJkAEUNc7ASBpIKRFCMOqcc3h8fpA331xATo4PRRHcc89CGht7sW0Hr0chc+gASJtgKaALhLRRMhItZDG0UGVhm4GoSaMYFgQ9CCSKYSE9Gka3QdPHCo0bQNEc5h/TRtk5gkBugKzIMIuZnGqXIBnWd113ZC6x96FB+4Qg/O3IwbTNWILRk0ZmfS5VaDaJlluImlNAvQVtBjxVZ/JGYQfiZxOxrlDJSB0pBQKJqgpapxzJXWf8kDtrXVHdq/Lg4N2s7wOqT2XtcW9R+MarfRdGIFQd74V3QHDveWH/HQgBF0yDaVNhTW2E5pp1fPDG81zw4+OJjMgje4aBVtHjEm3bre7DlAwyzxrOodpsGpvDXHDV0fQmvAQDXtZtUHnz3eHcc/0IJtS/BbVJCDsgghA2qaYOkLxlnsjz1nl0yihnaU8wLXQhfJZKvd0ATheo/d1c5JeE/YZwP77RaFqwgNUP/QNRGEDT3BvG7k7S9dOnCL9wCa1OC0tu/TPrX33VZf2QkvyhQznxrrt2IE2WUjL7F9eQaO/h24ElZLxBDKGzwiglnlNMakkDz63r4dQD+vrHhk6iqAhGjl1BINHHnygdFEWl1xPG2Z61pI91bfjn3E1CCC6+eDzf+94YYt1pop6HWPrgL5lrxnhNno3h9ZJjp3EsgWEY/PwPC8gdfgDeqiheAXPSrp7cI2Uu5djn4Xs5cFuHm4vc4vylHFfu6MKpVdSdOIiZM2u2itnqusqdD5zN2EEXsuyxxzCSSYaffjrxQAW3nvsspaXhrUYzmTRIJk3Ky8N0pXrxl2bJloDdbpM1HJACj2Jjmyb1i5uhOYhj9OA09iB6M6jluQjALwXZVkmwfBAV+Vn6rfsjlb9+mZrf91IyJULuz05CGV9Ijd7OobrBHHM8xWpP3zWFbhnm4sDeV1yU6nDvqCBX3XYKC/70Pta6bjBAL6/GM/pw8jVBmwm6sKkoXEFEJOg+vARzdD8Cq+swRRhFUWk8fArLfvRjSgqjFKsufdyVLa7Cw4E7VbFqQmfEqXfTOeF8MmveQ9dD5I36NlrerpR0XxVywmD0buKm33TQWD8U27Exl3qwPphH+A/lRAflguIDmUaikCfiIMI8/K/RxBMRorkGUuiEI156U4X89p8lvPDg+wh7S2g/DlhowEB1I4KZjPUZ9NcyFIv1YI0Fz0G7DszphfgvwZyH2yQjIPBD8J//pdDX7TeE+/GNxrpXXkFVdbJSo7atCFsqVOZ0E2hpw17bRtuK91n70kuES0sRfSwunWvW8OFtt3HUzTezcsYM1r/yCmXpWsYlVjAkGCJf+xBVODQ5ufQE/DyQncqTyqEo3SmgzxCqGoXn/i+e1F/IehS8GQs0HVVTCUmL9bGhGB5AcavEo8vgij2kB/V4VIryaiD2BPXzTFrsUnqNIBE9BToYKTef0qQH8X6wnqqL3PxpLrDZhKd7YfoeqJP/ogAWpN2CBPoEUvNVeKoCNFVw881Hcfrpw5k/v5FIxMvRRw+gqMhdwCZff/3W/Sx+bT1CiB1CqbYtAUlkRJapv83QLFYwW1ThLRY4vc101ZXghHW0Tb2Y9WlULYjducRlqOtMYsezBEv9FGRSHFgpqKraSPfqRwhveJKsL49uu5ycDZsxf/QYeU9cQuOACq6PLOcH3eW02EU4UkEVBiO1Bi7Mn7RnE78TJvrhw6MLWTH5DJ6vTfPwHSrZbi/9AtCYBacHooN6CPdLgPAS9ZpsvvFqjr7pCoyeTlR/EbO/dza5iqS4qBCljyCg24Z7u3c1hODmrgvKD4HyQ/ZpzPsEaUDqYcg84xJke46E4BWgliKl5He/+5D6+jiaGsTjNfFh0pk0WfiowdG3eFCkScLx4MHiOP1dEIJ3ZldQ31nOupYwoBDyZxlSbdHUsJzuzkbycrewJOwYkhig1jKAZhBHuSTc2XcheT84DSBCbsuG/0xI/hmMOaCUuIZPGpC8C7QB4Dls5zP8t7HfEO7HNxpWNktjoowZ64dgO9sy64cULOck00/zM2/jjUQQ27G4BAoL2fTee7z6ox/RsmQJQZFhYE4dyZRBfjiOLTUM4aFIidPO1GddAAAgAElEQVRs5XCZ922WZao4d+SO2jLqwAkc33wpr9rPk7DSSF1DhPI5xT+WVEMVL3e793K/FrjtRBgzdC9OLPs2AOHyEOn521dRSBRFklW94PWgxDM7bBZS4L3knhlCXcBLVbA0425TobmhVb1vGoUQjB9fyvjxn51fKyoK7vIQHol4kUhGXtODogmKeuoZYs9lbWgS3jybqBMjtcGP/vduwk4rmbZ5mEoMJ+TFyioM97aSF0+QH4Aj9BYOa3qGmetb2RQcglQ0hBTY/ghqqoP4k7MZdNM1lEdP5xn9Hp5duJaPV+XSovZDrZrArJwAJ0Z3ZZLZEygCxvgFY0YGuOrPcP8MeP1DN+Q6/MBGbrSuYdif1tKVU8CsQ6Yxa/TxfOupI0m/OZuu5Hf418jR9IuGEco2Fz2swPrdtIB8bYj/CrJvgpIPIgLZN8BaDLlPEU94WbeuEwBNd1lcBT5yUz2YGyStdn80qRIUCe7w/5ZSZSOZTJh1zaNJpC0CfgMQpLI+lq3LMLg0QzBUDnSyjdV1Z2Rdkm6nHtKPgdO4NVSMtRQyM0DGXaaaLT884QHhg/ST+w3hfvzfQ9mUY3j+rylyvVlsj4MlVCxTMqdtDDfo32Vtdt5WVo0tEIpCNpGgcd48Eq2tlOamkUHTpRLDJCl1Vz1HkQRkFlvCTw9qY2rpro/wpaWTOY+JbJIbMYRJKaXkU8DRpwvuykIiBXk527iY9xzuBqNPL2fFG59AB5i2QFcljmXjCXgQQiV64I6sJ4Z0vbq9wTif+9pXjB9fQr9+uWzcGKOw0DWKqZRJ9SSNQH6GbDeAYHBiAcPMxdhFeUTNBEMf+Yh1a2p4t+BCui0/AY9KxrSxjQy2tOlRfaQyHu6qyWNue5iDnPVEydAhg0gp0X0WEhWnppPxTAAlyKz/kbwzYy2v3vQXTH8QPZHkjTmb+PuoATxcofxbzdnRHPj5pe5r/trV+O+8GFWkSPqDRBIxzn3lfiJmLyOO7ERcNBCZ92uq6gUpCdvX+MYdt8fxGwFzJaSfww0v6q4xVEvAaYHsTPx+tyBr56LVPF+ckQUrucGZQSiiMkSpQacNsPl4xSGESvqTiK3EcVQUVUFT0iQTkrHjVbzeTB8T+qfBAbsFFK2PpUaAEnSNodMNhMBpA7Xa/a6zpUDK48o4fQn4hhSv7sd+7B7ra3s50p7FET0vMLXzZUbEl5EorqazYhhnv9BN4+TjSMV2zBGlu7rwR6PEm5tBShRNQ1MF0aDAo0KOkiJAFsVx8Aub/pW5nHlA7qemHnz4GCqGM5oxFFC4tZzf54WC6L4YQcB7NAAlo4Kc/OtBHFe2lKQZIJYJYoVK8VWPYOghVRjjKrcuUoZ0X2d/DkH1Fw1VVbj77pOZPLmStrYkra1Jhg3L5/pHBpPfTyVUoBLKVykf6aHS10K1tZbCznWIZIyOwgOIq3ng2FTbGxlGLZptkK+nCCgG+QGLQp/J4lgxKVsjnO0Ew0d+URZNd/AkBRPGnESUKD1NLbz+1xd45rJ/0hUcTCpRTE+6P2ZzilUtMR7+ApuzD3z/QfJV6AyWkRZeYt4c0oEQP579N4TVAYELEIrgR3luFW7ccY1Jjw1ZCT/M+/xjfOnIfgBdJ4BTC04NWIvA+MjNv6GBtQZdVznttGFYluP2zuL21Op6hmOO2MyY4GpGqsvRRQqXR9RLZ7yIYH4RA0YX41gSI2NhZk1ySko5/PB2sNfy6aZFuMcmAU4zrufYC86WiycBx/UIjeWQ/QjMpWAuAmsZaOO/lKna7xHuxzcOZjrNkocfZsWTT1K3eCW6HUJ6gthSUiyamZiYzYe530KRkhnHncdxH32A0rTBrSiUEk8oxMATTqBl6VI8wSA9WYeIx3b7tQBVgZAwcVQPI/8fe+cdHkd1/e/3TtuuXm1LbrLcey9U0wmYFkgC+YaS0EJIgYSQAukVAj9CKoGEFEIPzaEbYzDYGPfeZEmWrV627067vz9GLphmY2MM2fd55pG0ZfbO1c6cOeee8zmjhyMS3TDhxIMacyJhMnfuJhbP30ifYpWzL5rO4GHl7/4GbTQEvwjpu6k9QXLnUbBowSbmrf48pjqA448fyNiZ/bm5S2Fxek8ZxHVFb+2qcLgoKQly222nkEiYOI5Lfr6frWylmXUEh3lZwKDj9BmAlUwyMTaJSf/+AYMaVJqvu4/bCv5JqZ7klZ7+/M0ah09xSDoq0pUURCBqG2QCA9FS7QypTlJe5ifTmkYPFTDlM57m5dJnXuPFku+SrilC60r3igQIonp/9KZ2nq8s4luHqDhbNK6nT16EAk0nbveguy0U6DGUmAXyAgh4XdZPD3tCY3/o9uofhxhwTaHXQ/Ejxe2C2LW9HpbSu0kgBdlV0N4HioogLPnZz2azeHETK1a0oihg2w6XXLWUYYN7WLupmppBCgV5yV7vLcWwfk2oKpQPLKKyupBM2kQ3VLqSfRg1aCX7rgu+FdE7llTvz13LAibImPe7sxXIgGzsfY8K0vA6V1iL2J3ddQjJGcIcRxRSSp677jqaFi0i29mBz05gOhEc10IJ+Elpfqq71hDyT6d8ahWhgjye+8W93F43H7luFXlVVQw++WS2vfgirmWR7u6mb4FLPAMZyzvlQjrkB0DFhJY6mHE2jDrqA485Gs1w2ecfon75Bnx2AstVePieV7j1JzOYeem71AIKAaErwH8qmG+iRfzM+sxMZu3Tj+jOSmiyoMuBwcYHWwc7lITDe2J+AxjAMIaxnvWYvZ3JpQJF8WEUh04iMqCCY/ukGTvjRbatStNhhpEoOCgYio0rPFF1v1/Db0hGjenLyE9fwcq5L5Dq7GTg7NlMuuIKIr2NmFsy5cSVMIon9AkuiN611aQoJHgor419a2DTEoJ5foJGKcgiMONeDLT0ur1qGeHkiLd9CNfnD475qhdGFFrv+psFrgDLhZ4eeKwHQt+BPn/Hf8LfmDfvCzzyyDp++tNXaG1NMGSIoE+lRXfUYM3aJJPHNeIzvPXA0YNe4JgxJ/HSiuMI+ZMIRdITD3Ps2PmMHbzyfQZWBEQBA8jy1t6Fu7rbW/u8x/Feo47y1hPtDaAPPzTz1EvOEOY4omhbvZqdS5YQ6dOH9LaNaAr00brYaRWRdVRcKXEcGDjBIFLiuXiKz4c87mRmnH4yAF1btvDaLbfgy8sjE49TEXZI9iYvKArEs5C2oTIflGgbzDgL1A9+Kjz88Dq2LVtPH70HAl6JRyLj8JMfzuOp40egDniPk1athkD1e+6/n+5tRxoqKnM4h5FsZhUr2bEpw5M/SJDY6vAPdy5utJ0vhP/L+YUrCNqC0qDK+ECU/DabNjNCoZ7BdS16EhohxeX4r/wfBaeez9BLrkRKrwnv3trXQ2ZNQdy2nuCSDlLTSlC6Lc8QCokoinD+oQwZn3wZrF8EySgE88DsXRA+66ugvfM/44gxguBlWe7yzJSIt86WTnlNHMPAhYAeB7kEUuPwBe/kxBMv5PbbFzN1ahXPLDibIQPuorQoik/rRvTqxYKKEIKff/HbzF10Oo8tPBdwOXPG45wx/an9mINOPB9awbur6E1pfkfJnb0xvVCqWuKFTQ8xuTXCHEcUPQ0N3iqBaaKILOg2+eEk/fVmSsImvjI/akUBW0/+HEs2BdjR7vXErtrr2rT2oYeQrkv5uHH4wmGiGfDre7pZaJrAcSHjKF6Tu4d+9fZsgQPgpf+uISLjXp+6XsJ+QVdWZ8fcxz74ZHwMUFEZyjBmd53Jw5c6tK9RqK/voXHNFlrrGnlhRzXoBqqqEMq0UKynuHH0KiqCGeqyJbSEaiisreV3/7mWglPPB2BDFi7cAac1wgkN8PUW6LRhVK1OxdCBBJ7qxLcphpuv4eTpyOIQp4R1LjiUNemDxsJVd0BRH+jc6SV2fPoGmP35Q/ghHyLGFBD53vdaAo7m2UUvY8y78pt46mjSgtj1dOxcgqp6ZTJNLZVc95PLuPu+0SiqS31jHs1tQSQ2YKNrDmfNeoK/3fAl/nbDJZxz1GPomr0fA+sNg5Lo/VnA/pkh10vwQYA27MDn433IeYQ5jigiffrg2ja6bzGn3+kw/zbI9EC4r02oeQcpkc/SM6/CCYVwFJUNXTCjB6oG7dlHT309mt+PEQxSXFvLmm0rGVSSQVN2FZdLfAY0x1UG5+vQuQNS8Q+s7lEYhG3yramcrgSJQijz3u1wPim88koD7e1JWlqSKAoU2j24wLKuQmJplfyIQk/URU12UBgMcMuIeTxafgXn3XQZQ4cW71YC6rThymawJJSp3mVzYQqubYF/9oVf3BDhe/9vIj1Px7FKJEp5mJn9NO456Z17EB4Uw6fB8PvBsT3poCPK5Xsf1GoIfQMSN3uJKBkTmiX4gXKgEXgJ2ImXAzMrTdX0fyPEFEzTIRrNsH69zZtLx3L+GUtIJHQKC9qRrkS8xW7tG8Z8f7a39cOydTKWD59hUZpvkhfcpQ70biUXACYErgLl0Kvw5AxhjiOKyvHj8ednmHVVnGxSMPUSybaF0L4FKsY7vBD7Ei0DPkUyP4B0dYrWQ9ciiE7xVDIA+kycSPPSpfjz89EDAZqzPl7ZLphcnibkA0cK1rdBQZ4C+aVgBMD39uwGxzRZce+9rHvoIax0moHHHcfkq68mXFHxltddcPEUXntuBabtYmgCKaEtqTGrvJXiqe/cJeOTRjpts2NHnGzWQboOea6Ng0YajbvWV3HNsE2EdBfp2hhqDGXULL57yzcR+4QZn01C0vU6eoC3glSmwhYTVmfhlKNgcJXCUy/n0xWFoyfBMZPeu0fkQXMQYfOPlNAXwZgJz38bnpwPsRRoNlTiRSg1IA/P9rzoEHZe5/++cBU/+12UlmQVbolAy2xkwZJXOe/UJRTmZ7EdgaHsHT1R2WO83j+qsr2tinNufpgxg1YzedgyYqkIr64+mq9/+l6OG/s0nqf4TvtRvAH7T/7g8/EefEz/wzk+qWx4/HGK+jUgVImdBiMMI04FnwayQOO1BzIsei2C8YZE0QVJAdsysGIHHNMbMRl+7rmsf+QR4s3N+HqN4YY2k26nkHy7m5gJmqJw/vgy8AXhmAvecd1n3ve/T93zzxMoKsIIhdj89NPsXLqU8+6/H19kj+7hzOOHcu1lw/njPWu8rjRSYWJJBzfPcWHSqYdr6j5Sior8pFIWiiJwXYGNjui9QG7sMLh9RRWzKjpQIkW8aJzDyiWD+Vdziqqqty7s7bDYp2mW0+uIqbT3Rt6GDICvD/jQD+mTQV0Cnt7h9QiMrQOzBba5nu3aVaLqU0BXkK/HWVcyAl+Fi9za7HWKiIzk9qd+wUVnHY9QJMLdOylI4O1Iw1ugeH+efuMk/EaGtp4K5i46E9RyYimH3/7nKo4b9yzIvdcNd+HDq4HMA3MhBM45VLOzm5whzHHEkGhtZeHN36V6ioUQoGoqUniyaTIYQlGzdBX3pabuaUZtvZdguo2uvKEsrv46z2uTd/ehCBYXc9a997Li3ntpfPVVBhx3HIqu07lhA91drVQaCWaNLydQEoGZ58DpV75tLD319dTPm7dbug0gUlFBoqWFrc89x4hz92SDCiH4ws++xNmfXsTWx56gUPYw4PiTYNqZ4D8y1PU/bJYvb8Hv18hkbCTQQQl5RBmiNFGqWWQzkmebBrB13JcJRPykWxI8/vhGrrnmrfJoY/3wQAxUkaQs7xUi/jpAUpKtpMg3Hc+d8WixocGEPvpb14hz7MUrD4Gqg+GHgWOgvQA61nl2y8EzggjQ89jQPZ5Fy+MMrd6JGbeJJ1R0PUk0XcrqzYM4atJaVEXuFSH24+1o1wNvFUR/J15acRyR4C6vzwG1L5FImvqWEK4sQWEHnvfn4HmbAe9vpRiU/A+toD5nCHMcMTTd/RvclnoSG3VwvTNVuC4uCo6dJVhURMeKTiatvAczUEDCX0kotp1T665h9Za7oGbM7n2FKyqYdcMNcMMNux9zLAvpOGgK0NUCecUQfGdF+2hjI0JVdxvBXQhVpWP9+nd8T974aYwfP+2g5+HjSEtLgsGDC9m0qRPLcrGFDy1/AhNizxKx2rBEAKkYRHe6KPl+DCNNY2P0bfs5Jgi1hkOo4DGCWg/ZNAR93fQJtDNXXUsDIzlensydHSEei4MqwJZwdBB+UgaBXPrfW0l074l2qBpUDALHgfZ6rzOEgtdd3jGoTwmEaEK4OxhRq7J6fQnJlAaaQ0F+BsdWUA3HyyuTIGUa1/ZjmpUE830gd/ZmdL67MRRAIh0mPxTFMz8BktkS5hz1MIpi4pVXqJ7BU4aCGvLqB/F7yTL6uA9lmnJfmxxHBpaJWDIXVI1MS4iGeTrBYggWS0LFDpFKH4Hqm6ltfwgzUoQpgyAFSp8C9BKF6ofued+PUHUdze/37o4rBryjEezeto3nrr+eZ6+7js6NG4nt3IncK6PUdRyKamoO5ZEfObQ2wJvPwIbFXoLIATBtWj98Po3hw0vRNAUR6Md0cy6WGqTJN4FW3wgyehGT2u4k2x4jm7WZONHz7lxcdtDEGyxio7KS6ys3Uu2Lkm72EdS6wAI7aZBMSzakG7jLeoaHY5ISFUpUbw1xfhJ+1/VhTMrHnHHHQ2afNlWRYtD84A4GdQpYhdC9mcrprlfIIBU0VTJ6eAdFBRbjatYS8qcRio4QCq4Druttqx7z8dBlHUTb+oLYV07n7clFV5zxZ1LZIPFUGCkhEd+AId/kq+f8CrShoE8AfSwoleBu8tpAyYxXOmHMAn3i2/Z5KMh5hDmODLqaqSrzoagqTjrNtn+4dL+hUDTOxbRgwrQLoOZsytS7aR4SIQwIpTcZOx1m8PbNBz2E+M6dzL3k8xRb7QwrDrCl00fz+vXYqRSFgwaR7OggUFhIzSmnHPRnHVG4Ljz4S3jtUXZfvIoq4ZrfQ0nf/drF6afX8vDD69i6tZu+fSOke+IUp7aS0YpRemNprtBxUchvepmCqSdy2mlDcHB4gefYRl1vr0RBVs2gZlSCWhZNeNm3KN66VGurjlLRSrmvG9X1LryKgFLNa1H19WLPS8zRy7Qz4fUnYcdG7wbQtryJvOoO2LwUNi1BhrOsOu9Ynqk9E3uzwtbGQQwoaEQRNsVlAWaOb6CmfxeqMYqOLdvB6QJcFEUirQxmwmbZ3zdz3HUxvHDmrpuoXSHOXWgcM3Yht175Df7fI9+gvm0Y/Uqj/ODiH5IfbAKxVxKaPhxsFdRyUPuBcRr4T2GflNVDRs4Q5jgyiBQR9OscM62Kl19aR0ZCfJXCjlWCWWNLCCnLcKfvoCo/hGWn6dQDiN4SqcpsnJHTDl6Rvu4vtzFbXYnhVxFIRtYKNpQEWbB+O6rfz4Bjj2X617+Ov6AALBPWLYT6NZ7RmHAihLzED8/D2cF2GvHhYzA1FFDwPp/+EeEm4bUbYf69kO8HvRKUvtDdCvd+D677637tJhw2uOeeOTzyyHrmzavDaW1Bf01HDRr4DBXblqQzNtLRGN3f4rq/nkUk4mMLW6hjK2EiuzVcHenQGc+i6vuG2AR2xoCMi1/JYO+l5KXi6bDuWlnK0Ys/BN+42/P0177qfVenz4G+Q+AETybuLy2v8OdoESKr45yfovkPDk3rhlJgJDh58ny+ctYfUBUFSQnJ1nVohgtCwZ8v6W4MEChI07xiB8ggnvHbtVa4bymEpxBzzNhXOGbsUhx1Nqomwd4JToy3/fdExJMh9H/4mdc5Q5jjyCAYgRlzGPL03fSdFmB7TAXXpV8BhEYMhVQCZetSplx1JeYvf0m/sI3jD6LGo6gaTPjiZQf3+Y5NxdJ/IxWFjOgtjJeSYUUmnWP6M/0v91E6vFchJpNE/vYq3K0rQVFRVRWe+j1c+yfcvoOZxwtsYZeHKniTJczmBGoYcnBjPAQ4OKxnHetZh0gnqMm+weiFj6IagOKAvQ2UKOSNhIa13lpqUcX77hcgEvFx8cXjuPjicdiZDL8a/wA7umwsV0co4PMplIZsPvedT1NU5JWrbGETKhoCwfJn4KHvZ+moFyjBMJOvMpl2bQqEgqI6OBkfTkYjX7FozhZTutc1s8uBCX4wct7g2/EFYObZ3rY3boxmy+Ku5ARKlJW43Q7Lv78c2ZHF1f3kmVtZvzBO82lxyop0nNRijFAWIcAISbobNTq26r1rh2mvTZJMvcsgdhnIXcbRRFW6gEJQiryH3Swovcll0vS8P/2D9Zo8UI4IQyiE+DVwBp7UwFbgEillT+9zNwKX4U3VtVLKZz+ygeb4cDnnOmhtIDjvXwwtA/QAVA72JK4ySfCFGPHpT6OHQiz/y1+INzdTNno0U7/ylT1G6oOyfQMBQ9AdF2i7zgohcCVUaT1EKvdkK0b/+Wucl54knvF6qAWKiiiuEKj/+hHbv/VjNrOZMOHdHo6FxXzmUU1/DD5gwVsmCds3eOUeVcM+UHG3RPIiL9C+8zUmPfAiRXUNuMIibrrk6woC1dPQdLtBiXlvcg68YBpA8/v59G03MvebN9MTi+GikRfJUjN9HDUnn7T7dZ5Yt2TtfMHvPx9HSg3NH8RKubzyMxfVCDDlqjRWPIQZCxIoyHCyfxYrFB8tttdz0ZKeBus3iz/QUP/3cDsg/lPIPkvIyfBLYwQP2l/h5UfbMDuz+Mt0MlJHV/woyQQ/uvUYbv/yo+T3kUgpie5wad/op3lNBMeCTNRmwkWVYEyH7GN4l/F9vze73Pdd3qLrdZVQaz0Dqo3xRLed3i4UQoXQ9aC+d6/MQ8URYQiB54EbpZS2EOKXwI3ADUKIEcBngJFAH+AFIUStlPK95AdyfFzRDbj8Vmhc5yVrRIq8C3425aWAjzkWIQS1p59O7emnH9rPlpJgSQk93Ukcy0LVdaTr4loWJUOHeuFQvBKP7vvvxMBF8/lxHMnmTa0sXN5GUekOUrMmoMzw7zaCADo6JiattFJF1QEPLfPq0yy9+wkcRzIusom8yiK44jYoPbB9ddLBzvgqTr79QTTTIlsQRDhJ/DvSOB1ptMHanp5SyQ4oGQYl/Q54vLsYcsopfH7QIDY+8QSpzk6qjzqKQbNno/n2SNENZRhb2MLDP3GxHA0ZCGBJgRqQGNLg5R/68bnjKR5bD9kgl48dySyjH+P7wZNxWJOFGgPOjEDZkXI1O5KREqJfhszTQJKAVJmqtjBKWcJJddehRQxA8bq0CMgL29TVhYklA0Rsl0RHHu0bW2haliXVpeO6CqPPLWbkOaPA2eYZNmf1ewxgL0NIGpxVIEqh6ClQCsFcAChep3ptwIc+Hbs4Ir46Usrn9vpzEXBe7+9zgPullFlgmxBiCzAFeP0wDzHH4cIfgqt/C3++DqK9TTt1Ay79xX6H6D4Q1cPRCksp7mfSXt+EmUigGjoFFcWUfvmm3S/b/N//EnFcAj4Nx7VJdEbpH3IoN2Bzh8mGa34PNxxF6IJj3/YR2oGuXknJspca+Mb3ykhzLSgKKi43Rf/EKX/8Gnzv4QPyDDvooOqNtejpDJmiApBZpKLQ07eA4OYMdGTAUAELgj74vx8dtKxYcW0tM66//l2fr6Ka8UygYf0GHEvx1l4BqSrIkIHqmjTKQWzuGU3eaI07BfQxYZABFx2hy65HNNabkH0BT8FFQxUKcTdAmBg/v+Q+PnfTxeDXUHApFt1YGU+SMOBXUHSb0sFR+k+GKZeCY5sIfQCBgmLIvwl6vgHOGt67DZPEC5Ma7DKIJhpPdzzFrdmb6a8P4kuFcPRhtkxHhCHch0uBB3p/74tnGHfR1PtYjk8yg8bCj/8LdSs9z3DwuHeUQDukqBqNw89CX3wDQc1FCRu4UrI1HWFo/3HsavCe2bYRVfqopBvHslH9DpYLIQOm9JOEfYIFt7+EdcpE9F7NtwxpggQp4z36E+5NWyP853acVa+gbFU4Rz+K+docLHxkHJ0f1F/JqNA36dewFgaM2u9DDBIk0tqJqwqyMovrmgihoSguVoGKPsEPeQEorIBZT0Fe2QFO4oEjEOR3TsAx68COIYxeb9GR2NEktqax9d5HMDQHZ/pAtl4+kyudAE9UgT9X/HVgWKvI9nwZ1Y165YPCRkEloqTJuBqja3egY2HZOsONTehuhp2dQY6dsIH8UodgfobupgjpbYVovgT5/QL4Ig7k3w36rnC9jWdW3s0Y7kqA8kKnLgqmG2U2t7JInclC6wS+3gq/KIMTwx/6jOzmsH2VhBAvCCHWvMM2Z6/XfBdvJv/1AfZ/uRDiTSHEm+3t7Ydy6Dk+CnQDhk6GEdM/fCOIpyv60l8fZr6YyHrfSDb6ankjMIXFO31seKy3g8TyFxnX8BiVagwdC+k6ZHozxR0XMq7CMNFGoQyRXLuNJAmSbgy9J8qQu3bwqyv+zFevfpwHHlhDImG+80DiXfCbS2HtQrpFKWnXz2nGE1zE78F18KsWtqsyr2vS2+vD3u8YcWkcWIDpZLCxvPtxqWBLFUdVoTYOY4bDyYfHCO7iqRe3oRcAQiAt01MSkraX6j+oP+FyP0ZhkM6FdTT/+Bm6bcnCd8vJyPGOSDfDfa1P8Lnoj0nKIDEZISFDSBxU1yLgpohoKfoWQaAuht3lo60zzDEz27n24uUIJKkeH9IVqIZNJqYjjLEgCsGp9z7EbWFPycR7RRJ2ZZS6pGUYEx8CwUXqDylTU+QpcEfXQTWEOWAOm0copTzhvZ4XQlwMfAqYLfdUMO+Atyyq9Ot97J32/2fgzwCTJk06jFOY45NAtLERK5XCKC1lJ3sK7Y2wS8PLLzPu/HPhHzfhK6+kq70H0zK9hqey94RVFIRtQypOcbSDaYkpOIka9Pt/xYb/9vDV14ZRoW/h1JJNhNeYPP6vGubc/gPCg2rfOpA3/uv1wCuqwO1wsV2VTreAUcoyytKbaFP6IWU+WWlA9Yj9Pr5mdvKMfJrmUbUML3mDgrYo6UgA1XIIR9NsGjeC0a+GMDqRcAoAACAASURBVGwNfnz4jCCA6E6h+JKEh1aSakzjZnpADUJxOWr/BCIuQBUYpWESdR0kNrTRU7qf3nUOAF6ObebW5LkUq2k2OiMYoy0nI/2kpB/DzYAUpGMBKrUKnOIEoyeM4Ac3H0O/ynbq5p3Ihud+xqSLmgjkp5FSoPl8aP444NLU4vD84jc4bXSS4jyBqu4yg4K3JsnsMo57HsuiY5ClR5ajYdFfrCarTKXZhoyEwGHKAj4iQqNCiFOAbwHHSPmW/NsngPuEEL/BS5YZArzxEQwxx8eZeDcsfNRTTCmtgqPPh6qhb3mJLy8P6bpI132LrJpjmgRLS0luW0RSZDBXb8HJZLCki19j97mtSNera3RAj7VS/fvLEE8MxTIdLl5+PMPzO/lWxXNo2GREgAHxFpJfnkU44kJeCZzxZW/bsWl3twOleweuk4+JD9PVCMoYdtZCIcP08ye/qzzcO7GUpcQchbTf4JFrz2XG3IUMWNNINuRjw/jhbJ81lAEP11Pcduibnr4fR40ro1CDeGGSwkIBsox4Jo3V1oS/p3B3hfyuVk1uV5IRvvfaY459+Xu8mKBoxSdsbjdv5gfKV6kQzZjoaK6JZRn8+YH/Q1E0+vXLZ9WqNvwBA7QhDDqxhqpRD6E4LbiOgWr4vJwqZzMvrTybG+85CsfcjpM4g5MmzaU0vwO/L+s1Te5lc9Mw7pp7Bcu3jKWsoIGzp/+boZUrGTg0hYmfVgYQIoolfaRdKFLBfxhLYY4IQwjciScx/nzvl32RlPJKKeVaIcSDwDq8kOmXcxmjOQ6IaAfc8gXoafVKD+pWwBtPwRdvIdZ/Cn//yyIe++druNEORoliauPbKBo0EKEo2JkMZljQeX0tf9NfR35xLP7ocCb9axnGmnoCIk1pCDqSXrDH0KA5BrNreu3j1uVkA2VkLckFFUsRSHrcMLgOtgM+xyYiHH7d/HmeXzYR351b+NaxwznRfppoDNSuHfTRMuywK5AorDZH0yVK+Xzf5xl53lcPaA7S7jZigRCOYpDJC/DaWTNYfOZkHF0jb3sKQ1UItbbC+E99SP+Id2fy5L7MmdmPua9uJ+73YbsZgrE0hnQI64IOxyuzlq7EdOHEYYUM+zDbLn0CaXdL8IkmkC5Ru5BvNN3ONP98qv3biSzfzgvzz6Rxpxd8EwKyWZsVK5qZPXsQwtmMrkdBHYgqW9hVC5jOqNz81+sI+1MEwp3MX3UmowauIWMGqCprJmCkAR/1rZO55Fe/wHIMFBJ0RWv52rLbCMRfZMaYZVx4UxsVoShxUcR6dxRdDnyn9PC2fxTycAZiDxOTJk2Sb7755kc9jByHk3gXbFvtrScOHr9HaPixO7Ce/ivLGi3Wr2/GcSSDqvIYPbGGKxovZulLK/HJNFLRyJgwVqzipPI6AuUVCF2Hf83Brg6SXtdIJNqIGfLhagon/ugZCnY0Uxzw1PhtB7IOhI3eE1goICUugk3pYoq0NI1mES4q2Bamq1Doy6Ib+Xyu+wFKlA4cV2BWjeWPgQvxm90UJzeh4qBjknBDvOlMp0cp5dzatah3vvH+V4rOZvjHzVC3gh6ibC+P8NDnzsWuthHSxZ9JIpAEurJMeHMnxy1Pe0oyBYc3NApgmg5PPLGB++9fSzZrc8YZtTz/fB1b67pxIgHaTYkZz3DCqUP41y+Pz8moHSA/74BHe7ooSq6idV0a23LJ+MIQTTPy6pvYWvNZ+lQESSQdNm9LkMnaDB1aQlVVPr/6aSVDym4ApcSrMXXbAFiyYQJf+e0vKS1Swa4D4cfQMlSVbuDESa9z+rRXwZjJd/8wg+eXTCTi78B2vH/cprpyLFul2v0Tc+a0M+fKTn5s/paEMozLC+GcyIdjCIUQS6WUk/Z9/EjxCHPk+ODMfwD+cxuulKTTFpYvH+OrdxIcMhK5egHPvdFOU0ucYMBAU2FLfTdzt3SxVttCnpJAM3xo2RgYDuvtoUz1b+G48wdSeOlNPFXwEkFCxDNZogQpSKdJFQRonlpNyRNtJC2bkOF5g8bus0l464eAgiRftwiKLP2NTrZli3EBF0FYV9jhFFOptYKU6MLFF5RcZ97DhfyOOWITfpkiix9FUZihLQTXJdP/QkLvd5VwbLjzaujcCQVlBCmguGMrF/3ub9z1navQ8lJkgwGUmELp1gBHlRwNN558QOHWgyWRMHnmmc28+WYzFRUhNm7spKEhiqoK7rprGTNnVjNjRhXz5tVT61c577yJnHvuiJwR/ABcnA/zEoVsaeuDVDqw8vy4rsKM++6hnCbaNj7H5obBtJgFSCEYOrSUioowbW1Jrv5qHU/+VcMfyHq1fkohSImhCyR5XncIsQ1wMW0/r609lvIShdNnroe8X7Ky3iIv3IJjQipl0NYRwXUVbBmhoFDn2edG8f0ffJP7hA9DHF5PcBc5Q5jj403DOnjkFhJqhLUbezBNhzxlO8mrPkviugcZn9bY2dxDJBLcfYaFwz427izGElECPo08pwfD8Pro2cJhkF9nyUibfk33IwoqEAh8BfnEdyTp9BWAC9tKqmlXBFOySwkb+2aA9kZZFAWESqlusTMZplSNY7gmCdegJtCJqoaYmzwdbLv3PRLRuo2O/HEsqPk2E9Y8SyUNaNhowoHehqiBvvtRQbTpTejaCYWed2fgozhvMFrnNmpXbeSNSceyIzaOElnL1UftpaZzmOjpyXDppY/T2BjFMFSammJ0daUZP76CgoIAritZsKCBL31pAk8++dnDO7hPIJU6/Elr5dvPP8qq0mGUdbQydN5zFNdvJkCUscpqGpyh6CqU+RMobVEYUERRUYDW1gSvr7mC4ybe4e1M6CAzjBxSSXFhHt0JlcLQQHC2YVkKUlqcPm0+hL8PajkDqmDZ2nw2rGtAKdYxJwRxDRV1i4OetknYFkIx8H2ENzg5Q5jj482S/+JKydoN3Vi2xOdTyZJHoRvl9z/+NwVnjELKJ+kZnE9iah9wXPJeqSe/08Y1Ffwyi09YWChICbYr6GckKXt1DfMurwBZiitc8vr0Jdnahpm1ID9AYkWKbHcGu7wMhZ29tm+f2ilFg1ABWp/BVHc0YXckGFaYQFdBNROszA7k+cwJlKteuY9UdJxEAju+hQvmdOOuM1gnJ1Mi2vC5CdIiRFmln1DH9vefl1in11ViL3z4KBP5XBwfy1TtAsIlMFD/CO7ApeRf/1pFQ0OUPn08D3Tjxk50XWHLli4mTeqDogiKi4M8+OBarrpq8mEe4CeTckPh6GcfpKTBxufzLv2amUDBoUBLMS5Uz3qnljw/OFmbdFc3wZISXFcSy06Cgr9C5jFwu8A4Ds1/IrfdqPKVn0JbTz+QpSATfOVzdYyZdKvXOQK45BxYvFJDnxym48wyhCaQqBgnxlm7+CQ+l1i+OxHqoyJnCHN8vMmmSGVcTNPF59uj3CKEIE/G6FnbTMsXJ8F5NQjpuVQt5w6j4DeNhF/Jx+6O4WieCes0QwwJttG3Mk1+iyQYS1Eiq9gumtD8GkUTRhLtakZs6KIqXcm4732W/gvugHagu+VthgfbgkwCtq4Ay0RDopWUQnl/6G5lcMM2ipVOWp0yipUOMpZCh1vIeP114n+8hYkDXXxaId3JPvgNqKqAAqfV0199P6qG9mavu3tk06TX6EgfNJox/vd68/7T0NDD2rXt5Of7mDi+HF/AeOeLmpTw8gPw3F8h1sG8+ceTH+m/+2nHcTEMlXTaJpt18Pk0dF0hFssemoHmIFRWRr8J42hoXEDaDqNrCsK1EVKSUfMYWm6zoXlP/Z7r2DiOixCC0aPLQS8EfeRb9jl0oKc3/+ZaSKV9jBvuo6TwraKvE0fCj77p53M9xYiogJiLT3SjK1HiM0Zz3NBBh2sK3pWcIczx8WbMscjnHmLvAl5dmrgonOt/GdMfIXX2SNSWGEIKr7edoRD52mAuHDeZ137XQmNHGBeYnNfINWOXYvTPh2Qa4QsxWhnHCEaxljWYvixTKmcwIr8ao2QtpOKQSoCm7V4TfCvS00ndPTADYh0QKYT8UsLqJu4u+iK3xb/OvMyxGMLi3PCjXBK5h3WyL5t37GT8sCZqqio9rdVkD6DDrHNg+Yuw4kUIhGHKp2DQmLd+dJ8amHQaLH7Se41QIBWDIRNh2NSDnnYpJbfc8hoPPrgWM54g2daK345z6bgmBp/2KeSwoxg8uIji4gAvvVRP3zUPMbn1cYKlpYiiSvJklLaGzZ4kTyBCcXGAzs4UIFB7FwE7OlIce+yAgx5rjj0c/+Mf0VJ3KZuWbUFmXBzXxlQC+ItKGDkQtloxVrdHUB0Xn+Mj1prg058eyaBBhe+6T8OAGePf+3MLRgtqmw3yzTX09Dik0irBgINTXMbKSH9OPcTHeaDkskZzfLxxHNJ//Bb1/3kEoSooQiARLGAqM6yFvD7neNYcW4bR3INuJwGJrQbIVhRTumww36n2sfPvt9Pp66EgYKGoCr5Uhq6KQp7+zBkM/n2SIcfNZkilD3XFC9C5A7av9zRR493QvPVdjOA7oPSKWiuqt2USAFiomKaDIkBoGgkRZr42lZ6WNs44ayLl3Rsgm/a6Tpx7Hcz7J6yaD5oBruPdwp/9NTj+wn3mxoY35sKrj3pdJCafBked5zVoPUheemkb11//HPl+h461a1BUlYRtoGV7OFU8z5Y+p7LFGE13d4b+ffz8sfD/kZE6RWUF1NYW89zWPG58vpzy8jDagOGkUiZvLG5Cl1kKRRSpGVQOqOT+Jy+jqir/oMebYw+OZbH2uQUsmreWqF5OSdMCzHWL0Pw+HBfWdeWzs+Io+k2ayBln1HLssQMOOnS5JA3XNEOZJkHGQdogIrQ4Op/Jg+tKDtHBvQ+5rNEcn0xUlcBVv6bJGc/ahx4jJf0scUcxUmzi1FKN/mHJKinIanlkRBB/Kk1ePIObn6V66wuIcV+i78TJFG9eSMLsxlUc0sEA8yaNxX/zUtp3Zqla+QjxAof86gGIpo3e55b0ha5m73ch9kMPSoBrA6pnoKTX3BShoLo2Ps3bhZAOm9VqLKEjhUp0/FmUn3KyF2b1BWDNq8hV82lK+amr60AIweABBfR57A7E5FO9jh2750bzmrBOP/SNTR9/fCN+v06iaRuKoqBoGiKdodsOE4tUMCS6kIVOfywbFFuguBZtPQ7bW7ZjGIKTBsPmNoV/rNdQwkmyiRSjlHX0yWyhR+ZRYvcwfHsLL9/hZ9zlVzOy5qPJJvwkouo6Y06fzZjTZwPgOudRP38+W55+GkXXOeVTn6JqxoyDMn62DcvXQ08cRtbA2FIIKxBzBHlqHvS2zwI4+TBqir4bOUOY4+OPonDitRfRZ/ZJzJ27mbEpi7NrBtF38WoKOnqYr5TS5Tqc+NpixtVtRWoKioCCbBCeNOG7D+Hf/Cba9jW0qybP/OV+In+MoSohCgssquws0YQkEI/iU1SvRrG5zjNo7G9ERXoenABQwMr0SrR53qRUvKeyrqBB6YeVTqNoGlXTp3sGTdVIpy22zH2Jhtc72dncgwBS0mD1hnYm1YaZWr8GRh/9oUzxvpimgxBgJlMIVUUCluUghI6r+CDbjqbaKD4/ZsN6XqxrxnK8/o0tLV1MnzaILw8P8Nkzq3ht2Axe/NsdJCItDFi7Fp+ZIeUEsKKCtb/9EXfWXcKQmgC33wjFuY4ThxxFVRk0ezaDZs8+JPvb3gxf/gm0dPQ2XZJw4Rlw63nwtVZotb2zRgBXF8KoQ7RefTDkDGGOTwwjR5YxcmRvMbhlQv1DhLfX86m5JjsDcSY1bCGWF0QA/s3tCKsDqboIxyIa7sva1Qupnz+f5KoGArW14NcodGO9UlECMxpDMTMIJ4aC3O2h7Pd9s6KALwThAs+QIr2iCQGq9H76dJgYWcaa7lLG/OhWAkWeh/fkQ8vY+fufMDr5OnWbOmhVKnnJmUWLXYhAMvzNrfyhoZOq0Yd0St+VU0+tYfHiJoxwmGwsiqIYmFIjoGYppAtLD2O5BhEnyonxfxNXJLpwkFKQdF2WLtlKdUkt/645iVV//AX+tSuxCkp49ks3MeGp+wlubUQRLrqTYID9Jhu3HcVP/gi3ffvwHN//CmkXVmVAEzDaD8YHdQJlCtwepKvz4rz7mTO5jc2ts1nbcAyOI/jH4zBuGPx3IixKQ8aF8X6vrONIIGcIc3wy0Q249o/w+B1kfnsXM3wtOGE/eUoKJWEhXElSOoTiUXo2b+XJK6/EyWZxHYd0RwfNiQQV48Zh+jQkXugzFk3g0y1URe4W2z6g6JGZhXAR9LSBpiNtE1dVEI7rJXgqCo6uURR0OHW8wK+0wp+vJ7ZqCbM2LcNnWGxJ+4nJEA8mT0QiyDeSuBJWm4P41m+3c9/p8rCkop9ySg0vvriN+c9n6GlPI0yBpilMU99As1O01p6LVq8wrnseGhaWGkAljSEtXBfi8Qwvm9Vs+86NFJlJkrpBqLOVcY/8hVUnf4ap9X9AOBKJQOBQWgQLl0E0DvmHr+b/E80rSfhum4np2iAMworGbyo4sIxiaUPyt1iZR0jLOEFzI+fPVBBCIPgNa7fP5If3PY1hqPznBThmMhwf+tAO6QOT6+iV45NLXjF8/odkJp2JbvgQcRM1lkW4XvG660hkMI/XbrsN6bqEKyqI9OmDEYlgZ7N019fTppRgu6C6FpZUMBVP5FIKb0Puf3AUJCR6vExSIUDVyAYMpCJwhQBVRSgKTsCPmWqDu66H5+7Gv3UReSKBFILqUBqzoAYTjaCSJWurpNwgQV2yZptJXV33hzOX+6DrKr/5zcn86Z5zufprR3P+VJtrhy2jJE9hdcXZbHBrqagI0080IYWKLk006SBVDc0wMB3oXvASwUwcJ68A1dCx/UFsX4CaV5+hc0ANwjVx/EVkikZ5y7CAaR2Ww/vE02pl+dbORnR7BaVyGaXuIiyniWtbJKn9zP0CkMnfs8p5nr/njUM4dbiKiyk0MmaQjBliZNWrnDXtVlQF0pkP73gOlpxHmOMTT//ZJ9K17iEK/VnMrNc+yZXgDxgoM8+m9S8vE66sBLz6w7LRo2lfs4ZUWxvdJSUsKRjOlKIdGPEuTHQMLECiIHGRKEL12k7sD47tuZKKimv40awUQkqvc4VlI2yH/OZ2hOWAGgBFYKGjYOPDJKsGiIt8dEV6IxAKNiq2rZKMR2jcnmTw4KL3H8chQFEEU6f2Y+rUfsDZAKRSFgsWNLBjR4whQ4qp+9F/aFq0GM1KoaoqQhG4rkQIBVdCidlDk1GJ5tjYqsARPoxUEksJ4hgFtIy9DjtQQk8MBvWDknfP4s9xALzU9QKWHEBQkSAMkJI8WUerHWRRumj/vDZpUu88y8JgLdXZJvwyg42Gqru4WLimD8dVOXb0P7nz8W9xylEf+mF9YHIeYY5PPENOO43tfWbguGD4DAyfTkFQkF9ahDrxBPRwCMfcI5Om+/0UDx9O36lTOevvf+e0ua/Q/tlfMz8+kIXaJDYwEAE4qLi73EKxH6eSULzXqipYWRRFRTUtRK9LKaREqiq4EtV2PM/RdVENfbfXqSswvrADhIrsNehCUcgUjyCZVdje+dFaimBQ55RTarjssgkcfXR/xnz2M+RVlHrz5Ups28W2HQb0L6KisgA1lUBqfoThx2+o6Aqg6/jNPiyfcBdr+3yFlnZQBNx0VS5z9JAgbZLmSqTcS1ZICEBDup377xHKBCt8pehI9N6OFLat4WQ1XOlpOUgpkNJm2lg4/fDkcX0gcoYwxycePRDgmHseInnJb3ALyskPCALFxRhFpYh/3MzxU/uS6ujAdbyT2bVtsj09TLz8ckqGDkXRNGpOORWzsC8tbQkaKScl/ViOwNH8XrcGZX8MofBKIPwREAoiGYVAGMtn7DZ0qmWhZ0yUXSo1jo3hN7CF7injSIcTS+qpjlhkfKXECkYTKxxHylYoqR7OmrojIBd9L8ZceCFDTj2NgoIgoaBOMGgwcGAJZ589nmnDCnD1AG48SkYPYml+fKbJ7Msv4VcbH+fan5/GnBMEX7kQHvl/MHLIR300nxQspmgrUQS9N3IettQQ2Ezc3zVCUUBSCaNKi23JwUQTeUgTLFPDzuqAi6Hb6KGzueM7XuH9kUouNJrjfwLN72fw7Nmw5C7svEFIVUWiIxyb6uhmxn9qNiufXeA15ZWScV/4AiPOO4/tr7/O/Jtuon3DBoLFxeRXV9PWvIN0voqvrC/BQTWInlavDdT+IBSvnjCvBFJR1OrR4KRh84o9LxFKr9Hzyg0UO4sv6Mc2VVQHAkGDE8ZW8XByOrHmdgzdYEDNKIgMo+gIKy/Q/H5OueMO2meOpPsftxAuCNOnugyRihIoj3DeHd/l2X88SvvWrRhFxcz83o1M++z5CAGnHu1tOQ41fkb5dM4yX+I/2eNR8L5nUlpcmbeBSn34/u1GKFSJCaxnGS/9ZwbL22r49UXfJORLghAIKWjpGUb1iBu9hpJHMDlDmON/htSWV8i6ncRUb9VeQ6dELcEnJVNOnMror91IoqWFSN+++PPzqV+wgPvnzME1TRTDINPdTdeWLRz3059SPmUA4t8/gY4maNkGqo50HaTreZW77rPfEsmTEjJJ6FfrPZOKgmOiplK8jV0FWKoGmoFimxgDhmEcfQGBT13Nuh+E0RtgynjP0UxnIRaHsw5NKdghRQhB2WeupGzUKHj2bmhvgpEz4dTLqa4aypfO+gyOZXlF+bnY54ePEIjIt/iOcxUn6It43pqBIVOc7F/L2NKbDmhX4/Xz2ObGWbloDKpicd6f/sMFUx6gKhxn9dZTeHDB51jwdwMjZwhz5PjocXB4Q1/JSGzU3ttTG5tWWukrNFTDT6CoaHfdHsCL3/42rmVhhHvDjYaBnc2y8Oc/Z0pjI2r5QPjT16GrGTuvlO6GBoqUPUbNFQpKIIJipT2JM93nSatJ6YVTd26Bpk1ecf3euC67S44Nn/fa066EM64GRUEAt3wTvnkLbKr3DKGhww+ugWEfvX7xuzNqlre9A6p+hBSU/a+gj0EU3sfUzENMtZeBPhb8PwW19IB2k0ce5ymX8We/Rdw0icVLuP/Fo/Dhw96VF/YxWIDLGcIc/xPsZAf1o/oywvCjZkwcv88ziJkkGT1EaOTbL9CdGzei7rOwofl8mPE40e3bKRo8BkbMgGyKjoadmI5GRqpowun1BF3cVAxVV0kVRdBMG38ihejcCemUF/rMJN9Bq3RXP0MB/YZ6otkv3OupxvSKa1eUwt9/AduaIJGC2gHg9x3qWcvxiUbrD+HrD3o3YcJ88US48z4o9u1RHGzvgjmzOey9Lj8IHwNbnSPHwZMmTTYSZNkXL0BxXXw9Mfw9MYQrqb/8Gk/tZR/8RUW7E2h24dg2iq4TKOzNzhxzNNKMku3pRHUsYlkvAUEAQhE4fpWuygI6+xXTOqCUeFEINx3H7d6B69rI97hddoRGR1MLja8vpn3DBrbd9j3MRGL380LAoCoYMzRnBHN8tFx0BsyeBq0d0NbpbeOGwdf+76Me2f7xMbDVOXIcPKWUIYH2ETW8+NPrKdrSiBSSpsElnGSc8I7vmXzVVcz7/vdxLAtV13EdBzudZvDJJ3shVGsp9P0lDOkiuDGFUCS4KklbYWssgO/YEio6ozgBA82W6CkTfyJNtDiM7TfIa4+imdbuu9G9V8ckkDZdku3tqIaBkA7Ny5ex+mtf44y77sqtpeU4ojAM+PU3YUsDbNsB/5+9846Tqrr/93PunTt9d7YX2GXpRRBQmg2w9x5jidGo+WmsSYyJSZR8LYmJMSamWWMvMdZY0Kio2BXpnQV2F5btbXZ6u/ee3x9nlgVFBQugzsNrXuyUe+bOmd15z6cPKIU9vkaN0nMWYY5vBYUUMpoxRIkQd1o071HFhjEVlDtrqKJqm8fsc/nlTL7oIqRtk47FsFIphhx0ECc9+KCa0h36KWgm4vuTaBtbQV0UGqTNe50B1vhLePG7B7Bh9ECciTTuSBxnMkUs4KOruhjT0NFNK9vlZtvdaTSUK1ZDJZz0BobSvmwZ7UuXfmX7lCPHF2F4DRy2nyp1+bqIIOTmEeb4FmFjs551rGYVFhYjGMloxmDw6Yka6ViMzpUrMapLWFO5iRXJ9aTiXeyVeY+9oxHynSYZy8XrV75N48oMIu4hUxgg+tAxlEYjOIUTFxqDFqxl4Mo6EnleKte34Uym0Ex7K0tQGG7VdSYVJ2Y5yOgeJIJ6rZr1Wg3Rjg5m/OY3jDruuK92s3Lk+AaSm0eY41uPhsZIRjGSUTt0nNPno2zqXjzJ49QFe2lu9FCSn2ZxaRXrozGOXPMuVZ4ejphVQ+/GRiJvFJHsHMw9DV4ay4vIBKHIncZVVcrAVfXkh1I4TIuMy4nusDCSfQ00hcoqFYJ0+QgW1KUxAkX0iAJiwkvfl9ZAdfWXvDM5cny7yblGc+TYDhqopzMdoqk+DwcOMr0OPOkEoeI8VujDSUoXhNsp2LOasnGHc8WCfXjv5hKEnYe3xke0KJ+3Rk6kZ/Qo/N0hEBqaBBD0VhQSqSxVXWfKB8EpP8fxxzm0FY9ndYdGxHJipdNE29ooGzeO8gkTdvV25MjxjSJnEebIsR100UUsnq2LsjMk4h5CoSLcxTFEiUksoeN2ZKBnEu/WXE2b/1XK8/xsuNHGOzKB7rNoWmRxyC//jDt6EQMWr8b0uIkX5pHK81FGGSIvAtc8A8UDcADH3303H/7jH9S98gqarjP21FOZfNFFX0mijG3DvGUwfzkU5MPh+6kSjRw5vg3khDBHju2gkML+tM6si7KlrZo8EcS9SjBEpije6EOO2YdX59VRVxdk48ZeCgs9VEcDuN0Ooi1hIs0eSs/4G5mWc0l4dHR3HhW2H1ewV9UkFg/Y/Jy+0lIOuv56Drzuuq80S9Q0VXH+2wv7b7vjMbjllzAtZ3zm+BaQc43myLEdscaaywAAIABJREFUDGUYJR4vrkAUS9NBkziKUnSuGsD9/7yU4iUDoNnPi/UlPPV0LTHHGIL+k1ifOZb3amuIxDU0TTBkSAGBmn0JnPNPKqigtFfi6g3BnjPhB7/d5nN/1aUSr74Pby2AihKoLFUXlxOu/itkduP5f1JKbNPkm5jwl2PnkrMIc+TYDly4ONV5Ehnv29RqG5EuneY3Kml+fAS/G3IbxbH1pPc8iBseSJAOzMCWZdiZBELYJJ0j+KB1ICce2sC4vdTcQ/Y+FMbPVL1KPXkQKNllr+2V98Dt3Drd3e9VnUHWbtg9pz6s+9//mH/rrURaWsivqmLKxRcz/Mgjd/p51G+Cp+dAaydM3ROOmQn+3XACe45PJyeEOXJsJwECXDzwWDp7LRYsB62wgWnff4YCUQB73UyDcwyJe+cSTA8g4EuQSkIyY8MhGpnDq1k4ZAhHNAouLITT8kE4DKgYsqtfFh4XWB/p8ialujh3sxagpgnr58zhzVmzcOfnkzdgAOlIhNeuvhrNMBh6yGd3He8zIL+oof3uIrjiJhVfNQx4cwE89hJcfyk8/SrMXwFV5XDOiTkX8+5Oro4wR44vie7uOAcc/jqt1jRcDjXoN7Gvh9gRPhxxjSGVBtXV0G3DDaVwVN4uPuEsHyyFS38HpUUgdIsYMXp6oapU5/m/unGIXT86IBKDvz4IL7wJnbXrGaot5YSy2RQ7ugBIhcN4S0s59cknP3GNzh7424Pwyvuga3D0DLjsTJUctKNYFhxzIfSEIeDvb3G3sRlSGXVbvl9NBUkkVQuymVNg/Eglmjl2DZ9UR5iLEebI8SVRXOzl4JklpDMS25YkTRfRAwLYEQOnyOBydOIWIfKF5N7eXX22/UwbDz88GdqCGVZ3ddPYHUfm9bDfz//LC+I5Muy6QGE6FmPti//jrHNreeLZCAV+G0+6lQZ7HHd1XEbC9gDg9PsJbdz4yeuk4UfXwMvvQkkBFOTBs6/DedfBqjiktncqe5b//A/eWQQr1sLbC9SXiZYY1IWhrguafBB3KGuxfhNcdxtcdJ0aIrJi3RfZkRxfBTnXaI4cnxMzlaJuzhw2zp2Lu7CQ0SeeyM03TGZhfSdL1uaRdrjBByO866koakcIyCTA4/DTKscCarJFrKODdf/7H6GNGymfOJFhhx6K4fXutNchBFx4hkQe+hzr1rkI+DQG79GNbti00MxqVjGene/bCzU28vwFF7Cus4Alyd+Qx2K6wz5cPjeOdDcRu4Tl8QlM9X9AKhKhaMQnBzPfXgib2lRJSNCEul7otmDJGlj6KlSNh18Ww9EfsdK7ghBPwMBy0LOGcW0D/PY2Ze0J1P71hKB7KWgGONyQkrAiArIOXA5lQRbkQyoNP74BXrwz1yh9dyInhDlyfA6sdJoXL7mETfMXgsOJoUvWPPss03/9a1595GSmnGrT1C4oNJvJK45iW05AEEtCxqMzznyLrq79kJ0bmX3hhWTicTRdp/a551j24IMcf889uAOBnfZ6IoTRS9vZu9SH2GKssBMX61i7S4Twrd/9jmQwSKZgEnq3haHFSYWieAqdpGMaUjPpSJeS7O0lk0gw5ZJLPnGtpnYVB23JwMp6SHkAN5CEDUvBMxCusWGQAePc0NML196qLD0hoKgArr1YxfqengNt3eB0gGmp++0iYDBYgNwA6Xb1c8YGjwBbgOFQ2bjt3apmc+aUnbGLObaHnBDmyPE5WPrMC8x/7g26U16ElkR3aAyr8fPezTfz/cMOx+fxM32S5EjPndzFaSRx4yVFxPZgRBOckLyR0y86hgOCsynxRnCW5ePSPDhw0NvQwLKHHmLqpZfutNejse04oMTGsQs+JuK9Ed6dnyaWfzJ2uhspTaQUpEyDcItGzBVAZCTe4HIKpwxl8sUXUzVt2ieuN3ggCA3W9UAmChQAKXWf7YX1dTBIg9P/AvYGaO5Q95UUqCzQzh74yR/gsT+rGZCWBT4vxBKQngwcy+ZAk21D7AnQVoONihlWlW8xoFaquGGO3YecEObIsb3EQhDpgeKB/Pv3j5CJS5w+BwKwLJu1dWHG1Bh0165h2KBJ1C5pZkT3e1ytr+EZ90k0OarYO7WUk9L/ZYRvA0OmRLBmr6Ir34WI9SCEIJDJo6CglLpXXtmpQujHTzkVtNOGDz8AEkmGDGPYY4fWamuL0tERo6YmQCDg3uFzicbgohvcvJ35BYRcaHaImCwglvLgzHQhNMg4SzCIsti5F5ddcQxV48s/dc19J4DLD/EVYKeBJJAAytUl9SpsrFdDRVxCuUNBDT12GWrMkNsF192ubkunweUCXw1kTgBCgAWaE4QX7FPBugG0JAwcoIQYIGOq//cas8PbkuMrJCeEOXJ8FukkPPEn+HA2CMHGeICmFp1qB2Q/19B1DTNj0tMdx5mXx1nTN3HJy1HeXHwQZx//MOVdHZimoMBaR/Ok8bxQdARlP4+gzZHYGRtD15FC0usIIzrSDNxz753+Mg/mUGbzHBEiKBmEPRjHcD459mbbkmfveIN59z+OGQvTExjDymQRmsuJlHDWWRO4+OLJO9QU4N6nYVWDQVmhJNXbjG4kkKaGmQrjECZJZzGV3qUM8n9IKDKYhx5axp/+dNg214rGlCvzz/dDSxdYaSADtAB7AzOAbmA9mGlwG+AUytID0AWYNng06OqBZ1+DIVXKugyGwDlFuT2xAA2EE2Vbe0HfH/boAD0FncFs2YaES86E8l1XNppjG+SEMEeOz+LpW+D9Z6CwHDSdYFiny86n2l6FsDJI3QApcdlRkp5qikeOpGreQ5zufI1FH3yXaePnM7pmDY5UFyuGjGdDWQ3dXYVoxRnkjEGItxqx/W40BFJKwsEODjv11J3+MvPJ5zTOoJUWEiQooVS1lvsUbj7vDlqfvRvp8JBMg6tuBSN9gwhO/S4WOvfcs4jq6nyOP377J37MfgOKAmAERtC+LIGZiOMyu4hrZewfv5bm6v0JuOoRegUej0FjY2ib68TicN4sWLxaFbwbDiVsFoALCKJcpBsAHUQaPNnElj5MCzQb4kmws3WVlaVQmAcfLIOUpVyeUgOXG2yHWl8IKDPgJyfD6GHw2gfKsjxyOozbDRsUfNvJCWGOHJ9GMgYfPAcFZWpEEjC8XBJ3FtHonkJ1YjkinQRp02uUMOnyaxBCYLjdDNNXMrGgh/RTNi3Di4gVSFYNHk5oVQCzwI1PprAvnYLek4RlHcisVaGPrdiuwvCvAh2dKrZvzFPDmi5an78f6S8DzUEilUA43fgTmwg1b0LUDCUvz8W//718h4RQCGU96S4XlZMm0bi+hZ42kxCFvFj8F7RejXLPckYUdxCNpjjmmG0ry52Pq6L3aFxdNwzIE9CbASIoAZwHug1aXFLkhlRKEP9I/M6WkEyBQwefqtbA7Ybpk6A+Clo1BDVIxMGpg60rgazugWMOVPWZ++QK6ndrckKYI8enEY+AtEHv/1PxO21+PLmNmxcMpWn4wRSYnUSSgvIxIznxrAMAqJk5k/dvuYVMIoHh8dBRW0J3fj6xoEYk7UMLgadCIArd8I8jYEU3xqperNEFVDwWxun376pXvN0sfXstEoHQHarfpwShC2zhwBXcSLJmKE6nTjC4Y5khxx2k3KMDyiCV0WiMVCE9JoVGL8lQAmHrtMan4si8RXkgxpln7vmxNebOgz/eDbGk6kbTd8n3K/en0JTY1iyJ49I7CDrcFBeEWd80BMtyAFu7cgUqS7RiC5emZUNJBi4YCH91QVMnxGIgJExaAbf/Uolgjt2fnBDm+EaRTlu88sp6XnutAZ/PyYknjmby5AGffeAnESgFfyEko+DuF6fTa9YzZJ+JPN49lJ6eSk49cDAnnzwGv1/VBvrLyznk979n7qxZpKNRpJR4LT9lI4fhDbmIhDIYIYksjCANASOKsIr9eGY3ccDxF3zljba/DIoHFCGkDVIihEB3CCxLokmLjLsAgN7eJCeeuGODkM89CRauhJXrVX1exoQ8r4PRQ0tIxn20toQIxXRG7DmBW28op7Jy6+I/04Qb7gSvB9IZJWCRmIr1JVOqhKGsGDKZFINKljJ2yEYOmLCe+144hK5gPrFkGWSLSIQAhMoE1TRViA9KRLuCcOaxcJgJrz4HoSj4TZjkgD+fD4O+wK9djp1LrsVajm8MmYzFj3/8Pz78sBm328A0bWzb5tJLp/GDH3wB39SS1+HeX6lPQqcHEhEljj9/EIorP/XQdDRK66JFCF1nwKRJLHQvYRELcOJCR6c33kU82I33iQ2UNXvY97hzGTJj5uc/152IZUmuGPcDRPsayCvFtCAR7EGTJrV7nENcGBQVebn//hM+JlafhW3DghXwwlvwwDMqizOZUpZcaRHkeeAXP4TvHfvxY+sa4cwrVZbnkjXg0CCZgURCHV8UgJoBkEk14dAiSOlA0+Dkme9SU9bC+Tf9FI/Hg6YJMqYSvUxGieDA8mzdoISp4+CGn8DZV0F7F5Rkw6ldvVCUD0//HTw7njSb4yvkk1qs5YQwxzeGuXMb+MUv5lBR4d9sUWUyFr29SV588UyKijyff/H6ZTD339C1CUZOgQPPUMkzO4hEsoY1LGMJCRIMoobJTCafnVc8/2XSsKaLW79/DVb9fKQQ2N4SnIecSSqvgIkTKzjppDGfvu8yDeZaEG7Qh32sE/bq9bDXKUqItKw70+GAQB4seVolrmxqhYefh+VrYfgglZDy0z8owewKwvpGJaypDFQUw02/gL8+AC6xFK87DuhYtqAjWMBfLrubE351NRI/hqGRSisBNgwYVg1P/RUaW6GqAkYOhncWqsbbfVmgvV5YDYSaoawIfvYd+NlBO9bg25KQkeDONcD80vkkIcy5RnN8Y3j33U3ouraVW9EwVILLypUdTJ9e8/kXHzpeXb4gAsGY7L9vAkNGl3DzgltpWNNFPJJg9F4D0R3b+Qmeegui14BMADboQyD/ZtD7k3Wuu139r+tKBIVQoqhrql1ZXSOcczWkUsr6WrcR5rwPQ6vVfeUlylILRZR79K7r1HqmBYV5TrDDIHQ0IdGE5N3lIzh08gLeX3UgHUGVQWo4AAkd3fD+Ujj7hP6X0NHTP7kj7IN5TWD1AA7oScBvboG1G+HOcz97OzIS7g7CoyEIxeIUNazhqFceZd/KQsafdRaB6u1LYsqx4+SEMMc3gq6uOHV1PTQ1hZFSUlLiRdfVB7KUkvz8XGPHr5Iho3ewMM7ahAxfyXPvHsIDLx1DRzBAdVkztZtS1DVLDENw3IGqobXbpUoPMhagcnIIReCxF2FDsyqTCEWhe6O63+uBwnyYNBYWrVIiKgT8+nzVIm3+8uw56NUEwykaWgcQTXgRSNY2lnDV+UlO/bWGJsDrBYQS3hE1cNujcNh+EI4qV+mIGtCyWa5rnaog3+EDMwx2SN3+4ENw1VFQU7HtrZB2hnDdIyywF5F2VjA4OpMly22avHncdcQ5ZG6fRd0rr3Di/fdTMHjw53yHcnwaOddojq89q1d3cuGFLxAMJqivDyKEIC/PyZ57lhEKpRgwII8nnzwVTds+/1RPT4JoNM2AAXk4tte6ybFjxO7ggadb+ftTZ5HvjWNLeHf5HkgpyPfbIAxSKeXSdLtUFxdQscK+cgivG9ImeJwq9udSeUqkM0qAGl+D3jD0RpRbs29gbjoNR/8IghGoa7TQRAJdS5NMu6gqzbD3YJNXlxcSjChvgs8DE0er49c2qNIJAcQkjJ0ChTYsWwqryiBTD3YUiIMjO27JSsPh+8Hsv/c37u4j3tlG8+sHkT+wiebyoRh2GiElDzSfyWvBIwj7A+y9+kMOe/QfDD/ySA66/vqv9G35ppNzjeb4RiKl5He/ewvTtKmpKcDvd7J2bTehUIo1a7rYd99q/vSnw5QIrpkHrz8MPW0weh845PtbxfkikRTXX/Mab82pRWiC/NJCrpo1gwMP3PXDc79pJJMh/jX7JEoCYZyGyYr6QUgEmmaTyQg8HtA9SsQsqVyfsEVNoEP1+kwEIRJXhe7pDLidqt4vlYb3l6h44ZYO8fnL4Oc3qySaaBw0oeN1+7GlZKivFdkd5bnmgVQ5G0jpVbh8LhIpQf0mZWU2tqn4ZGgaZA6AepRIH3sAbJwNwQyIBOiu/qQa4YBNm+DD5bDvxK33YcndP2LE/o2E08X0WkXoZgbNjHPWwP8wL7w/iYyH9uJK3IEAzfPn74y35ltJTghzfK3p7U2ydm03ZWU+urritLREcLl0AgEXgwYFePTR76iY4fvPwSPXg9MFhgve/A8sfAl++Ygqlgd+c/EjvPPKSso9CTQhiAUd/PLHQR6YVc3ophchk4LJR8IB3wHXF0i8+RZR16hqApfWKqvs3JNg4hjojE7HNHWchhpgHIr5spV7kkTKQapvBKKEgE9ZUm1qBi+6puoBQbklLalEEPoL3ytLVTeZLVmxFo64QCXNuJ3KqrNslXyzd/FG3KFGFlpjQQjKPDHaQmkSMY1Y2kkkpiZYSKBnNMiDQYuAsFTPhXeGwZnnwJ1XqFgfKBG0s0N6nQasXLe1EMY6OnBoCxG6gZHMvgAhMKWBlAmGe9fTTBXVrRswk8lPHTOV44uR8/vk+FqTSpn09iZZvLiVZcvaCYeTJJMW7e0xli1r57zznuW3/zeH8IM3Iv0FkFcEbh8UVUA0CHMfBaC1diPvzVlBuT+D5nSB4cTntJEt63ny5sehZa3KGP3vLXDrpWCZn3FmOWob4LiL4dZH4a358OBzcOrPVNyvqGQqmu4jk8kAafI8USRgWkbWMuzvMFNSABefoYrZXQYUBpQwmh95C/oc35alhHHER3KjrrtNWYp+rxI/p6GO6Y1I2tpN2u1Swhk3SInHkWGgu4NIyoGdFTZJtpRiPyAOwlZPqkuItsPSPPjxOaoBkW2CNNX4pinD1fmWFW99Ppl4nEzCgdAkum1T3NuJ6TCQ2RH2Xa5iXOkkU+bNIR2PM/Gcc768NyfHVuQswhxfW+rqerjwwtn0hiy6EhUIVz6m7MVjtxGLZYjHMzz66HKG50U5flwrBTXV1NT0W3JJzcPaJ5/nituKmCBXIK0Mmp5NEbRMsG1cMk1zwgs+VSCOJw8alsHKd2H816Peb1fxq7/AxlYV43Ma2XmAHfCrW+Cdhx187/hB3PtUMcX5bQwdmKCpU1Wv69kyCctSomU4Ye894MofqjX70hoyJptFqi9RWKDuj8ZVKcWWLKtVAtiH01DDdZFQGxuEGdMBgYHJ/OBIbCnRkGi6Kt1wOVXmKXlgZeOXfc8tLIjYcN0psPptaGyH8kI1taInBPk+OOgjU6Lyq6vprK9hpNmD5rAo6e3EYWbIFDkIinLshWHOfv0aAvFepl51FTUzZnyp70+OfnJCmONriZSSa699g+6wQab8FIhpSAlpIJ3qhvALaCKDrmtEhZdMxmTTxh7Ky3243Q5SKYu61a0sSQ1DuMCdDmFlNMJhi3wtrj5NpU3C9DK5oIW5DQW0Rg2GFaaY7G5Hr1ucE8LP4O2FSmwc2QQRhw4YsKpOJb1cdIYDvz/Ag88ECKdg7HBlRfaVI5QUwYTRaupDdy+ccTQ88jwsXatcWX1JMZrot9ag35JctBqOLu0/n+rK/jmDmYyKLWaPIIMBSOUuRZC0HSQs1+b18rxKDGMJsNeDHAUy3L92xgljnSpx545r4dp/wpJaQMLIIWqob55v6/3RdJ3Jl9zEwvvPYeJ3mjA8khIzitVcyB77PMbTFw4hefrh+EpL0Z3OL+dNybFNckKY42tJd3eC2tpuerQZpC1D5asDIMFVAoEJEJ6PpmkkhI+Xg6M5oWwN4d5i3BX5dG7qBMtkQd6BuISDTudIzq55jgcaxpA23Lh0m0jGQakR4cm1ZXTXlWBKDV3AuICffx5bhu9Tz/DbjZTKYvpYnq5QZRBzkjD31ffw/fsujm/byLCxYyk46kJ+9vA4SguV+PQV0OsajBqs3Iyzb4e7n4QnXlaW1obmfqsQ1HFGtlXoM6/C0VsYUb86H066DCJR5SLd8jgAh7BUv1QhMbAwhYWlOZQ1K9XkCo8LYi8Bw1CTKxJAFBLtMHghUKW6z/zrt0q8LUsV9n9SQX3VtGnkVb7MuhefQqZXULLH3lQfcC4OjxcApy/3W7Yz2K2EUAhxBXAzUCql7BKqMvpvwNFAHDhHSrloV55jjt0DTRNYtk5Pqph0sndzLZdEgJUA/wgIL8DlUubI31tm4PW5+GGmBXqThGMmd1un8MwyN01Na5ESbh8T4c+jXuK/3ePpyXiYMbCZJb0lLOwtp7wgDoYLO5NhcVc+9y0v59KjdvEm7MYIocYNLVqlhExoaip7LAX6OTDrnjkccu+vifs9rPV4CS9YyIDF/4/p0+7mzaZx+H1qjXAUDthbJdiAEsNp4+HJV1TtoGEoUdtM1kKsqVTjl/qK8AEO3Rd+fi5cf9vHxVNKgSkdm9dICg1h6NgZoWYTJtTr0AToXWDcAckRqAkWYSACN2lw9kQYno1NFhds314FBg1i8oWXf659zvHlsNskywghqoHDgcYtbj4KGJG9XADcvgtOLcduSFGRh4l7lZNJW4BAd2gITaC+0gPSxjA0dF1DSkkso/O062Rcf34N/u+/PD/1Fh5aUcamTeHNLrU5PcMod0a5ctQCHjlyGT840MGC5DBK3Ukw00R6ozS3R0klE9z1h/+y4M47sbccXpdjK373E5XgkjHVgNx4AowR4Bovmfz434g68sh4C3A6nbTll5BCcFD6Dq78IVSVw4BS+OUP4aZf2CwTS7jfup9Zm+7itOu7WNGQoadXJcz0CZ2hW3hcSfzeGANKY+T7t7bE1m+Eh57LxvsMZTn2xRQVYvMlZTpIZwQuIyvkKJdteQkYOtgdIN4ENoLoBUxVn3jaFer/z8I0VXu2R19Q/3808SfHzmV3sghvAa4Ent3ithOAB6Wq+v9ACFEghKiUUrbukjPMsVvx2+tm8NKxDbSmSxAyjqYJDKeBLfzYXfOQEmKxNFLC4MEB/nX3cdQVtrCC5RT+NMrw7gjxB3QS3erTcnG4kpjlZGOHl+qpw8AUIATS6SVeUElP/UZ0rxcdN5qwWXjnnTicTiaeux39s3aASCTF22830tOTYNy4MiZMKP9aTKPYEiklo1wr+MM+85nbarBudAnuMo2Qt4Y1ViW+3jZihZXEk1CQLTxP+/PpqV3FD26H047uX+sd3mU5S2lu8rB6UQVSNykc0k7ryoFYtlDJKtjoegqnkUYjTXd3N5ed0QVMB5RlefoV/TFCM/v9pS+euO3XAPl5WTevBNtSVqjXo9yym6dTZL97GYbqQzp/Bey/98fXS6bg5Q8yzFkaZmlDkkzETzoYwKGrqfd3XKMyYnPsfHYLIRRCnAA0SymXfuQPfiCwaYvrTdnbckKYgwED8nj7vyOZdEwTsbQP05RYpkTPbKTYWM9J50xACMHMmTWccsoefOB4j+UsxYkLw6sx7nSbmpmSZ850kI4KGhJFPNw6nrMHLMXuacetCWaWF/BWYiB2ex2abgMZoinBwTW9eIuLWfLAA4w/+2y0j7YM+ZzU1nZx0UUvEImksSwbXRfMnDmYG2889GvT5UZKyds33MCaZ58lMbmAiktHEsgIwtogrJL1lGjlpAsK0NMJTKM/i1fE4wRGDttqrThxVrICR8pPV5eGUwcro+POT+L0pknFXOiahSYyWLZOMu3C64ozbY8POO2Au8D8Dzhq+O8cVYfocipLMJEEBGoY8kfQtGx8sy8TVaj4IFJNq7/gFLjxHpWg0yeCuq6yY0G1f/soPb1w1GVJNvRGQLOQtoHhjjFlxgas2vHUbRL889/wm4u+hDcgxw6z04RQCPEqsK1ue1cDV6Hcol9k/QtQ7lMGDRr0GY/O8U1hyCAXB41cxDMvh0DPQ5hB7HQnZsDFqFElXH75vgBEibKS5fjwo2UjArF2QV4lDD/aZvUTSsj+0TiNeeFq5l02CKTNlacPpf5377DkLQ8Z20E440EieGMTBFM+ZviWY6VSaF7vF34tUkquuuo1UimLigo/ZbITr4wy740EL744iOOPH/2Fn2Nn0PTBB6x55hm8Ayvo+NEw4iGJHc3go4HWxFSKx7TT8MOzKLn5Bdx5KdLSxh2P4kknmHTBBVutFSGMQGBmNAQQqAzh8qdIRV3kl4fpqi8FaSE0SVlBJ2MG1fGTU+4l3xskGErjK/kQHDW8vQiKC1VZhcuprLxkqv95nFkXqKapn3sj/ck6fVZjxlTF8Z1B1d6tb5K9pqmMUNXIW8VGP8oPrrap747iL4mTSanfv3TCyapVXiYOaabErOLFt2DWhTs2qSLHl8NOE0Ip5aHbul0IsScwBOizBquARUKIqUAzsGXL9arsbdta/y7gLlC9Rr+8M8+xO7JkSRtPPLGS2tpuXn11PXkOgab1gA624SSRMPnXvxZxwQWT8PmcBAki0DaLIEBxsZdYOkb5BMnqJ/pcZALvxP3ge+cAUBa9mcf+tYg//8jBba9NJM+ZpMQdASRL2gppD0zjMueX09B706YwmzaFGVIK55h3M0w2YKNjFtrM+88GOO4vX4tPyfo5c9B0HbPKTcLWaK0dQGvjIFxGBN84mxUvT6Nh8TAy5cfgD3Uwvv15Tq1+m4N/dhXV++231Vp+8pBIXG4b0NANyaRTFrLwqUnEe/wIAW5XioMnvsax+81l/LD6zcdG4gKE8ruWlygBDORBe7cSLWkrl2dlCQTDyqrTdVUioWnKFdodVLWATkNdRtSosoype8IHy1QJhy1VDLSwAP7fKR8fyNvSAR+sSuMuT4KtbU6lNdxpQq35GAeuhfqqr8Nb+41ll7tGpZTLgbK+60KIDcDkbNboc8ClQoj/ANOAUC4+mOP552u5/vo30TSN3t4EsZiJwyHw+51EoxlsW2LbNuvWdXPuuc/y0EOf2P05AAAgAElEQVQn4Xf5kNhIZHb2OOyzTxUL1jTQu8HaHCeaMmUAc+Z8v//JZBzDqTH00Er0uZJCPYSQAtuyyNdjxPLGsHhxG1OmDPzCr6vvg/AM+2mGyQZ6CagYpUxzaPR5WP1d2GO/T19kN0A3DKSU2Al47dHj2Lisz90pib5SiO6yMdwmI4aW4nQUU988ioYjriRv748rgQ8foxjNasdKqgZ6aWzSGTC2hYJBXcz+9Wn4vJDnNrnqzJt4ffHR/OQfP6A3ms/wgXWcd9QjjJl8AADfPRxeeUcV6HcGVTeYjK2yQHvCKjHH41b3xeJqqr3uUqKYSqtJE987BlasVz1HvR6YORkamqA7pM71oRvVZIqP0tAEhmEjLQ0cFlp24n3fJA2H06SrB0469GvxPecbye4edHgR1dd2PfAv4OJdezo5djXJpMnNN79HQYGHsjIf+fluDEPDNCXhcArLsrEsG9sG25Y8/3wt55zzDAWykIFUESWKnRXEmBXHEAZNrzlxu3UKClwkkyYvv9xvVeA8EKRJWPoJVPvwljgROrgDDsrHT8Dp99PSso2g0OegqiqfCYN1RmbWENosgpCyNPKK8uHNx76U5/mqGXbkkUjbprZ+JBsWDcNbHMGfH8KXFyEdd5OMuNETeTgtWL1a0NGjc/ujguMuhide+vh605nBZKZSWW4zdFQUZ7iStQ8cT6HhZ2g1jBlRyE9vv4+H5nwXIUwqilpp7qxg1n1/p3ajGg81cQz838Wq/6hD70+W0TSVsdnYqgSutFA10c7zK7ErLVRuUIcOf7xCGXNa9lPTMFSx/LTxMGQgzMjONFi8Cm59BO57Wg0NriwFn8NFOuFEIjdPoEhGPQwY20LrkiGMHAyXfO8rfVtyfAq73CL8KFLKwVv8LIFLdt3Z5PiqCREiSA9+8iimeLO19kk0NARJpSzy890AFBd7cLsdmGYa05SbGzcLlIWo6xovv1zHBx80cdi+R/Au77COtUgkdQuTPHaZhR1y4fcLUimThoYgs2a9zpQpAykr84Fzf3AdzPDBSzHcRRRXG2QyLiwxDKe3ABJxhgwp/FL2QgjBb34xidgsnWTaRkqJEFBW5qOwxAmR4JfyPF81FRMnsvcFF/DErV7cdgiRZ4PPgSW8IMBMGhSWOFhdr0TF7VJu6YJ8+NO9ao7g0C0CIjo6U5jKZDEFmSfR9tVAhX5VO7UYjDthFJGoSSLdi0Ti9xfiz3PwwLPw+5+qxx53kHJtBsNQt0mJW1/8z7ZV3SFCdYfZjFCWYiiqMkWPOAAefh4GlPU/pCsI+0xQrtff3Q7Pzc0m0gB3PAbXXQqH76Pz8jKdaMSN0GxsW1A2tIuTTwhziGsC0/bcuv1bjp1Lbutz7BIsLN7kDdZRi0AgkVQygCM4ChefHHMLBNzE4xmam8PoukZRkYfx48tZtLCFeDyNhkQXkqH+EMLlo90qxLZtHn10OXl5kzlg5IEc4JzO82ve4d3gXI68UxJqSPPhPzR633Fg25K1a7u5a9Z9DO94VZUBHHcM0046nOrq93nrPUkqpSFEEiE2cfjhQ9lzT/WpKKVkzZouursTjBhRRHm5f4f3ZdDeY7H2Gkp5d4SUcOH3u3B5XLy9OkEDk6l+vYH996/G5dp9/3SFEEy+4AImtIfpfNdCS5q0hvyYAKaBQ2ibE1Z0XY1YKi3q70f6xodbC+HmdbP/tn4uCMeU1TV6qINUugS3U1lr8YSaH7glU8fDY//rPxbUeRiGEi892/lG32J7TVOdW74fzjsZ5i1TQiqzGaPFBaoP6ofL4Nm5UF7cbzUmUvDbO+Cpv0LlU4U8/34crbSVocMTXHZsCdOqRn3ml78cXz27719Tjm80K1lBLavxk7dZCFto5j3e5SAO3uYxUkqefno1bW1RotE0hqGjaYKhQwsYHQjjDcRoCRtUeJJoQqLLOG1pNynLwZNPrubDD1vweAwOPa+QtkkL0dyScJPEVSQ55E+S136m0fS+IJXI8PhjK7hiUhghBAtuv5OGdyZSv3Ey4XAXtm0jhE1RkYfm5ghtbVFcLgc/+9nLrFrViaYJbFty+unjuPzyfXasBlB3oJ/5G4ru/gXIOL1JydnP11CfKMSsd6C/+SqVA/O47c7jqCrfvdtvfee4fN5YCqXFMFBT7siWTmhqU+ULpgUypSyhmmyCiSA7iX4HKC7ItlWTW/fzjMQ/Pv/vx2fCs6/11wYi1ZN63SqLdP+91XSMjKbOy7ZUjPAHJyrL1e2CB/8A7yyC2g1QXQEHTVWzER99QcUctS0CTh6XsljrNqmM0CvP82Jaw/DmpnjtVuzuMcIc31BWsBwX7s3fhgUCLz7WUYvJtttsLFvWzgMPLGH8+HLKy30IAZZlU7umk4HuEGUBDRtBc9zD8t5C3u+upDdqk0iYhEIJOjpimKbFP29aQqhep3u1Ru0zOkvv12ldCBPON7FtCdKixwpgOvMRbh9LrLFc8p9Clq/owDA0DEM1+O7qSrByZQcPPriU3/72TVau7KSszEdpqY+SEi8PP7yMl15av+Obs+d0+OXDMONU7mzdl/XaUMrHj6NoYCGdBX5eq48w9foP+FU7dPdtldUMiSfVxdpmYvVOZ+w42PNIWNwBS9uhJagyNP/7dzjreCU+laVqsoTHrYRRaDBjkjo+EoPXP4C587JTHz4Bt0tZau3d6nGWpfp8OnT4wQlbP3bEYLj3BvXcoCxBr1uVRhTkw53XwpnHqvviCXX7d4+Af1zdv4bTCdMnqftvugeOvAD+eHd/E/CPIqWyKPuOzYng7oeQn9RW4WvM5MmT5YIFC3b1aeT4FB7gfiQ2jqxTQpoWkY522lNhNt3nwVVazAGjJvKdmVNwu9SnyC23vM+//72cioo8QNLREWfjxl6CPXGG+kKU5UNXTGdjr4HXYRJOG6SlDkLD6zVwODS8PgOrMErRYEHXeoll2ZvjRIMPliy+S8cQJkIIDq3pwJKC1d15tEWdmMLY/EHXJ4a2Lamo8JNKWZimjculU1WVT0WFn1AoxYgRRdx77wmfsAufzcyZ9+N2O9ANnQVJNfTVsC0G++uZ9vcpuI0qbixcg564WQ3AAzUS3XcleL7zRd6iL0SvBec0S1b3pAlvSJBuNQiUGNx3mMHMAvXl54U34Po71MQribKmzjsZLjpDuUcvv1GVHiRSSuyuufjjCSVSwt8fUkLU3auuFwZUPPCnZ6thwLqmBGjLY/7wL7jrMSWcmqaO+fX5cO7J6jE9vWqE1JCBSiD7CEXg9v+o42Px/rmGrmxpRSKl5g72CV84qqzVF+/Y+hxy7BqEEAullJM/envONZpjlzCUoaxkBX782BmTtiVL6PQayGIn7hNiCC3O+452Fr6yhD8edQGGw4GmiawQSerqgjQ1hYnHM0gpcdlJnJZE0/KwhY7DYeMwwUJDaBqplIWua4RDSZxuSeOHksIagdCVG1PoUP+KmoVn2ho6kvdbikBCoTuNLcVWs++kVK5aKSUdHbHNghqLaUQiKTIZ5TqNRrej8eSnoF6zpMuCtISAM81vTnuEvQavw23MxsaiLRShwqhB15WZI80OXp/7GP9+o5xgbDgH7VPKmcfpFG1nE+gvg0dDkg82Rkg2BpVrOACdmoNLlntZdkABmoBjDlTZnG8tUNbUfhOVxdYVhJ/8ATY0ZTu2OFX/zl/+WVmN+T4ljAdOVWJ61d+U9RfIU27VSByCIfjHw7Aw2/R7+iQYORg6epS78u0F4Pcp9+jwQfCHy2HahP7zLypQ4hgMqRimy6VcpxdcA3M/VO5OXVfWZ7YTH03tcMwMWLymvzON3wt//XVOBHd3ckKYY5cwiclsYiNhIsS7O4h6JKLSRbReEm7Xs7Ebk8CgNp5f9iEn770fBx88hIcfXkZzc5j163uwbYllSTRNEDJKCKSaCSclDmETS+s4DJ2UqW1+XCyWUtPL28BbKtGcEtsC3SlASGIdIjsLT2BJ6E2paenRtI6NtoUAKpcsCDRNYJo2mqZ+BkilLOrre9C0Is48c88vtE/HHz+Shx9eRrokDxCcPmMuk4asoVUMRLNL0Eijk2BeooqjfGE89lr+9ezB3Dn7HLxuC8PRyANPp5nzfhUP36SRv+P5O5+LZ5qSxNojeF0OpAXSFDgsmw2hNIuaIkyuzgPUyKIzjtn62HcXQWuHstT0bGanywmJCFx1ixrJlEjBrL8pC00T/e3NnNluMM/NhfGjVNPvaAzufFxZbVUVsL5RPX7yWNAHQEc33POUSqTpC+f+ezb8+hYlyg4dDt8fzjxGNe5OJJULV2QFMGMqoYzElIU4+3aVgepxweRx/eeWY/clJ4Q5dglevJzCaaxnHa/P/RuZDhvfyBQz3lpGZbCb1sIS3hqzJ93FZcxpmcfJe+/HuHFlnHTSaK6//q3N4gPKMutMuogbNXjcNqkEOAwNSwocDkgm1aTXvkERmbgg1gWeYonhgVRYEqwXmHHVwFnXVbNJMzsg1hI6fr+TWNzEsvpDCR6PjmlKbNvC53OSSGQ2u04TCZOBA/M57bSxX2ifzj9/EsuXd/Desg4yKclRE+bRkylEDC1CAxzYxKWf4doSHkuczknJ+dz70lmUBrowDBcINz73Blrai5j9hp/vHfuFTme7SXRGsNCIf+AhtcKDNAVagYk4JEm9o2OzEG6LUES1OOvbyz6r0DSV6HWHIRRWGabRuLrf6ewfAGxbyiLze5WluXiNiuclU6Bn6wgzJjS2KddpeQksWaMK34dWq7jkj65VIufzqOd54S1YWkv/RIuPRJTs7O9WzQAoKdx2Yf2OIqVk1apONm0KU1WVz9ixpV+75utfF3JCmGOX4cTJHoxl3RwTrXsDRy6rxTIhaTgZ1dLI6JZGHgkczfIOL+m0idPpQAiN8nI/nZ0xnE6dVMokk7FJJk2EMIimbUxp4dB0rLSpaguFSum3txhCZ8YEbYs+/qHSV1O21UhZIUilt05lzM93MWpUMYsWteFwaLjdjs3no4r6JXfddSx5eV/MHPD7ndxzz/F8uLCVXywO4Rt6D72eAoTQsACHcGLaOi47zi3rZ8C6d9QIKocFot8f5zbCzF+x84TwwHiU998vI73Eiea1EC6wkgbafyTpS9xbPdY04frb4eHnlKWnaUp8BKBli9/D0eywXl3F7zTRb71ZlnJh+jzKIosm1Pu4fK0SvHhSiaUt1bEOh3KLdvUoIVRfflSMcWg1/P5OdXxfUotDA59QRfcjByuha84o8RNZCzRjqbKJU45Qx2w5B/HzEI9nuPb/Hmdw2ZNMHr+GrvUu7n79UL533vX4/F+8r22OrclljebY5Yw9/XQOCi0lYwtieV4yDgcRjxdbExz64XzWPCcZP+JPTKiexWv/eoIhxSncbgcOh4bf78Lp1LFtSTSaJpm0KC31UljoxuMx0DSxedr59qJatKmfhVDXUykbIQQul47LpasPSq+TCRPKcTodZDIWQpCdECEYPLiAysp+q8dKpzGTyc+1P0IIpk0ewOzzxpAsPJYSrRsN8GrgtCRGIs6rXfvQuMnFLbcOoWHpaizbQ/+ftyBtOqneVsv7r4izx5ajv29DvkS6dKSh47CTuMMJVrf0n4iUcOKlcPN9SohiCdX9hWx5QyajhE6irktbiWU8qazBLdeJxqE33F+f2CeCfQiUCzNtqjX7OrxkTPXlZ3i2V/+Gln7rsg9NV7epjkVKHBH9cwRLCuH+36tawiPPh8mnwFm/hIUrP9/+3X/fWxw3/UYOm74Uh8NBYX6GA/Z6nMVv/+TzLZjjU8kJYY5dzrDDDmNQuY9QkwAJuhMcLkjmeyhe3UnDnDiyYwOh9i6WbZQklr/PlEEJpJSYpp2N14HXa1BQ4CaZNDFNm0mTKvH7DWwbTHPHsqOVeIrN3+odDoHTqSGEIBBwsee4Uior/Zx22lg0TRCNpunpSRAKpTBNJYqPP76SRDDIa7Nmce/06dw7fTr/u+wyQps2ffqTfwL5OkwvvQjhqKZKa6OCNrypZrrNYm6P/JTBda1UD8wg462srZVYtkBKk3Dch+7I5+TDPtfTfi40w8fwIheBxi48m7rxNnQS6Agzeng+ja39KrNkNbwxH3xucGdbmWmiP4u0D4FKekmk1M+2/OQ5gk5DPSaR7B+8a9nZUgmP+lIUT0BRvuoy09kDP/xO/yzAscOVOG6Jlb3udKhYYDrT76qdMBIe/4tyrd54tzq2sgwamuGS38LKdTu+fx2NT1FWEiMYKsA0DVJpF+FYIUX+V5Hm7lEe800i5xrNscsRQuAbuSdju3tYXBtB99voTg0ZTLIp6KPADpIylSh5RJoGs4qD173EXvtcRGtblLq6IKNHl9DTEyeRsLAsm1AoyaJFrVnrQLlFd6RSSLm2ZDYmIzFNibRN8ogws+sVBkRCdC+rYPaiwxkyZCDr1nWTSJgYhsaECRUUFLi46aZ3qbvnQ0qCy/CVqvhO07x5PH/++Zz61FM4fZ+jIF4roazoEf5S/zTOVCMrw8N4LzYV/4ZuBqyMIJyTGFq1DqkvpDt0JFLqVJaXMetiJ4O/eF/w7aayFAoKXFSWDyCdSiGEannX3i2YMKr/ce8tyYpeVhv7rDQpwZL918mWRXQF+y3EbaEJNneWCUX6u8X4vUo802lVzrD/XsqVKiUcfRyce1L/GtdeojJZo3ElzH3F/x4XxJLZ4vs0BJxK9IIRuPwPSmxLClViD6jepZ09cP8z8Kdf7Nj+DRrQiGnpWLb6AqD2QMcyBVgbwLET38xvATmLMMfuwRE/xCfSTBpfRqm/EHdcJ1+mebptNIYws9aZQNcFmq5BOoFMxSgt9VFTE2DIkAJ0Xcu6R03SaYtIJE0mYzF4cACXa8cG52oauFw6hqGO03XBBNdazhCPkacl6E668SXamdz2H2ry4xiGTkmJZ7M71jAcyHSSOUts/OXlaLqO0DT85eUkurtpmDv3c2/Vi08tYe7Ve/H43Wfyyn0HkvinB/veGKTaQPhAH86h02t49R6d2XcN55nbipjyxZJXdxiPGy74LvSEBGhunC4XHd0Cv4et4pSDKvutNujPFN1MtvuLJpQYuZ1btz/7KPYWcwZ9HmUdapoquRhYrlq5nXsS/PBkNai3rUs1xz75x1CfNdT32kNlfg6rVrWCQqh6QoGy+po7VElFKKqeLxxT7tRFq+HN+TBvaf9wXr8X1m7Ysb2LxKC5dyyJZIZQRFmtaRPSaZOCAgOhV+7Ygjk+k5xFmGP3YP+TIBXD87+7GV5sQmUBrxrf4cW3e3GLKLBFWywhCLjSXPX3Y4mnNc444ymWLWsnGEzi8eikUvbmekPTtGluDm/OHN1ebBuSSWtzbNHvtJmcfoeE8GKjgZBYLj9CJvBt+hAhJiKEBtjU1wepre1GJMOU2xCJpsnPJs1IIJHMMO/lhZhDprLHHjuWCdjZLbnrySLKAiEGJGPElntJJjV69SH0JFZQ4LExTcnxJ+5DXkEVn5yb+dVz1vGqfOH+Z5RlNHmcciee/SvVCs3rVtMZAMIRlcCibRF3A2Vl9TXmtm1lOXqdKnnmkzDt/rIK8f/bO+8wu6py/3/W3vv0M30mM5NMeiMkkIQk9CJNQBCsCNLliiJwUbyigP6wgAIqCIp4UWxYcgUp0rsUgYQaSCF90qf3OX3v9fvj3WfODKQTSJhZn+fJkzm7nbXOntnf877rLZZYcq2d8m/2NKk2c9E1UF4iQul5kjx/xuUw92cweoSUWjt8tghZLCIFt9c1FFymyl8jTPhtmrK5QnhVa6fUIz1wuuybPW3z49Ra5hEKDkyx+N7N8OY7F3LUrAcoL+mgtT1Ob8JlzIgE5TXHgzNuZ2+JYQsYITTsGSgFR58Fh38BejsgVso+bRnCd9xIb3OGlBshpUNoFLV2E83Fe5PIKC6//HFaWnro7c2htTw0IxGpIpNO50in3QHRots7lEK+oLweX6uw1kLOUyilsR2LTNqlLQVe7ztkQ/tiWV6fNRqJOLh2iFqvgbcXNLLf7OEEgzbvLGmht7GDpdlOfjXvfqZPr+bGG4/b7ujSxStdLJXFseWxu/fkFAuXROhNw/q2UbjhBi64YD/233/3u86Ukvy7jx8CK9fCOVdIMExTm1hiaOkaP3kMLFlVqPKikbU82xK3JMh5oSB85lh4ecGWhTAclPMzWRHNkdWQ9sWrulzcnX+8T6zGjU0iYHl3ayYHJ10IZ38K/uc8WLoaNjZKfVQYuG6Y6xdE7OVkrAHHL9jtt3ZauFyiTPu7XfMseEeq06xcKxbwiUfAZeeIlfnim1BVPoY/vHA3J828mgkjF5LNhVnfexqTqy9778V0DrJvgO4AZxoYi3GHMUJo2LMIBKFUujlUVwe55Xdf4PRP/5m0q3BwiasEvaqItyNTuPPOt1i6tA3LkvqfmYz0IUync8yaNZLXXtuE1u4OrQ3Ce4MwtIbFa11meeBYHjltkc244gIlTUdwAp7n0d2d7stvTKXkqRkNpXDS7WxaE8CyHXoaNpEurqOjdC8sFPPnb+SXv5zPlVcetl1jK4o7aKJAFxAgGvGYM7OXlesCfGL/BJdfcvpOdb3YXlxXujfc84SI1Zc/B4e9p2DVe7njHmgcDev2gkwcWAn2k9C2UYRj9jQRnngUmtrFSguHRFwyWbGsxo+Eow6Q9ZxV6997nyxLxDLfziiblWvl19g6u8Vlm0qJK9O2IJH2cw4jEghTUgRzH4Z9J0m6xNoGOW5bv0O2LZZjIgXpbMFF+9P/kbFvbBKR/ucT8Id74fXF8kVhWDmMGQ73PSUpIF/6rLyfZUFj197c8exdBOwUbZ0OUyc4HP1xeb9NzfDiGxANruPIvS4i7DT6RSg0hM+C2MWmy+8OYITQsEcTDjuMGF9LW3MX5DK4KkppRTHxogDz/+8B6IwSszSeVUJOKZRfhq29PUk6ndv2G2wHSkEqZzM/cBAHu8+T0iGy2ER0CpwAi50ZBJSDUm5fDmG+6sjj6cNodko5JrOeRHcvSyuOZn7PRLylbX0BOf959iG8rzdiBfYGZ8pWH2DTJ0NtdQWNTQkqijtQlkMi6VBakuYbX531gYqg58FxX4aX3sx/MCKIXz8HrvnvrZ/7sIYNR0GuE0iBngjueLB+BT2dkqzuuiKE2pOfe3ol6MX2Uxd6k/Crv8GiFSJM/RvsAtRUSB7ghiapFpPOQkm8sOZoeSI2ritC4wRkLJYlQTBFUUmY10gfwUSycCve7VSw8l4D/3UsIhGtmZxv1WoZ24kXSkWbgCPuVNeDtnbZZ1synp4EzJwCz78OXzlVjstkC/VKs26YngRM3ws2NIrF+LPfQ87VXHPOt1m2qpmqimHUViHWYfKPEJwBwe37cmUwQmjYw1m3rot4PMiECWNIJrM4jkXAhndeXkhVbwNZbxyduSiQwVIBcq58/a+v70RrCIdtUqkd7OvTD8uS3MFUKseGkv14MxdibOfLREmwQdfyuj4QN1xJWTRAa2uv5Jg5qm/dT2vFq+4MZhz3JRYtambRoiYsyyLoWISCGYZVbGDRohDXXXsXRxzUwQEHz8Qpu0YKZ28G24Zffq+Ub/3UYtXaUiySxKIOP/tGiLq6D9Yd+pcHRARjEX+NDLG6bvkzXPA5GDV88+elPWiaBbSASoOlwesGXQL6CMjeKyIVDEpAS3M76CykXREE28/fqyz3BSYnIphfP8y7sTe1iGtRAyFH3Kod3RIok874lporuYbKEgHKu9NBAmlsWwRqU7NYpFE/7zD3rl8hTxdSOgIBEelMduAxmaz8K47JuLI5sVYdx48B0nJtZUnnjHhEigFc/EW48Y9i3QYDMofepJSJu22uuFNrq2DGpDWMq11Be/cwVqyVqNpw0AEVguS9Rgh3ACOEhj2aCRPKsfy6jrGYxKV3bViPm0kzptri9fVRLDzpP0ga7AgacU0Gg1Js+/3gOArbtrAsRTrtskhN4e3YZBIJadmkXFCtSVpaEn3uM8lZHGhCLFrURDweIpNxicflz668tJEVq4tJpx1uv3Mc9z6cY+a0Rm655UHCZZ/a4phG1sLff17M6vXFpDOSCB4IvK9pbhd3Pyb/50UQ5H0zWbj/abjkzM2f1+JCRSV0NfnCo8ADSIIaLcIWCEBFiYhXKtNvPc7PAQR4fZG4Nj1dKGmmkOu5+bxCLe7N3qTsd12JusQ/zs4LqJZgnWRazgsHobRIfu5NwKGzJAIURV9xhf6ofhbhSUfAg89u+XNLpvvaHpLLQTggXUSkjZiMq6tbxjN2BMyaCmNGSH/DfDsprcWNumCpiOXKdRAJZOlNyB+H1jLP2koQ5/HOFW8Yqpj0CcMezUEH1TFxYgWbNvXQ1Z1j8Yosry930JERjBg5koDl4aH6IkrzSdiu6xGJBPrSH3YW27bI5TxsW5FO57CsfOUZEbp8KyY5duC5/T2cr73WwPLlraTTLolEBu0l2bgphOdZBINQUuxSPSzHawvK+efdT25zXEqJG3DK+A9HBKGQH7c5IluJ9Sm3oTgCUyaKizBvySkbvMcKVt66BilWnc1u/jqbS6L3+gklQDIjgTSeN9Cd6diALzz5BPtEyndZIuLb2SNBPHP2kWa7maxcJ9+Jvj/5cXieBMWMrBEhywfNDBijXy6uv3h6/py1v5aYzkrfw+5e6bJx+c9hyWqpg7qpBUYMkyCjjm55D6Vg1caxtHWXgu4uDFBr0EkIHr/lG2J4D0YIDXs0gYDNbbedyN777cOLb2jqNyhSRQfQVPkl5vZ8g3jFGCoiOZRtoyybaCxIMGgTiwXp6pLglf5sbvnNstR7jpF8NoXrejiOwrJEEDs60iQSOf+8wjl6Mw/p/HspJf0Lhw2LE406hMo0VSMipDMBwiHZX14q1WiKinI89Pie2bn1/M/KWPunN6TS4r479YQtnxex4IwScIvgwNkwaSzEqyDwb6hukrLEzxYAACAASURBVLy9fOky913W17vvV3obXa08T6zKvEWYJ+cX4u5/i7SW6+W/QCVTEpCzrkEiOl23kJP4HiVE5l0Slwoy0bBfjk8PdKPmT1P9LMtef+1RI/NNZ2Vt9K7HZE3xqZclUOc/r8Otf5c8xAVLZX0wHARfSslm4fq/XQ1eloqiBiqLGkA3QPBACBsh3BGMa9Swx9PQFualtQcRmXSQ5Fu5OXo7e+nMleCUHkmgZx1BlSEUCeIFHHJuloqKCOvXZ/sqw+TZXPRf3qITF6wk7dfUxFm/vgtQfcEvoZBNMun2fbPfnMts4HXlgVdcHBKRG5/l1O9rrMoUASfDiscVb9yiqavKEAppVq4OsXGTzbJVUW677RXOPXcGkciuNffcbJbmxYsBqNp7b+wdMCePO1TE8Pf3iAUFEghy+w/YZnunr5RBVMGfOiFUApMWw5qFUFcln1MkLBbYu4Ww//3S+r1rdZtjB7NlABGyvceLCFVXwNg6iEUlOjXnbv4LlOfJGl8oCI1t4hF4T8QxMievX2CN5f9KWpZYd+GQrI2+s0pSOQIBsYqVRZ9ruMO3cqNhTcxJ0Z0Ogpdl/oJpnP79v/LXHzxMoCgNwTkQOHCLa8yGzWM+LcMezyPP+73e/N50OA7Rogi9PSnSXpAMlWjayOkQmc4UxTGb3pY2QkpjOQFSWGRzhU70+YeaZam+tkpitUm7Jc+TRPy8+HmeJpmU5Pp8cMbmyAtfoX2Q8t/DI1KlmXJ5C7aj6GkOUDosxl4ndVE51WLh8xOYtyJAdulaLJ2malgZv/3t67z9dhO33vqJXdZ6Z8381/j1V6/jnaYgcSfLrLoEp//yR9TOnLld5ysFN18JXzsd7n9KhOKLJxZqdL6bVFrWFR98Vh7+pxwFv50KX7sZNjRAbzcsT9InCiVxSVzfGSHbGuLO3voxqYx0q0imZK2t6S2JGs0L7+buuUZKttVVSwBONisi5rr0qV4+qCYf5Zp3C0dCsi0Slvd86c3Cumje6nWgz6RM+105UokctpujPJihLtJC0g2xr72Cg996A4767fv6nIYyRggNezw9fsPT/lF5VjAIdhrthDngoOE4qoxs1qW7sYnFy7sJ2zlcz4FMipwbJBoNkMnkJFjBb5kkuX4enqf7Okp0dKSIxwO4riYUcohEgiQSWZRy/QhDeTL1r126OZdovmuF64qo7nW6Ju16WN0hbNtm4oQKVmTKSdZ20xg6kM5ZxVif9aj5y0vU6RxKwbx5G3j22TUccsjI973W2dnUytkn/4Y1vaNwHAut4eU2l8ZzruaHL/yFUHHxVs/v7k5z//1Lef75NQwbFuPzn5/KvvtWb/F4z4PLrhf3XsCR5Pbrfif7Ao6sqW1skp9TftpBX95gbtt5ezvCtkQQCqXSsjkR43j0vdbp5q4bDslxxxwkwTWJVOH3NJOVOqfF8YLI9SYKFZISKamCk0r3CyLqN+/+1m/OlYd1xEkQcRJUhnuwLU1tsI1rZv4DVr4JnS1QUrlDn41BMEJo2OM5fA787WF5iOSLELsu5HIeJcEOiqI5IEAoaNHZtI7qaJjKaJbFrcWgLCxc3KzC86vCaA3ZrEsm4/ZZh/no0mg0QDBo09qawLYV2axLNusOsByLikJ0d6cHPKzz14nFAoRCNu3tKbQTh8holO0QrFhDNt3M6qeytMwL8WjHJuKzQkw9F2LJNlSLjV0UInHB4Xh3/JuN9e2sWtXOBRc8QF1dMRdeOJvPfW7vnbYO//arR6jvjlMe81BKnvBp1+K++tGc/cxzTD1ly40Ku7vTnHfe/dTXdxCNBnjzzUYeeWQl3/7OEcyZXU006jBs2MAC4q8tgkdf8CM2daG/X0eXJM8H/G7xazeJS9Fy5f7aNtjee12gm+mFu8uwLT+i05Xfr6Aj48htKw1VixV4yEz4+MGwfK2UlAP5/Zz/liaT8wjaPQSdCFk3SMJfK0xnCv/rftbj1ohHYVJoE98a9WeWZycwMtrMcTVvUBbogQ5VCKU17DBGCA17PIfMhBMPh3ufkLWYfGDK6OoU8Z55gKxz5ZIJPE/j2Iozp24gkW3g/uU1vNFUQiariUQdcjmPTMbtty6oCAbtvm3JZJZAQCymTMbDddMDutJrDT09BRHsn8cGUp+0pycLsfFQ9TEJbVcWC57cn5aVj9O6oJ5QcQYVtdj0vEfLq5rcOWFUR4Lsk+/Q0ZHg1cYOUvUdKCVrlZaluO66FygpCfHxj0/Yqc/whfltBHBRqmBZhmyPhGuxYmU7U/1tuZzk0EVj0BWGIgsevX8p9fUdff0VW5OVLGucwOlXRIiGeqgMLOO4AxP86EdHUlkpTWMffUEiMGORwmfUmxRrr7tXUhzGDBcX4bJ6EaGSuFR6qa6QSEndL7jlgxLBfGpOvtdhSZG8WSq1bWGKhGVcN3xTAmfuuEfW+CpKAZ0hGupCezYThi/CtjQt3aNIpUeRSqu+tAm9nSVwFZI+kaWSIq+Nb02+p/Dtq7sdasf3VWQy7DhGCA17PLYN110GJx8JT7wk36KPPxTqygOceWaG3l5NLBZE2Q5d2SCjypJUR9MoBZ+auImNXTY9qgQCNo4jopdK5VB4BLSLk/XI6gAgAhgI2H7EqN5sH8P+rjbPKwRJSN9DD+wQVB4BbgpwsR2bdEeO1U8mqZriECnOgtKE4h7Nq8P03LMSVjWjsx5EArSubUOlclRVRf1AG0U263HHHW/stBBWjx2O+/yqAYukWiqYM3KGVIV+8iW4/newrgo2HgJFFVAzDLI6RqxMBK41WcmbjfuTTmXRXgLbtmnV03n05bfo/vqj3Hnnp1FKsXhlIfoW5OdQUNyBnT0wwh9GR7fk2Tm27EumZG3ugyAfN5W/o+GQWFn5Em4zp8CoGimr1tkDq9cVgoI2RyojPQ03NcvYf3gJ/PFeifDE3cAJ+/+b1ZtGsbZxJK5WRILd1Fa00NJZRToj4v/uJPwtYVm+e7WknHTtVGh/TVRUWRAtgrN+YEqqvQ+MEBo+Eti21LQcWNeynBtuOIYf/eg5mpp60VozZVyEY+15oMtAWbR1uQQszaz9auhOWSQSWXI9XSxfk8ZW4gaTXDHJxlZKkct5O1ioW/kRpbK2p0PD/agZ10/I1sTKe0m2aFIdMcKxDgmAty2sqEK/sQ67JIKuiOG5HqSlr2E47PS5QiMRh02bttJyYRt88fyDeeSBxaS7G3B8C60nG2DipCpmHjmDhcvhql8AddBwHATS0NsIHRlIjqmi6aQZhB+u5/WGg8l4AbQdAjtO2ssQC/bSpafwztIHeOedFqZMqaI4VqgTGuxX+zMWlka2G/yC1+sbZV8k7LsJ+415e4Jctsa7S7C9Z79VKKN22Cy440ciahdfKxZYJrdll2xeVDu64fgLxM2bycCnj4VrL2mhacWZdCVLcWybJWsmsGpjNbUVaygrtrno5l9iWyKk7xbnLaE1NLRKGs7sb38NWmZL2G3pMNj3SIhtfY3XsHWMEBo+0hxxxBgOPngk9fUdxGJBKorguWtc6p95BpSipqiaoro6wkVFREvkqbP2paU4qhjbhqynUNj+g0gTCgVIpXIDGvrCwAjTd+O6mmxWnrj59k9A31PU8zxiw0MkV4Cb0aQ7fYssBAGdhmwQtzhMwLaIbmgnWhElm83R1ZXue4/OzjRz5myhhtl2MHv2cK748cncdP0zpDul/NyUyTX8759PRynF3Iflodw+Veo2Oy44QWhthyljQ7w1bhhvZUtxteP78ySEN+UGcHIutuWiVYjWVjHnPn6IFIVu75JSZiBRpqNqYe7PYdkaeGYe/OwPUgzbcd6b+/d+RFCpQkWWfNBLPjE+HJJ9X/2CWIFz9oGJo+WYUcPhtv8Hn7vUD16RjlsD6ormr5VP0G9ul4AXZcFNf4INDS7fOClHMCD5D1PHLmfq2OVYdFNcMpKbvg1fvtoPCFXvdcEGnIGdLvLvl86IeFZV2TDsINj7oJ3/gAwDMEJo+MgTCNhMnFjR9/rY668n2d5OpqeHotpa2q9+jkceWU5xcQjLgo6EYmxpkkTOIuda5JQ0+02lFY6j+gJnwmEHx7Ho7s4M8Dr1T5HYLMkNvjUYAJ1FoQiEIsTqxqN75pPLaCwHsi05lONfbFULIe1RFg+j02k6ej1CAY9kIkN3TwbHsbjwwu1o87AVzj57BiedNJl33mmhqCjItGnD+izOjU3iukzHQPVPCFcQCYeoqS6mIdwLOocmDCqfFK7ozhZTGmzFJsNee0m0yIlHwF2PwrOvSmsi7QFJKSH29Dw48gApafbzPxbKmm0tWX6bn3k/bL+4dTY3MPIz4MiapGND7TC45IzN10ctLYLiIsndSyYLeYB5HNvPD3QLqREaSXbPZGDuo8M4cspMpo9/h2SmSAqn4hGNdNKaOJcvnABfv07WSvsn3ONfKxiAgC3WdM6DoKOxVRJyaTY1hJj7Lzj9lOj2fRiG7cIIoWFQEikrI1JWBsAPfvAx9t13GP/85xIyGZfjpiSZEVpGsrqYoiObGTe2lS4vxtz/HMyCp8pIt9o0NfVSWRll3LhS5s/fSCZTSA6zbYXWGq0LFqPjWCjlkc1qefA1PQnDjgUnhFaQaQtyzBVJ1ryYof7fFpluKBuvqZnp0Xx9GpQiEaokkwtgqTCeSqOidXSvX87Rn53NeefNHCD2O0t5eYSDDx45YJubyTAxvIqX15YTXRam/dA4gYwljXAVWCEoLwlTUhJkZQx6kwrP8/qsaACd6+LcC/btC5bJl1KLRUQ4WjskF+7h5+C5V8Xam7OPpFFsaNz2uIOBbVeVgYHVfvJpLHlhdP3gm6oKKQ7Qv2Sc1n6PRGTNsn6DuE0tv6g2/ZL988E//dIF+wTNcSCbU/zsru/y96s+R2m0CdezcKwsqxrGUzu8gVAwyyH7BXjuVblO3k2azUli/7SJEkCUzkFJXJPpaSKbSYl3QQW59OpOxtcW7RE9JwcLSu/KhJ09hNmzZ+tXX311dw/DsIey9sUXeeHar3L4jzehKmwyaRtH5QhFwzzByfznzwfwjxs2kkzKWl08HqSzM01VVZT167twXY9QyPHXBbNMnlxJOBygqamHxsbevrVC5YSw46OxLJvh1e3M+cEGisd4eK7GzSoyPfDIBQ66yUMXjaW7OwF4hG2P8vJanPLp7MO/ueWPR1E5aSJFsW1ObYdJtrfz4AUXsKG+nTuS36cjXEnHf9fi1RZhZSyGD4eiEvh2Bfz1SnjyZT/oJanJ5qTmajyUZMp4i5fvivRZmPMWwCXXSgTo64tFXJQqFMAG6RBfWgxrN/pWVz/rTSnfKvJdhPlqKzuCZYkIF8XEIs2XNqsdBlWlImJnnQyfOByu/hUsrZfjWjpg3SY/2MeWrATPj0vJj7H/2qFSYkUGAiL2JUVQWQbVxfM4Zua9lBe38+aK/YiE4/z4yz9Hxb/FC0tO58IfiFs14dfHLi+Gu2+W9JKHnoVTvwGW10OquxVlO2hPuqkEnQyzq5/loYfOwHFMlcwdQSn1mtb6Pa4VI4SGIUnXsi/TG32Q3kwYy7ZxIhGClktXyuNnS79I/fVj6enJ0NjYC2huueV4PA8aGnpYurSFVavaCYcdXnllI8XFYYYPj9PQ0MuCBQ1ks++tbxoK2WBrxhwO8TFZOtdC/TNSH7kilCE14hyy6SSul6GqyKYqrkl5YdalRjCqVhGuLmfmdPjheTB8F0bJP/+Tn7Dk7rspGj6cbreIl3oOY0FqEvrEUdR+ZiJTquC0EpgTgUXL4ZAzC9VS8iH9leXSyPY335cWQU++BK8ukmT6uhqY/5aIRFunH2Xbr0JPWbEI1MTRYpG1dsp18xVY8rl8mwt8sa1CR4l8dGd/gbKUpDLku2V0dMm2CaNleyolQpTNSbBOTQUsWSVC2FcdyKJvLTBfGWZzwS1FMb8xcEByCb91Xooy69vc/8LxaG3xyUPn8YkDX8OxusCqhKIfsn7tfSxZ3sqzbx5BUh3Llz4TZupEuZ7nwSFnwBsLWsilE1i2TTQM2CFqohsoyb3CnXd+mkmT3r+XYCixJSE0rlHDkCResY5OSghHw+QdWxnPJphrprIqzaaQQyjkUFERpbGxh1df3cTVV38MgLa2JBdf/DArVrQRCjmsWNHG+vVdjBhRhLuFciTptCTvL39cYVkBPNfF9dMXgkGLtGWjLAfLDqKsHJ7OsT49irQK0nuyx8ZpsNSDh+bD3YfDIe8jSLBrwwZWP/UU2USCJXffTaRCHqZFdjcfL3mYY+IPkHiohf/63suofr7GqRMlReCXf5Hoz7ISEfmuXjjv07Im+NPfi1D29HqsbVBkc5DJKlKZftaUf0nbliATT0sAyyHHNrBwQ5LWjXE8DxKZIkiG0Z0D1wdrKqA7UXB95nPxYpFC9GlPQsQq50LAL8DguhAIw/oGSeRPpkRolRIhq99QEOl8EQCtxV2Z8YtzB2wZdyYrx5WXiMArpEdgKChtsT51dJp46kVOPHjlwLQGbdHU2sMf7nySF946lPLibs449kGOPegeVOltQBiQa9/wTTj1Eofu7hChILieg2NlqStaRVebVEMy7BqMEBqGJJY9kkiunl7lYSMPFOVm8Syb9nfKBhwbiQSor+/oe33jjS+xbFlrX4L5qFEl1Ne3s+++1SQSaZYta5frKbAsS9bTdCGFTwGW9nD95i+2gmreYY29D8rtJhZ06fHipHWIwNQs7fvGCSYhoKHHggvWwP2TYcJW2iJtiZVPPMEz3/senm9qtS5fTqyzk8q99ipUrdFaBHAzeWkXnS7pEH99UCy5ilK45nwYPwou/TEUxzzWrWujqTFB1i1jxZoItnJxGdinydMQ9ccfcSAba2XEGfdSnLJZ98pomldUEalJ0jG+hkTreIbdL8Elq9ZBW1fBWrRtwK/ZGYsA/fIVXVdE0bJEcC1LBM0K+j0NvUIyPfgWpy5YlaGgCF7W75c4cwosXinrgJWlYu0GA7BmowThTJsIR8yWFIp4rARy+0JuMajKvs+1rSPH2ddcTUtXNaXxJKs3lfGd2y9jbePf+K/THofIyX2f0WGz4YZvJLn8mk0oq5SyWDsj4mvpam9l4sQKRo3aQpFXww5jhNAwNImeR3HnfFI6jasCWNojZHXxUu901v9roLspkcgwa5aEF+ZyHk88sZKKigjr1nXS1NSLZSnKyyMsWtRMJBIiEnHIZMSHprWWoBNr4INXa4WFxragO2NR0vsqodBI7GCE3myAJHGsoIf1xRihhNUXjGGnJYjiri64YgfLSmZ6enj2+98nGI8TiEirp2wiQUd9PdloDaGSMkqKNMnmZsadeDL19R2UloYpLQ3z4ovr+Oc/l2C1rOPI2cX866dHoyMVlBbL3B57Qea1cmUrza0uTiCKk+sl5yo8stgBW1IvKLg7u3vl3I/tD8dc/hL1GrKpMGMOa2T8yY2kUlk8VvLA0rFMGmNhrRERqquWCM3iIhG4lWtFxBIpuV4wAEVRsQpjEVmfLC+RY9s65bWbb+ybL7GXLbhkHX9tMh8t6rpyve9fBD+4FYJBEV6QLzcBRyzl8SPh5bekSPzhs6C69LvozgtQbgPKb817z/Nn0dJVSW1FJwCRUIZoOM3vH/40XzjubxT1E0KAMz9fQ3fLev74x6dQGUVnm6a2tojrrz9mlxVjNxghNAxVggcTLLqW6t6fkfKayODRFfk8vW9/lo1LFhKLQSjk0NGRpLQ0zKmnShGyfCHtRYua6e7O9JVjW726g/LyCHV1RRQXh0gms4CipyfTt0bounKuZYm7rSacIOBYTKtOcNioXg4s/hZd089lUekx9KYt/vROlE3FCvWuZuMlIajfjgjKd9OwYAGe6/aJIEBXfAzNpVmWTdmPZUd8iilPPc4YeyR/fXoE6cf+idaaYcNidG5s4tLi+9k7sIbs/dD8eIAxF12Bdfw5gKKzB9ZudGlqK8VSoLKanAbLyhDsfZOR5XFW984ZsM7nacCThrNtHRYl00Oo4aATDm/+bS9WPDMSPE26xqM7bLF6g1hek8YUokPdHCxZ6Rfq9r9o9CKCedAMEcLmdhGz9Y0FF2ufp1UX1v7yFrvjFAQQoKoMRg+H79woifcPPit1U8NBSfsYMwKefhm+e0vB/XvVL6CkZCTlI3/Lx/Z+hVMPbGbC+Km8umpfoqHVA+5LwHHRWrOmaQzT3pXOoZTia1+bw2c+M4XFi5spKQkxY0YNtm2CZHYlRggNQ5fwSQRCxxPwWsEqoUKFGfN5zcjyGu68822am3s55ZS9+NKXZvYVlQ4GbcaNK2PJkmbi8YK7T6rReHzta3P4+c9fYtWqdpLJrN/ZwmH06BICAZv6+g5c12PypCp0Qz1FdoJfHPgmNZEUVNbBRSdwZHEFrgtLroW7pBoblp8XV14CKgSzwzs+XcsZ+Ofe3uuxMpkkHAqTjQ+ne8Q+PPNfs8k9sJBDlq6guDRAKpXl6afr+cXUJ9g3tIZ2SsBW9PSmGPbXnxIcOYH53qH89PfQ3G6hUX4wjQfk8FSYbHgKq7viaKvQYcGxRdQsS8Ts4dvm8Pnb7qPNc3jxptk0LakgWprA1ZqWJTZvtImrcswIsSgdR85t86NQi6MSMJPzg1mCAan7uWKtv56nxAK1HfwiByKGGvl5dC00t/mfcSm0dcj1y4rF5dnYIoE0TW1SE7Xd71CRc2Vt8el5Ei267yS5TmDccvb50rOUlmdpQnPNiuF8mTmMrovw+qISSvRGuZH4X648i2E1h2/x3tXUxKmp2UbTR8NOY4TQMLRRDtiFdkJKKY45ZjzHHDN+i6fMmFHDY4+tJJ3O9VkRsViAqqoYZWURHn30TJ55ZjW/+c2rrFnTybhxZUQiAbTWhMMOK1e24dlBTjj7aC6Y006NNxJG7w3Tj4Kw5OHZNtx2OVgvwD0RsLJQVwLhcqhw4DPbGSyjNfx7cRf3zuvAy5UyQQWwu7rQrsumt5dSlctguy4j33iG1YecRldQkz1qAj31m4iSIZnMURVOsV9wBW2U97njPCtARyJH0dNz+X8LD8V1xV3YmxRby8NCWRLy76kINmpAcns45Ae2ZEWMgqlishviZDIOTUvLiVb2Ai6ty2uxWhQ5DW4SFi6HZashHpM1upY2sa5nT/PdnimpYLN6A30WpO3XMc1m5WfXBSsKXgh0Aryc5AyecJj0WrzzX5LvOKpW0iAyWVi5TsQ3HILRI2D1evl38H7Q0Czu0URShJeyZo6++EkyPUFS7TGCQU3J+I3c2/EoF57wae5/eiSdiQzF0RZcz6axvZzjDrYYVjNpR35zDbsQI4QGww4yeXIlEyaU4zgWyWSWWCxIeXmE5mZJwh82LMYXvjCNeDzI1Vf/u6/LvFKKioooSsGDD36RqqqtJwZGwnDH0fDFXrizE9pcODQK55aKGG4PV/65kb89qFAqhCZEWfyHnLTmMqKbloNroS2b9uFjCSa7Oey2i5j71dsgGCZVGoPuDIGATczO+JaTJcEp+I1mwyHWNDp0dIkYOY4iHnZJpjw0Fv3jZz0VHpBzkM8rtPyITc8LcVzkUB5sWkrIzuEoSHVUkWkOSZcGCm7NTE6svxVrIByW7u6BAFT3WzN9Y4lYej1uYd1QKbH4gqMhF0MCY3ogciIUz5dao0Vx6VT/9jKxDC1LBNb1iwuUSHwUrb7F2NmVr7AjFmtzO8z65GIUmlw6AFEAhdcTwytroHxkB7+8qoyf3L4X6xvSWJbLZ493uOzcgcFEhg8XI4QGww5yzDFjufXW+aRSOUaNKkFraGlJMGpUCbNm1fYdd/TR4/jLX95m2bIWiopC5HIeyWSWc8+dsU0RzKMUHBOXfzvKovoMcx9SlJSn+wTMLZ3IvPYz+bj7azqcUjZFS/CwsWybeGczIzYsZfXU6ZS7UvgzHg+w1CqnPRthmJUmTZhczkMpqIp7tO4zB285xItFKJxQAMt26ekFsFCWwgk4BINSqSaTLeTiJZIiXp4HU8bDIcNHUuvV8aSnGaYUb2xUsubmu1NtSyJWszmIBKGmym/6q/zGtf4cN/kFvfNVZTxPLEXbAjcMORusJJCGwBhwpkBbRWHdcVgFnH0K/OEeEdpkSq5fXiwWIsgc8qEqFaWSjpFP4yiq7CGXsUHJ+AByOUVR0CJFkjn7lPHPWxTtnWEi4ULgjWH3YVZcDYYdpKQkzG9+cxJjx5bS2NhLc3Mvs2bVcuutnxgQxBAOO9x++0lccskBDB9exNSpVdxww7FcfPH+H8o4n17UKY2M+6Wb2Y7GySZpdYNsavbQARs75IFOk3MzOEGXqlVNdK9sprs7TXNzguraEh4rPw0rmyKWaafYSjB9lCZUN47hnzyJfSbJmtmkMeB6ilTGQSsHx7YIBhSetkhnJMAkGvYb4Vryf00ljBspbbaUgvEjFUcdYLFwmaLFz1jJpzfk/1dKIkbHjBARPOsUWQ9sbIGmVmjvlvU72yqIk0ZqngaOA6sKnAqIHg3xT4pVaI2G3n7pKBd9EX52OcyYLCJdUwmTx/kJ9sharetJZZx4FMbWiZUbdKB7+WisYI5ICFB+qyU7R3WFooLKvjmUlxoR3FMwlWUMhp1Ea01raxLbVpSVRbZ9wofM755s4Uf/61FelR6w3VmygIMXX0pZeYKiCpd11iiSXphgWwevfON/uftTM7n/Hwt5+eUNjBhRxGmnTWPGjBqa33oL68V7KfXasPc+EPY/CaJFNDTDpT/x63OmYHm9POTrqmXNrDcpAS6hkAjFhNEiKFVl8NNvwV7jBqYsNrbAQadLlZmunkLifL4qDQoO3FcsuJpK+L8bpV7pW0vFzXrjn2QsK9f5PSL7RapO/xXUZyHdgZ/QIG7V4RPh0XFQK15sPE+6ZyyrlwT5dQ1w818KkaQgop5KF1yj4aCUbBsxIsOyCfewKdNCsidILO5SXak5ooABPAAADeVJREFUNnIY09jnA7nXhu3DlFgzGIYYje0uh17cgh3MEYmIaZRM2GRSOU5c+WnGd72NZwfQnjzgx34ySsfsQ/jyF+/b4ffSWgJZXl0Iv/67uC1B0gtWrJXuFo4Nk8dCtChD0Yg2rvlKhP3Hvjcp/NWFcPE1UFUua3XrGwrRnSAl5kbWyOtbroIDpw88/9s/h2dfkf31G8Qis20R3iv/BDd1QSQBiYSIs4rD2DD8bYSIWnMbHP9lv/aoH4E6YwrcfIUIo1Jw8EyxCp97FRatkPEcc1BhDTFDhndYQj2riRBhKtMYjimSvbsxQmgwDEEeer2Ny27KiHtOQzDs8t9f6aR06XnEGzbQMC+DFVCUH1hKdHyAkaNcps2YB9bO1XBr64ATvgLRUJbmph56EznKy0JkdJzJYyyqDl5A9cdepqJMEwh51FHHMXycMAUf4ap18IVvSsFukGCV9Q2S/jC6DuqqxKo879Mwfa/3jmHRcvjSdyV4pTgmLsv2Ljj/s/Bfp8FljTA/Ka5WS0GxBbfVwiQ/XuXY86VOaiwqopdzpSLNGSfB//5gpz4Wwx6CEUKDYYjSncrx9NIWXFw+tlcZ5aEoT//7MKzeJtLExO3o50HOnmkzbOQTUhh6J7n0B138Zm6GbCaN9jywQkTDmgfu7WXppEeIEsHGQaNZ3wD2ukl8svygPhep1vDV78Nri0UMLUtcpJkszP2ZpC9si3kLpNfhqnWSanH2yXDOp8Qy9DS8moKFKahy4GNRKMoH2jTDXidKkEv/tdV0WtyuKx+TyjKGjyZGCA0GQx8dbbeycc3N1K+JgAYraDNhos34ynGosrmbrTO6PWitmbn/Xby9bhhedCJKBSDTjO54k8PPT3PGTRmiKoqbU9z3m2nMf6IOV7mkGqqYNt7mnlskarOzG350Gzz3mqzjVZXB9y4U6+w3c2XN7oB94evniFh29UjQSv+aAfmu7sHAwD6FW+OtpXD4WbJu2P+cTFbWN9c9bYTwo4zpPmEwGPooLT2LEp5jdO0KMjmLWBCCdhSKvrfTIgiwYUOC5Ss7wc0RzGzq2+6pHCvnd5BMFBGNwfzHR/Kfh0cRLk4QCrl47S6vL7Y5/Cy48iuw/z4StdneKQE4tVUigFf/StIZUPDKQvjd3RLNmfNbKZ37KbH88jXDwzuYnjd6uFSTae2kL+oTxDV6wD5GBAcrRggNhqGIVYwq+xOx9FPEsm+CXQehE8B+f80OXe3guvQVmS7gkU4V051JUxEL8cIDY3DCGZygBs/CzQZIZyRA5YKrpd3RcYfA3Bul3VNTq0SDprOydocScWrvlmCVw2fJvl/9TVIzzv3Uzo2/pAguOxe+f2uhYa7nibV585Xv66Mx7MGYPEKDYaiiwhA+EYqugug571sEAUaOCFE2fBw610t+2UVrD+1mwDqYSLaUbrpJphR2MIeyPFJNFXR1q748wXBIojkffgGuuEm2LVwOnT1+grqvr6m0BLtkMhIQEwpKJOef7i10uNgZLj4D7rwODt0PJo6CMz4Jr90tEa+GwYmxCA0Gwy7DsuAXNxzKWeencHs3ovz6aE7lfowZO4WzI+PZyBIen97Fs/8aTU6FyCRCfXVIFeBYEqgScOD/HoFrL5VAlXwgTR7XrzqDKnhzwyFo6BJhLNrJp5tS8Ikj5J9haGCE0GAw7FK+8MkQ6k8nctm1HXS09RIvKWfvyRF+cQVUFYWoYgY3fwHm3A1dCRHPvMAFAoVoTcuS4JjuhDTFHVcHC5fJfsvvJO9moaxIktlBehBWV4or02DYXowQGgyGXc6pJ8Dnjy9l9fpSXFc62PePwhxVC8/fCf/zU3htkQTAWJafkO63iHdzMGIUlBaJ+M39OZx8kVSMUUiCfsAR4UtnpYJNOgPf/er7ivcxDEH2GCFUSl0CXAS4wENa68v97VcA5/vb/1tr/djuG6XBYNhelJI6olti0lj4169lPe/BZ+Gc74iYWb4LtDgO3/9awUIcMwIW3AsvvAaLV8K0CVLr84/3SdrD1Alw/mfggOlbfk+DYXPsEXmESqkjgauAE7XWaaXUMK11k1Jqb+DvwP7AcOBJYJLW2t3K5UweocHwEeSNxfDj2yVydPIY+PrZcMh+u3tUhsHEnp5HeCFwndY6DaC1bvK3nwLM9bevVkqtQETxpd0zTIPB8EExc2+46xe7exSGociekj4xCThMKTVPKfWsUmqOv30EsK7fcev9bQaDwWAw7BI+NItQKfUkULOZXVf54ygHDgTmAP9QSo3bwetfAFwAMGrUqPc3WIPBYDAMGT40IdRaH7OlfUqpC4F7tCxYzldKeUAlsAHov9xe52/b3PVvB24HWSPcVeM2GAwGw+BmT3GN3gccCaCUmgQEgRbgX8BpSqmQUmosMBGYv9tGaTAYDIZBx54SLPN74PdKqYVABjjHtw4XKaX+ASwGcsBF24oYNRgMBoNhR9gjhFBrnQHO3MK+a4FrP9wRGQwGg2GosKe4Rg0Gg8Fg2C0YITQYDAbDkMYIocFgMBiGNEYIDQaDwTCkMUJoMBgMhiHNHlF0e1ejlGoG1uziy1YiuY1DBTPfwc9Qm7OZ7+BnW3MerbWuevfGQSmEHwRKqVc3V7V8sGLmO/gZanM28x387OycjWvUYDAYDEMaI4QGg8FgGNIYIdx+bt/dA/iQMfMd/Ay1OZv5Dn52as5mjdBgMBgMQxpjERoMBoNhSGOEcBsopS5RSr2jlFqklLqh3/YrlFIrlFJLlVLH7c4xfhAopb6plNJKqUr/tVJK3eLP+S2l1H67e4y7AqXUT/37+5ZS6l6lVGm/fYPyHiuljvfntEIp9Z3dPZ5djVJqpFLqGaXUYv/v9lJ/e7lS6gml1HL//7LdPdZdiVLKVkq9oZR60H89Vik1z7/P/6eUCu7uMe5KlFKlSqm7/b/fJUqpg3b2Hhsh3ApKqSOBU4DpWuupwM/87XsDpwFTgeOBXyul7N020F2MUmok8HFgbb/NJyD9ICcCFwC37YahfRA8AUzTWu8LLAOugMF7j/053Ircz72B0/25DiZywDe11nsDBwIX+XP8DvCU1noi8JT/ejBxKbCk3+vrgZu01hOAduD83TKqD46bgUe11nsB05G579Q9NkK4dS4ErtNapwG01k3+9lOAuVrrtNZ6NbAC2H83jfGD4CbgcqD/AvIpwJ+18DJQqpSq3S2j24VorR/XWuf8ly8Ddf7Pg/Ue7w+s0Fqv8tufzUXmOmjQWm/SWr/u/9yNPCBHIPP8k3/Yn4BP7Z4R7nqUUnXAicDv/NcKOAq42z9ksM23BDgcuAOklZ/WuoOdvMdGCLfOJOAw373wrFJqjr99BLCu33Hr/W0feZRSpwAbtNYL3rVr0M65H18CHvF/HqzzHazz2ixKqTHATGAeUK213uTvagCqd9OwPgh+gXx59fzXFUBHvy95g+0+jwWagT/47uDfKaVi7OQ93iMa8+5OlFJPAjWb2XUV8vmUI+6VOcA/lFLjPsThfSBsY85XIm7RQcPW5qu1vt8/5irEpfbXD3Nshg8OpVQc+Cfwda11lxhJgtZaK6UGRci8UuokoElr/ZpS6mO7ezwfEg6wH3CJ1nqeUupm3uUG3ZF7POSFUGt9zJb2KaUuBO7RkmMyXynlIbXsNgAj+x1a52/7SLClOSul9kG+aS3wHxp1wOtKqf35CM95a/cYQCl1LnAScLQu5BN9ZOe7DQbrvAaglAogIvhXrfU9/uZGpVSt1nqT79Zv2vIVPlIcApyslPoEEAaKkfWzUqWU41uFg+0+rwfWa63n+a/vRoRwp+6xcY1unfuAIwGUUpOAIFLQ9V/AaUqpkFJqLBJAMn+3jXIXobV+W2s9TGs9Rms9Bvll209r3YDM+Ww/evRAoLOfC+Iji1LqeMSldLLWOtFv16C8x8ArwEQ/ojCIBAT9azePaZfir4/dASzRWt/Yb9e/gHP8n88B7v+wx/ZBoLW+Qmtd5//NngY8rbU+A3gG+Jx/2KCZL4D/TFqnlJrsbzoaWMxO3uMhbxFug98Dv1dKLQQywDm+xbBIKfUP5IPPARdprd3dOM4Pg4eBTyBBIwngvN07nF3Gr4AQ8IRvBb+stf6q1npQ3mOtdU4pdTHwGGADv9daL9rNw9rVHAKcBbytlHrT33YlcB2yvHE+0p3m1N00vg+LbwNzlVLXAG/gB5YMIi4B/up/oVuFPJMsduIem8oyBoPBYBjSGNeowWAwGIY0RggNBoPBMKQxQmgwGAyGIY0RQoPBYDAMaYwQGgwGg2FIY4TQYDAYDEMaI4QGg8FgGNIYITQYBhFKqa8qpW7r9/oapdSdu3NMBsOejkmoNxgGEUqpKLAU2Ac4FPgRcLDWOrlbB2Yw7MEYITQYBhlKqRuAGNJ891it9crdPCSDYY/GCKHBMMhQSu2FNKM9RWs9qApqGwwfBGaN0GAYfPw/pGlpX1F9pdQ4pdQdSqm7t3yawTA0MUJoMAwilFLfRHrSnQpcmt+utV6ltT5/tw3MYNiDMW2YDIZBglLqKKQVzUFa626lVLFSaobW+s1tnWswDGWMRWgwDAKUUqOA3wGf11p3+5tvBr6++0ZlMHw0MMEyBsMQQClVAVwLHAv8Tmv9k908JINhj8EIocFgMBiGNMY1ajAYDIYhjRFCg8FgMAxpjBAaDAaDYUhjhNBgMBgMQxojhAaDwWAY0hghNBgMBsOQxgihwWAwGIY0RggNBoPBMKQxQmgwGAyGIc3/B2tLVn/ZWxz6AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "alpha = 0.7\n", + "\n", + "fig, ax = plt.subplots(figsize=(7,7))\n", + "ax.scatter(emb_transformed[0], emb_transformed[1], c=emb_transformed['label'].astype(\"category\"), \n", + " cmap=\"jet\", alpha=alpha)\n", + "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n", + "plt.title('{} visualization of GraphSAGE embeddings for cora dataset'.format(transform.__name__))\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/demos/node-classification/directed-graphsage/example4-directed-graph-fully-directed-graphsage.ipynb b/demos/node-classification/directed-graphsage/example4-directed-graph-fully-directed-graphsage.ipynb new file mode 100644 index 000000000..17f418247 --- /dev/null +++ b/demos/node-classification/directed-graphsage/example4-directed-graph-fully-directed-graphsage.ipynb @@ -0,0 +1,1043 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Stellargraph example 4: fully directed GraphSAGE on a directed CORA citation network\n", + "\n", + "This example shows the application of *directed* GraphSAGE to a *directed* graph. In contrast to example 3, in this example we **do** sample from both the in-node and out-node neighbourhoods. The model should be similar to example 1 (undirected GraphSAGE on the undirected graph), except that here the model can learn separate weights for the in-nodes versus the out-nodes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import NetworkX and stellar:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import networkx as nx\n", + "import pandas as pd\n", + "import os\n", + "\n", + "import stellargraph as sg\n", + "from stellargraph.mapper import DirectedGraphSAGENodeGenerator\n", + "from stellargraph.layer import DirectedGraphSAGE\n", + "\n", + "from keras import layers, optimizers, losses, metrics, Model\n", + "from sklearn import preprocessing, feature_extraction, model_selection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading the CORA network" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Downloading the CORA dataset:**\n", + " \n", + "The dataset used in this demo can be downloaded from [here](https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz).\n", + "\n", + "The following is the description of the dataset:\n", + "> The Cora dataset consists of 2708 scientific publications classified into one of seven classes.\n", + "> The citation network consists of 5429 links. Each publication in the dataset is described by a\n", + "> 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary.\n", + "> The dictionary consists of 1433 unique words. The README file in the dataset provides more details.\n", + "\n", + "Download and unzip the cora.tgz file to a location on your computer and set the `data_dir` variable to\n", + "point to the location of the dataset (the directory containing \"cora.cites\" and \"cora.content\")." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_dir = os.path.expanduser(\"~/data/cora\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the graph from edgelist (in `cited-paper` <- `citing-paper` order)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "edgelist = pd.read_csv(os.path.join(data_dir, \"cora.cites\"), sep='\\t', header=None, names=[\"target\", \"source\"])\n", + "edgelist[\"label\"] = \"cites\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the graph with directed edges" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "Gnx = nx.from_pandas_edgelist(edgelist, edge_attr=\"label\", create_using=nx.DiGraph)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "nx.set_node_attributes(Gnx, \"paper\", \"label\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the features and subject for the nodes" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "feature_names = [\"w_{}\".format(ii) for ii in range(1433)]\n", + "column_names = feature_names + [\"subject\"]\n", + "node_data = pd.read_csv(os.path.join(data_dir, \"cora.content\"), sep='\\t', header=None, names=column_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We aim to train a graph-ML model that will predict the \"subject\" attribute on the nodes. These subjects are one of 7 categories:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Case_Based',\n", + " 'Genetic_Algorithms',\n", + " 'Neural_Networks',\n", + " 'Probabilistic_Methods',\n", + " 'Reinforcement_Learning',\n", + " 'Rule_Learning',\n", + " 'Theory'}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set(node_data[\"subject\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Splitting the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For machine learning we want to take a subset of the nodes for training, and use the rest for testing. We'll use scikit-learn again to do this" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "train_data, test_data = model_selection.train_test_split(\n", + " node_data, train_size=0.1, test_size=None, stratify=node_data['subject']\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note using stratified sampling gives the following counts:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Counter({'Probabilistic_Methods': 42,\n", + " 'Case_Based': 30,\n", + " 'Theory': 35,\n", + " 'Rule_Learning': 18,\n", + " 'Genetic_Algorithms': 42,\n", + " 'Neural_Networks': 81,\n", + " 'Reinforcement_Learning': 22})" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from collections import Counter\n", + "Counter(train_data['subject'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set has class imbalance that might need to be compensated, e.g., via using a weighted cross-entropy loss in model training, with class weights inversely proportional to class support. However, we will ignore the class imbalance in this example, for simplicity." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Converting to numeric arrays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For our categorical target, we will use one-hot vectors that will be fed into a soft-max Keras layer during training. To do this conversion ..." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "target_encoding = feature_extraction.DictVectorizer(sparse=False)\n", + "\n", + "train_targets = target_encoding.fit_transform(train_data[[\"subject\"]].to_dict('records'))\n", + "test_targets = target_encoding.transform(test_data[[\"subject\"]].to_dict('records'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now do the same for the node attributes we want to use to predict the subject. These are the feature vectors that the Keras model will use as input. The CORA dataset contains attributes 'w_x' that correspond to words found in that publication. If a word occurs more than once in a publication the relevant attribute will be set to one, otherwise it will be zero." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "node_features = node_data[feature_names]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating the GraphSAGE model in Keras" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create a StellarGraph object from the NetworkX graph and the node features and targets. It is StellarGraph objects that we use in this library to perform machine learning tasks on.\n", + "\n", + "Note that the NetworkX graph is *directed*, so we also treat it here as *directed*." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "G = sg.StellarDiGraph(Gnx, node_features=node_features)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "StellarDiGraph: Directed multigraph\n", + " Nodes: 2708, Edges: 5429\n", + "\n", + " Node types:\n", + " paper: [2708]\n", + " Edge types: paper-cites->paper\n", + "\n", + " Edge types:\n", + " paper-cites->paper: [5429]\n", + "\n" + ] + } + ], + "source": [ + "print(G.info())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To feed data from the graph to the Keras model we need a data generator that feeds data from the graph to the model. The generators are specialized to the model and the learning task so we choose the `DirectedGraphSAGENodeGenerator` as we are predicting node attributes with a `DirectedGraphSAGE` model.\n", + "\n", + "We need two other parameters, the `batch_size` to use for training and the number of nodes to sample at each level of the model. Here we choose a two-level model with 10 nodes sampled in the first layer (5 in-nodes and 5 out-nodes), and 4 in the second layer (2 in-nodes and 2 out-nodes)." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 50; in_samples = [5, 2]; out_samples = [5, 2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A `DirectedGraphSAGENodeGenerator` object is required to send the node features in sampled subgraphs to Keras" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "generator = DirectedGraphSAGENodeGenerator(G, batch_size, in_samples, out_samples)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the `generator.flow()` method, we can create iterators over nodes that should be used to train, validate, or evaluate the model. For training we use only the training nodes returned from our splitter and the target values. The `shuffle=True` argument is given to the `flow` method to improve training." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "train_gen = generator.flow(train_data.index, train_targets, shuffle=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can specify our machine learning model, we need a few more parameters for this:\n", + "\n", + " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use 48-dimensional hidden node features at each layer, which corresponds to 16 weights for a node, 16 for the in-nodes and 16 for the out-nodes. This is higher than the 32 dimensions used in example 1 (which also uses 16 weights per node and 16 per 1-hop neighbourhood), giving more expressive power but having more weights to estimate (and thus might require more training epochs if the model underfits, or else the model might be more prone to overfitting).\n", + " * The `bias` and `dropout` are internal parameters of the model. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: Logging before flag parsing goes to stderr.\n", + "W0819 15:38:48.459014 4613264832 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", + "\n" + ] + } + ], + "source": [ + "graphsage_model = DirectedGraphSAGE(\n", + " layer_sizes=[48, 48],\n", + " generator=train_gen,\n", + " bias=True,\n", + " dropout=0.5,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we create a model to predict the 7 categories using Keras softmax layers." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0819 15:38:48.474189 4613264832 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", + "\n", + "W0819 15:38:48.479403 4613264832 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", + "\n", + "W0819 15:38:48.487928 4613264832 deprecation.py:506] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", + "W0819 15:38:48.540890 4613264832 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", + "\n" + ] + } + ], + "source": [ + "x_inp, x_out = graphsage_model.build()\n", + "prediction = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's create the actual Keras model with the graph inputs `x_inp` provided by the `graph_model` and outputs being the predictions from the softmax layer" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0819 15:38:48.817672 4613264832 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", + "\n", + "W0819 15:38:48.823397 4613264832 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", + "\n" + ] + } + ], + "source": [ + "model = Model(inputs=x_inp, outputs=prediction)\n", + "model.compile(\n", + " optimizer=optimizers.Adam(lr=0.005),\n", + " loss=losses.categorical_crossentropy,\n", + " metrics=[\"acc\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Train the model, keeping track of its loss and accuracy on the training set, and its generalisation performance on the test set (we need to create another generator over the test data for this)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "test_gen = generator.flow(test_data.index, test_targets)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0819 15:38:48.996465 4613264832 deprecation.py:323] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n", + " - 3s - loss: 1.8718 - acc: 0.2544 - val_loss: 1.7467 - val_acc: 0.3089\n", + "Epoch 2/20\n", + " - 3s - loss: 1.6894 - acc: 0.3422 - val_loss: 1.6491 - val_acc: 0.3495\n", + "Epoch 3/20\n", + " - 3s - loss: 1.5481 - acc: 0.5120 - val_loss: 1.5117 - val_acc: 0.5492\n", + "Epoch 4/20\n", + " - 3s - loss: 1.3784 - acc: 0.7357 - val_loss: 1.3860 - val_acc: 0.6596\n", + "Epoch 5/20\n", + " - 3s - loss: 1.2675 - acc: 0.8312 - val_loss: 1.2752 - val_acc: 0.7149\n", + "Epoch 6/20\n", + " - 3s - loss: 1.1246 - acc: 0.8720 - val_loss: 1.1875 - val_acc: 0.7354\n", + "Epoch 7/20\n", + " - 3s - loss: 1.0080 - acc: 0.9187 - val_loss: 1.1052 - val_acc: 0.7506\n", + "Epoch 8/20\n", + " - 3s - loss: 0.9084 - acc: 0.9153 - val_loss: 1.0398 - val_acc: 0.7510\n", + "Epoch 9/20\n", + " - 3s - loss: 0.8475 - acc: 0.8871 - val_loss: 0.9856 - val_acc: 0.7531\n", + "Epoch 10/20\n", + " - 3s - loss: 0.7502 - acc: 0.9363 - val_loss: 0.9358 - val_acc: 0.7600\n", + "Epoch 11/20\n", + " - 3s - loss: 0.6963 - acc: 0.9348 - val_loss: 0.8979 - val_acc: 0.7699\n", + "Epoch 12/20\n", + " - 3s - loss: 0.6018 - acc: 0.9619 - val_loss: 0.8593 - val_acc: 0.7777\n", + "Epoch 13/20\n", + " - 3s - loss: 0.5553 - acc: 0.9585 - val_loss: 0.8312 - val_acc: 0.7781\n", + "Epoch 14/20\n", + " - 3s - loss: 0.5075 - acc: 0.9797 - val_loss: 0.8131 - val_acc: 0.7797\n", + "Epoch 15/20\n", + " - 3s - loss: 0.4729 - acc: 0.9677 - val_loss: 0.7855 - val_acc: 0.7801\n", + "Epoch 16/20\n", + " - 3s - loss: 0.4345 - acc: 0.9686 - val_loss: 0.7669 - val_acc: 0.7847\n", + "Epoch 17/20\n", + " - 3s - loss: 0.3999 - acc: 0.9797 - val_loss: 0.7461 - val_acc: 0.7818\n", + "Epoch 18/20\n", + " - 3s - loss: 0.3487 - acc: 0.9898 - val_loss: 0.7334 - val_acc: 0.7900\n", + "Epoch 19/20\n", + " - 3s - loss: 0.3345 - acc: 0.9932 - val_loss: 0.7278 - val_acc: 0.7884\n", + "Epoch 20/20\n", + " - 3s - loss: 0.3194 - acc: 0.9754 - val_loss: 0.7182 - val_acc: 0.7892\n" + ] + } + ], + "source": [ + "history = model.fit_generator(\n", + " train_gen,\n", + " epochs=20,\n", + " validation_data=test_gen,\n", + " verbose=2,\n", + " shuffle=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "def plot_history(history):\n", + " metrics = sorted(history.history.keys())\n", + " metrics = metrics[:len(metrics)//2]\n", + " for m in metrics:\n", + " # summarize history for metric m\n", + " plt.plot(history.history[m])\n", + " plt.plot(history.history['val_' + m])\n", + " plt.title(m)\n", + " plt.ylabel(m)\n", + " plt.xlabel('epoch')\n", + " plt.legend(['train', 'test'], loc='best')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_history(history)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have trained the model we can evaluate on the test set." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Test Set Metrics:\n", + "\tloss: 0.7124\n", + "\tacc: 0.7900\n" + ] + } + ], + "source": [ + "test_metrics = model.evaluate_generator(test_gen)\n", + "print(\"\\nTest Set Metrics:\")\n", + "for name, val in zip(model.metrics_names, test_metrics):\n", + " print(\"\\t{}: {:0.4f}\".format(name, val))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Making predictions with the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's get the predictions themselves for all nodes using another node iterator:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "all_nodes = node_data.index\n", + "all_mapper = generator.flow(all_nodes)\n", + "all_predictions = model.predict_generator(all_mapper)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These predictions will be the output of the softmax layer, so to get final categories we'll use the `inverse_transform` method of our target attribute specifcation to turn these values back to the original categories" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "node_predictions = target_encoding.inverse_transform(all_predictions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's have a look at a few:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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31336subject=Neural_NetworksNeural_Networks
1061127subject=Rule_LearningRule_Learning
1106406subject=Reinforcement_LearningReinforcement_Learning
13195subject=Reinforcement_LearningReinforcement_Learning
37879subject=Probabilistic_MethodsProbabilistic_Methods
1126012subject=Probabilistic_MethodsProbabilistic_Methods
1107140subject=TheoryTheory
1102850subject=Neural_NetworksNeural_Networks
31349subject=Neural_NetworksNeural_Networks
1106418subject=TheoryTheory
\n", + "
" + ], + "text/plain": [ + " Predicted True\n", + "31336 subject=Neural_Networks Neural_Networks\n", + "1061127 subject=Rule_Learning Rule_Learning\n", + "1106406 subject=Reinforcement_Learning Reinforcement_Learning\n", + "13195 subject=Reinforcement_Learning Reinforcement_Learning\n", + "37879 subject=Probabilistic_Methods Probabilistic_Methods\n", + "1126012 subject=Probabilistic_Methods Probabilistic_Methods\n", + "1107140 subject=Theory Theory\n", + "1102850 subject=Neural_Networks Neural_Networks\n", + "31349 subject=Neural_Networks Neural_Networks\n", + "1106418 subject=Theory Theory" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results = pd.DataFrame(node_predictions, index=all_nodes).idxmax(axis=1)\n", + "df = pd.DataFrame({\"Predicted\": results, \"True\": node_data['subject']})\n", + "df.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Add the predictions to the graph, and save as graphml, e.g. for visualisation in [Gephi](https://gephi.org)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "for nid, pred, true in zip(df.index, df[\"Predicted\"], df[\"True\"]):\n", + " Gnx.node[nid][\"subject\"] = true\n", + " Gnx.node[nid][\"PREDICTED_subject\"] = pred.split(\"=\")[-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Also add isTrain and isCorrect node attributes:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "for nid in train_data.index:\n", + " Gnx.node[nid][\"isTrain\"] = True\n", + " \n", + "for nid in test_data.index:\n", + " Gnx.node[nid][\"isTrain\"] = False" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "for nid in Gnx.nodes():\n", + " Gnx.node[nid][\"isCorrect\"] = Gnx.node[nid][\"subject\"] == Gnx.node[nid][\"PREDICTED_subject\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Node embeddings\n", + "Evaluate node embeddings as activations of the output of graphsage layer stack, and visualise them, coloring nodes by their subject label.\n", + "\n", + "The GraphSAGE embeddings are the output of the GraphSAGE layers, namely the `x_out` variable. Let's create a new model with the same inputs as we used previously `x_inp` but now the output is the embeddings rather than the predicted class. Additionally note that the weights trained previously are kept in the new model." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "embedding_model = Model(inputs=x_inp, outputs=x_out)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2708, 48)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "emb = embedding_model.predict_generator(all_mapper)\n", + "emb.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Project the embeddings to 2d using either TSNE or PCA transform, and visualise, coloring nodes by their subject label" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import TSNE\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "X = emb\n", + "y = np.argmax(target_encoding.transform(node_data[[\"subject\"]].to_dict('records')), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "if X.shape[1] > 2:\n", + " transform = TSNE #PCA \n", + "\n", + " trans = transform(n_components=2)\n", + " emb_transformed = pd.DataFrame(trans.fit_transform(X), index=node_data.index)\n", + " emb_transformed['label'] = y\n", + "else:\n", + " emb_transformed = pd.DataFrame(X, index=node_data.index)\n", + " emb_transformed = emb_transformed.rename(columns = {'0':0, '1':1})\n", + " emb_transformed['label'] = y" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "alpha = 0.7\n", + "\n", + "fig, ax = plt.subplots(figsize=(7,7))\n", + "ax.scatter(emb_transformed[0], emb_transformed[1], c=emb_transformed['label'].astype(\"category\"), \n", + " cmap=\"jet\", alpha=alpha)\n", + "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n", + "plt.title('{} visualization of GraphSAGE embeddings for cora dataset'.format(transform.__name__))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Observe that each class of paper is separated into three distinct regions. This can be explained by the fact that the resulting embedding looks like (node, in, out). Hence there are three distinct types of representation, namely: (node, 0, out) if there are no in-nodes; (node, in, 0) if there are no out-nodes; and (node, in, out) if there are both in-nodes and out-nodes. The fourth case, isolated nodes having no in-nodes and no out-nodes, does not occur for the CORA dataset (see the counts below)." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 nodes have no in or out neighbours\n", + "486 nodes have in but not out neighbours\n", + "1143 nodes have out but no in neighbours\n", + "1079 nodes have in and out neighbours\n" + ] + } + ], + "source": [ + "counts = [0] * 4\n", + "for node in G.nodes():\n", + " have_in = len(G.in_edges(node)) > 0\n", + " have_out = len(G.out_edges(node)) > 0\n", + " idx = 1 * have_in + 2 * have_out\n", + " counts[idx] = counts[idx] + 1\n", + "print(\"{} nodes have no in or out neighbours\".format(counts[0]))\n", + "print(\"{} nodes have in but not out neighbours\".format(counts[1]))\n", + "print(\"{} nodes have out but no in neighbours\".format(counts[2]))\n", + "print(\"{} nodes have in and out neighbours\".format(counts[3]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/stellargraph/layer/graphsage.py b/stellargraph/layer/graphsage.py index 11e6fc0da..2c7d25ec5 100644 --- a/stellargraph/layer/graphsage.py +++ b/stellargraph/layer/graphsage.py @@ -49,8 +49,6 @@ class GraphSAGEAggregator(Layer): a bias term should be included. act (Callable or str): name of the activation function to use (must be a Keras activation function), or alternatively, a TensorFlow operation. - share_weights (bool): Optional flag indicating whether (True; default) or not - (False) to use the same weight tensor for all multi-dimensional neighbourhoods. neigh_dim (int): Optional value indicating the maximum number of multi-dimensional neighbourhoods; defaults to 1. """ @@ -60,11 +58,9 @@ def __init__( output_dim: int = 0, bias: bool = False, act: Callable or AnyStr = "relu", - share_weights: bool = True, neigh_dim: int = 1, **kwargs ): - self.share_weights = share_weights self.neigh_dim = neigh_dim self.output_dim = output_dim self.other_output_dim = output_dim // (neigh_dim + 1) @@ -248,14 +244,6 @@ def build(self, input_shape): if self._build_mlp_only: self.w_neigh = None - elif self.share_weights: - self.w_neigh = self.add_weight( - name="w_neigh", - shape=(input_shape[1][3], self.other_output_dim), - initializer=self._initializer, - trainable=True, - ) - else: in_size = input_shape[1][3] out_size = self.other_output_dim @@ -271,8 +259,6 @@ def build(self, input_shape): ) def aggregate_neighbours(self, x_neigh, neigh_idx=0): - if self.share_weights: - return K.dot(K.mean(x_neigh, axis=2), self.w_neigh) return K.dot(K.mean(x_neigh, axis=2), self.w_neigh[neigh_idx]) @@ -806,7 +792,6 @@ class DirectedGraphSAGE: bias (bool): If True a bias vector is learnt for each layer in the GraphSAGE model dropout (float): The dropout supplied to each layer in the GraphSAGE model. normalize (str or None): The normalization used after each layer, defaults to L2 normalization. - """ def __init__( @@ -886,7 +871,6 @@ def __init__( output_dim=layer_sizes[i], bias=self.bias, act="relu" if i < max_hops - 1 else "linear", - share_weights=False, neigh_dim=2, ) for i in range(max_hops) @@ -905,7 +889,6 @@ def __call__(self, xin: List): def aggregate_neighbours(tree: List, stage: int): # compute the number of slots with children in the binary tree num_slots = (len(tree) - 1) // 2 - print("DEBUG: input tree length={}, output tree length={}".format(len(tree), num_slots)) new_tree = [None] * num_slots for slot in range(num_slots): # get parent nodes @@ -944,16 +927,10 @@ def aggregate_neighbours(tree: List, stage: int): # Combine GraphSAGE layers in stages stage_tree = xin for stage in range(self.max_hops): - print("DEBUG: stage={}, input tree size={}".format(stage, len(stage_tree))) stage_tree = aggregate_neighbours(stage_tree, stage) - print("DEBUG: stage={}, output tree size={}".format(stage, len(stage_tree))) - for out_layer in stage_tree: - print("DEBUG: intermediate shape = {}".format(K.int_shape(out_layer))) - print("DEBUG: number of outputs = {}".format(len(stage_tree))) out_layer = stage_tree[0] # Remove neighbourhood dimension from output tensors of the stack - print("DEBUG: final shape = {}".format(K.int_shape(out_layer))) out_layer = Reshape(K.int_shape(out_layer)[2:])(out_layer) return self._normalization(out_layer) From fc79a8f81953aa629d75f619ef19dfdf794fa8fa Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Tue, 20 Aug 2019 16:00:56 +0930 Subject: [PATCH 12/30] updated from featuree/464. --- stellargraph/data/explorer.py | 115 +++++++++++++++++++++------------- 1 file changed, 73 insertions(+), 42 deletions(-) diff --git a/stellargraph/data/explorer.py b/stellargraph/data/explorer.py index a45bcaaf2..3b85b2c8c 100644 --- a/stellargraph/data/explorer.py +++ b/stellargraph/data/explorer.py @@ -26,7 +26,7 @@ import networkx as nx import numpy as np import random -from collections import defaultdict +from collections import defaultdict, deque from ..core.schema import GraphSchema from ..core.graph import StellarGraphBase @@ -42,6 +42,7 @@ def __init__(self, graph, graph_schema=None, seed=None): self.graph = graph # Initialize the random state + self._check_seed(seed) self._random_state = random.Random(seed) # Initialize a numpy random state (for numpy random methods) @@ -61,10 +62,34 @@ def __init__(self, graph, graph_schema=None, seed=None): "The parameter graph_schema should be either None or of type GraphSchema." ) + def _check_seed(self, seed): + if seed is not None: + if type(seed) != int: + self._raise_error( + "The random number generator seed value, seed, should be integer type or None." + ) + if seed < 0: + self._raise_error( + "The random number generator seed value, seed, should be non-negative integer or None." + ) + + def _get_random_state(self, seed): + """ + Args: + seed: The optional seed value for a given run. + + Returns: + The random state as determined by the seed. + """ + if seed is None: + # Restore the random state + return self._random_state + # seed the random number generator + return random.Random(seed) + def neighbors(self, node): if node not in self.graph: - print("node {} not in graph".format(node)) - print("Graph nodes {}".format(self.graph.nodes())) + self._raise_error("node {} not in graph".format(node)) return list(nx.neighbors(self.graph, node)) def run(self, **kwargs): @@ -82,8 +107,7 @@ def run(self, **kwargs): raise NotImplementedError def _raise_error(self, msg): - full = "({}) " + msg - raise ValueError(full.format(type(self).__name__)) + raise ValueError("({}) {}".format(type(self).__name__, msg)) def _check_parameter_values(self, nodes=None, n=0, length=0, seed=None): """ @@ -96,13 +120,11 @@ def _check_parameter_values(self, nodes=None, n=0, length=0, seed=None): n: Number of walks per node id. length: Maximum length of walk measured as the number of edges followed from root node. seed: Random number generator seed - Returns: - The random state as determined by the seed. """ self._check_nodes(nodes) self._check_repetitions(n) self._check_length(length) - return self._check_seed(seed) + self._check_seed(seed) def _check_nodes(self, nodes): if nodes is None: @@ -135,25 +157,10 @@ def _check_length(self, length): # Technically, length 0 should be okay, but by consensus is invalid. self._raise_error("The walk length, length, should be a positive integer.") - def _check_seed(self, seed): - if seed is None: - # Restore the random state - return self._random_state - if type(seed) != int: - self._raise_error( - "The random number generator seed value, seed, should be integer type or None." - ) - if seed < 0: - self._raise_error( - "The random number generator seed value, seed, should be non-negative integer or None." - ) - # seed the random number generator - return random.Random(seed) - # For neighbourhood sampling def _check_sizes(self, n_size): err_msg = "The neighbourhood size must be a list of non-negative integers." - if n_size is None or type(n_size) != list: + if not isinstance(n_size, list): self._raise_error(err_msg) if len(n_size) == 0: # Technically, length 0 should be okay, but by consensus it is invalid. @@ -181,7 +188,7 @@ def __init__(self, graph, graph_schema=None, seed=None): neigh_et = [ n2 for n2, nkeys in nbrdict.items() - for k in iter(nkeys) + for k in nkeys if self.graph_schema.is_of_edge_type((n1, n2, k), et) ] # Create adjacency list in lexographical order @@ -209,7 +216,8 @@ def run(self, nodes=None, n=None, length=None, seed=None): List of lists of nodes ids for each of the random walks """ - rs = self._check_parameter_values(nodes, n, length, seed) + self._check_parameter_values(nodes, n, length, seed) + rs = self._get_random_state(seed) walks = [] for node in nodes: # iterate over root nodes @@ -302,9 +310,10 @@ def run( List of lists of nodes ids for each of the random walks """ - rs = self._check_parameter_values( + self._check_parameter_values( nodes, n, p, q, length, seed, weighted, edge_weight_label ) + rs = self._get_random_state(seed) if weighted: # Check that all edge weights are greater than or equal to 0. @@ -429,7 +438,8 @@ def _check_parameter_values( Returns: The random state as determined by the seed. """ - rs = super()._check_parameter_values(nodes, n, length, seed) + super()._check_parameter_values(nodes, n, length, seed) + rs = self._get_random_state(seed) if p <= 0.0: self._raise_error("Parameter p should be greater than 0.") @@ -478,9 +488,10 @@ def run( Returns: List of lists of nodes ids for each of the random walks generated """ - rs = self._check_parameter_values( + self._check_parameter_values( nodes, n, length, metapaths, node_type_attribute, seed ) + rs = self._get_random_state(seed) walks = [] @@ -549,7 +560,8 @@ def _check_parameter_values( Returns: The random state as determined by the seed. """ - rs = super()._check_parameter_values(nodes, n, length, seed) + super()._check_parameter_values(nodes, n, length, seed) + rs = self._get_random_state(seed) if type(metapaths) != list: self._raise_error("The metapaths parameter must be a list of lists.") @@ -599,14 +611,15 @@ def run(self, nodes=None, n=1, n_size=None, seed=None): Returns: A list of lists such that each list element is a sequence of ids corresponding to a BFW. """ - rs = self._check_parameter_values(nodes, n, n_size, seed) + self._check_parameter_values(nodes, n, n_size, seed) + rs = self._get_random_state(seed) walks = [] max_hops = len(n_size) # depth of search for node in nodes: # iterate over root nodes for _ in range(n): # do n bounded breadth first walks from each root node - q = list() # the queue of neighbours + q = deque() # the queue of neighbours walk = list() # the list of nodes in the subgraph of node # extend() needs iterable as parameter; we use list of tuples (node id, depth) q.append((node, 0)) @@ -614,7 +627,7 @@ def run(self, nodes=None, n=1, n_size=None, seed=None): while len(q) > 0: # remove the top element in the queue # index 0 pop the item from the front of the list - cur_node, cur_depth = q.pop(0) + cur_node, cur_depth = q.popleft() depth = cur_depth + 1 # the depth of the neighbouring nodes walk.append(cur_node) # add to the walk @@ -634,7 +647,7 @@ def run(self, nodes=None, n=1, n_size=None, seed=None): ] # add them to the back of the queue - q.extend([(sampled_node, depth) for sampled_node in neighbours]) + q.extend((sampled_node, depth) for sampled_node in neighbours) # finished i-th walk from node so add it to the list of walks as a list walks.append(walk) @@ -684,7 +697,8 @@ def run(self, nodes=None, n=1, n_size=None, seed=None): A list of lists such that each list element is a sequence of ids corresponding to a sampled Heterogeneous BFW. """ - rs = self._check_parameter_values(nodes, n, n_size, self.graph_schema, seed) + self._check_parameter_values(nodes, n, n_size, self.graph_schema, seed) + rs = self._get_random_state(seed) walks = [] d = len(n_size) # depth of search @@ -760,12 +774,6 @@ def _check_parameter_values(self, nodes, n, n_size, graph_schema, seed): The random state as determined by the seed. """ self._check_sizes(n_size) - - if graph_schema is not None and type(graph_schema) is not GraphSchema: - self._raise_error( - "The parameter graph_schema should be either None or of type GraphSchema." - ) - return super()._check_parameter_values(nodes, n, len(n_size), seed) @@ -808,8 +816,15 @@ def run(self, nodes=None, n=1, in_size=None, out_size=None, seed=None): [node out_i out_i.in_j out_i.in_j.in_k ...] """ - rs = self._check_parameter_values(nodes, n, in_size, out_size, seed) + self._check_parameter_values(nodes, n, in_size, out_size, seed) + rs = self._get_random_state(seed) + max_hops = len(in_size) + # A binary tree is a graph of nodes; however, we wish to avoid overusing the term 'node'. + # Consider that each binary tree node carries some information. + # We uniquely and deterministically number every node in the tree, so we + # can represent the information stored in the tree via a flattened list of 'slots'. + # Each slot (and corresponding binary tree node) now has a unique index in the flattened list. max_slots = 2 ** (max_hops + 1) - 1 samples = [] @@ -857,6 +872,22 @@ def run(self, nodes=None, n=1, in_size=None, out_size=None, seed=None): return samples def _sample_neighbours(self, rs, node, idx, size): + """ + Samples (with replacement) the specified number of nodes + from the directed neighbourhood of the given starting node. + If the neighbourhood is empty, then the result will contain + only None values. + Args: + rs: The random state used for sampling. + node: The starting node. + idx: The index specifying the direction of the + neighbourhood to be sampled: 0 => in-nodes; + 1 => out-nodes. + size: The number of nodes to sample. + Returns: + The fixed-length list of neighbouring nodes (or None values + if the neighbourhood is empty). + """ if node is None: # Non-node, e.g. previously sampled from empty neighbourhood return [None] * size From cec08c3d84ff8dc65788a731e9dfb9920bc9397d Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Wed, 21 Aug 2019 12:34:05 +0930 Subject: [PATCH 13/30] Added tests for directed node generator. --- ...ndirected-graph-undirected-graphsage.ipynb | 110 ++- ...-directed-graph-undirected-graphsage.ipynb | 104 ++- ...rected-graph-half-directed-graphsage.ipynb | 124 +-- ...ected-graph-fully-directed-graphsage.ipynb | 114 ++- .../directed-graphsage/experim.ipynb | 799 ++++++++++++++++++ tests/mapper/test_directed_node_generator.py | 232 +++++ 6 files changed, 1305 insertions(+), 178 deletions(-) create mode 100644 demos/node-classification/directed-graphsage/experim.ipynb create mode 100644 tests/mapper/test_directed_node_generator.py diff --git a/demos/node-classification/directed-graphsage/example1-undirected-graph-undirected-graphsage.ipynb b/demos/node-classification/directed-graphsage/example1-undirected-graph-undirected-graphsage.ipynb index 057d76d2f..6ca711d14 100644 --- a/demos/node-classification/directed-graphsage/example1-undirected-graph-undirected-graphsage.ipynb +++ b/demos/node-classification/directed-graphsage/example1-undirected-graph-undirected-graphsage.ipynb @@ -25,7 +25,31 @@ "name": "stderr", "output_type": "stream", "text": [ - "Using TensorFlow backend.\n" + "Using TensorFlow backend.\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n" ] } ], @@ -210,12 +234,12 @@ "data": { "text/plain": [ "Counter({'Neural_Networks': 81,\n", + " 'Probabilistic_Methods': 42,\n", " 'Case_Based': 30,\n", - " 'Theory': 35,\n", " 'Reinforcement_Learning': 22,\n", " 'Genetic_Algorithms': 42,\n", - " 'Probabilistic_Methods': 42,\n", - " 'Rule_Learning': 18})" + " 'Rule_Learning': 18,\n", + " 'Theory': 35})" ] }, "execution_count": 9, @@ -396,7 +420,7 @@ "output_type": "stream", "text": [ "WARNING: Logging before flag parsing goes to stderr.\n", - "W0819 15:17:31.165925 4583318976 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", + "W0820 17:26:50.029736 4429116864 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", "\n" ] } @@ -405,7 +429,7 @@ "graphsage_model = GraphSAGE(\n", " layer_sizes=[32, 32],\n", " generator=train_gen,\n", - " bias=True,\n", + " bias=False,\n", " dropout=0.5,\n", ")" ] @@ -426,14 +450,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0819 15:17:31.181749 4583318976 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", + "W0820 17:26:50.045212 4429116864 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", "\n", - "W0819 15:17:31.192147 4583318976 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", + "W0820 17:26:50.055482 4429116864 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", "\n", - "W0819 15:17:31.199890 4583318976 deprecation.py:506] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", + "W0820 17:26:50.062143 4429116864 deprecation.py:506] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", - "W0819 15:17:31.228713 4583318976 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", + "W0820 17:26:50.088773 4429116864 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", "\n" ] } @@ -466,9 +490,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0819 15:17:31.395158 4583318976 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", + "W0820 17:26:50.245335 4429116864 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", "\n", - "W0819 15:17:31.401324 4583318976 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", + "W0820 17:26:50.252226 4429116864 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", "\n" ] } @@ -507,7 +531,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0819 15:17:31.487915 4583318976 deprecation.py:323] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", + "W0820 17:26:50.339275 4429116864 deprecation.py:323] From /anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" ] @@ -517,45 +541,45 @@ "output_type": "stream", "text": [ "Epoch 1/20\n", - " - 2s - loss: 1.8596 - acc: 0.2474 - val_loss: 1.7286 - val_acc: 0.3043\n", + " - 2s - loss: 1.8869 - acc: 0.2670 - val_loss: 1.7227 - val_acc: 0.3462\n", "Epoch 2/20\n", - " - 2s - loss: 1.6507 - acc: 0.3628 - val_loss: 1.5946 - val_acc: 0.4286\n", + " - 2s - loss: 1.6700 - acc: 0.4314 - val_loss: 1.5809 - val_acc: 0.4262\n", "Epoch 3/20\n", - " - 2s - loss: 1.4967 - acc: 0.6068 - val_loss: 1.4402 - val_acc: 0.6173\n", + " - 2s - loss: 1.5122 - acc: 0.5406 - val_loss: 1.4226 - val_acc: 0.6300\n", "Epoch 4/20\n", - " - 2s - loss: 1.3392 - acc: 0.8068 - val_loss: 1.3155 - val_acc: 0.7112\n", + " - 2s - loss: 1.3389 - acc: 0.8018 - val_loss: 1.2970 - val_acc: 0.7699\n", "Epoch 5/20\n", - " - 2s - loss: 1.1888 - acc: 0.8941 - val_loss: 1.2048 - val_acc: 0.7514\n", + " - 2s - loss: 1.2074 - acc: 0.8628 - val_loss: 1.1941 - val_acc: 0.7908\n", "Epoch 6/20\n", - " - 2s - loss: 1.0651 - acc: 0.9246 - val_loss: 1.1160 - val_acc: 0.7535\n", + " - 2s - loss: 1.0721 - acc: 0.9120 - val_loss: 1.1014 - val_acc: 0.8097\n", "Epoch 7/20\n", - " - 2s - loss: 0.9226 - acc: 0.9628 - val_loss: 1.0428 - val_acc: 0.7740\n", + " - 2s - loss: 0.9553 - acc: 0.9560 - val_loss: 1.0335 - val_acc: 0.8080\n", "Epoch 8/20\n", - " - 2s - loss: 0.8495 - acc: 0.9754 - val_loss: 0.9774 - val_acc: 0.7797\n", + " - 2s - loss: 0.8790 - acc: 0.9415 - val_loss: 0.9720 - val_acc: 0.8142\n", "Epoch 9/20\n", - " - 2s - loss: 0.7556 - acc: 0.9560 - val_loss: 0.9289 - val_acc: 0.7875\n", + " - 2s - loss: 0.7737 - acc: 0.9585 - val_loss: 0.9096 - val_acc: 0.8232\n", "Epoch 10/20\n", - " - 2s - loss: 0.6583 - acc: 0.9754 - val_loss: 0.8976 - val_acc: 0.7867\n", + " - 2s - loss: 0.7082 - acc: 0.9619 - val_loss: 0.8611 - val_acc: 0.8253\n", "Epoch 11/20\n", - " - 2s - loss: 0.6153 - acc: 0.9788 - val_loss: 0.8492 - val_acc: 0.7879\n", + " - 2s - loss: 0.6381 - acc: 0.9686 - val_loss: 0.8174 - val_acc: 0.8269\n", "Epoch 12/20\n", - " - 2s - loss: 0.5498 - acc: 0.9788 - val_loss: 0.8198 - val_acc: 0.7888\n", + " - 2s - loss: 0.5646 - acc: 0.9865 - val_loss: 0.7880 - val_acc: 0.8290\n", "Epoch 13/20\n", - " - 2s - loss: 0.5072 - acc: 0.9754 - val_loss: 0.7897 - val_acc: 0.8007\n", + " - 2s - loss: 0.5009 - acc: 0.9932 - val_loss: 0.7499 - val_acc: 0.8249\n", "Epoch 14/20\n", - " - 2s - loss: 0.4472 - acc: 0.9932 - val_loss: 0.7813 - val_acc: 0.7888\n", + " - 2s - loss: 0.4505 - acc: 0.9898 - val_loss: 0.7308 - val_acc: 0.8310\n", "Epoch 15/20\n", - " - 2s - loss: 0.4149 - acc: 0.9932 - val_loss: 0.7586 - val_acc: 0.7830\n", + " - 2s - loss: 0.4207 - acc: 0.9831 - val_loss: 0.7203 - val_acc: 0.8232\n", "Epoch 16/20\n", - " - 2s - loss: 0.3863 - acc: 0.9865 - val_loss: 0.7402 - val_acc: 0.7892\n", + " - 2s - loss: 0.3895 - acc: 0.9865 - val_loss: 0.6956 - val_acc: 0.8277\n", "Epoch 17/20\n", - " - 2s - loss: 0.3516 - acc: 0.9898 - val_loss: 0.7368 - val_acc: 0.7892\n", + " - 2s - loss: 0.3683 - acc: 0.9754 - val_loss: 0.6828 - val_acc: 0.8249\n", "Epoch 18/20\n", - " - 2s - loss: 0.3064 - acc: 1.0000 - val_loss: 0.7239 - val_acc: 0.7920\n", + " - 2s - loss: 0.3322 - acc: 0.9932 - val_loss: 0.6686 - val_acc: 0.8220\n", "Epoch 19/20\n", - " - 2s - loss: 0.2936 - acc: 0.9889 - val_loss: 0.7167 - val_acc: 0.7875\n", + " - 2s - loss: 0.3039 - acc: 0.9822 - val_loss: 0.6568 - val_acc: 0.8290\n", "Epoch 20/20\n", - " - 2s - loss: 0.2756 - acc: 0.9966 - val_loss: 0.7248 - val_acc: 0.7863\n" + " - 2s - loss: 0.2706 - acc: 0.9966 - val_loss: 0.6618 - val_acc: 0.8224\n" ] } ], @@ -599,7 +623,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -644,8 +668,8 @@ "text": [ "\n", "Test Set Metrics:\n", - "\tloss: 0.7112\n", - "\tacc: 0.7896\n" + "\tloss: 0.6534\n", + "\tacc: 0.8187\n" ] } ], @@ -762,12 +786,12 @@ " \n", " \n", " 1126012\n", - " subject=Probabilistic_Methods\n", + " subject=Reinforcement_Learning\n", " Probabilistic_Methods\n", " \n", " \n", " 1107140\n", - " subject=Theory\n", + " subject=Reinforcement_Learning\n", " Theory\n", " \n", " \n", @@ -796,8 +820,8 @@ "1106406 subject=Reinforcement_Learning Reinforcement_Learning\n", "13195 subject=Reinforcement_Learning Reinforcement_Learning\n", "37879 subject=Probabilistic_Methods Probabilistic_Methods\n", - "1126012 subject=Probabilistic_Methods Probabilistic_Methods\n", - "1107140 subject=Theory Theory\n", + "1126012 subject=Reinforcement_Learning Probabilistic_Methods\n", + "1107140 subject=Reinforcement_Learning Theory\n", "1102850 subject=Neural_Networks Neural_Networks\n", "31349 subject=Neural_Networks Neural_Networks\n", "1106418 subject=Theory Theory" @@ -956,7 +980,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -995,7 +1019,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/demos/node-classification/directed-graphsage/example2-directed-graph-undirected-graphsage.ipynb b/demos/node-classification/directed-graphsage/example2-directed-graph-undirected-graphsage.ipynb index 328eb98c0..35d2ee1c0 100644 --- a/demos/node-classification/directed-graphsage/example2-directed-graph-undirected-graphsage.ipynb +++ b/demos/node-classification/directed-graphsage/example2-directed-graph-undirected-graphsage.ipynb @@ -25,7 +25,31 @@ "name": "stderr", "output_type": "stream", "text": [ - "Using TensorFlow backend.\n" + "Using TensorFlow backend.\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n" ] } ], @@ -209,12 +233,12 @@ { "data": { "text/plain": [ - "Counter({'Genetic_Algorithms': 42,\n", + "Counter({'Neural_Networks': 81,\n", " 'Probabilistic_Methods': 42,\n", - " 'Case_Based': 30,\n", - " 'Theory': 35,\n", - " 'Neural_Networks': 81,\n", " 'Reinforcement_Learning': 22,\n", + " 'Genetic_Algorithms': 42,\n", + " 'Theory': 35,\n", + " 'Case_Based': 30,\n", " 'Rule_Learning': 18})" ] }, @@ -398,7 +422,7 @@ "output_type": "stream", "text": [ "WARNING: Logging before flag parsing goes to stderr.\n", - "W0819 15:19:59.184190 4566578624 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", + "W0820 17:28:17.259684 4354622912 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", "\n" ] } @@ -407,7 +431,7 @@ "graphsage_model = GraphSAGE(\n", " layer_sizes=[32, 32],\n", " generator=train_gen,\n", - " bias=True,\n", + " bias=False,\n", " dropout=0.5,\n", ")" ] @@ -428,14 +452,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0819 15:19:59.200346 4566578624 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", + "W0820 17:28:17.275812 4354622912 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", "\n", - "W0819 15:19:59.209197 4566578624 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", + "W0820 17:28:17.286010 4354622912 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", "\n", - "W0819 15:19:59.215890 4566578624 deprecation.py:506] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", + "W0820 17:28:17.293032 4354622912 deprecation.py:506] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", - "W0819 15:19:59.242011 4566578624 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", + "W0820 17:28:17.324139 4354622912 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", "\n" ] } @@ -468,9 +492,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0819 15:19:59.402662 4566578624 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", + "W0820 17:28:17.485618 4354622912 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", "\n", - "W0819 15:19:59.408103 4566578624 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", + "W0820 17:28:17.491878 4354622912 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", "\n" ] } @@ -509,7 +533,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0819 15:19:59.497935 4566578624 deprecation.py:323] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", + "W0820 17:28:17.585758 4354622912 deprecation.py:323] From /anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" ] @@ -519,45 +543,45 @@ "output_type": "stream", "text": [ "Epoch 1/20\n", - " - 2s - loss: 1.8928 - acc: 0.2517 - val_loss: 1.7296 - val_acc: 0.3380\n", + " - 2s - loss: 1.8759 - acc: 0.2296 - val_loss: 1.7155 - val_acc: 0.3966\n", "Epoch 2/20\n", - " - 2s - loss: 1.6574 - acc: 0.4221 - val_loss: 1.6054 - val_acc: 0.3683\n", + " - 2s - loss: 1.6638 - acc: 0.4686 - val_loss: 1.5716 - val_acc: 0.5119\n", "Epoch 3/20\n", - " - 2s - loss: 1.4946 - acc: 0.5528 - val_loss: 1.4422 - val_acc: 0.6144\n", + " - 2s - loss: 1.5172 - acc: 0.6314 - val_loss: 1.4221 - val_acc: 0.6637\n", "Epoch 4/20\n", - " - 2s - loss: 1.3341 - acc: 0.8052 - val_loss: 1.3190 - val_acc: 0.7211\n", + " - 2s - loss: 1.3586 - acc: 0.7348 - val_loss: 1.2962 - val_acc: 0.7252\n", "Epoch 5/20\n", - " - 2s - loss: 1.1975 - acc: 0.8162 - val_loss: 1.2131 - val_acc: 0.7432\n", + " - 2s - loss: 1.2279 - acc: 0.7889 - val_loss: 1.1894 - val_acc: 0.7666\n", "Epoch 6/20\n", - " - 2s - loss: 1.0917 - acc: 0.8253 - val_loss: 1.1185 - val_acc: 0.7691\n", + " - 2s - loss: 1.0991 - acc: 0.8763 - val_loss: 1.0952 - val_acc: 0.8019\n", "Epoch 7/20\n", - " - 2s - loss: 0.9588 - acc: 0.8975 - val_loss: 1.0361 - val_acc: 0.8093\n", + " - 2s - loss: 0.9647 - acc: 0.9357 - val_loss: 1.0214 - val_acc: 0.8084\n", "Epoch 8/20\n", - " - 2s - loss: 0.8650 - acc: 0.9314 - val_loss: 0.9699 - val_acc: 0.8195\n", + " - 2s - loss: 0.8706 - acc: 0.9458 - val_loss: 0.9461 - val_acc: 0.8212\n", "Epoch 9/20\n", - " - 2s - loss: 0.7632 - acc: 0.9551 - val_loss: 0.9118 - val_acc: 0.8179\n", + " - 2s - loss: 0.7655 - acc: 0.9643 - val_loss: 0.8892 - val_acc: 0.8228\n", "Epoch 10/20\n", - " - 2s - loss: 0.6954 - acc: 0.9551 - val_loss: 0.8643 - val_acc: 0.8269\n", + " - 2s - loss: 0.7107 - acc: 0.9797 - val_loss: 0.8512 - val_acc: 0.8285\n", "Epoch 11/20\n", - " - 2s - loss: 0.6265 - acc: 0.9686 - val_loss: 0.8211 - val_acc: 0.8339\n", + " - 2s - loss: 0.6266 - acc: 0.9695 - val_loss: 0.8037 - val_acc: 0.8290\n", "Epoch 12/20\n", - " - 2s - loss: 0.5531 - acc: 0.9763 - val_loss: 0.7932 - val_acc: 0.8236\n", + " - 2s - loss: 0.5707 - acc: 0.9619 - val_loss: 0.7714 - val_acc: 0.8285\n", "Epoch 13/20\n", - " - 2s - loss: 0.5008 - acc: 0.9856 - val_loss: 0.7622 - val_acc: 0.8273\n", + " - 2s - loss: 0.5125 - acc: 0.9763 - val_loss: 0.7449 - val_acc: 0.8294\n", "Epoch 14/20\n", - " - 2s - loss: 0.4382 - acc: 0.9856 - val_loss: 0.7404 - val_acc: 0.8281\n", + " - 2s - loss: 0.4601 - acc: 0.9898 - val_loss: 0.7109 - val_acc: 0.8335\n", "Epoch 15/20\n", - " - 2s - loss: 0.4193 - acc: 0.9788 - val_loss: 0.7150 - val_acc: 0.8281\n", + " - 2s - loss: 0.4220 - acc: 0.9932 - val_loss: 0.6968 - val_acc: 0.8310\n", "Epoch 16/20\n", - " - 2s - loss: 0.3718 - acc: 0.9932 - val_loss: 0.6941 - val_acc: 0.8244\n", + " - 2s - loss: 0.3883 - acc: 0.9898 - val_loss: 0.6823 - val_acc: 0.8244\n", "Epoch 17/20\n", - " - 2s - loss: 0.3552 - acc: 0.9889 - val_loss: 0.6781 - val_acc: 0.8277\n", + " - 2s - loss: 0.3549 - acc: 0.9831 - val_loss: 0.6658 - val_acc: 0.8244\n", "Epoch 18/20\n", - " - 2s - loss: 0.3236 - acc: 0.9856 - val_loss: 0.6848 - val_acc: 0.8199\n", + " - 2s - loss: 0.3252 - acc: 0.9966 - val_loss: 0.6518 - val_acc: 0.8322\n", "Epoch 19/20\n", - " - 2s - loss: 0.2852 - acc: 0.9966 - val_loss: 0.6735 - val_acc: 0.8183\n", + " - 2s - loss: 0.2904 - acc: 0.9966 - val_loss: 0.6423 - val_acc: 0.8277\n", "Epoch 20/20\n", - " - 2s - loss: 0.2699 - acc: 0.9966 - val_loss: 0.6630 - val_acc: 0.8183\n" + " - 2s - loss: 0.2651 - acc: 0.9932 - val_loss: 0.6476 - val_acc: 0.8253\n" ] } ], @@ -601,7 +625,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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wjlvDVEqpM2kiqEjN+lkDyEnz4Pu/OF1jUKdWDT68oyuFxYY7P1pNVq5WK1VK2UcTQUXrfBtc/pi1qc3i/zht1jS8Nu+N7EzyoeM88IlWK1VK2UcTgSv0eRLaD7N2N9vwqdNmlzQ9Xa302W8367RSpZQtfOwOoFoSscpQHE2Fb+6HoPrQ+PJSmw7t2oCkQ8d4f+EumoTXZnSvxm4OVinl6bRH4Co+NWDYdAhtCjNHQto2p00fH9CKAW0iefGHLczbetCNQSqllCYC16oZYq0x8PW31hhkHyi1mZeX8MawjrSNCubBGevYklr69FOllHIFTQSuFtIQbvkUcjLgE+drDAJq+DDx9niCa/oyespq0o7muTlQpZSncuUOZZNEJE1Eytx1TES6ikihiNzkqlhsF9UJbv4IDm6GaddB7pFSm0UG+TPx9niycgu4e2oCuflaoE4p5Xqu7BF8BAwsq4GIeAOvAHNcGEfl0KI/DJ0GB36DKX+C44dKbdYmKpi3h3fit/1Z/OXT9RQX60wipZRruSwRGGMWAeeqofAg8AWQ5qo4KpVWg2DEDDi0Ez4aDNmlDwz3ax3Jk4Pi+GnzAV6ds93NQSqlPI1tYwQiEg1cD7xXjrZjRCRBRBLS09NdH5wrNesHt86CzH3w0SDIKr343Ohejbmle0PeW5DEZwn73BykUsqT2DlY/CbwuDHmnEtqjTETjDHxxpj48PBwN4TmYo0vg1FfwbE0mHw1HEk+q4mI8NyQNlzWPIy/f/kby5My3B+nUsoj2JkI4oGZIpIM3AS8KyLX2RiPezXsDrd9A3lZMHkQHEo8q4mvtxfv3NKZ2LBajJ2+hl3px2wIVClV3dmWCIwxjY0xscaYWGAWcJ8x5mu74rFFdGe443soPGFdJipl0VlwTV8m3d4Vby9h9JQEMo6dsCFQpVR15srpozOA5UBLEUkRkdEiMlZExrrqPaukeu3gjh8AsZLB7xvPatIwNIAJo7qQmpnLqA9XkZWj1UqVUhVHqlqhs/j4eJOQkGB3GBUvIwmmDIH8bGv8ILrLWU0WbE9jzNQ1xEUFMX10NwL9fW0IVClVFYnIGmNMfGnndGVxZRHaFO6cDTXrwJRrYc/ys5r0bhnB+Fs7s3l/FndOXs3xE4U2BKqUqm40EVQmdRrBnT9CYCRMvwF2LTyryVWtI3lreCfW7j3C3VMSyCvQ1cdKqYujiaCyCYqykkGdWKs20c65ZzUZ3L4+rw3twIrdGdw7bQ0nCjUZKKUunCaCyqh2BNz+PYS1gBnDYdsPZzW5vlMML13fjoU70nngk3W6w5lS6oJpIqisaoXC7d9B/Q7w2W2w6cuzmgzv1pDnr23DL1sO8sin6ynUZKCUugC6Q1llVjMEbvsaPh4KX4y21ht0HPGHJrf1jOVEQTH/nL0VP28v/nNzB7y8xKaAlVJVkSaCys4vEEbOgpm3wNd/huxU6PWotR2mwz2XNyGvoIjXftmBn68X/7q+HSKaDJRS5aOJoCqoUQtGfArfPgDznrf2NRjyDtQIONXkwb7NOVFYzDvzE/Hz8eYff2qtyUApVS6aCKoKX3+44QOIbANzn4OMRBj+CQTHnGryf/1bkFdQxMQlu/Hz8WLc1a00GSilzkkHi6sSEej1FxgxEzJ2wYQ+sHdlidPCk4PjGNWjEe8v2sUbc3faGKxSqqrQRFAVtRwI98wDv9ow5RpYO+3UqZPlq4fFN+DteTsZP//sqqZKKVWSJoKqKrwl3D0PGl1ijR38OA6KrJITXl7Cv25ox3Udo3j15+18uGS3zcEqpSozHSOoygLqwq1fwC9Pw4p3IX0r3DQZAuri7SX85+YO5BcV88L3W6jh48WoHo3sjlgpVQlpj6Cq8/aBgS/BteNhzzL44MpT+xr4eHvx5rBO9IuL4OmvN+mWl0qpUmkiqC46jbT2Ncg/DhP7wfafAKjhY+1ydlnzMB7/YiOz1qTYHKhSqrJx5cY0k0QkTUQ2OTl/q4hsFJHfRGSZiHRwVSweo0E3GLPAKmk9Yzgsfh2Mwd/Xmwmj4unVLIy/fr6BT1butTtSpVQl4soewUfAwDLO7wauMMa0A14AJrgwFs8RHA13/QRtb4R5z8EXd0N+DjVrePPBbfH0bRXB37/6jclLdQBZKWVxWSIwxiwCDpdxfpkx5ojj4QogxllbdZ58a8KNE6HvP2DTFzD5asjaj7+vN++N7MLANvV47rstvLcgye5IlVKVQGUZIxgN/OjspIiMEZEEEUlIT093Y1hVmAhc9qhj8VkSTOgNe1c6xgw6MaRDFK/8tI035+6gqm1XqpSqWLYnAhHpg5UIHnfWxhgzwRgTb4yJDw8Pd19w1UHLgXD33NOLz1a8h4+X8MawjtzcJYY35+7k3z9v12SglAezNRGISHtgInCtMSbDzliqtYhW1uKzpn3hp3HwyVC8cw7xyo3tubV7Q95bkMTz32/RZKCUh7ItEYhIQ+BLYJQxZoddcXiMgLowYgYM+o+1F/L/LsVr16+8eF1b7rq0MZOXJvPk15soLtZkoJSncdnKYhGZAfQGwkQkBfgH4AtgjPkf8AwQCrzrqJBZaIyJd1U8CmvcoNs9VlmKWaNh+g3IJQ/y9NVP4+/rxbsLksgvLOaVG9vjrZvbKOUxXJYIjDEjznH+buBuV72/KkNkGxgzH35+Epb9F9m9iMdu/BA/nxa8MXcH+YXFvDa0A77etg8hKaXcQH/TPZVvTbjmdRj2MWTuRd6/godDVzJuYEu+3ZDKA5+sJb9Q90BWyhNoIvB0cdfA2KUQ3Rm+uZ+x6f/knwNj+HnzQcZOX0NeQZHdESqlXEwTgbJWI9/2DfR9BrZ8w63rRjKhdyHzt6dx95QEcvM1GShVnWkiUBYvb7js/2D0HBCh/8o7+L79UlYkpXH75FUcO1Fod4RKKRcpVyIQkYdFJEgsH4rIWhHp7+rglA1i4mHsEmh7E222j2dl1Buk7tnJqA9XkpVbYHd0SikXKG+P4C5jzFGgP1AHGAW87LKolL38g+DGD+D69wnN3s78Wk8SkzqHWyeuIDMn3+7olFIVrLyJ4OSk8kHANGPM5hLHVHXVYTiMXYxvRHP+6/Mmt6W/zp8nzic7T3sGSlUn5U0Ea0RkDlYi+FlEAgGdW+gJ6jaBu36GXn/hZq/5jM8Yzcx3/0FOXp7dkSmlKkh5E8FoYBzQ1RiTg7VC+E6XRaUqF29f6PcsMmYBJrwl9xwdT8Z/upG/bY7dkSmlKkB5E0FPYLsxJlNERgJPAVmuC0tVSlEdCb1/Lsvj36IoP48aM2+meNqNkL7d7siUUhehvIngPSDHsZ3k/wFJwFSXRaUqLxF6XnMHywf+wIsFt5K3eznm3Z7ww1/huBaQVaoqKm8iKDRWjeJrgXeMMeOBQNeFpSq7EZc0p/7Vj9Er5zWWhAzBJEyCtzvBsv9C4Qm7w1NKnYfyJoJsEXkCa9roDyLihaOSqPJco3s1ZvSAroz6/WbebD4Z06AbzHkKxneHrd+B7m+gVJVQ3kQwDDiBtZ7gANb+wq+6LCpVZdzfpxn392nKWxt9eD74OcytX4CPH3w6Ej66BlLX2x2iUuocypUIHH/8PwaCReQaIM8Yo2MECoC/9m95anOb/+yKsYrYDX4d0rdaeyV/fR8c/d3uMJVSTpS3xMRQYBVwMzAUWCkiN7kyMFV1iAhPXxPHLd0bMn5+Eu8s3A1dR8ND6+CSB+G3z+G/nWHBK5CfY3e4SqkzlPfS0JNYawhuN8bcBnQDni7rCSIySUTSRGSTk/MiIm+LSKKIbBSRzucXuqpMRIQXr23LDZ2i+c+cHUxcvAv8g6H/C3D/SmjWDxb8C97pCpu+0PEDpSqR8iYCL2NMWonHGeV47kfAwDLOXw00d9zGYE1RVVWYl5fw75vaM7hdfV78YSvTVuyxTtRtAsOmwR2zIaAOzLoLJl+t4wdKVRLlTQQ/icjPInKHiNwB/ADMLusJxphFwOEymlwLTDWWFUCIiNQvZzyqkvLx9uKNYR3p2yqCp7/exKw1KadPxl4KYxbCn96CQzut8YNvHoBjaU5fTynleuUdLH4MmAC0d9wmGGMev8j3jgb2lXic4jh2FhEZIyIJIpKQnp5+kW+rXK2Gjxfjb+1Mr2Zh/G3WBr7fmHr6pJc3dLkDHloLPe+HDTPg7c6w9C1df6CUTcq9MY0x5gtjzKOO21euDKqU955gjIk3xsSHh4e7863VBfL39WbCbV2Ib1SXR2au55ctB89oEAwD/gn3rYBGl8Avz8C7PWD7jzp+oJSblZkIRCRbRI6WcssWkaMX+d77gQYlHsc4jqlqIqCGDx/eEU+b6GDu/3gti3aU0psLaw63fga3fgFePjBjOEy/AdK2uT9gpTxUmYnAGBNojAkq5RZojAm6yPf+FrjNMXuoB5BljNHJ5tVMoL8vU+/sRrOI2oyZlsA3653k+ub94M/LYOArsH8NvHcJzP4b5JQ1zKSUqggu27NYRGYAy4GWIpIiIqNFZKyIjHU0mQ3sAhKBD4D7XBWLsldwgC/TRnejTVQwD89czyMz13G0tM1tvH2hx1h4cJ01jrD6A2v9waoPoEj3TFbKVcRUseux8fHxJiEhwe4w1AUoLCpm/Pwk3v51J/WC/HlzeEe6xtZ1/oQDm+CncZC8GCJaw4B/QdM+7gtYqWpERNYYY+JLO+eyHoFSZ/Lx9uLhfs35fGxPvL2EYe8v57U52ykocrLZXb22cPt3MHQa5B+HadfBh/1h05faQ1CqAmmPQNni2IlCnv12M7PWpNChQQhvDetIbFgt508oyIM1H8HK/8GR3RAUA93ugc63QUAZvQqlFFB2j0ATgbLVDxt/54kvN1JYbHh2SBtu7hKDiDh/QnER7PgZVrxrXTLyDYAOI6D7WAhv4b7AlapiNBGoSi01M5dHP1vPil2HubptPV66oR0hATXO/cQDm2Dle7Dxcyg6YdUz6vFnaNoXykomSnkgTQSq0isuNnyweBf/mbOd0Fp+vD60A5c0Cyvfk4+lw5rJsHoiHDsIYS2sHkKH4VCjjMtNSnkQTQSqyti0P4uHZq5j96HjjLmsCY/2b4Gfj3f5nlyYD5u/ghXj4fcN4B9iTUPtdg8Ex7g0bqUqO00EqkrJzS/ixR+28PHKvbSJCuKt4R1pFnEeW2QbA3tXWOMI274HBFoPge5/hgbd9LKR8kiaCFSVNHfLQf72xUZy8gt5cnBrRnZvWPZAcmky98KqCbBmKpzIsmYbNb8KmveHJlfopSPlMTQRqCorLTuPxz7fyMId6fRtFcFrQzuUbyD5TCeOWZeNdv4MSfMh/xh414DYXtB8gJUcQptW/DegVCWhiUBVacYYpixL5l8/biM6pCYf3h5Pk/DaF/6ChfmwdznsnGPdDu2wjoc2s3oKza+CRpeCj1/FfANKVQKaCFS1sGbPYcZMXUNhseG9kZ25pGk5ZxWdy+HdsPMXq7ewe7E1FdW3FjTpDS36Q7OrILjUrTKUqjI0EahqY9/hHO76aDW7Dx3nxevaMrxbw4p9g/wc2L3odG8hy7F3UmQ7q6fQ9kar9IVSVYwmAlWtHM0r4MFP1rFwRzpjLm/C4wNb4e3lgplAxkD6Nmsl885frMtJpggi20L7YdDuZgjS3VVV1aCJQFU7hUXFvPD9FqYs30O/uEjeGt6RWn4+rn3T4xmw+UvYMBP2J4B4WZeP2g+HuGt0BpKq1DQRqGpryrJknvtuM63qBfHhHfHUD67pnjc+lAgbZ8LGT60pqr61rLUK7YdB48utvZmVqkRsSwQiMhB4C/AGJhpjXj7jfENgChDiaDPOGDO7rNfURKDOtGB7Gg9+so6aNbyZeHs87WNC3PfmxcWwbwVsmAGbv7HWKgRGQfubrZ5CZGv3xaJUGWxJBCLiDewArgJSgNXACGPMlhJtJgDrjDHviUhrYLYxJras19VEoEqz42A2d320mkPHTvD60I4MamfDtfuCXNj+o9VLSJwLxYVQr71V86jtTRAY6f6YlHKwa2OabkCiMWaXMSYfmAlce0YbA5zc+zgYSHVhPKoaaxEZyNf3X0rr+kHc9/Faxs9PxO2XPX1rQtsb4JZP4dFt1v7LXt7w89/h9TiYfiOsfOJeBqQAABf6SURBVN+qmlrsZDMepWzgytG1aGBficcpQPcz2jwLzBGRB4FaQD8XxqOqubDafnxyTw8e/2Ijr/68naT0Y7x0Q7vyF62rSLXDrf2Xe4yF9O3WAPOmL6yeAkDNOtaitUaXQuyl1kwkHVdQNnHxNItzGgF8ZIx5TUR6AtNEpK0x5g//XRKRMcAYgIYNK3jeuKpW/H29eXNYR5qE1eaNuTvYdziH90fFU7fWBZSlqCjhLaHfP6xb5l5IXgp7lkDyEkdRPMA/GBpeYiWF2F7WJSVNDMpNXDlG0BN41hgzwPH4CQBjzEsl2mwGBhpj9jke7wJ6GGPSnL2ujhGo8vp2Qyp//XwD9YL8mXRH/PlVMHWXrP2wZ6m121ryUjicZB33C4KGPayk0KgX1O8A3nb/v01VZXYNFvtgDRb3BfZjDRbfYozZXKLNj8CnxpiPRCQOmAdEmzKC0kSgzsfavUcYMzWBE4XFvHtrZy5rHm53SGU7+rsjMSyxvp6sg1SjNjToDjFdIaqTddPBZ3Ue7Jw+Ogh4E2tq6CRjzD9F5HkgwRjzrWOm0AdAbayB478ZY+aU9ZqaCNT5SjmSw+iPEkhMP8bwrg24r08zokPctN7gYmUftBLCnqWwZ5m10vnkldPAqNNJIaoTRHWEWhVUf0lVO7qgTHm87LwCXv5xG58lWPMXbuoSw329m9GgboDNkZ2n/ONw4DdIXXf6dmgn1v+jgOCGVkIomRxq1rE1ZFU5aCJQyiE1M5f/LUxi5qp9FBnD9Z2iub9PMxqHVeHyEHlHra05SyaHI7tPn68TezoxNLoU6nfU8QYPpIlAqTMcyMrj/UVJfLJyLwVFxVzXMZr7r2xG04vZ56AyyTl8RnJYD1l7rXN+QVZCaHIFNL4CIuJ0+04PoIlAKSfSsvP4YNEupq/YS15hEde0j+LBK5vRIrISzjC6WMfSrNlJuxbC7oVwJNk6Xivcqo/U+AorOdSJtTNK5SKaCJQ6h4xjJ5i4ZDdTlyVzPL+IQe3q8eCVzYmrH3TuJ1dVR/ZYey/sXmglh+OOWdshjazE0KS39bV2hJ1RqgqiiUCpcjpyPJ9JS3fz0dJksk8U0r91JA/1bU7b6GC7Q3Otk3sv7F5kJYXkJVYBPYCI1o4ew+UQ1tLag0FLblc5mgiUOk9ZOQVMXrabSUt2czSvkL6tIniwb3M6NnBjZVM7FRVaYwy7HZeR9q6AwrzT5/2DISgagqKsW6Dja1C0lSiCosA/RMceKhFNBEpdoKN5BUxdlszEJbvJzCmgT8twHr2qJe1iqnkP4UwFedagc+YeOLrfWvh2NNW6n/27Nf7AGX9LfAMcSaL+H5NGULS1B3RQNASEarJwE00ESl2kYycKmbIsmQmLdpGVW0D/1pH85aoW1XsM4XwU5sOxA44Esd9KEtm//zFpZKdapblL8vYr0ZOIOp0gTh2LthbJabK4aJoIlKog2XkFTFqSzMTFu8g+Ucjg9vX5S7/mlbOOUWVTXAzH0+FoiqM3kQpZKad7FieTRnHBH5/nXaNET6IB1GkEIQ0dt0bWcV0XcU6aCJSqYFk5BXyweBeTl+4mt6CIaztG83Df5sRW5YVplcGpZLH/dM+iZLLI3Gd9LXkZSrytnkRII8fNkSROJozA+lrJFU0ESrnM4eP5vL8wiSnLkykoMtzYOZoHr2xe9UpXVCWF+Vav4sgeq6x35l5r7CJzr3Xs2IE/tvfyheAYKykEx5QywB3lEWMVmgiUcrG07DzeW5DExyv3YoxhaHwDHriyGfWDq0hxu+qkIM/qRWQmn04UR/Y4BrpT4djB04X7TvL2s2Y7lUwOZyaM2pFV+hKUJgKl3OT3rFzGz0/k09X7EBFu6daQ+/o0JSLQ3+7Q1ElFhVYyyC4xsF3ylu34WpT/x+eJl1XAr2Zd62tA3dOPA+qUcs5xv0Yt572N4mJrWm5BjuOWaxUWLMgt/VhUJ2vzogugiUApN0s5ksN/5yUya20Kvt7CbT1juffyJoTW9rM7NFUexlj1mk7NgHIkh5wM63juEcg9DLmZ1uOC485fy7uGIymEWK9b8g98Qc75xXXJQ9D/hQv6ljQRKGWT5EPHefvXnXy9bj/+vt6M7NGIu3s1JiJIewjVSuEJR3I4ckaiKPn4iNWr8A0A35pQI8BxP6CUYzXBt1bpxy7w8pQmAqVslph2jP/+upPvNqTi4+3FzV1iuPfypjQM1UFl5R5lJQIvF7/xQBHZLiKJIjLOSZuhIrJFRDaLyCeujEcpuzSLqM1bwzsx/6+9ualLDJ8npNDntQU8MnMd2w9k2x2e8nCu3LPYG2vP4quAFKw9i0cYY7aUaNMc+Ay40hhzREQiytq4HrRHoKqHg0fz+HDJbqav2ENOfhH94iK5r09TOjfU3cSUa9jVI+gGJBpjdhlj8oGZwLVntLkHGG+MOQJwriSgVHURGeTP3wfFsWzclTzSrzkJew5zw7vLGDFhBUt2HqKqXbJVVZsrE0E0sK/E4xTHsZJaAC1EZKmIrBCRgaW9kIiMEZEEEUlIT093UbhKuV9IQA0e6deCpY9fyVOD40hKP8bID1dy3fil/LTpAMXFmhCU67l0jKAcfIDmQG9gBPCBiJxV59cYM8EYE2+MiQ8PD3dziEq5Xi0/H+6+rAmLH+/DSze0IzO3gLHT1zDgzUV8uTaFgqLic7+IUhfIlYlgP9CgxOMYx7GSUoBvjTEFxpjdWGMKzV0Yk1KVmp+PNyO6NWTeo1fw1vCOeHsJj362gd6vLmDa8mRy84vsDlFVQ64cLPbB+sPeFysBrAZuMcZsLtFmINYA8u0iEgasAzoaYzKcva4OFitPYozh121pjJ+fyNq9mQT5+zCsawNG9mhEo1AtcKfKr6zBYpcVzjDGFIrIA8DPgDcwyRizWUSeBxKMMd86zvUXkS1AEfBYWUlAKU8jIvSNi+TKVhEk7DnClGXJTF5qbZTTp2UEt/VsxOXNw/Hyqt4F05Rr6YIypaqYg0fz+GTlXj5ZtZf07BPEhgYwqmcsN3WJIbimr93hqUpKVxYrVQ3lFxbz0+YDTF2WTMKeI9T09eb6ztHc1rMRrerpzmnqjzQRKFXNbdqfxbTle/h6/X5OFBbTvXFdbr8klqtaR+LrbffkQFUZaCJQykNk5uTzeUIKU1cks+9wLvWC/Lmle0OGd2ugpbA9nCYCpTxMUbFh4Y40pizbw8Id6fh6C4Pa1WfM5U1oExVsd3jKBrbMGlJK2cfbS7iyVSRXtopkV/oxpq/Yy+cJ+/hmfSr94iJ48MrmdGhw1tpN5aG0R6CUh8jKLWDqsmQ+XLqbzJwCrmgRzkN9m9GlUV27Q1NuoJeGlFKnHDtRyLTle5i4eBcZx/O5pGkoD/VtTo8moXaHplxIE4FS6iw5+YV8snIv7y/aRXr2CbrF1uXBvs3o1SwMcbbHrqqyNBEopZzKKyji09X7+N/CJH7PyqNjgxAe6tuMPi0jNCFUI5oIlFLndKKwiFlrUnh3fhL7M3NpGx3Eg1c256q4SC1hUQ1oIlBKlVtBUTFfrdvP+PmJ7MnIoVW9QB64shlXt62PtyaEKksTgVLqvBUWFfPdxlTe+TWRpPTjNIuozYA2kbSPCaFDTAj1gnWBWlWiiUApdcGKig0/bvqdiYt3s2l/FoWOXdMiAv3o0CCEDjHBtI8JoX1MMCEBNWyOVjmjC8qUUhfM20u4pn0U17SPIq+giM2pR9mYksnGlCw2pGTyy5aDp9rGhgacSgodGoTQNiqYmjW8bYxelYcmAqVUufn7etOlUR26NKpz6lhWbgGb9ltJYeO+LFYnH+bbDamAlUSaR9SmQ0wI7RsE071xXZqG19bZSJWMXhpSSlW4tOw8Nu7LYmNKJutTrK+ZOQUAhAf60bNJKJc0DaVn01Aa1g3QxOAGtl0acmxF+RbWDmUTjTEvO2l3IzAL6GqM0b/ySlVxEYH+9GvtT7/WkYC15eaejBxW7s5gWZJ1O9lriA6pSY8SiSEqpKadoXskV+5Z7I21Z/FVWJvUr8ban3jLGe0CgR+AGsAD50oE2iNQquozxpCUfpzlSYdYviuD5UkZHHH0GGJDA+jZNIyeTUPp2SSU8EA/m6OtHuzqEXQDEo0xuxxBzASuBbac0e4F4BXgMRfGopSqRESEZhG1aRZRm1E9YykuNmw7kO1ICof4fkMqM1btBaBFZG16NgmlZ9MwLmkWSpC/bsdZ0VyZCKKBfSUepwDdSzYQkc5AA2PMDyLiNBGIyBhgDEDDhg1dEKpSyk5eXkLrqCBaRwUxuldjCouK2Zx6lGVJGSzflcFnCSlMWb4HHy+he5O69G0VSd+4CBqF1rI79GrBlZeGbgIGGmPudjweBXQ3xjzgeOwF/ArcYYxJFpEFwF/10pBS6kz5hcWs23uE+dvTmbf1IDvTjgHQLKI2feMi6BcXSeeGdXTlcxlsWVAmIj2BZ40xAxyPnwAwxrzkeBwMJAHHHE+pBxwGhpSVDDQRKKX2ZBxn3tY05m07yMpdhyksNtQJ8KVPywj6xkVyeYswAvUS0h/YlQh8sAaL+wL7sQaLbzHGbHbSfgHaI1BKnaejeQUs3nGIeVsPMn97GkdyCvD1Fro1ti4h9YuLpGFogN1h2s6WwWJjTKGIPAD8jDV9dJIxZrOIPA8kGGO+ddV7K6U8R5C/L4Pb12dw+/oUFRvW7j3C3K0Hmbc1jee/38Lz32+heURt+sZF0r1xXdrHBBNaW2cilaQLypRS1VZpl5DAWrvQocHpGkntooOr/aUkLTqnlPJ4x08U8tt+a5XzBsdq532Hc0+dbxJeyyqF4Sii1yYqCH/f6lMnSYvOKaU8Xi0/H3o0Cf3D3syHj+dbyWGflRyWJh7iq3X7AfDxElpEBp5KDO1jgmlVLxAfby+7vgWX0R6BUkqVcCArzyqg56iwujEli6xca9VzoL+VTC5tGkqv5mFVqoCe9giUUqqc6gX7Uy+4HgPa1AOschh7D+ewfl8mK3ZlsCTx0KnS2xGBflzaLMxxC6V+cNWsk6SJQCmlyiAiNAqtRaPQWlzbMRqAvRk5LE06xNLEQyzakX7qclKTsFqnkkLPJmEEB1SNAWi9NKSUUhehuNiw/WA2SxOtxLBy92Fy8osQgbZRwacSQ9fYurYOPuusIaWUcpP8wmI2pGSyNPEQyxIzWLv3CIXFhhreXsRFBdHGcWsbFUzLeoFuSw6aCJRSyibHTxSyKvkwy5My2JiSyebUo2TnFQLWDm7NwmvTxlFwr01UMK2jggiuWfGXlHSwWCmlbFLLz4c+LSPo0zICsAaf9x3OZXNqFptTj7I5NYsliYf40jHOANCwbsCpnkObqGDaRAUREeTvshg1ESillBuJCA1DA2gYGsDV7eqfOp6efeJUctiSepRNqVn8uOnAqfNhtf249/Im3HN5kwqPSROBUkpVAuGBfvRuGUFvR88BrIJ6W1OPOnoOR4kIck2NJE0ESilVSQX5+9K9SSjdS6yGdoXqt1ZaKaXUedFEoJRSHk4TgVJKeTiXJgIRGSgi20UkUUTGlXL+URHZIiIbRWSeiDRyZTxKKaXO5rJEICLewHjgaqA1MEJEWp/RbB0Qb4xpD8wC/u2qeJRSSpXOlT2CbkCiMWaXMSYfmAlcW7KBMWa+MSbH8XAFEOPCeJRSSpXClYkgGthX4nGK45gzo4EfSzshImNEJEFEEtLT0yswRKWUUpVisFhERgLxwKulnTfGTDDGxBtj4sPDw90bnFJKVXOuXFC2H2hQ4nGM49gfiEg/4EngCmPMiXO96Jo1aw6JyJ4LjCkMOHSBz3WHyh4fVP4YNb6Lo/FdnMocn9PJOC6rPioiPsAOoC9WAlgN3GKM2VyiTSesQeKBxpidLgnkjzElOKu+VxlU9vig8seo8V0cje/iVPb4nHHZpSFjTCHwAPAzsBX4zBizWUSeF5EhjmavArWBz0VkvYh866p4lFJKlc6ltYaMMbOB2Wcce6bE/X6ufH+llFLnVikGi91ogt0BnENljw8qf4wa38XR+C5OZY+vVFVuhzKllFIVy9N6BEoppc6giUAppTxctUwE5Sh25ycinzrOrxSRWDfG1kBE5juK7W0WkYdLadNbRLIcM6nWi8gzpb2WC2NMFpHfHO+dUMp5EZG3HZ/fRhHp7MbYWpb4XNaLyFEReeSMNm7//ERkkoikicimEsfqisgvIrLT8bWOk+fe7mizU0Rud2N8r4rINse/4VciEuLkuWX+PLgwvmdFZH+Jf8dBTp5b5u+7C+P7tERsySKy3slzXf75XTRjTLW6Ad5AEtAEqAFsAFqf0eY+4H+O+8OBT90YX32gs+N+INZaizPj6w18b+NnmAyElXF+EFY5EAF6ACtt/Lc+ADSy+/MDLgc6A5tKHPs3MM5xfxzwSinPqwvscnyt47hfx03x9Qd8HPdfKS2+8vw8uDC+Z4G/luNnoMzfd1fFd8b514Bn7Pr8LvZWHXsE5yx253g8xXF/FtBXRMQdwRljfjfGrHXcz8ZaY1FWDabK6FpgqrGsAEJEpP65nuQCfYEkY8yFrjSvMMaYRcDhMw6X/DmbAlxXylMHAL8YYw4bY44AvwAD3RGfMWaOsdb7gM1FH518fuVRnt/3i1ZWfI6/HUOBGRX9vu5SHRNBeYrdnWrj+EXIAly7KWgpHJekOgErSzndU0Q2iMiPItLGrYGBAeaIyBoRGVPK+fMtKOgqw3H+y2fn53dSpDHmd8f9A0BkKW0qy2d5F06KPnLunwdXesBx6WqSk0trleHzuww4aJxXR7Dz8yuX6pgIqgQRqQ18ATxijDl6xum1WJc7OgD/Bb52c3i9jDGdsfaSuF9ELnfz+5+TiNQAhgCfl3La7s/vLMa6RlAp52qLyJNAIfCxkyZ2/Ty8BzQFOgK/Y11+qYxGUHZvoNL/PlXHRFCeYnen2ohVEykYyHBLdNZ7+mIlgY+NMV+eed4Yc9QYc8xxfzbgKyJh7orPGLPf8TUN+Aqr+11SuQoKutjVwFpjzMEzT9j9+ZVw8OQlM8fXtFLa2PpZisgdwDXArY5kdZZy/Dy4hDHmoDGmyBhTDHzg5H3t/vx8gBuAT521sevzOx/VMRGsBpqLSGPH/xqHA2fWMPoWODk74ybgV2e/BBXNcT3xQ2CrMeZ1J23qnRyzEJFuWP9ObklUIlJLRAJP3scaUNx0RrNvgdscs4d6AFklLoG4i9P/hdn5+Z2h5M/Z7cA3pbT5GegvInUclz76O465nIgMBP4GDDGnN4g6s015fh5cFV/JcafrnbxveX7fXakfsM0Yk1LaSTs/v/Ni92i1K25Ys1p2YM0meNJx7HmsH3gAf6xLConAKqCJG2PrhXWJYCOw3nEbBIwFxjraPABsxpoBsQK4xI3xNXG87wZHDCc/v5LxCdY2pEnAb1jbjbrz37cW1h/24BLHbP38sJLS70AB1nXq0VjjTvOAncBcoK6jbTwwscRz73L8LCYCd7oxvkSs6+snfw5PzqSLAmaX9fPgpvimOX6+NmL9ca9/ZnyOx2f9vrsjPsfxj07+3JVo6/bP72JvWmJCKaU8XHW8NKSUUuo8aCJQSikPp4lAKaU8nCYCpZTycJoIlFLKw2kiUMqNHJVRv7c7DqVK0kSglFIeThOBUqUQkZEisspRQ/59EfEWkWMi8oZY+0jME5FwR9uOIrKiRF3/Oo7jzURkrqP43VoRaep4+doiMsuxF8DH7qp8q5QzmgiUOoOIxAHDgEuNMR2BIuBWrBXNCcaYNsBC4B+Op0wFHjfGtMdaCXvy+MfAeGMVv7sEa2UqWBVnHwFaY608vdTl35RSZfCxOwClKqG+QBdgteM/6zWxCsYVc7q42HTgSxEJBkKMMQsdx6cAnzvqy0QbY74CMMbkATheb5Vx1KZx7GoVCyxx/belVOk0ESh1NgGmGGOe+MNBkafPaHeh9VlOlLhfhP4eKpvppSGlzjYPuElEIuDU3sONsH5fbnK0uQVYYozJAo6IyGWO46OAhcbafS5FRK5zvIafiAS49btQqpz0fyJKncEYs0VEnsLaVcoLq+Lk/cBxoJvjXBrWOAJYJab/5/hDvwu403F8FPC+iDzveI2b3fhtKFVuWn1UqXISkWPGmNp2x6FURdNLQ0op5eG0R6CUUh5OewRKKeXhNBEopZSH00SglFIeThOBUkp5OE0ESinl4f4fyaDw7BkVq8AAAAAASUVORK5CYII=\n", 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" ] @@ -646,8 +670,8 @@ "text": [ "\n", "Test Set Metrics:\n", - "\tloss: 0.6709\n", - "\tacc: 0.8117\n" + "\tloss: 0.6440\n", + "\tacc: 0.8294\n" ] } ], @@ -958,7 +982,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -997,7 +1021,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/demos/node-classification/directed-graphsage/example3-directed-graph-half-directed-graphsage.ipynb b/demos/node-classification/directed-graphsage/example3-directed-graph-half-directed-graphsage.ipynb index 8798d8974..8ef1e3d11 100644 --- a/demos/node-classification/directed-graphsage/example3-directed-graph-half-directed-graphsage.ipynb +++ b/demos/node-classification/directed-graphsage/example3-directed-graph-half-directed-graphsage.ipynb @@ -25,7 +25,31 @@ "name": "stderr", "output_type": "stream", "text": [ - "Using TensorFlow backend.\n" + "Using TensorFlow backend.\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n" ] } ], @@ -209,12 +233,12 @@ { "data": { "text/plain": [ - "Counter({'Neural_Networks': 81,\n", + "Counter({'Rule_Learning': 18,\n", " 'Probabilistic_Methods': 42,\n", - " 'Theory': 35,\n", - " 'Case_Based': 30,\n", - " 'Rule_Learning': 18,\n", " 'Genetic_Algorithms': 42,\n", + " 'Neural_Networks': 81,\n", + " 'Case_Based': 30,\n", + " 'Theory': 35,\n", " 'Reinforcement_Learning': 22})" ] }, @@ -398,7 +422,7 @@ "output_type": "stream", "text": [ "WARNING: Logging before flag parsing goes to stderr.\n", - "W0819 15:22:59.292147 4432573888 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", + "W0820 17:29:59.615134 4619847104 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", "\n" ] } @@ -407,7 +431,7 @@ "graphsage_model = DirectedGraphSAGE(\n", " layer_sizes=[48, 48],\n", " generator=train_gen,\n", - " bias=True,\n", + " bias=False,\n", " dropout=0.5,\n", ")" ] @@ -428,14 +452,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0819 15:22:59.307858 4432573888 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", + "W0820 17:29:59.630453 4619847104 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", "\n", - "W0819 15:22:59.313180 4432573888 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", + "W0820 17:29:59.636193 4619847104 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", "\n", - "W0819 15:22:59.320682 4432573888 deprecation.py:506] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", + "W0820 17:29:59.642680 4619847104 deprecation.py:506] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", - "W0819 15:22:59.370659 4432573888 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", + "W0820 17:29:59.691473 4619847104 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", "\n" ] } @@ -468,9 +492,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0819 15:22:59.573738 4432573888 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", + "W0820 17:29:59.893568 4619847104 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", "\n", - "W0819 15:22:59.579632 4432573888 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", + "W0820 17:29:59.899368 4619847104 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", "\n" ] } @@ -509,7 +533,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0819 15:22:59.665999 4432573888 deprecation.py:323] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", + "W0820 17:30:00.058759 4619847104 deprecation.py:323] From /anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" ] @@ -519,45 +543,45 @@ "output_type": "stream", "text": [ "Epoch 1/20\n", - " - 2s - loss: 1.9548 - acc: 0.1576 - val_loss: 1.8109 - val_acc: 0.3035\n", + " - 3s - loss: 1.9150 - acc: 0.2314 - val_loss: 1.7971 - val_acc: 0.3175\n", "Epoch 2/20\n", - " - 2s - loss: 1.8039 - acc: 0.2973 - val_loss: 1.7576 - val_acc: 0.3031\n", + " - 2s - loss: 1.8004 - acc: 0.3296 - val_loss: 1.7488 - val_acc: 0.3035\n", "Epoch 3/20\n", - " - 2s - loss: 1.6934 - acc: 0.3492 - val_loss: 1.7003 - val_acc: 0.3105\n", + " - 2s - loss: 1.7122 - acc: 0.3465 - val_loss: 1.7000 - val_acc: 0.3097\n", "Epoch 4/20\n", - " - 2s - loss: 1.6138 - acc: 0.3889 - val_loss: 1.6203 - val_acc: 0.3737\n", + " - 3s - loss: 1.6186 - acc: 0.3790 - val_loss: 1.6162 - val_acc: 0.3532\n", "Epoch 5/20\n", - " - 2s - loss: 1.5234 - acc: 0.4966 - val_loss: 1.5189 - val_acc: 0.4791\n", + " - 2s - loss: 1.5003 - acc: 0.5246 - val_loss: 1.5056 - val_acc: 0.4877\n", "Epoch 6/20\n", - " - 2s - loss: 1.4155 - acc: 0.6059 - val_loss: 1.4104 - val_acc: 0.5673\n", + " - 3s - loss: 1.3483 - acc: 0.6637 - val_loss: 1.4063 - val_acc: 0.5697\n", "Epoch 7/20\n", - " - 2s - loss: 1.3081 - acc: 0.6874 - val_loss: 1.3269 - val_acc: 0.5923\n", + " - 2s - loss: 1.2484 - acc: 0.7366 - val_loss: 1.3357 - val_acc: 0.5919\n", "Epoch 8/20\n", - " - 2s - loss: 1.1641 - acc: 0.7569 - val_loss: 1.2653 - val_acc: 0.6050\n", + " - 3s - loss: 1.1346 - acc: 0.7551 - val_loss: 1.2837 - val_acc: 0.5956\n", "Epoch 9/20\n", - " - 2s - loss: 1.0262 - acc: 0.7982 - val_loss: 1.2255 - val_acc: 0.6124\n", + " - 3s - loss: 1.0483 - acc: 0.7526 - val_loss: 1.2417 - val_acc: 0.6087\n", "Epoch 10/20\n", - " - 2s - loss: 0.9155 - acc: 0.8086 - val_loss: 1.1813 - val_acc: 0.6272\n", + " - 3s - loss: 0.9278 - acc: 0.8068 - val_loss: 1.2093 - val_acc: 0.6181\n", "Epoch 11/20\n", - " - 2s - loss: 0.8801 - acc: 0.8271 - val_loss: 1.1599 - val_acc: 0.6210\n", + " - 2s - loss: 0.9240 - acc: 0.7871 - val_loss: 1.1892 - val_acc: 0.6263\n", "Epoch 12/20\n", - " - 2s - loss: 0.8641 - acc: 0.7932 - val_loss: 1.1410 - val_acc: 0.6288\n", + " - 2s - loss: 0.7247 - acc: 0.8661 - val_loss: 1.1747 - val_acc: 0.6300\n", "Epoch 13/20\n", - " - 2s - loss: 0.7044 - acc: 0.8745 - val_loss: 1.1228 - val_acc: 0.6325\n", + " - 3s - loss: 0.7574 - acc: 0.8526 - val_loss: 1.1725 - val_acc: 0.6296\n", "Epoch 14/20\n", - " - 2s - loss: 0.6694 - acc: 0.8874 - val_loss: 1.1130 - val_acc: 0.6366\n", + " - 3s - loss: 0.6766 - acc: 0.8578 - val_loss: 1.1701 - val_acc: 0.6280\n", "Epoch 15/20\n", - " - 2s - loss: 0.6119 - acc: 0.8849 - val_loss: 1.1079 - val_acc: 0.6423\n", + " - 3s - loss: 0.6190 - acc: 0.8745 - val_loss: 1.1707 - val_acc: 0.6259\n", "Epoch 16/20\n", - " - 2s - loss: 0.5858 - acc: 0.8948 - val_loss: 1.1106 - val_acc: 0.6403\n", + " - 3s - loss: 0.6101 - acc: 0.8524 - val_loss: 1.1637 - val_acc: 0.6333\n", "Epoch 17/20\n", - " - 2s - loss: 0.5372 - acc: 0.8907 - val_loss: 1.1156 - val_acc: 0.6395\n", + " - 3s - loss: 0.5425 - acc: 0.8711 - val_loss: 1.1559 - val_acc: 0.6366\n", "Epoch 18/20\n", - " - 2s - loss: 0.5024 - acc: 0.9000 - val_loss: 1.1208 - val_acc: 0.6358\n", + " - 3s - loss: 0.5056 - acc: 0.9077 - val_loss: 1.1601 - val_acc: 0.6354\n", "Epoch 19/20\n", - " - 2s - loss: 0.5256 - acc: 0.8729 - val_loss: 1.1160 - val_acc: 0.6415\n", + " - 3s - loss: 0.4844 - acc: 0.9111 - val_loss: 1.1712 - val_acc: 0.6337\n", "Epoch 20/20\n", - " - 2s - loss: 0.4661 - acc: 0.8975 - val_loss: 1.1261 - val_acc: 0.6382\n" + " - 3s - loss: 0.4269 - acc: 0.9348 - val_loss: 1.1881 - val_acc: 0.6362\n" ] } ], @@ -601,7 +625,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] @@ -646,8 +670,8 @@ "text": [ "\n", "Test Set Metrics:\n", - "\tloss: 1.1261\n", - "\tacc: 0.6382\n" + "\tloss: 1.1881\n", + "\tacc: 0.6362\n" ] } ], @@ -744,7 +768,7 @@ " \n", " \n", " 1061127\n", - " subject=Genetic_Algorithms\n", + " subject=Case_Based\n", " Rule_Learning\n", " \n", " \n", @@ -754,7 +778,7 @@ " \n", " \n", " 13195\n", - " subject=Probabilistic_Methods\n", + " subject=Reinforcement_Learning\n", " Reinforcement_Learning\n", " \n", " \n", @@ -764,7 +788,7 @@ " \n", " \n", " 1126012\n", - " subject=Theory\n", + " subject=Case_Based\n", " Probabilistic_Methods\n", " \n", " \n", @@ -774,7 +798,7 @@ " \n", " \n", " 1102850\n", - " subject=Genetic_Algorithms\n", + " subject=Neural_Networks\n", " Neural_Networks\n", " \n", " \n", @@ -784,7 +808,7 @@ " \n", " \n", " 1106418\n", - " subject=Theory\n", + " subject=Case_Based\n", " Theory\n", " \n", " \n", @@ -794,15 +818,15 @@ "text/plain": [ " Predicted True\n", "31336 subject=Neural_Networks Neural_Networks\n", - "1061127 subject=Genetic_Algorithms Rule_Learning\n", + "1061127 subject=Case_Based Rule_Learning\n", "1106406 subject=Reinforcement_Learning Reinforcement_Learning\n", - "13195 subject=Probabilistic_Methods Reinforcement_Learning\n", + "13195 subject=Reinforcement_Learning Reinforcement_Learning\n", "37879 subject=Probabilistic_Methods Probabilistic_Methods\n", - "1126012 subject=Theory Probabilistic_Methods\n", + "1126012 subject=Case_Based Probabilistic_Methods\n", "1107140 subject=Case_Based Theory\n", - "1102850 subject=Genetic_Algorithms Neural_Networks\n", + "1102850 subject=Neural_Networks Neural_Networks\n", "31349 subject=Neural_Networks Neural_Networks\n", - "1106418 subject=Theory Theory" + "1106418 subject=Case_Based Theory" ] }, "execution_count": 27, @@ -958,7 +982,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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hQqFQKPZqlBAqFAqFYq9GCaFCoVAo9mqUECoUCoVir0YJoUKhUCj2apQQKhQKhWKvRgmhQqFQKPZqlBAqFAqFYq9GCaFCoVAo9mqUECoUCoVir+YzudeoQqFQ7CoFCqxkBWtZQ4Agk5hELYP2dLAUHyFKCBUKhcKjQIGneZIWWjAwcLBZyQoO5wgmMXlPB0/xEaGEUKFQ7N20NcJrD8Ca+XTXlZI6MoYcWodEEiKMjc07vM0YxuLHv6dDq/gIUEKoUCj2KtLpAi+/vIZlyzqYXJnkuAX/g5HuRvr8yA3tHDXH4oVLT2bjPkPQ0KihFolDB+3UMXhPB1/xEaCEUKFQfDZJxaGtAbpawB+CsQfQHrf41reepKkpgRCCSt/DrDJWMry0gM8oENUs4hUlzHjkHZ666mvYwqGNVkopVa3BzzBKCBUKxWeLbBoevh5eexA6m0HToXwQVA7hUe1cNmzIMWhQFKTDtJ5ldOX9FHp8jK7oBEdS2tqNAPRMgZyukdUyJFK1/CpTwbQgnB6FMn1PR1KxO1HLJxQKxWeLf/4W3noUutvAFwTDhK6NkOhi2uzfU1Phc+0lu+jJG4T1PJ0pDceycYRA2A56Nk9nPk86lyedlNz/7RTP3D6LWzokZzdCi7Vno6jYvSghVCgUn2qklMQbGmhfvhynqxXmvugaCEDT3BYhAtJxIlqOkfZaIu1LGLPgLyxf30m53Uap7ISCBCSaI2mORMkvzxJv9rFhfphUe4zOx+cTXLSBNhvu7ZJu16utFPGzgOoaVSgUn1qSGzfy0k9+QtuSJWTRSOo6x9QmmFgOYUBICVYeCnno6aAiVou1ZD1DGt7E0Xws6o6h1xscPSROPiUJ6QaZkJ+3j90fny9Pe8pg7j21CF0HXaP11VWc4VvGUS/fBLlW8AfhmPPgCxeCrvpLP60oIVQoFJ9KpJQ8f9lldK9bR6K8mlUFgZFK8O67jaQOG8vUnKAkG0fHASlBOlTaG6jU2kgUHAqmCdJkeYeJnrPRNThmkkmTWcvqx+MUHI1nO2cgchr7sIELos9w+Jp66v63k67YEBg6whXZZ//qBujEi/Zoeih2HSWECoXiU0nb4sV0r12Lv6qaeVmBiWRyOMWE4WFCTY2kfDZB0yYvBAEDhOlHqxjM9PWLaRoTw2quJ2BkCIssAkjngEKWYCLHwfPn01M9hWHRJxkl1nGwtoIuM0iJDkjJoO4GCOpQNQxKKuHlf8Cx54Hp28OpotgVlBAqFIpPJdl4HKFpJKUgQo6jtbWMs9toJEock1FmHBtBe6GESGmMilHDwR9icMkCWpetokzLgiaRDlg2aAJiPptKNjB8XJh8YTZ+WSCLjiFtgn4L3Rbo/gCGdKBxFfR0QiQGmgnZJJjlezpZFLuAEkKFQvGppGrCBJAOU6wVnKxvpEakyEsNI2izQgwlpYUpJY0wfayMR6nobIbOFiYF0ixz8iRykoAJlgMFGw4dBX4TQBKykwQMg7weosRK4fjD4PNhZBKIApDPgGNDRxY6msAXgEQXRJUQfhpRs0YVCsWnkmB5OUedOoN97AbyBYkjIecYlJh5hpudNMsyhJCERZpwpgUal0Oqm5DIcdZkyeRB4DegOgInToDJte5EU+G5rzkWATuJLgSmU8AQGlI3kDlPBBEgNBDCnT1626V7MDUUHwbVIlQoFJ9aRhnt5MaMw2qPI9MbEEIj6+hUiAQbRS0pIggry5hghyta0gYg7IdDRg7AA8cBHGTexs5lsKWGkA66BpomELrhzhwVApa+C1bBXbeo+FShWoQKheJTi8j0ECirYNDkSYSG7UNpUMPUJLp0iFk9rM4N4oqGS1jIdCwzzOb23sCRQN6WCMDQJBLoykBb3gfhEk/4vLZkLrNb46f4eFBCqFAoPr1MORqS3QCImuGYdaMoDemYfpPn9a9zgf0yS2MX8P32P3PGhodYbY9hW2Io+znAaxQiyOCn2wmwNh1GExqZTAHL9pZmFHIwaLQ7VmjbH3WsFbsZJYQKheLTQ/sGeP4ueOQPsOhN2PcIMP2wcS30dIBuoo3cl4YfPcfD0e8xfGQrdSN7qA520mnH+O+Om7Hk1tWeLD4pOiTujNKCJclmC4h8ju6ERXNCENQlMpuFXJouO8BL83u4b1Qlj02uZc0vvoPMpJBSbuWX4pOHGiNUKBQfCikljz++jHvvnUd7e5oDD6zjm18fzeAySemwYZjBIAUJ72WgzYKxPpjkd4fVdoqFb8LdV27e1uyxm8AugOGDZBfoBhx8Cpx7Kc8/NRdpl+E3e8jLPLlBgtyqPC1OJfML+3KAb14/EWHLxqKnYbrmnpYYDpYUJAmysjWP6ffzpYP2oztYxRN/+zeW00wwHKQ7ZfHCbX9n9NNPsEYMoXz0aPY77zxKhw4lMmgQkZqanU9kxUeKEkKFQrFNCpkMje++Sy6RoGbffSkbNWorO3feOZc77phLNGQgEm08ddcCnr49z0Vj5lNbrjPu0v/mhkO+zAYLHFytOTQEv68B30DFsJCH+3/pTkwJhN2WYSYB+awrhIEw5HMw71WIraEndT6aZpDNQSolME0d3wid5tUlbLQqyJfE8OV6QDrb9lMA0hXCXnQkiVSejK1z4IGjMf74GvMv+CKW7RApjQBgWhJLWqxb30n1QROonzuXlc89R+nIkeSqBxE49Yuc9J1vMiKsFt9/UlBCqFAo+qVjxQqe/e53yfX0IB0HIQQTvvQlDr3ySoTXnEul8tx773zKS006Fi8kG+/GX7BIEeaN1WG+FOnh+et+R+rqIdQcNANwh9TeSMHDcfhGbICB2bCCDe02z7eOpDVlcjBNHB7VMHv7MDUN/AF3ssoHizjiuCU8/taxpFMFNAGOraMbDsIwqDWbWWEPZXKowV0GkUls32+xuVWoASeNzkAggj+aglyGlsVL8fvcfUYl7suDoevkCg7ZDWtJt3Uh/D7WHHII3ftPZ2VkCLcu6OLEkTX8TzWE1ADVHkcJoUKh2AopJS/++MdY2eymrjzHtlnyyCMMOfhgRnz+8wC0tLjjYJmWjVjZLNJ20Awdn3RocSrpWr+QRN1IRj/7II2eEAoBpTo8mRi4EL4zv4cfPjcVS/jQBDyW+RzTosO4Zfyz+E2vWSmEN3nT5NCKtzly6lE88+Y4TMNxez2FZFxsIVEzQ2c2CAHN3SjbDkI+s1XPaO8FQdEv4BcS8gloXg0/O4G6kTUsa6jHDHq3OQ4FG3rSDh3LNhD2wWEH1aJn3sF8+20EkteWHMq937ydPxk6P63c1VxS7C6UECoUiq3oXLWKRFMT4aqqTdc0XQefztx//YPFCxt4/a0WnGg1iUQeva0DoWnYUoCjkcOg2hd3xVFomO2tW7gvgIF+wMiyHO55/DV+cOXbDB/aTmNDBS88PorZ82t4rm00Z45qcS0W8hCMgqOht7Xzu1NuYp+qscxfOIVB2RYm5Reysmskz6UO4ozSuRCOud8pRILpx7EdsApIAToDGMN0LKhfwvRBE1iq6eRSacygH0dCZ8LC9Ok4BcGRo2200jDtZgmm7q5DPG7tK5Q9ezWrasZTOHga5ohJuzBoqthdKCFUKBRbIR3HbVx5lXNPKkur04pT6OKVBeW89cpStPYcWrKBlIiiWxo+y0baBln8WJgILUtaDxJN97By4n7QsQFKKpGGn7gD55QOLCyNa1/h8gvvRBeCTNxh4uRGJk5u5LpfHMaLzWM5c0i928Wp6e6YYR54PYzx6gd8M7iAQub/SKTCtCcqmGAtpEUvI3fxreBrgdb1sORtxPJZGGW12L4QWuMyRC6NYDszPnWv6kz3EGhfzUk33cCbf7iRnsZG8tJA6A5OIEqJL0d12GJZsAwhJVJKIrkkg1Mt1Lx1JxtjQ9He0WHG6fD1nykx3EMoIVQoFFtRPmYMwYoKerqSvLOhjEYLqmKl1CTjvGrtR3CQIFATILAmQ7i9nbilIR1JglJyGATJ8n5+PAutcZze9T7pA/anJd6NnUhQiNYR6cjSsmANsw8fyvTpdZsEtz8qg/9LV8GgYEXBhEK+QCSU5KyLN/LWM2dCXQv0tLmDj/ksjNrPFarW9ZhLl6EjSGGQsw2MaBkHlecIsRaOu8T1INUDV58Epg89EHYHBFfOdd3rF2/xvJTuuGQyzuAp+/HV2UvIJ5Msf+opXrvmGgqpFHrBAiEI9XSRrKhFt3KM7FkPuk7eCJCpGIzuc+DdJ2DKUTDp0N2el4odo4RQoVBshabrTLril5zz5X/QngDdJ1naDnpkKnbMjyNASger3MTp9tEuSzm8YhVLugxKpIVBgZz0I80grxY+z3/94zHkxDo+sCt46YU8+XiBN+xVzLuph31P+DxX/eVidNNESkk+kcAMhdAMA6Qk7F8NooR83sLn00E3SeZKGTWiiaoffBUO+ZErgL/5ivsFCMMHTauhvQGAnqooLfsNI+BIhmXKCbV3wEt/hyHjIRiBZ/4KiU63mzRaAeFSt2Vm+N2jd4PtTTNMvb1Fhdi8fMMXRAiBPxqletIkArEYlePGIQC7MJshPS2silUQNEAXAomkK1rNGB/uRB/NgPdfUEK4h1BCqFB8wrCxWcc61rIGHz7GMZ4aPqa1Z7YF8XaIxPj7c13kykejiRZCtQUcv5+Odh07LgiVS1cbDIFVsBgXamXfaBdNuXKiPp12UU0qpVEoOHRnTf4404/5Vp518Q4iWpaCTDOfINP86+h44A/cn/iAw775Vd77859JtrRgBoNMOe88pl54IZpWzYQJaRYtSpLJWgggEMgSKRnB5P2HuOH2BdyP5Gqau99nZzOYft4/fDSzT94fxzTBcdAdOOqBesYuaYO/fN8Vy0FjoHYklFZC50YYtS80LHPdQ0KoxBXCVA/uAhAH2dtxms+BZqBFyzYlYc2UKQw77DDWv/46vnCYWXIQM8xVTOlajyiP4XcK5KOVDK+uJbBpxqh0xVCxR1Apr1B8grCx+WfrUzxy7ypWvW4RKhMceM77XHzCMewr9vtoPX/3KXjiZsimKUiDN587gdJYJe3dMYI1eXAgIizaV0E+BcNCCU6w5jFxVANTYl38p2MMzzuj8DtZhhnN9MSGkTr3OJwDRtBqSKy5jTh3vY3R2k3EzFGQBnPy+xILJql/9WWenfsGpUOGEK2txcrlmP2Xv+DYNtPP+yYB53oOmFZBTxJsK0M0rGGW/WDLMbUDjodX/g9CERDQUVfGrFOmEexJo4sCODYFXfDq1w5myN9XEmxuckWzrQFyKdcN3YA3HvG+LgFkUyDS7jrFkjJIdlPQfCTTeQoFSc6SBIN55GN3U3XxNQCkM4J1+1/L2y/9jop5TxDV43SNGc/xXz6DYYNL4eV/4PcFwNCwHUlPVxojmUGMP4rIR5vDim2gVrAoFJ8gFnat5DfnL2XewxZOTiPeAM/8PMP1d7xElux275VSsnJlBzNnNtLent45jxe9Bf/3K/cLDbEqRDiCkWij3G5HSEG2yUAYEt2vUz5KMqmklRvlY5ziX8qhlRupCyQ4tmINAkhZBjmpUfj5KVj7j8DpTKN1JnD2GwK/PIWEL0qnXUqLVUanHeXV9HRe6p7E3Y0zeKR+HOt7ghh+P+GqKhbefz+2dhqEL0eQozTSTXmZgRn7CfhP2TIOX7gQakdAMg5WgXVjKkDX0TWf263pOJiWg/QH2DB+CKR73K7NeCs0rnCPhmVuN2sk5rYyzYCbJv6QO9GnYjCtKY1EFgoYaOESbMPPxgfvIN7QgGXBpdfCa7+7kZqmZ3FK6mgPTGbNRoMnb76PxYVByMvuBE0nsWE9y96bT/2yFdy0egRHfG8eTz29fOfyTbFbUC1CheITxENPfUCyTVI+yHs0gwJfSPDuvRlWfr2BfUvH9ntfV1eGH/3oBRYubEXXBbYtOe+8KVx66YHbnYiyiRf+5u7a4ncXwxk+kxPHJXl6ucG4fcaxYmUXuaREmjYTTvFxm7mAkrWCrg6HUr+FRGN4qIcrR73L79Ycwsaxo8lWliM6UggkuYKG6EzhVEQoTBtNeuZqNGwcoNUppzNfwmBfF8s7oyztKOHsCQ1MrEyS6e4ml0gQqjwXgl8DGQcRA9FP1RWJwZX3w/xX4Zm/IvwpN2EvFwMAACAASURBVD4hP5ks2FYWKXSyto9kGrcb2PIWcei6u27QLri7bEvpLs6XDiChuxUqBlFwBBZdGOESb2xPR5c5UrbOquefJ3PAd1ixpItD25/BitSgSQHpBG2pAP7Odv592WWsOPZYDvrNbVx9wR/x2TnWhYfSHCwln4vzs2v/w76Tqxk5smzr+Ck+MlSLUKH4BLF6TgYzsOU13XBXc7es23aL8Npr32DBghaqq8NUVoapqAhxzz0f8NJLawbmcUcT+IJbXLp8RgeTy+M4tsOoUWVURkv5/LR9+Ndl32BkdwfBkigRs4CDhkBio3FG9VJ+MvJNDh7TQ8yfQ2igmQKpCRwhwNCgIoLmrSJ00DA0iRACW/MR9Vn4dZunV9eSS6UJlJYSiHmr7oUJWmX/ItiLLwAHngi/eJwRE88GKUgUNGYlJ7PGGoGtO9g5SedLCayCxeYNRoteFoSAnnaIlEIo6qZLpAzQyKVzBO0kZHogFcfIxAFYb1WQbm9n+VowMu2ue0LHymSQlgWahuUrRWgarYsWccd1f+WVxCjei+xHiyhDQ8PvN0jYSf794sqB5Zlit6FahArFR0SaNEkSlFBKgMCObwCmjBzO7PeakEiEVznbjo2wNCZUD+v3nu7uLG++WU9lZZiWliQbN7q7vUQiPh58cCHHHTd6k10pJU88sYy77/6AlpYkkyZVc/nlBzN17AHw/ouukHiUyjj3fMliwRdPp6k5SVVViNyahTx7wdmcGtpAztEppDPEpUbMB3kbco7O4EAPX8y/zavO2Ugct4EFCN1dcqC19bhjcVLiQ6ILB0w/lshj59P4DZPujE5nR5pTfvMzerJ5VtWvY2h5lJraAU4a0nXKD/kmM+wn+Xt+JUJk6SFEtyxl+p3vU97VSt600YRwv1jhOG4Lz/C7rUJw1yX2LpMYNBq6W9CdAhtTJnURBwTkLYv5nVHijslBM2aQ0CAXHozUdISdx8rlEJr7omA6GQKllYQrK2l5Yx5a6MQtgixwZ5PGM6mBxVGx21BCqFDsZiws3uJNlrPMEzPJiO6JdP6nnPr1PUycWMnRU0KEZj0Mqz6AQaPgqHNg6Di+cdYBPPboMhLdcQKl7pyNdKvglKMnMXhQ/yvQM5kC4I4PtrWl0XWBEIKurgwvvJDn5ZfX0NaWpqoqRH19nFtvnUVpaYDq6ggrV3ZyySXPcP8NZzJm0VvQ1eK2gnJpsCzEOb9kv/1qeXfmXH57zQtMW3ozDjrDJ1QyrawL9AipRJL2QowRwThZaWIIeP7tKJ3jumG/wZDMousQHhFmaKCLjd3NVO5XTqXhY/X6JJmcK5SDRlSgJ1tIdyXwB/2cdOP13OdEePDV1e5rwco0J7e+ye+/dgKB6A6mlUgJqd8xsvVJ/nnRrQya2o4mJc/NO57n2g9ntLaSL5a+wqGBdwg4eXzhKH6f5n5X0NK8btO861bNCIhVQeNyQpVDcIZU8uz7a/HpgqwlyOc7GfLFkxl++OFU5yFWEWLN8IsZs/pmdNumoIUJOxsJBm2idXUgBENCPUghsfISw9f7wuMgBBx52NYbmys+WsRn8XtZ06dPl3PmzNnTwVDspcziPeYymzARNDRa1ua489ut6PEIES2Mk8sxNLWUu458n1A0QHe8QEWwgPm9P8H4z/H++8385rrXWbWuA0PXOOOUifzwB4cQCpn9+iel5Nhj72PWrA34/Qa2LUmn8xQKbsXq8+lUV0eoqgqxdm0XkyZVU1q6ueXX1pbiiCOG8/sf7gMv/h1WfwDVw+HY82DsNB57bCnXXvsGY7U1DFv1Lwq+Uux8nrMnxRkbTZDP27TbMWb6Z3BnwzSWrMuRtzSkX8M5eV84ejxogtjy1cRmLwIrSyBVQl1ljHg8x+LFLYTDPqZPrwMEra0pzjlnX4zDK7ihzSaWTmIgsTWNLn+Y8xrmce0lX99+JhTmQfe3mbd4OF/4/v/D0qswtTS5nCCV9oEwGBzo5LFhFzBGrsHEIuDXEb6g+43D1nr3y/PRMrf1ms/C8vdg1P7IYJSmpmaWLd9IPi8YVRVgzD8/QI9EAVi/AX5zu6T+9ZepXXY7pZmVDK2VVA+vxgwGSTQ3M/jMY3l6QjX/uT6LkG5L0MHhC6cN539//o2BjesqdhohxFwp5fStrishVCh2HxLJ37gTAwPd63C555J21s3LUlplMpgh0LicDY1xhlU4tKVMLCkIa3m+d0icsx68B4RASkl3d5Zg0CQQ2HHHzQ03vMPPfvYyti1xHImU7lCX9D7MAIKSEh/ZrEVNTYR9993cxZjJFAiFTJ544mu0t6eJRHxbiO4ZZzxEPJ5jcGoxg1Y8iuUvxXFcf46eUUmupZnbus8iMmY8s2dvQAhBPm+RTluguV2IQoNxR/qwHYdMi87oykHomkZzc5K2thTJZB4hBNXVYb797WlcccUMDn5lBdlkkkDRVmc504eWTjH7pMkESrezR1vyFsj8nfrm4Rx/3tE0ydMwRIZM2qJg+XG0MNW+JcwY1cB+zmLGpOdx3EkTqTnhTHdR+9KZ8LefbG4VahrU7QP1C6GkE5w4ICBRgJGj4YezQPi3CEJPErJdHbx2xcXE6+txLAuh65SNGMEpd9xBS3mcR1e9ypwXu7AzghMOn8h504/F2N4YqOJDsS0hVCmuUOxGHBzyFPDhVor5rMOaOTlKajQKtkVHdxqnNUHSMnllrZ8ZQ1L4dEmmoPObV8qo+M9SPn/CRIQQlJUFt3JfSsmyZe2sWtVJTU2E/adWs/KpJ5l9z0tIqxpD0ykAmjdzFNz9QoWAbNYin7dpa0th5bswDBtEhGTSpro6xGmnPUhrawpNE5x22jh+8IMZBAIG7e1polE/KXMUCIFwLDTNIJ+3SVomGUIcffoBPPNqK5omcByJz2cAgsqqIC1tCaTmoAuNUrOUksEltLdm+P73D+Kmm2YyaVI1fr9BT0+ORCLHiBExTFMnbviJ2F2b9/UETKtAPBojn0hsXwg1N+2GDclz9AFLeGG2j27tMBwtgkTiT7/P+KHtFISPOdpUnkuP5ZAvfhHyK+Cf17uzSL95vdsSlA7sc6DbXfr7w6GrDXQ/OBL8fjjBgsw/IHTRFkEoiUBJpIIvPvAA9W+/TU99PaUjRjD0kEPQTZMRlPODMSPIjclhYqKjf7jCp9hllBAqFLsJKeH9rM67qSPI0sO0SDu1Wje6Dqm4TesyiW6342TCpPKCgOEgbQsbgeXotGf8fPv7L/GTn+Y466wJxGJbTrDJ522uuuplXn99PeDOtAxnNnIyz7Ky5UB8mo3tOCB1pNw8IbxXEC3LXSSeyRRI9iylJGLT3WOSSVWyaFGB8vIgNTURLMvh0UeXkM1a/OIXRzF4+GDefruBWFkp5uCTGN74LLZlE/MbZDvamXLuuZz3nZPx/fFdbxJOCr9fY8yYcuLxDNLWiFTHaMyWowGDTEFAE9x77zyiUT/BoNv6jMUC+P06d931AV/5yiQmk2GRP0ypldsUl6Q/yKjmtURO3HKiyVb4joX0nSBz/PbqDQT/4PDGO8tIaiEaOyKM26eMSCSKlJLW1hTjx1Uw4r2/kn/jX+iGga4JeOtROOHbcPLFXgY78N0S7IXj6GkqkK4pITi1gvJgBjL/2koIe9F9PkYedVS/ZgIx4IlUio8O1TWqUOwGpITr2uGxBNgU6KILpMMxscU03zyXV/6UJWj68BkGVi5Dd9zCwGGfSAeWo7EhV4I0/RgBP6NHl1NbG+G++86kvHxzq/D++xdw443vUlcXRQiBlcux9O35TKjOsLYngnRsNsR9pGUA2XdJABAbKYnWOcTXC4aEs4SCkskT0viMFPOWTqa6tm6TXdt2aG/PcMAJ5/DWrCzr338Su1BAN0zGlmxgFCs452sTOOTrp1A1adKmMa329hTXXPM6M2c2YhgarW1pGjvTEPTjJHMIXUOvClMSNBnuE1RWhjCMzaItpaS5OcHMmRcxvzPB2XObKNgOfitPzvChOza3V+Q59rADd5wpmacgdR3gTlvtjJeQ0n7NzLkl3HzzeziOg+NIxo6t4KJjTeoeuozWfBiERlVViNEjSjDSXXD1Y1A1BKRFd9eRPB3Zn7RmuHtyC9gn38WR6XVola99iBKk+DhQXaMKxUfIwhw8noBqHfR4JzVta+mK+nkzNYILB73LbL+fQkGSd2ySKXenSl04pGyTrkKQgiPIZqHEJ0km86xc2clllz3P+PFVTJ8+iKlTa7nrrvcxDI1s1kIIgcykiZp5VnSVsE9ZgvlNfqJamrxtUih6tI2g5OjrbIbMkEgHNKuAeGg1Z7CAWLXOvbP2JeDrAjYLoa5rJNPwxntpRo6qoKb6LJpWLaa7rYW20Gjue/C7TJq49aLvysowt956EuvXd7N+fZx3Ogv8/IJHkd0ZtIABjqTQ0E1m6hBGTa6gYXELFRWhTff39OQYPboc09SZXhPjyYM0/jx3JUsLFqMz3Xx38jAOmDhlYJkSPA38R0DhA1qXNPL+ve/RvvxXVIwdy/1/PJ8u32CiUR+27fDwt6/ka0Ebn99ESmhpTWE7kok1jjt5qGoIUui8FDmEDCnCjrsOUkpYbkYYEjqafXat6Cg+ASghVCh2A7My7pCRnu6GxhWYukF1vICTzmLOXM0oESU8tJqVHT7icYlEJyM16jOl2Gj0tt4KBYcNGxKk0wWamnqYOLGTu+6aSzKZJ5u1SKcLCCEwTY2AKSlP58kafqaXNTGvYSgddgyBg46D7Y05Tb/UZuihktRGicgWGHTbcwRb25mTzGK2JRhir8SKTKbzoNE4httNl8tZJNMatdEShIBgpITRU90vzLd1QrKw/fQYPjzG8OEx/nrDHHx1pWg5i3xXGmHqBAeXYqdy7H/mZOoXbaStLeWOQabcma6XX/65Te5MqCzhr8cfsOsZo8VoXhLj2Ut+gtA0tHAJ9bPm0jhrFifdeiuDxx7Itde+QdrxIdxZRQgBfp9BR3uGQqWJ6XeFOkGCDrOOUGEl4K75EIApgiwL7qOE8FOMEkKFYjcQEp6UtW9wa1JNh3wGzdGYGOvEKfhIN66nxtFokrVIT/zsPhMkNE2Qz9uAxLIkpqnR2ZnFcRxyOQvHAZDYtk02CymqGSPX0bF6FRP0PLOdySAhI0w3PEIy/kxJuk0ipSD23jJ8De1YlSHMmIEvnUEUbIbEl+HMuY8NUy4km7XIZAocfuwhrOjeesmGlBAc4LBWanU7WshHuK6U8MiKzdc70gwr8/O3v53O3Xd/wOLFbUybNoiLLprG1Km1u5QH22LWLbeQkGFeahnHsk53icO4SBvG9X/mwkf/QX19nGZ9IpZ4Fb/MUcAgRAZNy5JnMOaEg91444DwgTkNnHaQaRAR0KI4wrdbw6z4eFFCqFDsBo4Kw5+7IGM7BDUNpEOP1AnaOfZrWsSplXHua5qCKWxKjBzd1tYzQsFtETqOg6ZpGIZGQ0Pcm4kJliU3LYnYZB+TFllFKpHHL7vwaRYFX9RdFO64yxZ0n9z0MYXgvPXIoIkuJJohAAfTZxIK+xhl1JMW3cQmjODcc6dQWTeM86+CXB78Xj3f0Q21lTC5/y1Pt+ILU6t4/e16chL8wl04n7Mc/NLhmNEl1FSHueGGL+xyug+EDQuWcH/DwXTnTKI+tym7LFHJLa/08OVkjnS6wOvz0lwRO4abRj/BWF+X+x1BXRDQB8HGtTBiMiWUUkopCZEkqA8C3OUyBRKMVe3BTzVqr1GFYheRUrJ4cSuvvbYOu6WH31RB2h9j1fI8yxamsbrS/PKJX3HVe9N4oHlfbEegScm4aOc23bQsx21xBQ103V2KIAReS3DLrw55V+iUZTyof4O3g8fTTYxkXse2XcF0LEHTLI2Q1xiT4QA+4eDzS/SeAmgRJO7km9q6GDf/dgZ33nkaRxwxnIljBD+5CBIptzu0tQMqY3DzT3vXJu6Yr541nskVfvydSbKWQy5TINiR5LtfGk9NdXin03xXaAqMpSujU+q3QEocx6HEzNEjS/jOd55hwYIWfD6d+kwJ6bzOkmQlSwpDiQ85EN0fgNsvg3wWgeAYjkNHJ0mSHnpIkWIYIxjH+I8lLoqPBtUiVCh2ga6uDFdc8R+WLGlDCEilCkyaVEWmsYnuZA+6ZZFqa+dH2hjSSY2YL4dtQX22hJWZiq3cMwyBz+d+yDYQMMjnLWprI1RWhlm5sgMpXYHc1iRv24G8YxKJuOvxipl1k8Gpd9nEBkv0syZj3tiAk7Ux20BKEzufx19Sgm4YlI0cuem+bNZi/zE9PPhbnQ1dJYSDgn33cZfYAWQ2NrH4D79g5YuvEAyHmH7RBQy54HLiGDyXhBV5GOcLc/vdp3PfX2fzxlv1lJb6Oec7n+Occz7ibysWEdp3Bvk5y0nksxRsgcBBkza5sipmzdrAuHGV1NZGOLBrIboGbfkQw2rD1FXo7ueX4u2wYg5MPowqqjmbb7CWNaRJU0stg6hD69OmyOdt5s3bSD5vM2VKDdGofxuhU3wSUEKoUOwC1133FosWteLLdtC6ah1WzuLxxYPQfFA1LkhlnUBPCJY0lzE+1oW0ICMNUo6PgtzcrNM0ME0dKSXptNt/aVkFNE3Q2ZmlvDyIabrKk83a/QqhprmL5ntbj6GQTj4vsW0HTRN0rYFnzgtwxLdCFEaWcei3qlj+yzso5PIIw8AMBAjEYoz/0ld57J1K7n9GsnbxYnrWvI4/VY+wcxw8Isuvb/k6+oTPu2Hp6OChIw9l1foEPXYQTXby3gfXM/X9FfztyjvotAUG8DRQqce4+7fH8Yf+d4j7yLCyWV7/9a+JvzYHpzAW6aQxNR1H95EJ1NCZEAwpdcdl/T6d8fpqKmQbEemgNzq0dRpUlofRSsrdhfUAtkVg+QdMaG+E6mEwdhDoW4rgokWtXH75v0km3V1pNE3w858fwUknDbA/WfGxo4RQodhJksk8r722Dn+2g9YVa7AdgWWEwJIYhQI9ayWBSBRROwg2ZunOmNSZGboLEbK2QfGIhONALmdv+t/b9SmEe339+ji1tVHq67spKfGTy9lks9YW4RHCrWzDYRPblkQiAUpKfLS0uLvE2LZDmT/C2if8fOc7B3Dm96fTccK3eO9Pf6JlwQIigwax79ln82jTqTx8VxY7vprGha/i2DqGMYLhodW8Wx/mqvNv40+Ph6mbPp1Ft/2RpWuTJEUpPlMiMEnaJn+ITMVJpBhcsnlT7BYLbu2E6wb44Yjdxey//IXV//kPY2oria7O0CUqCMk0WX8VGS1KOCxIpwtIKSlpX0Rn/TrM0Q6OZiN0g0zOoSueocJqdr9kn+yGW74Lzavc3dA1HYbsA5feBmF3l5tczuLyy/9NPm9TVeV2/WazFr/61etMmlTF8OGxjzcRFANCCaFCsZPk8zaO7dDT0EjB0dB0zf1+K+AgKCnE6W4OUjpEUNANNjgldGSj9OR2/Lj1tvgsSwI2nZ02tbURYrEAnZ0ZamvDSIm3FRqk05a3L6i7OL2pKcHw4SWUlYVIpQqk0wUcR2LbkoMOGsz557tr8CrGjuWkW2/d5O/a+jy3/eRV0h1rSPd0YRcK+P0hbCIknDJigXY+6B7Me/97N2dOn84Hz75O1vHh929uouqGTuO0gxnU2AITNwthhQ6vfsxfFnIsiyWPPkqoqopMTnJEcB4thVK6c356snHscZ+npNRPT0+O5uYkwza+TWMmREM3DI8JNF2CBDtfoFA3AnPO87BqLmxYARXuRBmkhPql8Mzt8NUfAzBnThPdBQdmjKYrGqCkJU6svgOnO8uLL67hooumfbwJoRgQnzghFELowBxgg5TyFCHESOAhoAKYC5wrpczvyTAq9m7KygKMGh5mzjIdhNuaM4WF2y4qYDgFsgUo2Bb+KORTOqmC+zmmXWH58g4CAYNczqa5OcmBB9ZRKLh7hobDJpqmEY36mDy5mqlTahDrFzNK5qjbr5qZ66MEAga33HIihx46dJtfNfjlNa/T07KGUDRCJtENQC6Xxq/5yGpBNB0cTadlVT0AdqQSweot3BBSYuRy5PQtlxLYEkIf87Q8x7Kwczm0sjJ8dp7KzBpqZAEhJEJKOpJdzPadzhVXHEI8nmXltQlsW7KgzUe746M2VMB2YE2n4OT9ajC7W2DxG+5X6+u7IVoOpZXuMeuZTUK4qgArvn8cWjSw6ZO/Jc3dVN7xGun0DhZfKvYYnzghBC4DlgIl3v/fATdJKR8SQtwOfAv4654KnEIhhOAXvzyaL70yh66cH2FLpNCoNrqxpaRHRtB0SSYuGXuqpOENjY5Vu76VoW1LcjkLv18nm7V4//1mQFBREaKsLEggYHDNNUdy2jG1WLddRoc+m86uLNKyaT/wc4y59i/UDdtygo5jWXSsXInh91OIVLPgg3Xo/igCgekPYOXdd81CIYvfnyNra5RoaUZMdWdH7v9fl7LsjfcQVh5p+EBKzEKSulffpPvyy7b4+kWHA9/6mHoE7XyemffczgcP/4PuRCOd/jh6Q5ogaSzbfRXJ+mL4WpcxpWIkX/7yRUSjfmaK8/j/7J13mJxV2f8/52nTZ7b33bRN742EhEBoAQRpgqhIERCQFxEVpNgFrD9RFFBeREEDKD30KkmAQBLSe8/2vju9PO38/piwMdIVxBfmw7XXNczO88yZ82Sf79zn3Pf3Xv+XP+OPxYnmJFHLyM+5V8cvs+ALQeuO/HKoouYTaAY6oW70YBgvJTzeUIOM9aHHMyhCIIFYTTHqQcOZO7f+PzMJBd43/1VCKISoA44HbgC+IfJfX48AvrDvJXcBP6AghAU+YiZMruF/fzaD235wD7v7vZSoMYaJJjLSy/rRn2fUyaVs3t1JdK0Ht09FUbKDJRD/CrYtcRwbIfKP586tJRDwIKWkry/NNdc8z9QvtVDfvJHK0cOoHFShHbDtcWg4Z/BcLa++yovf+x5mIoGUEq2slmIxm1RJkO5+8AQi5NIpbNtGuBaKHSPnuJw4ZA/Tv3wjAAd95mgePe4Cos8sxGOmAUm7ZwSTrCoiFTpLM6CKvNvOEf7/nBA+9aOrWf/Ug1DiQxlaTK61F9kbRaigOvnMW69iUzq0kqqypsFszklnncXu558nkUmiRdtxHEARHD61CGI9sOSv+5r1WvnGxYoKqRh07IZjzwegz4G9qDRUKezdlQUJilAgDv7jxjNtWvl/ZhIKvG/+q4QQ+DXwLSC07/9LgaiU8o3sgFag9q0OFEJcCFwI0NDQ8CEPs0ABmHfhmVQPKWPZzbfRuqWfbPEkDj3vy9z8paO49dbXWfx8ikTCJJ3OvW3Zwz+iKLyjWL5RPmFZDtFoDk1T2bixm0zGwjIdnr/778wcKhlXDIogH5IFi+Gl++HovBAm2tt59pvfRPN4CJSXI6Uk1dPO+KZ7ERMuRc8kiPclUH0GSVPFp6Qp0ZJ8dr7OeTf8gvKxY4F8VPzDh37EogdP57G7XsRUfXzqjIM57bRxeL0Ke0xotaBeh6H/IdOVeFsbm599FFEVRlM0pCuxwz6QUSj2ocddpOPgWAlEz17sov1prP7SUk69+262P/YYTc88Sd+WrfhUQSrZR1PapsajYPjDefFLxfJlFdLNC+IJlwD5OU+QQKtKUh9SSfS4OK6Lv1RjSklZodnufzH/NUIohDgB6JZSrhJCzH+/x0sp/xf4X8h3n/iAh1egwJsQQjDy2GMZeeyxb/pdR0eChoYi9u6NkkyabymE/+wS834ixubmGB0dCVIpCynz2Yqaa9LSmmEg1syMGTX59kaKAtb+usKdzzyDa1kYpaWDnyFYUU5dz25eW72UnCwiKFwSOYUKN0NVYwNWYDKPdClMbvFw3IS8kcC2bX288kozhqFy3W1for7+wN6Aw4z8zweJiYmNjQ8fgjeLykDLHizVwVDy5SbSsfPqFNDBcnBy+1ILpCQXjRJva6Nz7VqqpkwBwBuJMOmLX+SejVU8v2U3B5f2MsX9I/3ST7EVxzRACxRjmCmUUGleDA85FYL5cFeo/VR422nLVhIOZvEF89e331aYF+4FKj7YCSnwgfFfI4TAXOBEIcSnAC/5PcKbgCIhhLYvKqwD2j7CMRYo8J44+OA6Fi/eSyigkhYpTNWLI3Qsa7/a5R1kVEzTHewZ+F6QEvr7M4MNdx1HommCjBKgzOinP6OyZUsvU6dWIxL9MP/zg8dmenvfyp4Gj51gQfEGVqmz6U+plNvNTPRuR0nvpGnkl8lmba7/wQuMjMR4/NkW7n68B3ffPuDNN6/ku989lBNO+HBsxuLEeYLHaKUFFZUqqjmSo6nkwHqMSF0Dwpa4rkRRxP7PWeoH00EO5PJRmZTogQChqiqW//a3nHTHHYPn6OtL8/e/76GyMkgRe5EC+qurqIxHCdhpcsKDJTR0zcDr9cMhnxk8tlt2c0zwJe5LHM6AGUJoOkJRGB/ay6SQTUEI/3v5rxFCKeU1wDUA+yLCK6SUZwoh7gdOI585eg6w6CMbZIFPDIl9pX2hf7Fp+DHHjOCp/3c7WtvjOG4G11VZL6awTpuGtW+hX1EEmqZiGBq5XL4MYmAg+56WUd9IRtnfdFfy850H8b/ju6g0EmjpDGaXi6duGBxz/uBx5TNnYf/tb7hSouwTCte2ycVijBpVzsSirTR1WTSZMTyGgR7fi2JnKU9sp2zzg9z3RQ/dXQlmFdXRNu5MbG8RuZzNDTcsZe7ceoqL39pD9V8lRYrb+T0pUqhogMkedvMQD/BFziI0mFMHRXUN1C44lNanXkQvCyM0DRHLIUt80FCMMdCMa+dbWFVNm4bu89GzceMB7xeL5VAUgaII2mQVjk/H9mpsFyMYG91B0M6guA49UqPutG8hRkzZdz0kOxY+iDutndP61tKmDSWtBGls8FFRkSPMoR/ovBT4YPm/4DV6FfnEmZ3k9wzveJfXFyjwL9NmwSUdLzVeFwAAIABJREFUcHgTHNGUf9z2L2S9dy5bwvTsEoYMLcbUQpjozBQrmeHZjKrmI7lw2GD06FJs28Hr1QkE9HcRQUklnUxmDeOVzXjcA4vzdiWLOGnl6dweX8Dz1kx6j7sKrrobQsUkXfhRN5xVM4flY2ezsamD3r4B0r29pPbsoMiwEB07YOdqymLbMLABFykUjHQPtVvvxxEq7UYR/WMasQNpinsXYesqHo+G60pWrep4/xP1FmRjMbY8/DCr//AHnl7+B5JuEg8eNFQ0NASCKANsY9ubjj3j+7+j/ssnYVsWVm8Mz9FjKTp3AfayKDE1QFxEUOpGo3j9WOk0odoDUw7q6sL4fBqZjEWPKOeJkcdRnejEsE32FA2nzVfFxpHzOPfrS+iec9rgcZ1r1tB2yyKMBKgVfoZp7YyTW1C716DZKiNo/EDmpsCHw39NRPiPSCkXA4v3Pd4NHPRRjqfAJ4OcCxd3QFfWxdjYRrYrwZLKME1Ta3go0IFn4xKwsjD2YKgf85ZLjG+w5o478IVDFNWFqagtYdXr7WSTMDa7iq6GOXh9Gl6vhuNIhBAUFXnI5ey3PR+4zGcJI9mR98pEYRav8jTH0v4P+WNJ18ttG4cwa1YNX/306YP2X9/uhpfTUOHR6PvejUSXPkv30mc4yZtmat8y+nPDee31FnSfgdfMMVRtpy1bTKxqKpGedbiuS0rxoJYEIGeTDZfgTXTTPLuUYS/3AKCq/34ySM/mzTx60cX0tPWRiOcwjRzKYTV47joeYeRvVwoKFhZ99L7peK8R4Oyv3EzsKzESMs6adDE/afcjH/wBU166k6wSYKDdor1zD8OrNeZ+61sHHG8YKldcMYcf/GAxyaTJr0ZdzKriMRy26DfkmjtIesL0lKdJVj7FvZfdyfiDpjPra1+jaelShCOp/n0HPV+sJNfgBVSUthTT19Tjm/nBRsoFPlj+K4WwQIEPA9eVPLW+lz1NMQ4eHmbahPIDMvmWZaCtL0PXD54ktacPJZFAzaWRRTmaZzzHyPA+oXr8d3DkWXDSV99WDJMdHRiBvMWW36dzyCFDiEYzJNrbueDPn+agg4cSjWb53OfuZ8MGm+bm2DuOvZReRrGdJEEURUFRBMLNchTPs5Av4v5DX0PHkaxe3cVNNy3nsstmsbI9xX17XDJtMZo1QU1NiLojj2f94Sdw4d+vp379Bmoj5fRFc+za1YMQAi8mpreEDYHDaOx8GksqGCPK8HpUYlkbEAgpsH2SjtIQgWiGgw7KC3I6bbF8eSuZjM20adVUVQXf5lMdiJ3LsejCi9m0tomk5UFVVWzLj/fRZmLf38Kwn0w84PXV1LzpHI6ETTlIuxEiaoTru8B8bRe722sQw09kXPuLqE4SKQ1e9x3Opce8OdHp+ONHUVMT4t57N7KivZ9McxO74zp240zMbBY3nWLu766Hilq2vvASXevXM/zoo3FMk+zGZkJXN1M8ogLN7yW7uY2yX30J4v3wwp+haRM0jId5n4HyesxkkszAAMHKSlSj0NPwo6IghAU+EWyPWRz31WfpWtsOAoSEeTOruf83CwgEDNoseDwBe+9cgbOrh5KBvaiJOFJKEt1wdVuQWz9vU1kZBseBF/4CU46AoRPe8v2qpkyhfeVK/GVlQF4vfYpJ+YwJzJ47DCEE3/7233nllVYCAYN02jogkQYOLKcopR8HFU1TEYrAdVwsDAKkKKeHHqVq8LVvRGY337yCTMbinhVd9BWH8SggJtWy13TJZGzKG8vIJfpBy7vTHHHEGKZNa2BgIE1QphGX/oZXu8tIrvGz54Hf0RL2oqoCv18nG0/hAt1VIxCRvdzy86MIBAzWr+/issueOsBF5ZJLZnLuuVPe8fp0rF3DA7+7mtadW4i6YXTdQpoSVSi4uk78T9vIfX8cmldgY1FEEWMYe8A59pjwtc68t6kAet9oRbV4B4rXYFfRXHZXzgY7Q6PfhzWQZffuAUaOfHM3kKlTq5k6tZpdS5dy29n30VdWDZksuC5KVmC1xnmpo4R1vkMY07KHBW0LiW7ZhKJpqLqO2LWTscPCzC61qHrgauLfX04ua4EQSN1L6Mk/sUYZzeYXXgYJanE5B33rWsZ99rOFMouPgIIQFvjYIyWcfuMqOle3EqjM995zXMmS5e1cf/MqJlx0MDf1Q9yRRJfsQA16CPaDx/CgOSaVnhiNRTapDSuQmSpEWW3+pBuWvq0QzrzkEh49/3yS3d14QiGsVArpusz+xjcQQtDfn2HRom14PPlkGV1XyWRMUql81On36weISYAkAoltu3g8Kpqh5YUtRf7mum9vUVEEQghUNd/W6cYbXyVtujjVRWQcB7F0J/4TJtI9cwi++iKYNB92LRnMvikq8lPkV8DUYMo0Pu3xYS8Yyh07ltGzdj2q10fQtghIhx3nfY3KkdX89vBaZpcZmKbDN77xDFJCZWU+CrQsh1tuWcGMGTVMmPDWWZNmKsX9T/2Ygc9VkbtPRVEBQ0UaoKQdFBxcj8bAbkHZOEk1NZzOGRjsj6BcCZd35sWvYt9dLepCiw3FJYH97i+KiqkHsZFIKd9RdOKtrTx/+eUU93USdkzSikauP0Es5cdVNYqI4XWSrI6Vkerp5UivjpPL4do2s4cpTPInCNeMRG58BS8uqiEYyAl0M0vPxg1s2rYRfziEogrs7j28fPXX8ZWVMfzII9/Dv+oCHyQFISzwsWdTDvY8tQV/SWDwxqcqArXEz8uLltA/Ocf4snp6yoezRwJWjr7aYVQ27yAoLW4Y/TK13iReYeHE+tES/XmvSfXt/3zKxozhpDvvZO2dd9KzaROVkycz5dxzqZyYX95rbo7ti9zy41EUgd9vkMs52LZ8ky9l1KhFs1ejuC6OozBzZjUesmzZZTDQUwkOKKpAeKoQgVpcxUGqfcTjUfx+DU0TZEtCYNqkntyIcdBQgjmLKXMWwNqHYc96MLxgWfkhnfl98OT3tTSPh7P/93dccvfTuC+/iF5UzMCCk2kbM4VJOhy+L6Bat66TZNIc7LoADLaQeuaZXW8rhLtfe4n48RXQnyJXFMHTmcTWfEgFXBW0rElf+RQuj5xCI5WUUvqmOsKNOei094sgQIUKLRYoC8bhPr0F6Uqkkj9OJrLU1oYYMaL4ba/h1kceQQiBousoqQT+dBol5+AhhutodGt1uKk4YWCXGMmc3HIMKfGpDuPLXDLSwJ9MoEoXV4KugFeDrK2iCYvqMCQMA4RA08BIJlj/u98WhPAjoCCEBT72pFxwLQf8+yMILyY/MO5llrYV/eGH8eCyaeQ89hx2DLuf2oos9mH6Ahylb6bamybnCCwEaAYIN2+7NWb2O75v6ciRHHnDDW/5u6qqIOGwh0Qih5QKQohBEVRVUBTlgKXSZruazfo0xtlrEYCWi6EGg3zzmbvY8d2NPPvcbnLBucjQKBwEUsAeS+J6n8V12/H3JVFNm2zYh+OC/4n13Dr3UMK+MHz1d7DmeVi/BMIlcPDJ0HDgsqPP5+UX557Mb048mWeS+eeOD8L/lNrsEbtpcpvYlcsXvP8zQghM03nT828QFzEkoEoF9YJJZH68Gp+ZYp9msUcZSd3secyuHf+258i4vKnEPqJCsQJWfQmho8cw8PxWkFBtCELFHn56/XzE3++m79G72LqxhVSwhvozzqPx5FPR/X6ie/eiGAbe4mKie/cC4KAhkKjSJJJtQ1OGomgC05VE/B7MZBafBs9tk2TtHEO7e5lZDX5DIJEYQpLd16rEUOQBg9Y0SDbtOeAz2PnYFZ3/cDPHTxgFISzwsWecB4rmDifx0i685fklu6/XPsSnJ7yGHQkSTalEO8qYuG0xF0+r57pNZcSaB5BpmFjdhVRUSmQfHl2gSQukgJIa/tVuEpAXwpNOGs3tt6+mvz8D7K8J9Hh0/H6Nvr7M4JKnDBq8ctQFrJhWRQMxPnVIhGNmTkT3+7n33jFcdtUm/vRkJdJJYugqtuViSQVRdjjp1oWYZpaIlOixDOm0xQJZyeQh+2rwDC/MOiH/8zY4ODRpaxhRsZ6vlMMEJjFejOfx3BNs6NpJd3OOdQ/Ahs2wTfRRX1tEQ0MERck3DD7yyGFve+4hY2dA30soHoOy6To7vjmfDXdn0WyTqFVMvd/HL2/9zNseDzDemzeRybng2VcUJiVUanBekSB+7aEkzhhP1d5uKnGYNq2axiU/Z89jD/LCym6kBFW2sPf1tWx65FFO/OMfKRo6lNW33543IN/nb6fgkiKAhRc/KSIiSq9bjo5NRCSJaRDNwL5AmN1dNt398OkJkoABDvkuJJoCuX8St2zOZcTEcQCkSfMyS9nNbgDqqWcehxH+h7rJAh8cBSEs8LEnpMKPvj6Lb2zpJNmd5JRDV3L+555BCoHPCyVsYmBziOZHKpj9yl9p/OFTtK/Zxbxb/h+V3gRVnji2UULZ0FrQNQjs85wMvv2y2nsaV8iD16vh8eTr1iCfIJPJWDiOs9+CLWDg/uhkqImAaaNPmsIPAx5cG04EfD6dIWOnML7Zoq8LOjvzIVvQL8iYGr7iOsxYM6mUiaYp1NQEufji6dx33ybKy/3MmVOPx/P2twIbm7v5C8005Z8QghaaWSmXs7dzgP7dLi/9QqF/tyRcK4m32uzdG6WzM8nw4cWceupYZs58c4bnG1RWj2B4ewO7i5tRLA+Ns9MMnW8QG4gw6vEE517/fcIl/necy6AC15TBdT2Amy+QtoF5AbioBDQBa1I6l9y0jb1begjZWcal+lmQixPwGBhvKJeVo2/DWrYuWkTbypX5C7JvL1FKiYIkrYRBKCiOg5SSDF5O8CwB18Fy8pmrAF5dQaoaqZzFrl7JpBpwXZdyn8R2YVadRdIaYE1fgP6Eg27oTPvWd3BweIxFDDCAHz8CQSstLOJhPscXCtHhh0BBCAt8IjhvRICJ95/OHc+v5bJRv0aL6ngMH3bSJZfMERnWhxbUsHe4rDjxD0w/qJYv/eIqGtPrKH7ul+glFQhfIG+0PNAFI2dAxb9u7t7Xl2bRom2MH1/O66+34/WqmKZDOm0jhCSXc9F1BZC4R46BmghKX5LxEypoiHjIuvDLflgQBK8Cqirp6UqTjOd9RQWQzTpohoJA4vFoGIbKhAkV1NSEuOKK57BtF01TKCnxceutn2L48JK3HOtmNtFMExoayj4PDheXTtmJ6UB8j5eBvQ7+MhBCwRtxUfq8CEvl8stnc+aZE981E/Kz077O8+0PsTm0CVc4hMwAnxtyCBNOnfeesyg/HYLRBjyRgLiEQ/0wz58XwY0pl6MueBKzI4pfpLHTMXqjWXakNKbUu/xjdG8oDjuffpq+rVupnTmT3q1bSbS3gxDoCtSofbian0TGISQyfMH7JMPUNsx9q8KaAr0ZlRIh8QkbRVVoS+tU2DplRgbLF8Y3bDTa3i0YyRRzjRS76ocy8bpbCY8ZTwstDMgBfJYBiovQNAIESZKkib00MvI9zUeB905BCAt8YphZojPzpDQkgkAlMt5LMpVP0Rcu1I2N8re1U/E7LonmKNfesJq//OUUjDHD4K83QLQ7n544dg6c9cN/ayytrXEURZBMmriuxDA0FEUhm3X2lUHkM0QBxPQGFNPG59dxXYnZnwbbJVccYI8lGOuBuuJuYnGBV9cQwkZRwBAKpXTypcbFPNxST8pbhus6PPjgZhRFEA57URTYtKmH2bPv4LLLZnH55bMJBg+sZ9vERgRiUAQhX9SOBCMiiXfmvx+ASjw7ibQ5AYwAQU83PTH9PQmZLnSOqz2Do7GxsfHgeUtj7XdjlCf/889c/WIHzp52Knp2IaSLcB2yuTS269Cd1KgL79/bdIWKt6gov29pGFRMmIBjmqR7ekAIVGkRVNNMrskyJr4Uy4GsnY/eNSX/GNehJ63hC4dwrBwpu5gJR5yKr/tVREkVZjJJVI2Qky5+H9R9+mxCc44CoK1lMwO5XfR3JADwlZZQ2jgSabjEeOd60wL/GgUhLPDJQuxz+KgahpOIopMGIVCEQzLt4a7eeWiqIJ22CAYN7r9/M9deOx8mHALdzeALQtG/b55cXR3CcVxcd//NXlEEvn2OM319aRRFoKoKMpbB1hVSrVH23L2C5o5YvkSivpieH89k7DiX/vYmylXJABMQmkpApBnh6eDT7k08uKkcRw7gRDO8tDMICBQFurtTKApEIl5yOZsHHthMR0eSW2751AFj9fDmQm9JfrnQdSShSolQIJaZQ9oajXAzeJUsaVnOn5+N8PnPQtV7bMWn7fvvgyTjwo7WAXwdreBRcdX858lKi+50KWXJLmRIRTgWDiqOEWTyWWeR6u6ma/16/CUlVE6aRPeGDSS7uzHLq9h72HGEPa0c1/4y3Z1pcpZEV+HvO/a/r+7z4boSoXsoGdFI23NPMGx8CCudpnPduvyeo+EBN8OWhX+iP1DHqBNOYN33fo1zZSOariMdh3RPD3bOJDSlkVLx5prHAv8+BSEs8MlCnwqiGLQk1pAptK7bQciTIZtzueiJL9Jth5HSQddVvF6NpqZ938BVDaqHf2DDqKgIcPzxI3n44S24LjhOvgOFriuMH1/OsmWtqKogFPJgvryT6Iwh8NeVpKJpRMSHJ2BQ1NnO9y/5JQ/+cSWfntJC8+RxPPnEVGRwOF+r+ju1cgs/ap1NqD5LKGTipGPk9kra4+HB4nvXhWzWxjBUKisDrFjRxq5d/QwfXkJGgk/AFDGNbWzDxh4UKQcHjzCILy2nfGongVo//W0jUZUk5BTwSMJ+B9Xw8NBzcMkX3mEyPmR0ATVOB9oEyaQTTbwhl7ZXBLteDPG6eTDTPY+SiqcQviCirJaDv3EFbcuX07t1K8n2duItLQhFITxkCKsvv47lM44E2+I1VXJfqpNDH7qFcS89SVu3QyCkIJMZLEdgZ7MEKiooaWzEEwrR1+/HtiwSLS1I10W6LlY6haZL+rIa7X/4A9JxYGMv2sYSMqMDiP4MRs4kG4TKXqgvL/Ra/TAoCGGBTxZCh8hvIH45Xl8feoWHvqTGj359KFuaq2BfoXVVVZB02nrHJI9/l2uumUdFRYBbblnJnj0D+P06FRVBkkmTuroQyaSJadqk1rTAz5+FWAaKA0hFkOtJUulrJul4ePgehcOnJ/nSOevIJP0kV+1gjrGMPcMrEGmIFNm4jkD1u4wd20P/Wh/Z3P6EC9N0KCvzYxj5esOHdyd5USuhy4ESFS4sHs6s0ByWi1cxyff009D4lDiBUceP54/3rGR9eRvtbQqYOsII4akWBCMK6Qxs3vWhTeF7QhNw+IQmMrekwXHBllTPVWk8Ksaa78CcK65i/PHHkM1alIwcSfeGDbx6442Ea2oI19aS6e8n099PYsI0tp92PqM0gSqAbJLcjh7+eNJVfH3HTirUVrBzCE8x8aSJHghQPXUqANJ16TU9MH4G4rm/oZppcF28GuzoN2hp6ULzJ+jdtg07lUJe+hRDT65jthElnEzhxCy6xSrUv12wPyX1DWK9sHcj+EMwfPI71rcWeGsKM1bgk4c2CoofRdgbKWro47qrOlizqY90OoWqCurrI2QyFuXlfk49dew7nqq1NU4yaTJ8eDGG8f56NhmGyle+MpOLL55BU1OUxYubyOVs5s5t4Gc/e5mmpii2LVm7thNnfSuuBHxxdAFuNsd23c/oxiQdnSoe3QOu5MSTNvP7FbNwkQw5shexSkVIBXCRDkhFUF6aoqW9aHAciiIYNaoU15UMmJK7AkWUSajWIO3CT3oF3+UILglPZw+7UNAYw2gMPOCD/zl/DmecCsddCANOnMnnPkdZYxdIQbwnSPcrRwHV7/86vQ/SpFmZeoVNsVWIpMlYawyHjD4ZTTPIksWo24a9O4VtCRypILEomWZw0jFbGPWpb1E8bn+N4rZFi1B1HUXL3x4D5eX4y8pYV96Amcuh6t78C71BPMPGo+5qZnvdGCo69kBxFaFwFfGVr+MNh5FSIl2XZFcXww4/HOOyn7Ltte0YrUsRms7OuJfmhI5qSMxkknB9Pem+PspUm2O2bMF2IG05CCmp822k+4YLqPjBn/PvLyU8dyc8cdu+kUsoqoRLfguVQz7U+f64URDCAp9MhAb6FMqq4c67JFu29LB48V6WLWslkcgxb94Qzj57MiUlb901oKcnxTXXvMC6dV2D/pvf+c48jjji/S+fCiEYOrSYc8/dX45x1lmTuOaaF/D58gkyQghwJSJjYQOqIjEtiMYkDXX55VsvkilD+5g5PEs4ZNIwKk1jbZzd7WFKQ+n8vp4lKfVnOL5+O39qm0rO1Skt9eE4ks7OBNrhYyipDhPclxfjz+fEcNsAnBSKMEVMe8vPUFYMk8e4JOc/RrgyRmYggHTBH8ngPeUxtnWeyeiqwFse++9iYvK3xF20tW9BRE2kKngp2MPWR1/m/ON+Rqu1GzudxusLYDpJkPnifumoaIfVUz5u3AHns3M5hHJghzohBB4rh/jn2tFgMaFhHjxFlSRLRyA0DTeZovbgg9E8HlLd3QghGHXCCflOF6qGOeFwXnx8OarHg6JpSNfFtSx8JSWEqqvRfT7G+AeQriSb25fRqigkc5LgkgfYes+RjPnCObBjFTx2C4TL8ma2ySi07YBbL4UfPPqO3VEKHEhBCAt84pAS1uVgyUAWtXUrR3etY1xVJeMumM8ll7x7xy8pJd/85rNs3dpLZWUAISCdtrnmmr+zcGHkLU2c3y9HHz2Cjo4Uv//9ynxUISWapgxmkrquQFVdhtUnKK9P0Z/VqShL0b+zhGGRDKv6axlKEz+/ZDGX3Xg4bd1+XOnBFYIz565n3pYm5pc087WWzxMKeYhEPFx44TR+M2M8/n/qUuoX0O6AwzvfMBpndNBcGyfZG0RK0HXwCS+qkeD12A5GV72z8fa/yg65nc6uHai9JqqugwtuQtIzymX1k3+l6oiDQYBi6PiKinAsC6TECXgIKNqbslpHHHMMzS+9dIAXaS6RYELHLtZ6PSRd8Dkmqe4e4qaFHgzx9Wu/Tt+SKfRt3066t5dMXx/B6moOuvRShsybhxHc34Gjeto0ioYNIxuNYiYSKLpO0bBhKIZBpL6eOVdeSWjht7CcbH7cqjYoaqqus/H2Wxl52udRX30UFBWyKWjaPCjw9LbAot/AyV/7UOb740hBCAt8opASftUH9/bnoLMJXJ27gjO5bNlfOeupP8Dlt0PonQvld+7sZ+vWXir8JmLXdsgm8WseEloNix7ZyhVXzv23xymE4JxzJnPaaWO5/vql3HTTciCfVJP/vcKwhgHO/8IKnn91OD7DYvKYbrYvqqest4PiSZPZPOk82txX+cbsOO5z68gMWIwe0U304VLS4XImiRhnBdu4+K7rGDEiX0P4fDtszeX3Bt8g4cIwPb/X9k6UVWZoU6E4cuDzEoEeSf7bc/J2tCR3YWdyGPr+25kiBYoQbN24lOknn41Yq2OWWniyCprHg2MArsukyJu/+Iw4+mh2PfsszS+9lH9CCFTD4MRvX82UKoUrmtI0796L67p4clk+c/N3eVVmOOpnP2PT3/5Gap/ReqK9ndZXX2XOlVcy7jOfGVxqHTp/PuHaWoxgEF9Jft7Tvb1EGhqomTGD8nHj2PLMbUT6N2O7+21yvEE/UlVJxCXJzk4i2WReIJs340qJVA1UZZ8Lw1O3w0EnQM2ID23eP04UhLDAJ4pVO5pY+bs/M2fVYsySEvqOPYl441h+O/WLHP3oV6l65g447Yp3PEc8nkO1MoimrXnnEd0LroMebaV71SrgPQihlGCvBXsHKOVgzAWxv0xh5co27r9/M/39GQ47bAjnnDOFBx7YgOPYqKqGx6MSTwW56oYFGLqNaerEYwZe4fKnG2oYuCTMU9k+nMxIgsF+jFNKOXbn61gLK0js9iCEBFVw0YnFgyII8D/F8JUO6HMgrEDShayE69661v4AFowtZ2OfJGe7GKqCBExLEopIZlTUvuvx/ypFFOX91SSD3p0ScAVsWBfkU8fdQ2fnMDJ6JzO+mGPsEVkUW1L/tMXMy96czqpoGsf88pe0Ll/O1kcewTFNGo89lprp06lV4PJffZ11CQtvURGj2zcz0m5CeCxeuunrPLPgBBzLw/yVz2P0dhFrbubRCy7gtRtvZMwpp3DQV79K98aN+MvL6d22jVhLC4GyMoYefjjzrr0WRdPwhMOMu+UhBi6cRSAZxxQGHo+OoUo2yzocFLzFxTDtaLIvLyI7kCSWdkFKDFVSGtIwzBysfhZqvvKhzfvHiYIQFvjEMLBnD8+few4VyQyq4hLs6SK0ZRMtF15G30FH8PrQgzhh1TPvKoSjRpUiot2YroKh57MvpVDJoTLXfAmcS94yc6+rK8kDD2xm06YWzj/9HiaM2oPHEIAKaiVEfg9qLffdt4mf//wVdF1F1xXWrGmlsaGFxxa+xsuvCAyPymPPH8zfl3pRVS9SCiIRD56AIJW22DK5iljXNuJNWUz8eFEYHu7gWWUu5wfbqJtpYzsu/pyOctiBxuHTfHBbDfy+H7aZ+eL0i4pg1js7nAHQEIlwqDWR1/T1pJMaUgpKyk0mFdcwRPnw0v5HZIewGB3Lk0M3VVDADqvsvivDivUjqBriMmJ6gHgsyMpfS8IPNTEhtoWc4efBZZ9jzpVXMmTevAPOaWUyrP3jH+lctw4pJS2vvML6P/+Zo372MwZWr2JcVRW+RAsHO2tIhCJ0Riqpie7lqpW/5dbZX+FPC87mxGvORtg2iqqiejxsvv9+WpYtI9HejubxUNLYSKa/HyMYZM6VVxKo2F+fatQ3olxzL63fOZcqT5aM5mejrGJ3j8u4z56CJxRCTjmSlbtMRis2GhKQWBZ0xRxqtFbUZQ/D8RcX9grfAwUhLPCJYfUf/gC5LGZZJd7UALbXh5LJUHX/3eyaOR81myHrqnjepU9dKOT5ENbJAAAgAElEQVThssm7+eXyGlRboKuStKUwtjzLMVUtkE68aXm1pTnK5z9/J/19/Zxy3Ab86uusXF3MpIlVhMMecLohcQNJeSG/vvExSiMuhrcMV0aI9e/kmRdUdu6ezhmntDF1/HrWbNgMTERKBZ8v/2fsuhKwqS/9Kyc5r2PUOcSdEC8mDyGX9uIU2fSrFmUakO6Dkhq2Fx3JysfAtnLUFPUzcriPKUOL+P2/WDVyStkhTJLVrMpuQqo244xGxjIOlfeXUftecCyLl3/yE7Y//jihei+9p5fjjC1DUTW8S3rY/uhYysYHGXVpG1ppHGm7jLJh+69KqK818J40nnTM4ombvstpxTdTMWF/b8k1d9xBx+rVBKurB31Ge7dtY9VttyFUFem6NMhWcEyS0RSR3m3kAiESRcWct+pOvltyKinNIKIo2Lkcus+HEQqxd/FiKiZOHFwS1WtrSXZ2sn7hQg656qoDPl/5oQtIXXcvS37xC1Ld3Si6zsQzP8NBl14KQNfmLeyyyxgT7kfJZHCFiqopOI5LzhH4+9pgzwYYPukDn/uPGwUhLPCJoWPVKsqKi2iRAkf3oFpZXK8Xo6+LYPNuKl54gquN43nqlk1UHjaCyWU+TlNy7HhsE9Foljlz6pg1qw5VVfjcMRU0+jbwYPMw+rMqRwxL8OkhnXj1IPj/oUOAtRGSN/Hbn3WSSdRQU5Fjwbw1ZLI2tmWyc1c/06ZWg1IG5tPs3rIJaU/F0EywWtm0JcJATAck3T0uf3uojA1r5xAuTqCpLj6/gbmv4zxIvnzmCmb51pDGR8714VVynBR5ivvlSURliNYdvXT1JwlOPYSHqn7JPd/x0tmVpL8/gyIUavWlHDFb5ac/PSov0O8TgaBRNNLoa/zArtvbsfbOO9ny8MMIVcVa1Uz49SZcHcaeciplV1zErbcv49CvdqLUOmR6BUpWAWEy/dYyrN6DcZIWeriY1LwSFr+2kM9O+Ongubc+8gi+0tLBL0RCCEJlpQz8/REmzpnFhpfWEPJ1kHazaFreE9Yf7cNJxhHVldS3byNneLFjffjLy1ENAzubRbrum4zjPOEw7StWAPlErO6NG2l//XWMYJChhx3G5x99lMzAAEYggOb1Dh6X6u7GEgabzGomaHtRFIGULq6QdNkhhik67C0I4XuhIIQFPjEEq6uxdu5kdNDDdunHn4lR270LkTP55j2fp8UzlIfP/gr9Rpiubf20vRznd9c+hm7aKPuMqxcsGMEf/3gS/uMuYMbWC5hRvQECEcil85HgKdeCui/6sXdC7CIsK8prq2YRiWQxNBehgJQCXUsTj+s4rkTFRDpdeENjsB0dKU3icYjGdbwem1xOZVKgk/ns5a7XDsIcNhxqvMSa4giRrwX0eCy++JkNtLcECY3I37xz0oMqHGYVv87Dj49i8SobvzdAa0eGu2UvxXWSeH+MgEfFlRqdzkE88dQDdHcv4rbbTqC09D2siX5EbLjnHrIDA2QHBhCqClLi2jZbFz+F/xcjGT5PJVgjyfVLUBVcr8Twamh+idQEsicFWQXFq7F7jomFNdjZQbruAasCxTLKZDajeHOUmR5qy9toa0lRWwyuqqIIAaqKlsugpBK0jBxDafYFAhUV+BpriRJFqPlSCNU40LLOSqepmDgRKSUv3XADWxctQjoOQlFYcuOvaP/2L1gxcS6VWTgnAkcG8qudpSNHguvS7pYS6+qmIgQKkuYBmDZnKGguBIoo8O4o7/6SAgU+Hkw97zysdJoiK8MsJc2Y3l0oGZO6Ej+dbjW64XD1ip/jz5i43Qmavv0Y0nJw/QaBgIGU8PTTO1i4cB0MnYD9P79H1o6BRD8ES+Cc62HuqfvfMP0XkBaZdJbiSBYzp+FKwYrVNYQCZr7FkrTpc6Al10prMsyPF9axp93gleURWjvyZQiOIygSOU4JbeGmkRew96ZL6f7m6Wi/PBV+eAIy7EUIKCuxCYcVrLiHRItAKC5Ck1iqSlGsG/WnyykbWkuopobd/sNxzCx9u3ahqSIvmtksiZRLTzzE449v56ij/swrrzR/ZNfrnZBSkuruJhuNono8oGpIVUP1ekn29uDs6OHQ80K4Dji2QDoSxxUYZfn2SagC6bqYqSRmbxTV76WfvsHzNx53HOneXgB0aTHN2YRrmiilVahl1ahlNbQmdTweDwFNIlUVgUtYc9loNJD+/IUMnzyeTNih2+lkINVNX3czysFDSMX7ce28ybeZSuHYNpPOOovWV19l6yOPEKyoIFxbi1ZZzTa8xK/7NiKbpcmEq7rgvnh+jEVDh9L4qU+RSOYYyCrs6Ia1HQpKIMyQMi3vizvx0P/4tfm/SEEIC3xiGDJvHod+97tI1yW3dweO6TBufD0l1TVkhZ/OSCWzWldQlerAXtWMm7bQvHre0UUIDEPFdeG22zdw1lVw0DVTOHrTHSw88jXc7zwEM487IDFB2pvJJr24tsLnTt5IIunBtgVLlw9l8/YKIqEcNQ1JOsxuWrMRLrnyGF59IULDiAxen0VbZ5BsVkMA32t8kRdr5tL3hSPR0hn0RBIjmoDRlfDVw9E0hUQqQH+/hs9roSR8xDZ7ie/UcVtdOh9M0ji0FnVfobhAomgaaiaGUAS25ZDNWEhXYpoOtu2yc+cAZ5zxANFo9qO5YG/BwO7d7HzmGTrXrCFUU4NtmsTjJrFolngsRyKaBl3D3djBsPEBKobp+EsEUoInKAmIHIoC9GUGi+YlYOUy6HK/7dyMiy6ieMQIEp2dhPp2IKwceAIUD88bJgRKSxjICNaZdXhVnVKRw4vN6+lilo07ldsnlDNp4TUoJ49FtUAP+Cn5xqcoXnQR4guTyMaiJLu7UXWdI66/nprp09n9/PMoqjo4rjYbLH+AIivJEc/eynFrH2R0rJlbB/INiAEOu/IbzDtkBEEPGG6G6VUmJ0wLoBWXwiU3523XCrwrQkr57q/6P8aMGTPk66+//lEPo8B/KY5lkf7FRXg7tyIDRSxf3obfkERCKSqy3WwpHs3lbSex4hUFNZjfJyvet9qZSlkIbyWTjz2PohBkTeiPwkWfhXNOsnnuuV1s3dpLTXY79os/xhxoR/XqjD0xzcuZmdz36ASEkGRzBqefuIXUGaezPTiSVx4wKP7rIsrKs9joRESUdEpl3YZypg/r4HdVT/C5w37Ptsap+BJxdE0hZWlkHEGwweDY+35E9xadyuoMV1+6hKrqCsrLS8ik+xCKwVNXCLp7StjQ6ae730JqpayIXIHP6qXH04DtSNLZ/JKt2n43uuLSEMphZzKcPyfDtdcdDzOO+ch8LF3bZvEPf8jOp59GUfKlGY7t0rZuAxIlLx7SxUXBLiui4q6TqVgwkxQp+mQvtpnDtSxoSyDLfIh13SgWKLqOqA6h7U5w0fjvUTx02OB7OqZJy7JlyMX3UrPrBfQho1HU/bHDUw8vpyUXIjB8FF7FIRFPY9kup//1XoqGDOF+/kacOB7277VKJGlSfDZzOkZCDu5D7nzmGV783veI7tlDpKGBYFUVayyVOtnHye4GnJpaXJ8fqWjcN+9izjnjPOp14A/fgrUvQHElCBXivfll+qvugaHjKXAgQohVUsoZ//x8YY+wwCcOVdcJzZwPi9aDoVJfFyTYsRFPwsIxdHJC5yLlBTZrh5EwVYLe/J+JlJKc6VIzagLF+/JhfB4oL4GbF2b5f9+9g472GNVOM0eZj6P6vIyfGMDrMVmzUOWQs1Zw9l/WsXl7Nbbj5xH1BtZETmCTCebmp8joQylmBzo5bHRCgQyjhseoH54iETUIFqsI20FTAaEgNJuZE3pIeov4wlnbqLOaWL66ju/89BgW3pFCVfsJlhwD/i+TbXiAXy7aTdpSsaQKSIzUS/SVHYaFH8txkTKH0vMcCi7jgr1ERIqMofLEBg/X/Pm7iK2vwdk/+kjS8bcuWsSOJ54gVF09GDFt3J4kJsoJGRZCujiqB1c1yGQ82PVjSJJEQyMsIuQ8WapkOZmHlpBMtJD9YiNSSFzXQd2TIPjLTfCHA4MC1TAYOn8+jKiGn68G8Q+/dx2OnFPPK7457Fq2grSUhOvqWPDtb1M0JO/zqZCf539GAobPT8CXt5x76cc/ZvODDyJdFzuXo3/nTtK9vRSNGsuJznqcnEmupRMESK+XM5u/xbI7fk+wbihHiLV460cglH3f1CJl+cbRLz9QEML3QUEIC3wymXNS/mbR184Qv4njsck5gna1AidcREOJwpHNrTzdPgI3bZIiXwNfVF7LsAnTB0+TJkUv/WzbOoDSOUDApzI1uQZX6KRyOpu2VTB7RpxQeZQtizyUHPE5jNL5TBx/LEtlEU4yX7geqw+irsvR5jbgoCCEhzAmQbWTn1+3lOKtOb7c9ChXD5lAptNA2i6No/qxdQ96LofeGaXLDTJ7eguvrRnC0act4PjjR3HttfPQVYW7VwSwHfDKDL686yiJ+Cb8VivFjXOZeWglD/zlWby6TVnEJSJSmFJDKGAKDxRXw6pn4IgzoX7Mf/xybb7/fjzh8AEeoLKoilf0Og4LbEK34iAlpq+UjWUnMvy5KRw6togWmvETYCzjKPeWs31SJS9+73tEVqfosjpRBtK42/tIahob7r2Xedde++bSmbpRcMhp8PL9eUszANfBOPpMDv/MN5ibSmFnMgdkmQKMZSxL5IuQMMn09SMUgRPR8Xe4bFm6kKHz56N5vWx9+GGClZX5sgzHIdrURKavjxEdG9CLMsT1IGgaajIOyQSGF8Id62jetInoOAclYVIxYfz+uTG80Nv6YV+SjxUFISzwySQQgSvuhMV/RTx5G1oggFbTyOhQCSMRWBLmHLWJJ4cexcJX87VZp546lp3RSTyzTCEUhAxpeugh2i1wE62oqsRSLILuALaiI+T/Z++84+yqqj3+3afdfu/0nsyk9wRSIfQSShAISBdFFAG7PLtPEWwPCyr4lOajRaVLlV4SExJIIT2ZtCmZ3uf2e0/b748zJEAQEpoI+X4+88fcuWefffaZc9bee631W5A3IZEZR2Ghn2y6k9pxv2RczNMgW5CFJ1Iw19jOmaf/kv95ZhQkJJGYTb9djkxUMG/+PComXwOTJGeuWcTjvd2sryslk5PY0ShZW+Hynl/iEzY5qZBM+TjzpK3c/MAZPPLIVoqKApx11kSaGvoRRoBBM4ohLFxUcgRwLIvC/GbOmFfDQEsVS5e2YDgZpCqRQF4LcXRtCqEqXvHCXVv+LYbQeRMh7OpIjgFRy5apXyHiJpBCkPeXkOpKM3v6cCYxnElMft0xo088kaYXXmDN//0f0rJQNA3F56Nk4kS2/P3vDD/sMG8V+FqEgHO+4wWerH7KUxOacSKM8+TZjFAII7S3oPhYOY5VKx+mOdQOSFzLRm6I4/x0Pa84QV75858ZfvjhCEVBCEGuvx+hKITLy8nF44TCIU+Y23RQMileLSIpHNAMnbQ0cKwk9mAf2f5+giUl3onzGRh/yF79OcC/5oAhPMDHl0gRnPolKKyAe/4Hop5YtgL4kKC6nHn2FM789p6XaUMLPL8CegcgF42Tibsk2k1UuxnH9EoddYoyat1dWIRAen4hM50mWFKCL7IneGFGAL5WKKlI/wBnpOCCaxM8eX2Uru0hIkY/Z59Xx5e/fpT3ZSGITD+GW114IgnPd3RSlLiTaR1LKEq3kXeGRJmFSzBgoKoKpaUh7r13E2efPRGnMERqZi2pUCG+LW0om9vBkQjB7lXMrbeezvnnP0BPkySVzSA1P0UBh2/M6fL6oCgQ/veE44+eP5/VN96IHgzu7q8/08Xxk2pYO2iTNCKoqkJmMMWcOTUcckjNm7ajaBrTL72UHU8+idA0NMMgUFSEOlQNfttjj+1tCMEzhhMP9X72kcFtO8l9+UFqptWQq1DpX7ERbV0f+bxFyezZKKrKzqee8r7b3IyZSmHnct7WgxBsy2eYPskk4IApwdOP8yYoTQPgKjqrOg3m1jlY3W0Q9kM66fkLDztjv8b3484BQ3iAA0yfB0/c7PlWYqUgXRjshtHTofb1fpaRw+DGK12+e/U2nrv/eexUHC3gYsa9cHihwkoOptrdRUCk0NQoPjdLPpHn2J/9bK9VzXGRlTweKsUUPoKHwjmHwoSBHua6O9ADgP+i130/pMBZMTgrVkHr0w+jRxpIijCNyQgKFsUFeZYsmguArivkcjYNwQADv/o08VQOW6rYp09HXdWE8ofnCIUU1FCIww4bTnV1lEcfPZ8nHtpA/c1/Ymy0nflTbAr8DsT7vYnDhLnv3314C6acfz7NixbRu22bZw6kxBeNcs2NX2Vzp4+HHqonl7M58cRRnHzymKEk93+BlBih0OskzcCbELiO8571uXX5cqTr4u+ysda2oO7oRNF1LNtmoLERPRAgOzCAnc3iWBauaSIUBSklSIk0AjxTn2LeeBVD8STUpAurW6C9J4u/wEd9nw7FpcwdU+WVY5p7JpmjzuUp1aIz0c5EtYzDgtrbCqZ/3DlgCA/woaK9PcmuXXFKqqIMKw9x770bueuujaTTJkcdVcc3v3kosZj/7RvaH4IRr+rE/b+Bzcu8hPjDzoTTv7ZXYIiUkrvueIEtyzfiZgbRfGCn8ZaRrmdD+0QJD8nTOdRYx/HjJKUTxjL9859n2NzXGxELi6eUl1FdSUx6htQFthQWMjYVopK3filXHvEQqe1novmbIeyQcX1saZ7Oy+u9LbuBgRwTJpdx9aBGRUmAwV39CMerBuHMGokzu5lEb4LIRYexNBLlPCAW83PeRbPg2O/BHf8N3Z2e6nb1GLjo52Dsv9rMe4ERDnP6bbfRvGQJ3Rs3EqmqYtS8efgLCjhyLBx55L4Xoi0aPZpAcTG5eBz/0Da1dF3sXI6xp5yyz+1IKdn8wAO89Pvfk2xro3jsWI75yU+omTMHAM3no0DNMcmpJxDpo7Uizfo2SSYrGWxoYEgJASG9eoPSdV9XbsnKZtmVgjtedqiKQMiA9gTEcwAu+XicYEkJXXYE3w8WwvDhbLZ6udF6HKmkwYCNUuO+xEiOjiQIKQYTmcRwahF76dt8vDmQPnGAfz9OJ1bmFX72y17+8niWXql6UYx1xZhdSVjlJXVrmkJZWZAnn7yQSZPK3qbRd9oXGxB71GHewJYtPVx4wX3s3NpNPpdH0SRZ2w+KQPWBtMEX1AiWxKA4xIK/X8wFMTg6uHewZQstPCEfI2htBiwYUjVJCZXJ+RYO1y4B/8lv3V+ZB3MZvT0tfO+/d7Fucxl+v04+b2MYKt+58XR+ESiiYVU9jmvjugoJ248M+VG2tDNe5qm+6HAGHLijGia81s5J6QVdCAWKqz5S4s1dGzbw+Fe+gpVOeyoyqsroE0/k6KuvRvkX9/6NvPjrX7P4pz/d7WuUUiJUlXPuv59R8+aRXv406R+d6UnAuQKZiuO48Mgm6MupMLT6FK8awSFfIUIgHccb/7dB9fspqK1l7KmncsRVP+In2gMkXROVACAJ6O0oSh6fU06lLnBxmMEsZvH2dTc/ihxInzjAhw8pIftnSN/Cwr/V8ODDdZSXOZhiMn1Jndz6djh2PKrloGxsx3UlfX1ZLrvsMRYt+uxbb3+9U94mT27jmlYGGpuw8rrn/3NASBfpCoRUkAjkmCqymorPp7IlD9/qgi8WwiVvUuZQCkAbD/ZGID/kH/Ih1THgm/f2/RU+8B1DSQ384tdpHnhgC+vXdzFmTBFnnz2RXFmM3M40pu3gMxQsqXp18fwqqk8SX9nECHE4Engy+QZDKASUDtuPwfvPoXzKFM5/5BGa//lP8vE45VOnUjZlyluKreeTSXY+/TQ9mzaRTyZ5+frrkbYNQuDaNloggHQcnrriCr64YQOhJQtRhtXSs6sDO5/HthQCmsusYfBk/dBqX3gKNwC4LnJolfhWRlAoCorPh6IoxIYPp3DECJqef574hDDpC8pQZAgEaEoGRVhIqRB3bUYRw8XlFVYzkUmE2DvA5+PKAUN4gH8f9iuQvgmUUu5+qA5fAeiKQx2b6MhPhoAOy3biXjgbdVMHILFtl87OFJs39zB1avkH3uX09vVIyyTsVxnMKqjSRUFiS4GVB2oKyHanIJVHfmIKwnIo9an8eQDOjkLsNYuNCirQ0DAVgaHPArcPFxOEj1Ha+SD27/EsKwvxxS++frIrJVSpWXYEQ/jsjPeZEAhVQV/diB5+VWkGPjz6MR8M/liMcaeeuk/fTXd38/DnPke6qwspJT1btng+PU3b7bO0Mhn0cJhkezvp9jZCTRuRwSjhSkmmrw/HMsm7UBl19zQ8tC36akTokKbeW/ZFCoE2pFeqB4PkEgmMWIy2DSuB+bu/pyo5Xg2wUYTXvoKCAHrpeb0h7GiAhnWeLNvEueD/eBnJA4bwAB8Y3TYsz3rPenXPIL2vXM/wsj4SaYe+gfFoheCio5EnrKWJ2wFI5pGVeyIVpfSCQLySQx88lWYDYcMG1yGlqFiOsidlOhaArqTXyaBBbkUjK378JIdffRJCV2m0oC6Z48EHt/Dssw1UVIQ55Quz2Tn+JVIij1QDCEJMZRqVvMM6SG9ACPhDrc7R21IMqFFUXQB+jOX1KJtaqbzyGFzp+SaP+fDqa//bWXXTTaQ6O4lUVpLp7UXVdWxA2ra3igMvjzGVIlxaiuNKetu6yCdTOCg4+TyubePTFVoG3tC4677xdK9HiNevEB0H27IQUtI1VC8RQF0fIvSlmQxWBTGEghwSTiBnEf3LKjoe3wRItNMmol1wMt7uqYQHroXF9+wxyv6QJ8/2MUrIP2AID/DOkRJkHxAA5a1nkP9Iwk97IZ/I03nzUrrvXMmwwjLmHTGTBfNbGD+qk9XbqimpcACBpgAZEyaUIxo98WPXlei6Snl5mIkTS9/3y3szyseM4FPDXuAf/dO8pPS8StwUhKoMMnoAogFk0ACfhgjo2OtbaXixidKjRqHGsyw49x7Wru3EcSRSutx+u8bvbziOmecEsbGpoppiit/TPo/yx1g+aRmf+8MAzQNF2C2DdPX4KTh/IrnpU+my4RMRmBN4T0/7kaLx+ecJFr/hvrxqoIbSHQBwHIrHjaNp0SL6unWmxQRZR2CaJirQl3RZ0rCfJ3/jNqkQOLmcF+7y6taqlNh9Cfyn3Yyx+CvY/hCO5aIFbLRvPI2+ugu3IIArXJw/LeOVl39B1Y03IrYsh8V3Q0HZHrGAdBz+/G24+tF/6Sv/qHHAEB7gnWGugtQvwG0BVDBOhPB33tQgdtvws14II2n72RPElzcBkowI8vKacjZvL+KLF62jvqmYvi4fhmGQyxgoIQ15ylT43xewLAdVFYweXcQvf3k8hvHveUDHnHIKa2+/nYtCqzFDZTQN+rlvSznUjqGlT0MJG9gSkBLhmAhDklixjk+cXM2Td77C6tUd6LqCz+c9ermcxRVfep7mU76BEIJ4PIdd6r6n/s8N2+Cmey7F6e9nqr6Sk85pZuTxdVyrHscGU6NAhYCAtITwu42HMXOw+F5Y8Zj3Yp27wIvA1fS3P/ZDjBEMYudyqIaBv6Bgb+M09LseCqGHw2y+917yvjrCoo+qzFYCusRxYEkj5O133g/F5yNcWkqitfVNxNuArZ3Ejr2O5JePxy0rQn25CX1ZE7KuFMe0UbpzBLI6Dc8/z64XX6R8w0O4piQglD1xpKGYlz7UUv+xWRUeMIQH2H/sJkh8FdBBlAMu+fzfaWAFz0dPppQy5nI4NXhJzS9lwZZg1neS3NYNikBRFQbSYUqDAwzGNQbjkt98/3GWrBvLYx1nUl09lhl1CU5Y9XteKQ+xbcRBnHbaOM45ZzIlJf++PbxQaSmfuOkmll5zDd0bNhC1ovgLJhALx9jVlwIpUYXEkRIUAY6kqqyfOWUPcuMjGcBb1b6K36+TSplcfvlj7Nw5gONIQiGdK644hFNPHfeu+7uuHi67ysuRi4aLac+cxB+fPQlfDTgTYJLP2xa9NwnbLbil8l0EhzoO3PB12LbSe5lKCff+ErauhC/8+gOPOu3bvp3mxYuRrkvtUUdRMu6dj+ek8xbQ8PhvCJYUM9haQMHIkXSvX49iGKi6juu6qD4fTj5Px6pVuJaFFgiwYdgw/rktRMiAvt4Ejv3utvQ1n49wVRWp7m5c09zzh9esTtXNPRR+9R6MUAjHspCuS9CNkuvqBVeSGyq5df1nv8CSS75LoHo089Y/xWXlcUpKwiSiOq4SoIC9iwh/VDlgCD8GmKbNihXtFBb6333agZSQ/BHYW7zfRZQBrYxeJU/UXI/pTGSr2k8TTXyC05jIxN0z11xnAiRoYR/WYBbXdKjfXkIoYPLI01NYtzlBb990filv5yhfDiUTBb/NZ6dm4YQRcNqHI+S7ZNw4Ftx2G2Y6jaJpbPncY2zf3kdQV8lkLGRY8yL7BGjC4eyzfKSUfpQCZW93kOKiF1oon1rPzBKDxIoo7Y9pXH31YoqLg8yd++6iNv90F6gKFA25WYti0BiHxgfhsMne+1MFKlVYn4ONeZjyTtM0t61E7lhNyijBzkkiEQMtEIaN/4TmzR/Y6kJKyQtXXsmaW28FwB+NsvqWW5h5+eVM//zn96utnAvPDG5l8eFhQtMu4vjBB5iaX8rLd1SR7a9BDwQwwmGsTIZEayuqplFQV4dr2/Rs3oxQVWwH0q6PeNlwlGQcXyqBIt+ZkfFFo2R6el5vBAH8GjKsQdZGmC7CFV4gz5CQd7qpFS0QQPUZZDNZWkdNwsikKdi2ifrjz+BPE2azdfGNHHOSi1UdAjGMSPVa5lFOKe9TqtKHiAP1CD/i3HDDSqqqfstJJ/2F2bP/zJQpN7B9e9/bH/ivyN4K+SeHtoJ82DJNP/1eGgAqUdtCsTRybp7FvICNzaEB72WrVhaAIjBKQghVQeYsFCHI5ny8uHIki14+gmmin3K7nYxRiGc1ddveIRoAACAASURBVE/t5dk7YbDnPRmTfSWRgq2NEE+++d+NUAjN5+Paa09k3LgSRqou/lwe0ZdGz+WI2Sk+c6XByCkaIDj+s8WAfE2gj4uM5hhxgkOwQEUKl5ITBhj15V78fpXbb1/7rq9h0w6Ihl//mfSD2eWlfryKEF58YYv1zs+VWPwYie3bsDctI79lNRtf3kZ7R8pTGWjf/s4b3g+klDz7ve/x0m9/i5VOY6XTxNvaGGxs5OlvfpOHL7mE9n3MMc658MWOPFf3SpZbk3nCfxZXlP2V5cMv4pRrQlz45N1EqqoQQpBo9USug2VlBEtKCJeXE6msJNvXh2NZLD7ts9z1u/vpmDQDVzfIhyJvvrX5Rl6tH6kooChkurtJ9/SgvKbKvawKI8cWQlEAecEknIfPRo4uRFFVNL83q3FsG0XTcEyThC+IPxnHn4ozavkzBAb7Cce70H8wiV0qiJ4MwaLRpJUMj/IIuY9BPPGBFeFHmKVLd/Gd7zyDpilDFdYlDQ0DzJ//V7Zu/QqKsp/zIJmDzO2glIPbCgLywo+LQHUdXFeyK+UjJ/IouqQ130eHv5dh4Qp+UAI/k2VoEyvJrG/DN7oEtb6TfNpC0xTq6gqoqytgjLsIPzkCzWtwGQr59oc8PdC2bVDw/gfJOA7879/grn94BsJxXaYc3EJJ3RKKig3OPGwWo4N1u9U5KirC3HnnGTQ2DtKYbON55zl0W6dmYgh/eM8Yn3HGeF4+2mLRomYURaAGHWrnOIyeL0kNehZI7RcUjspQNCFKe/u/sMD7wbBKGNS7qDpuNf7yXnIdpQw8P4PWbAlCETB0DVJ65YGGv0NXnmx4Caf3f4lclsK1VXwrbcLrs+xscImNjBKKlrzra9kXerdsYcv9D+AKBYSGkA5WKoUlJUIRbLzrLlqXL+fYn/2MCWe8tR7n0ylYn81SKXoQig/IklN0ficv52TjFUpH9HLugw/y4q9+RU99PUIIMr29NHV3g+N4YtqahnP5N1l72JmEutrQzDyp4jL8qTi27kOz8m+9MhxSm3k119AxTaSUBIqKsDIZzGofclQMbBcOqUYeXAERHfdXx8IlLyBtGz0YRNU0L5cxm8Wn5DEyKYTjoKeSHHXDT/DpecL32zgzKoifOpFgKEYASJGiiUbGM+G9ukUfSg4Ywo8wv/3tMlwXDMO7zUIIQiGd9vYUzz3bwLyDh7KnS2r2zX/j9gEmUq3BtTrATYEWwC8EmrRYKmeScQPo0sSvmcSzgj/+cQ3X/PhkTovCrIDguWtP4PmFa9j6eD2pkYX09KSZOLGMQMB7AydTDpWBAVIZA8sGhCSYHcSXTaE2bQTN8DRAVdXbAnuhiXvu2Uh/f5bjjhvJWWdNfNc+xHseh9sfBmU6dI+W9KYyrF4eJXZ/AVFlHbdFd/A/NxzGGWOO2n2MEIKRIwsZQQGDbKGHHgwMJJIsWfz4GaOM5eGHJ/Pww/Xce+8mtBndFJzcTe82QaZfECoSOI4km7Vxohlm14x9V9cBcO5FrTznexRVASyD4IidHHzhNj755zaO/vVyls88m38cfhmdio/ZAc9nuN9IE6v1U/iPzSJzAkVxMM5wsYep1D3aQWuqgnHj57zra9kX1j3xTzo6Eih5G5kDzcmgColQQdUlgQKTdGc7y37zG8acfPLuFdOb8UIG/NiI19Qh9AuLpAywzalh2mALK754GjuWrabYsEimTdKm2JOGgLdr8JReiJrPognIxoqIdLbSN2w0ejZDWeOWt7+oofb0SAQrkUAIQaSqCn8sxq7TA7hzK6AngzL0TJByoTKEWe2DrSlmXnopKAoNzz5L37ZtIBQc3UB1c4CkbNt6MmOGIdKg3b+B3M5e5F2zPO1VXDJk3u1t+dBzwBB+hGlvT6EorzdwQgjG+Lupvv40+txOVF3HGDWZ4DdvJF05huUZyEqY7ofqodWBY5psuvdetjxwL056G8XTdNqOPZJ1FYdR5BtgRuFq+iIFbDOHc4K1iLFyJ7lelbvumszCOwS5AbjwwmnMnFnFhZUGF35nDnxnDlJKfvu7Ffz1rnrUeApFgUBMYNuSeCaHbXuzYFN3KXEEysN/QPiDUFJD/tLrufS7K/n73+txHBdFETzzTAMLF67nb387k9rad14lYeGjEJ8HfePAydjkhYJYECQ57SiGP7OFVK/NT7+/jHn3zSAsXr/vKBDM5xMsZQkN7ETiUkkVR3AUPumjuTVOUVEAEKxf1o9aL2l9EVRpE4o5zDg3Q7g4SLZb5eKvH/yOr+FV7CnLGDao0LbLT8Rop0DvRIu41F6cIbwwyLHL7iAa76Lv0z/nksJ3GM9i/hM12EmqS0dVDPzkUXM2+nSb3Isa9xZfyo8+gKhRKSV/uW87xfgIKhmQJgKJIz3hA4CCGkmiM0e2v5/B5ua3DKApUsESYU/+ZyhFQkpwpSDsZkhc/39sXVZPOBLEjJsUB8G0JdZrtpztXA7b56e4oZ5Zd/0RX08Xkd5OQv09IF0kAvF2m6SK4m2NOg6az4dQVeItLUSqqggPrySpKfhLSymfNo3c4CCDTU3kdYeiCeM44juXkDr1bO7b0ICxfDUBBLqVxxWCfCiCL5NCqhoyLxF+DUoCyPVdWNs60cdVoKBQQcV7cXs+1BwwhP/JSAk9LZ4+Znnd7lnoqxx1VB0bNnS97jOfk+S6kQ8QSw+S0Q2kaWKse5nUVefx6f96ggHDj8SPEAaXF8LFBZLnfvADGp9/Hn8sRioR5o9VP6RdzKTIdchaIX438E1myyVc6f8FdWIX3ZkY3//yXJq2R4iFeliyZAdLl7bw7W/P5dxzvZJGA7vaufOvPTy6ZjxO9Qx0Lc9nT00zbc0y+lcIHMdBVUAgMW3ozwiCMkC0sBz62tj102+w7qlRVKkO4YDLoIjRb/rYvr2P669/mWuvPfEdD2uHAn1jwJeGeNJCWC6YEqfWR2pYNWGnhf5dNmuaGziibupexwcIMI8TsLCQSAwMurvTXPydh9m0qZudO/tJJExQJLHhECuzUGyHfE5h2e0R5qutfGrsFCorw2/Su31HIumhh1HhHKfV3Eix2YKU0KpUsnbkQYyZMABNFYxveAb4CiiV7+xE1hqE7kcVeRypkBEBhJQY0qKttJSDT/hgVoNbt/axwxxGiS9AVkAg66kRecovUDBcQwiB4bewLctLg3gLzojAI0k/eYbjk7uQLvTIQsapOxnZ4mfX9jRCN7yVk5QIVQcsEMKTsRvazpy4/BmMpu3oUpKPFeJPJ/DFB1Dk2yTSv4rrIvGMavnUqSTb2jCTSVKdnajPWahfn0BpzXhUn0GovAytLIbqCi78yx+4pT3PtS/vwk1nKT/tYmZ1tBNyHXTLJCtUZDbtybGlTPy9Jm6BhhIwyDS2oY4LUcfI90zc4cPMAUP4n0pXM7mbvs/Alh3E04KMr5TU2T/niLOn7Z7Vf/vbc7nrrg309mbQdRXXlcwN1lPlS4I/iEDgGip52yKRzTBl6z1snXwwILGo5oaBEYzftZWmxYt3BwW8EpzLrlGziXW1Yfj8NBaMwsz62JqeSKggxQ5G8MzjNTRsi1FUkMVv2EQKElhuLddd9zLzjq5m5a9+xsoHl9AzqDJTk/RN+BxNtV/gtn+EUK0gJwlJVgkhgAA5NGXI/9hrM7kKiBZTsuIZnEwF5b48KhCTCQJ6CS35GIsWNb+roa2dATscz3u2W6VfKiiKRa6qlHBTC0KAgfGW7ehDItpSSr71raepr+/F79fI5bzrkQ6oLf1gGFDsw2dAqtlmw48SaIHr+NNDKcaMKeaKKw5hzpw3r6/3VggEUVdlzuBNGM4AKdePQFLldBBMphmckKCgrcTL9+tvh+J3aAiVKpTCEoK97aRzDo5UUJAI6bLZfwynnTD6nbW7n5imQzJlstqZxsT8MoQ/RCDnKbtQGCFQkPYClYRL7ZFHEi5/a4m+yX747xL4dd9wErIE100wVh/g1+XDEY8XE4qFkW4nkj01HTVVoAQEk84QjDzMxrYUtjz7KCsTpcSrRlG4czOqmccOhjHSiX2PHHVdtECAUEkJiqJgmybjzzyTgtEjaRnup8XfTo4kAlCFxknqKfTbGr/f3o6vqx2frmHVjsINRRkARod9lMX7Geh3kLZDOD2I0RHCFy4lpyYpKa/jII5jDGM/FpUqDhjC/0RsC+u6L9O4sY8euwxdFwSyccK3f4VbEg9x6SWeAkZJSZAVK77AlVe+wHPPNRCJGHzC346iKdQnCzB9PtTiIKLYT3m6h+LkAAgDpESXrSCLWLJpB0VCYCFIObBz2qGQdcnkQnQ11RAPF6AGLEqr+sk7fsxByfalGn4jj5Auqq4jZBbD8PwXf//x9dgrFtORK0eLKDjSonTzTZiROhoiJ7CqZwyTUwbDYjaWKwjgoghY0aoxvGLogUwOEBB5DNUlLw0M4QKSUnppJ0Qk8tYG6u349LHwfKNXXV7TdExTIIREVzJoqTTJQZvSGoOZtWP2qb2dOweor++lrCxEX18Wy3KG8q8lwSIQEd0Tbs7akDBJZEOElG5CfsE//9nMU0/t4JOfnMh3v3sYY8bsn+rM4XkTzUmSlgEQ4CJJixDluR76bB+BUBJfrwOlw9/0+NbWBG1tCWpqolRXR9/8JP4TIXQz+kiVcGsXdi6HDENOmcDpP70Zw/fBvGYKzHZynS10aTUkij5JRXQXNcnNBAYHqAhlyMVtpITauVXMu+66fWpzQRTmhQXb8iHCSojRRqU30awaQ/G2VZSXR+nsSmD4fNjZDAG/5IifSMonmrSliqkPHkRkcpYTn1zDsptSOOk0lt+PIoQnzSblvzYzb5BWs9JpmpcuRQDRGi91Y/Inz2ayKuignVa3hXxTN+ENGeyiRtaOCGElE4SG8lZd3WDjxVcw7s7r6e/qpq44hu7zESovp2jkSKQQZLq7qR43mzMOuuJjYQBf5YAh/E9k20rirT30OGW86us31Rgxq5OmfzxH/1nn7M4bq6gIc/PNnrBwOm1y6ay1zNB3kNcNsCV2exI1m8cs87GqakiwWQiQKsLtwyoup9WGjgyoAmxLklWCaDKJq2qgSuy0RsPgKELECXd0U2WE2GxG8QdyGEJnMOmnx8mQz9is3bGNykgZZofi/fMpOq4Womj7X+mcewKpwpk8vrGcigGbiYVphKvRN5ClI2swp2LoRdzfAbqPY8paebRzFCVGDkUIHBeCqstnPjPtXQ3vgpHwZxe2DIA7qKKpGbKkkBkLZ9lOwobOH39xFqrYN3WbZDKPqgqEEAQCGo7jvdwCxSDqoggcZM5BGApUhxgmWzHb/azf2IeiKrjS5eXWzXzrz818/4vHM6du/D6/pIY70Jd1IKDjCgXhugTTKTTHRNN1djSYTDrijL2icU3T4aqrFvHssw0oihfEc9JJo/jRj47aW9VHKYHon0C5Ci0cQpMuGIcQiFwF6rublOwPOx99iNOrt/Ng5xRyeY3EwGhai6o4auIaDhq/E1ULMmbeMGrnP4LQIvvcbkiBg98gP2fNPg3r0f9jdo3Fy0mb1u4sQkqGHyooGy/5W+5c7qz+xu7vR89Ocujy/yKwdBNG1sHyBbB9fqQEXz77eqP3Gu3SN+I6DpUHH0ygoID1CxcSLi9nygUXEOvU+Odl19D28suYqRQA2dETcH98C7zmfyVRO4Znf3ILn3j8To4/dz75wUHW3n777kLBo044gbnf/vZbVuH4KHLAEP4nko6Tzso3ugRRhKSQXhrb9iRQv5ann25gkxjP8+4OjlW3k1F0UCGQjPPCtNN4ruJwDpf1XsqAVMlJhQdGTGdc+QgKWhtIOQEKe1YhZs8jrwfIE0EiETFBrt1g0+MWh5wOJx7fwPOrxmCjkU3ZrJTFmMk8oq6I+y77FiPWvsjYLY9imiphQ8WnGqj5AZJpOOq4QxmRn8jW55extcOHJjRqAybj64KUKCnoT4Pux6is4QK1i958kJf7y73IQDTO+2Qtn/703n67/UETcGsd/DgEa0sEFUSIZTQO217PpO8dw/wjJhEO7WN4ZTbFeKeeqdpOWvOjAQ3DUDFtm4Mucdj2dxW/5qJokM8oGGGX7lgVq7bXkdddYuWCo39tUjDKxefP86jyED1M5CTm7956fSuEPgE3HaLY7sKWOsIBXAvN57KrpYDiGZfB2Z/Z67g///kVnnpqJxUVARTZjWv38I9HdjG8qosvXH7e3lE1+iQouBfcbhA6KEX7Nj7vIdn+fsYXp/hG9U7W90RJWyrjyxPMnNzP4V+/gHD14eA7AZR/sbLdR/LJJI9++0dkNuSgpwfTdCgLQbiymuD0HNu0cu4s+w6F7gCqnUM6Dmk1xuNfv5YFL52K6tjouQyp4griVbVUbF9P0HVQ/X7yg4NvXoJJCFSfD83nQ49GySgaakEh6xYuZMoFF/DClVfS+tJLOPk8RijkRY82bGXEon+w65hPELW9NI287v3fzjt4PBv/9je61q9HCIGq6ww//HCOvuoqVOODm7x8WDhgCP8TqZ2EX5eQcfBS1QEpkQIajAmcV9ANjgXK64uprlvXSSAa5X/UT/FiehWnGOtQhOSxxAS2jTsDW+i0ucXojsmJqx7i+2ueQ8+ZLD98JmufdgisWEVA9DPizqdZ89nP46oGRgCctMqYO57lpeZiuq0KDp/bzvc/8xI/v2EWzdoYbMPBqCuh8OQSgj2dNB18GBXT11D4YgOpgB+/O0hHzcn4fbBgZjsv3dFFxfASChMJQCACFYSnTkbMO8HLJ6ydiPK7Szh4rJ/f1/bR0N1HPOUwdnSM6t/87j0RCq7S4ZYqTyfVkoIqLYiYPH3/GlnzHCy8koDj8Iu6DI3teX6XPgtNC2FEVcafYeOPCToWC9ykTdmoPC0bAnSYUeLoCNslJ20c6WANauhBg3jcpXV4K6+wmjkc8vZ9MI5ED04mMZCiIJpG6BJVdenqLebWx77J/91+sScF9wbuu28TxcU+FGcTyDiKUCkuULjrry9y4Vm9GEVfRlXfMBMTAtQPvjTWq9QdfTSty5ZRWGBy9HBPNMLKZulYX4G/5hp4i1SJ/WHzfffRvno16e4+rDQIRaM9JRG9XUybFOYl3ymYtoLlSIQEIRQiJOkI1dBx8FxqVi/1ZAZtk+LGrWRixcRML/kfKRG6jrReo2wwtFqUjkOioIRWS0faIF0fJd39dHd207ZiBXYuhz50jUIIVE3j6Ptu4IGJBzFYVYsC6PkMFy17BJEfoOGZZzDCYQJFRQSKi2latIj1f/0rB1988XsyTv9JHDCEHzIaaWA1qxhkkDLKmM0cKvCCGKSEh5NwZ344R888h6OfvRuj2KHs+E7C4wZwg4KrnU9RpmSgtwKMyRD+CegTARgxogDbdohFwrzon8w/mUDO0UFmmTkiRbm+lkv8jzD20eWMWruVev8E0kaQIxuXMEnN8tmDrqDfnoi5azrKb9IEyjIUBXwMixShJR1iPgV1exn1DWV0daY4yNhJ1zcuIqgGEGGBL9ePhQ89l6H5yCMZu/RxcukoucJRjDn7M1xzEXTedz9CSorH7smhk1KyZWsXk6edSkFdnffhF36DWHglBSLJ9FIXJo/w9CzfprDu/lL2Tpvr64Db/9ur7+YLUFwARqSPn3c/Sou8lI07k5iJPNWzJeVTNFRVZeV1IbRCiWN526hCAV/MZd1ChXnf13Ecl2BQx4+ferbsmyEUPgprF7Kz+Sd09DyOrtksWz2Zp15cwO9+d/pe6TXgjXcqZVFanAUZB/wgIJ1V2brdzxHzGoiE/sR5n5rJ5y+d/f4USH4HjJk/n60PP0zP5s1oPh/OkDE5+uqr3zJfcH/Z+eyz5Pr7vXskBEIoCBVc2+aFJ2NkPl+EEnRwbXAQGLqLnRfkbR+pSdPJdbfhb96JL50iGy3Al01jphM4loVQVQpHjGCwqWm3jJpQVYQQ5Aw/PZW1BIVAEaDG+2iedgjXd5pUu+7e25lS4sukuOLu3xA441yS2RzjfYKMyLLy1ltRdZ3c4CCJtjZPCae6mi3333/AEB7g38t2tvEsz6CjY2DQSScP8xCncwYVVHDbIPxvv1fc9Ykzv0VnTRlfr/keeiiDE1GQiqDU6AICIDvB9kPiS1D4ICiFnHLKWG67bS3xRC/xYBDbUaE/gzalhi0Vs/lV+AbOjd8JG/owC8sRroIidboDJZSwhRMC9fzFrgQrC5kA+q48acUmNxIu+eyhmL8eIPVyI2nA8UfRjSq0qjCuLMQXb8IVKi4CVw9ihyNoBT78mMx76o/Mm+T5qLa3te21NSOEQKgqmb6+PYZw0mHwsyehbTsYfqgY8YGLOr8l6xd5aS2+Pc6lSGkxEc3m/LFRft5usvJ/sxz5YxcZdSmog3iXJFwF2BLFEXTXu/iigmSzQAiwbZdhw6JetC/7GHoPoBQw8+jf0tf3M9av72LWMQaX/lflvzRgQggOP3wYy5asoKzY015LphTWbwoyPNTDnN7F+Hcm2bRG4zePHscV9/4OX2TffW7vF3ogwKk338yOJ5+keckSQiUljD/jDErGj39Pz+PkcuSTSeTQKk0BUBQsdDL9Luuu7cf8oY5mOKgSMglBT2cELepSuH0j/uadIF00yyQ00IeQEsf1SlIUjR6NYhjofj/S58NKp1FUFUXXSReVE4wPoLk2ai6DGwiS+NzXeCZSzZeG1zLY3Ix0XYSiYOVyWKkUQtNINDfj/PU2ItXVbG5qom/rVpx8Hum6+KJRhBCkurrwvU06yUeZA4bwQ4JE8hLL8eHbHZYfJEiGDIucFTT2n8Zv+zzHfVCBsCqYMXcjPQxjpLITv1ARpPC2Sk2QGrhxQIf8cxA4i5KSIDfffConXvUgzgYTVQP/ccMo+vxkCmUvd6WO5NODzyLUDLpIMFZZR707g6wtMV2V8WYjjj0HXb5ATWQ8g8kSaiokv/4W2A9czxYhcEwLy3JxEx1UuN2oSUGuLEAgoZLvDpDaXIllFhDKpWmtO46S1EZmDd8j6ls1axZNL7zwurFxLAukpGj0G8LwNR1qJ771uEpJw3PPse7220l1dlI1YwYzLruMwpEj34O79hZYnmrHGzEVwWJfCwvuVcjmNNqXWUy8QGJEIVAEdgqMsCBS51AUjtHblyQ8zGtnzNhiikuCJEkyhf33gxYXBznmmBH79N2vf/0Q1q9ZS2e3ga6rNDZ7Serzcv/AGrTpdT3DZz/zJHdfnOcz9935oQiw0Px+xi9YwPgFC96X9rvWr6dn82ak43iJ7kLg2jYoCrYIkSbMwJOd9A/fRsdx07xC0k6eoJplwd9+R+mqJQjXwVUUz+9nmwjAF4uhaBpFo0d7otrji5AnjESoLqGtFsW9AZ488SKqO5spbmsiO3YSA584B7u8CmHDtKt/TurT5zHY3AxS4uTzoCgECgspHjeOREsLDU89RcnEiaiGJ7fomCZWKoUv6vlMk21tzP7qV9+Xcfuwc8AQfkiwsEiTIsyembWU0GL6aLO7ubsX8hLSLvQ7MMqAYf4tWDKAxPFeQhK8CDEXUEB6ZX9wu3e3OWZsEdFrjsWfUjEMiWKoaDJPodlLu1vJQEElRW49AoOQMAnbnfTJMowCg+3NxajZLEbHIHrFIOOKAtz2+wWUGw3ct+gFnMIa2nr8aFoGoQlsM0/htX8j/uOv0pMchbk4ilTAL1PEu0p51voaXz92EQWRPTUMx8yfz6Z77iHe3IwvGsWxLOxslhmXX44/Ftvvcd18330s/eUvMUIh9ECAxkWLaFm+nDMWLqSgtvYd36+3ZdwcEH8C19ld8FTaFk98chzlJSYibxAOqIw8wUIPgpmAiWc7rL5BQzcEwi/JhxJE8xHmf11n/BwVqUhSpCimmJnMes+7PNDYyJYHHmCwuZmqmTNZeMvJPPPUT6nfEcFxSqiJb8FqskiLCMqQHzZlh1n31FJa1mxg+PTXGGcpvQolbgeoI0B7nyceHxAr/vhHAkVFhCsrSXV0eLsQigJSYvgE25xJKA6U3vII+aXryU4dTT7lcuy2vzO31qLV0MnnBKqUnur5kP8vn0hQNHYsyfZ2rE+Owpw/FdNVyYgYdtDFXryLsSmTtV/6ASltz4Qj40JEgcnTpzJm8WJW33QTG+++m/iuXRSMGEGkshJV08j293srxXQaAD0YxLVtHNPEzuVwbJtAURHTL7nk3zW0/1YOGMIPCRoafgJYWLujARst6HZM4lYJYQVMB0xAl9BkQZtvNKN5yYvS41VdJ8luYygi3oOm70knEAhKVIVdAR2/6h2jSgsHBU2Ar0iFicWwsRctApGBbqoSFnl/iM1VR1G8OkFWQjptcd995zB+fAmNz68nLwWbEzZyWAlGzsKXzhHIxhnXW8+xa7by+PY5ZEUG/2ALeiaNkBI7UkNr6RdfNw6+SITTb72VjXffTeNzz+EvLGTy+edTd/TR+z2mjmmy4o9/JFhUtNtHFC4rI9XVxfqFCznyhz/c7zb3meET4KjzYPFdvBq+3jEsQsfU0fib/SRSFoahopk6qpHHycHUc1WCAYUVtzrYCTBCkuO/D5PmG8wQM3FxKaKY4QxHe48f3baVK3nia19DDsl4tS5fTvjecs6+5QqCvmv5xW/zrLwxjiuV3UZQSlBUBccVvPTsBsJ1Y3BdSXGhhZn8Fs2ijYzQKXVSVKqzUCI/8fJU/4Pp2bgRfyxG4KCD6DEMEi0tuwWxy8eMoHjbAD1WFL/i4t+yC7m5i5C0mVi5iXy8fHc1+Tfbxo/V1jLqgtN5YfZW1qZnskY/FBeByEtmztvIDwvG86OUoMOGkPAmxi7w8zIv0lkrLeXIH/6Q4rFjWXrNNUSrq/c6hx4MogwJcAtF8YyhbeOPxVhwxx0Ei/cvT/WjwgFD+CFBQWEGM1nKP73fpUa3Y6IJh82DszyVFeHpgOaBAPA383NcZSxHU0q9ahCo/dz8BQAAIABJREFUgIVXMNcF4QNtBuivkbjauIQrVi7i+6PPJKCkMUsLyBsqWbeEBf7VhJQ8nDUOCv2YL7WTzflZV30wt9deiLYDZisDuK6kuzvF1KnlpEnz3Lhe2v9wMLG8SubZATIv9JPPBShujONEK1BNSVSGGDcqhGtFsdJpVJ8P9CAbG/YeC39BATMvv5yZl1/+rsY03dODk8vhj74+XN4Ih+lat+5Nj5FSsn59Fx2dSdSp3XRV7iRHjmEMYzaHUEjhvp1cCPjkf8FBx3r+QlWj/7AxyNgORo70s25dF/m8jWILQi6oPggbBlMvshh3gUM+DrECH3VaCWnStNHKqZz+rsbjXyGlZMkvfoFmGLu3yfwFBSQ7Olh3zwYO/cZjzFtQz0N/+j2lbiO27QXygCQU0MByuO2hDn59318QAr7xvcVk5wex1HG4AhQkw8wWTszehRa86H25hg+KSE0N6c5OpOuS7e9H8flws1mQkv7t25hjb2W43MLT9olYwqCAQY5VFiP74oi6Sk967dW8p6FCugCqz4cAyi86gV3x0aztnUFUy6HhYkvBKmcuj6mDLKyG++OwPAtVGpwbg6lviAOqnjMHxzTpGEjQPGE6yeGjKBMBws/9w5OVk5LOdeuQUnqrRAS2atDwwgtUHvzu9W3/E/nQGEIhxDDgTqAcb1lzs5TyOiFEEXAPUAc0AedIKQf+Xf18PymnnEqqaKYZiYLlFrN54Dg6csPxCc83KD3ZS+9Hm4oeuw6f/XvID5Jxs/zdPJs7zIupUHNcXODn2NhcEEO3+aVH4S9XcYovSIdjcMvIE6AtQ75qGCcH1vJd/y0gA6BrdB9fzadn38mGlhrSAw5KQEdOURj7zAa0ZTs5+OBK8uT5u3yALcVJlN4IukwQ+1QZRrVO/rpNJGOFbMjW4mzrRvOZ5E0Dv8/YHQwTT8Kw91HPN1BYiFBVHMtC1ffk3FmZDAWz9t5aHBzM8bWvPUF9fS+158epqk0TTgUYM6qEJqWRNto4h/MIs48aoELA6IO9HyBEIwoNhMIG06dX0tGRIpUyUbIuesjBVWxsbBQdQiUKJXi5eEGCtNKKg4PKu08NeSPZvj6Sra2E3iA55o/FaF68mNpPXcH3/jSJ5PivkFqxnIg7SE4GCfoVwiTZ6I6iJRHk0FlJjjl0CbMOXUtnooyu8HgG9QIcBI16CVtyzzCF/2BDaOaY9MnTePjmhciG7ShC4Jqmp18aDnt+t2ySKto5T/4VSxqESSFcsG1o3tZBozOWUncLipCElBxCOiiqSri8nKqZM+ldu5EloYn4ZdILZhICTUiC5HlisJwrY3BpEVz6L7poZTIsveYaBnMmHV19yO5nyQ9v5P7LfsDko0+l5O4/kNi1C83nw0Sw8uxLWT/vLBxd5+7OVk78wkVM+sFEAlVlxP7WgrH5/9k763A56rvtf34jO6vHXePuIUGCQ0PRFAoNTnErhRoUaEuh1B5KKZT26VPDpZQWd4coJBB3PyfHZV3Gfu8fszlJSCiWhJQ3N9deh+zOzs7Mzs53vnLfd5yaCRMZcvzxfTdJX0bsNYEQsIHvSSnfF0JEgPlCiFeAbwKvSSl/JYT4IfBD4LovcDt3C+Yzj/d4F5D40JEIVnYfS84uRQCO9JRdVKBUgaPDcH8tIPbHyZzIpmyMc5M3Y0pBkYiy0SnkB92VXK+anFrk9yYYn7oLwkUII8jFTTM4vXU+m2yFskFjqDz7+5C5ADKPgUzzkH0D3cpohlRKFrW34+YshF9nxQGDGfnuOr797f1ZzSp6ZZKUE8EYMAK5eg2Btk5CB0VonjmON/ufTva372Ks7SGtz6StaH+GDQ7i0yGTg1QGLjhl9x1TPRhk1PTpLLj/fk+jUdcxEwmQkrHnnLPD8rffPotlyzqpHRig4dh27KROVzpHxJ+mrr6AFEmWsuST0RZ2gnrqCRMhSYJgIES/AYWkSROknAEMYB7ziBFFQaGEUkL5gOvioqGh7CYfbS0QQCiKN3G4DQfTMU38xcU88JRnUjxw3BgWJH5K9fqHabRWknJ8rC2YzGL/SE49JMoVZ92D5rPxKzkGO+toTG3mmcLjiGnFuMBso4RReb+F/ypIiXzjER5asYY/Dz2J+Pd+RW8sy6jXnmC/B+4kEAigGgbZ3t6+4+jHxM9WF/lOUc5z3Ucg/RHq9AqG2gvJuWnKAlkiFWVEamvpWrGCNVe+SuJPjxIkQTYDRkEEVAVduGSd0EdVVfvw/l//SvPcubQ2DEFKF2GaVDWv49CZL7Doqp9Qedh+uOeeimNZPDv1DBYfPo1gbyeKbZMpKuHxr1yB+O6NlLw3HzeaRpECVWgUDxjA9CeeoGTgwD1wwPc89g4CECClbJVSvp///wSwHKgFpgH35Re7D9g942BfIGJEeY93CRIkTIQIBYRFiMllb+BiUaV5gTDqQEp6fcIVpuTW+DLud+/lz9pc7ik4iKC/mwqlB5+QFIoExbTwx642LAkko5CKgRHsE66I2BlGml1Urp4FwkdT7EJ+9e+XOOvXb/PXlV9FNX1EIgbjx1dRWRkmqEBBZYTb7jmFUaMqaKcNNX8KSVXDHTKU9qGT6KwezLzzLiWplzB8eDkDBpQwpHIzIjqP3phNR7cnzH/DJXDkZ4spnxiTrrySiZdcgpXJkGxrI1BaytQ77qBilOeCsWLJZs46+jaGlXyPB3/1D8KdS/AVp725I1dB11VaWz3JKhWNDto/+sM+BhoaJzGNOupJ5f+ro45pnMwUDuFKrmIqX6WEEiJE8gY9kpibZbActdsCiC8UYuBXv0qqo2OrAaxlYaZSjD7zTN5d4rncCyHof/ApxA75Oa/0/znPV/2Ewd84j5r6Yk6Z+jyuqxBNFJGTBgk9goLLpMwHqEgELt1ahM0075Z92K1Y+AYvvzeTO0afgR+Xei1HmZ1k8Ylnsfgbl6IaBq5leaVGTdsxUikKs5iC40qKDItEqI51E67m1bLzWdJwJhMuuohhJ51EdP16IsXlDFq+mhwhpAa5XBqJRHcrmODXPpYhtOKpp3CKy3CFQFdVtEAAp6KKoteexo72Mr+skYZDDiEuFZYdegKh7jY020YgMVIJJIL3Gw/D7UqihAwI+9ACfnrXrePl739/9x3jLxh7U0bYByFEP2A8MBeolFK25l9qwyud7uw9l5CvGDQ07FxAeG/FBrmZTitCmeIjouUAr2fYP9jOTbXrmB8bygdZmJeFag2qVPD7l9FhvEnO0hgqsiSFjxHlC2ntGkgyW4iFhsChy4FOs5eaYAELekr43Zv1LOoMUxa0uWBcF9PruxG1w1jXBOffCJkshILQvR42WuCrg6KIjyGDS3EkdDswJj9sWUIpRR1zOGbOWtxUhk3Dh7BxyAAsVyWtFFNTYtJ6zVeIxtI0zF1LWfcKfvCNSg47YiglhaDtgbNP0TT2u/RSJlx4oae8EQr1jflv2tDDqUffTSqeJmxIkq7K8mbgudk0fGcgwudgFNpoQZde2YsqFEr5fE7rEQo4nhMx89nCtg4WGhqTmEyaNKtYScyK8GLXODZnGiikgGPC8L1SKN711VGmXHstZjLJpnfe6csKJ11+OQOOPpra96C1E4IBUBSF6gHDKW8YTjQBF17QxfvvraKuajO9sSJAYKZ9qIUOOQyqrVavni80AqKIFSynljrWbpxPc8syirUyRo49Cs33WRyB9xBevY97Rp5D2M5iuDYgKCn0k+3uYMGxp7Pfsw+A44CUXskxT4xHSqTrYqPRLisJkQARBOnRJerHV9BlORx643n845RT8BcVIYTg+Jee5C+Rb2H6/ai5JMEBgzF0nWs+gWqda1kIQ0O6Xu/XSiWx0xn8qRSx5maWPT+Tr40bx6z3FoLrorgufbLfqkDPZohW90PoKt6Ql0Qqnq1Uy7x5ZHp6CJTsefm83Y29LhAKIcLAv4BrpJTxbblJUkoptrWL3gZSyj8DfwbYb7/9Psbpcu/Ba0m4vrMfbW45KgrDQh2cXL6QoOqpYgwyHE6sgJs7oNWGUhWWm5KDS98jaRv0Sh/1uo5fmiQIUlLYSnO6BlXY9DglrJd1/L7H4ZxYnEvnHYqMthEI5OixVG55u4zO0XGuuvJ8/vQPyJpQmb/OD94EHzTCqhaYPNTLSNsdmBaGgvyFeNi8Jmof+Du4LlmpsN/MuawZO4g/nnAlUaOanoONPirdpsmDaPi/NwgFFSq+gME0RdPwhbfv7f35Ny+STGQpiXhZrd+wUUyL1a0hhsy0KD/dJd0j6dkgiLV1Ih2FTa93s7p8AYMHlzJ5cu1nVlX5KAsnFZUjOYoR7iROb9FJODqDVQ0JvJiE9SY8ULtTVbTPBV8oxDG3306ipYV0VxeFjY19dJVzToRZH0A6AwNq1nLQ0AcpDa9EN4YybuR5HHbYIGJxHYGJZevENmkUj5SowialhLCVIEEKCIkwGTfDn+f/hI5gL0S8Ot9rLz/JOaOvpbxxz9g1fWpEO+gIlOJ3tpY6/X4f1YUW68PlyFAENRn3BsAA1e/3MsT8soqQqNhIoSEdBz0UQg8GSCYtCgvz7/H5+rLxuo4mrnj0t8wcexgbiyqYOtLh/Eqd/orN4ocfY+ljj2Gl0ww4+mjGX3jhdpOeA485huVPPIEWqSCVTCJyWfzxKK3Dx2O7knGr5rPwhWUcd8kFPCxdHE1HtW2EqiCRWMEQVSs+6Bvo8WbQPetg6bresM+XEHtNaRRACKHjBcGHpJT/zj/dLoSozr9eDXR81Pv/2zAvA+dshhW5IF1WId12kIXJGh7vGEuUKDGivOPO5Y7YwxSmf8hN6jROkt+ligUEtRQ2OhLY4NRTrbRjuxpCs1Gkgy012mQNjWonL2fC3PyXhbSFyklUVxMiRYXopDrcy987+zG30OS9xVC0jThIUScMnwfpHLRbXiY4LQzXbkmIsikCD99Gka8GWVCCWxLGLY0wblET9WvqcEIGStZGM72Howiazp3CfgfVfxGHeqdY+EELPmFh442iG0EfqgKulCRnN9O2UCHRKjCKBF2LNR49UeWOmxZw661vc/XVL3DxxU+TSpkf+zmfBfOSBaScADWa5slpCa8SsNqED7KfY8VuL9gbQFo7fTlSU0PlmDHbcTYnjYafXQUDqhZy2dQzGVX3FGXBlTSE72Xt/OO57vsVEDybstIMgaCgvrSUsNAJSocNgcOoFg2UiXJsHHqbN9IR6kVPgpFW8KUEqUEG/3rrN55Q9N6IYQeyX9tior6tfFccG7O4gonDBnLDpo1ctmABJ/31r1SOGUO4osJzlAcUXUeVDsPFCrJ6AYquUzZ8GI4jicWynHnmaACGn3IKuXh8KxWjp52pj/6emxc+z631fgb64O2f/5xZv/kNuVgMpGTpY4/x1PnnY+a5geBl8YV1dZR+MAd/Vzt6KkmytIIFp5zP5Mf/TOmCubiWRVVjPReHLTLl1eQiBdi6QbK0Ep+TZczbTyJdF4mLioqQXqZZt//+X9qBmb0mvAsv9fsbsFxK+dttXnoaOA/4Vf7vU1/A5u1S2NK70/pWK8RcCAmBi0ZGuvTaAVpsh/eTEUYFVDqzmzkj93eyRoB2q4pxYjYT/TO41z4LgYWLjya3Br9wGaivZ1VmEO1UknILqFZ7qTcqiEnB3GVd9BoGZcEMdqlO1g0hVYHdreA2/5CK4mdp64ng28bQoHgNjFsBD/wJSvStmSAA778CzSvx2RaVAmS4CGoHI/QEq1I5giUKWVvB0lUQAuE4iNIQLYZCxYcPyBeE/gOLmf9BM+YWCiYCEfGjpxwajwry4m1B0p2SUInCpoUWVtpF90MiYTJ0aBkLF7bz8MOLufjiibt825rsHXVpthRH2u3PsEI3hR2/FdV8HiGjgAXGSRC5CdSPN+Q99lAYU3oLHa29mFYETUhSmWIct5clc27kkKMugNyhYC0GYZElwNzQBDb4xwAmJjkaaGBp9h3UhIOSn2QWgJ6GrlEKic2bKaj79AbEux1fvZDL/vda5lSOpt0XIWQmyRgRZEUDPyjz5O+KGhspamxk2Ne+RtPs2Sy8/37aFy0CvCBy4fghvO5MYP5qi1gK3ESa008fxde/PhzwAmHbggWsfeWVvtJ9YWMjh954IwDx5mZWP/cckerqviAbyZP61738MsNOPhmAQEkJjYceyuoXXqDMytFeNxAnVMDXfnwRRakY6UAAX2MjUkpuOOFQRjf3cvvsOG22YOic15k0558UT63FfLIbETcRuFiYhGtqOO7uu/f0kd9j2GsCITAFOAdYLIRYkH/uBrwA+JgQ4kJgI/CNL2j7PhcsCX/vhd/2QJvlUSGiLhjkRftR0aSfpCvZkG4kaVbxjmJzZ/ASBAq2pmHZPlJukAgxJqXmM6tgfzTXxXZ9NFHKYK2Q9YnD6a+pqIqNotaDMBAORAaV0fvOKgpDXVgYoAosU6DqCuGiHOd+9Xl+8pfp+H2eYbptQ1cvXDYd+n1YrziThMd+DWYGjCAAIhmF9YuhtA5FgKupiJCCIvP0ft3jKyU/hUTm7kb5pcfiPrkcK2Ph8wtcKUhnBOP6Zxl66mT+cUcHkVIV24R01EXzAUKQydgIAcXFAZ55ZtVuCYRDfOwwGiPzN1D9PiUnffnyTtYuuJxh/WZRURrDZwgMn4vI/A1yz0HRw2Ac0rd8zIG5Ge+GbXIAyjSQrk139zq6MsWE/K7XEwNypp+CwPvENm+msCAHGBA4C3/oAsYLi7BcQcZppY566tVxLNHeBleyHQvElWAIXMdhr0RZLQOuvJ2HZj7LA/FCFpYPY2BVNec0RBj+odamHgwy4KijGHDUUaQ6Oki0tFBQV0ewrIxvAJs3x2lrS9LQUEh5+dYMU9E0jvrFLxj6zQt4fnMva4qqGdlYQ6ZAIQT0rlvniW9/yHtNqCrtixf3BUIpJSuefBLNMFB0nep1y2H9CsCT3ND8foSmUbf//ggBE1pWcdb/XIMRieALh7H9RaTe7WbEt6/DSiSJbtxIwyGHMP7889GMvbiP+zmx1wRCKeUMdvztb8FRe3Jbdgd+0gH/1+sFRJ+AhOMR4y3Ah3ctSLsCBRdFSIr0LL22TonVSdoXRBWScl8vLdly4rKAseZS7uy4keFFCwjpUVJWOans/hRQS7u6daDClZCWcMX54/jprGX09PoIFQpyWYVkXOWUC9opDPg4bv836cpO56+PQyzpfRFnn/gR9IYtgtL+ENiWp/mpG5DLQC7FuOpqXpCgIFDz36gL6MD7WTg4tJN17gQSSTvtdNJBgAANNO68tyYlZkszK9eY5Ir7MXqYivEJgsXMuhoG3X0OnT99hFhbDp/iMGyin8xttzBabaF04EvEmyVGSPRtj2JrBIMaIJBS7tS9YVfgkBAM8MGaHJSo3vHrdeGQIAz/FIFw8+Y43/vOI9x2w3wKIzmEgHTKBdfE73fB3Qy9p0LJs+CbxIwUXNfhnacSrxd5UjPMfFSyouWfuD7BmCMWcMLRTzGsaSWFbjvRuEE61UFhOO19aOouCHyNAnsTE5O3gIwCDmj70WgOZHUkjZreSqOwg4KCpSaFp+7FQ24lVdSfeBE3fIq3hCoqCFVsX/+orS2gtnbn5cWoA5f7BzG/BhIuiHa4tRser4O6qiqk43iTqdvOTTgORf2314+1UimC5eVkurvRAgHsTMYTCM+LbB/6ox/1Dbx88Ne/ovn9ff1zze8nVFrGupde5txXX/3S9gQ/jP8/9vILxjrTs09ypJcJgteLzrjeBS6Vz5K8UpggrHhCvCHVolVWU+CuRioCgUpYgZybI0EhTZkhbMgMxRDQT4fqIPyiAi5vhTZ7K/fwyCBc3L+U4B+P5+k7f8HyZUUUlzl87bwOjjw2SqWaQRgTOe9rMP1YaOuC0iKIfFTA6mnzJgEbR8KGJWDl+2SOA5NO4LShA/ndesjikUMF3ok2woD3Mp/smDk4vMrLrGc9EheBgh8/JzKNUraZtunaTM/vrqdj4QqEIzCVEq6K3Mr5103kwHH/+TNMCcHDxjLkzbEQjSN8Kk4gRLsDoyjn/Cub+fXVi3GBQDFkuhQUVaFfvyKklMRiOc47b+x//pDPCJ+AP1d7VYQXUuAHriyGswo/ncnG448vw6cnURWVYCCDZUsiYRMh5NaLquyGxA3EC1/mhx0Cv/CCL0DPBvjlb11qC7owSm2EneODF/YjbQY48sxXmbrySWa9Vs+hByz35NNkPrgm7wbzNXC78lZOgN3GsaUH09rRQDKUzEuNgRZ1mTbm0r1CtHtPI+3A32OwIANtDryd8m4+Ap58KS02fGMzzKwfSOsJZ9K1cgXh1Z2kWnMYVjflFWEGHHNc3/qEENTuv3+fP6GVTqMFAljZLEUNDZz3+uvbaexGN25EDwaRrkuipYVkWxtSSjS/n2RHBwU1NV/EYdnj2BcI9wDWm/mAsM3vXBFeJmgDYcUTzxVAgaJQrUssbHxC42HrTH6l/xBb+kg5ETRMitRu7jOvIawIilWoVKHLgaNCMNSAp+rhnbQnzj3S8ALuL7pAGTCAH9w1hTrrbhIySEAY1KhRfGo5+L3Uz29Avx0lCrdH/TBvZ4wgDJ3s8RMd23Nc+OoFNKpdjDAiOPiIuYKAgDrNy2jqP95UHfAsqdaxlnCeTweQJs1rvMJpTPeecxzsu77F5oVtdGm1BHWbiBvnquTVXP/rf3Hf/1ZS+h+cZY4Jw2NxT6qKogIk0Gl7WZchVK6YchJD7xzGH/7wHmsTMbqtLJGID8tyaW9PcdBBdZx++qhPtkOfAYUqfKfMe3xWrFnTQyxRjmnpuFLgNywQEtfd4l8sAResD5ib7sWUJdvRM9pfB00xEcEcBkFSjoBSyaJ3JjBg+hquCP6ZC79yH0Lxym8IxVtl9gWQTXgFuS0pbJxC5xWuGPQa8zZuZHPPakpkCZNHnkBBYfln38n/Umww4YgN0OHkD1k+Cy/2nK8QAgKpHGtfWUHDsErEuDNwR1hEnCiH/e0PlKyvYnb1dVhPlHLDpVvXe8B3vsMzF19MQU0NjmVhpVL4S0o49dFHdxCarxg9mk0zZpDp6iLd1YVQVexcjkxPDw8ffzwn338/laNH78Gj8sVgXyDcA6jSvDt8uUUPOw+f8E76cX4vEK61YJgPNErpJkvctXlPTmGGfxpjUnMJ00uh7qNHv5zZ1tlUaF6rpdv1ejlfybMDIiocF/E+73+64Z9xbzkJPMk3uSFczym+h8DtBt8xEDgPlE/BDRp+APQfC2sXQLjQM8PNpmDMQRC5i6L4fI5VL+ZZ8wgG+8rQlCKS+ez3jE9oILGC5ej4tiORBwjQSw8J4hRQiL12Ab8vmcy9l56IaRjUd7Vw9ownOKBpJhPSL/H2vHM5+eiP/oyLi70MdeM2A5RlKnx/m4TzqClDOGqKZxJs2y7vvruZ1k091FX5mHTIkD4B6r0Vo0ZVMHt2M0+8cgKXnXEPZSVpXHd7DqeUgri08ZnzgKmAAzIFaGQ6A6h+C4lHdTD1IIqUIAW53iC1JT3cG/w2X3fnUab0eBkhAkiCNEHZpqwgDZBpDGc+U4ZcgJlKsWnGDJpffZuqceO+tKolH4UrW70gGBJe0DMd77Yh4XoZuZSS5F1v4EwbhyolStIhmXLp9Zfw6g/uYtKLOpEUPPkanH8KVOfvJUoHD+brjzzC8n//m64VK6gYMYLhX//6DmVagImXXML6118n2d6OahiYySSubaMHAnSvXMmDX/0qVWPHUjV+PCO+/nWqJ0zYswdpD2FfINwDGGHAZL837ZdxvftjK++adEAIHq3z+me3dMJL2Rb6F79GnZZEIEmb1Qzy3cK4AgPcXoRaSK0weCQCzyU9WsOUABwaAv1DlaWVJjweh4IoWL0QKAetUPCr5NFMqDv6Uw9d9EHV4Irfw1uPwtxnvX8fezGMehrsdaCUc33kBfSk4LncAbhakGLVx48rYPTnNArfdpLyzkyE+8ecjC+WJmKm6Sgs47YTL+P2fzRR1NVJMv2f11WiwoO1Xva8woR+GhwZ9jwfdwaltxXt7vOJvTmXHkeyurKKA2+9jYEnnfz5dmo34pRThvPPfy7j2VeG0N37LX7+vV8T9Hv8CyEEy50R3Jq5gVXuEBBhWmWKsL2ckOL5KUb6DSSxuQStUGBKAIm0JLphUVQdIxOLYCGYaQ5jmu95QAOlHNRhngWTBK/u4bCFoA0KHUuW8PxVV2Elk31msiOnT+fA733v/4sSadqFdzNgiK2VIh+QIX+0JLhrujARiICOL5Ul54CiqJCzyDk2PdU61es8l6/1zVsDIXgTpZOvvPJjt6Ns6FAmXnIJb918M2Y67bnaFxT0uddnurvZNGMGqc5O1r78MlOuvZaRp522Ow7JF4p9gXAPQAi4owqquuCRmFeyDAs4rxiuK/MyQ4CLS+J0Ws9gS4HrBvEJCBqt3G8/xwj5DYq1bQxsdS/ANlmeBcvOGFhzY9B0H2QX5itWLlQdBKGTvaGVzxwIAfxBOOYC7wFgLYXY70BUgBD4sfhR5Cm+E7yPuHEZFZGz+gZnPgmGMZw3eA0Doy8rzJDJy48V0BVL8JBTRkVqKWnHB4ogkk3REyrkmXFTyb01nGNHfoLdULxM+isft6CZZd65X+WD+esIRQKoioLZ085rV1yEv7KG2v33/7g17HnYGyiPrOCe/2vkf/9mMnOmxv1PnMXF0x9C10063EouS/0RG41KkcAVGWKunyXuQOpkGwIL4+Buylda9FQUIv0m7noXEVeYdN4sTOkjkwsjEVhoeKNfivc3cBpYs7cOygBeTcCPm5nFy9dcjxl1sTIhHFvBX1TEogceoGLUKAYec8yXPhgqbL0t2IKQAlnXe86U4HQmEJqCKrwLtd13gyZwXRuZ7yM6DlR8DrGXmokTKWxoIN3V5cnsOQ6WZeHadp+Dve73owUCzLnjDgYfe+wO4hT/7djR8lnjAAAgAElEQVQXCPcQClX4RSX8tMK72wvsJOt4xV6NIhwMwn0lVClDGFoPL2c7mB7y1OW6bLi01es9ZiX4BQzywf/VbC+/NeNpiM+HcOnWQNg6A0pKITj9k2131oXXU/Bu1iN0nxiB2p31+WQvSAFLu+C1TdCTgZowoa8UERq29qPngT8CgxnCBmcD70eX0RNN41iSQiPMkQUnsGbuC/zrT38netFNBNM5/EoWWwaQQhDKxVlYOpbjj9mPYbvQC9ae/xpLFm4gFAmiqt6X5wsGcRIpFt71G2of+ueu+7DPC+lA/AeQeRRIUR/S+MV3hsEt/wT1LOiaD/YqnrWmkSZElRIFEUKVCYYom2iW5ZylP0Cp0k66yuAv11xMOm6QyEZwDlIY41tISb8Ym7sGY0mBisskdQmgg1IJogTsVaD1A3sp3mVG4F3+TboW/5ue9SnSXRbQBQSINTUhHYcnvvlN+h9+OIf95CeUjxjxhR3C3Q2/AgcH4cWUVw0SIj8k49qUtDcx9sn7sXttFq6qJX3aeFxF4PNJMjlwBPgUjXALtHXC/mNg4EcM3MaJkSJFMSX42Xk5pnriREKVlbQvXuxJtImtVBahqn2KMpphkIvH6V69+ktn17QvEO5h+AQfGRRSxHeizS9wgaTcWuf7bTfMTnu0iC3Ldjvwxx64MV8ekRIWvQFaoffD0fCCoQhB7C2Y8s2P39aU6wXcFTnvx2oDD8Tgd1UwKfChhdUh8EEv/Lsd/BqEdDIbEiR+00X42z6CU4CWNbDgdW+wZtQh0G/UR45AqqjM/3WIlxf4qBhpQEZj5jswI/IqA2sW0XHoiWQKS+iRLpFsFF3aKKFiegrqObYhwrVjP16g+NMgsWoJtu0QUBUkYJkOjuuiKILYxg277oN2BdIPQvovgJo3wnXAXgDRM6HsTSi8G2KXsyk3Hk34QYSBHIgAAge/TLC/Pp/ByhJOSj5Bua+TunJP7ne93Z+lcizZWAifcBBYXGb8iUZ1HX1CVWolWG/mg2I3yB68bNEEXALFPThZgaKrHufUySAd0TfFGm9u5tnLLuMb//oXofIv7xDNH6u9YZkmOz9A6zpUrl7M+X+6iXozgWvbyE2NLHqkitTpB4MA1S/BVSh9TyfXDNOOgu+cu+PPKCdN7ml9hY1yI66jUBSRHBaeyIH6pB2uMIqqUjJwIJtmzCCXzSJFvl7rulipFIGSEvRQqI+Csa3q0JcF+wLhXoSRai0rnGXbcYWk9HiFE/Ty/L/hoRgkXe+uckt5pdfxnr+x3Fvmnm5YlgBZBHHXC2SG8CYFGx1voObj8O84LM84VKc6EakY+ALECyv5aYfBMw0f0rtUyuF1DQIu0nCZ816GpctzIBXknN8y5Kj3OTiwFlXJ13FfvgeOPBu+9u2dBsPW1gRPPbmS0tISWl/J4TiScHmId08Yy8KKgyg0JLaisq5+MLXtTfjjPfjq+1NbWMB1tZ+OYvBhmKkUyx5/nLUvvYSUEjOZJL5mBdFojnja43I5jkQIcGyXQaP3sgtD+g/eX2ULAVoBqYI1H5wm8E2B8I2MzS3gOfNgwPS+P1GAY63ERWWgsoo59gE4UsFQTLYw4PtrG5CuxgHqHCaq73O4/jpD1VV4Z5gJbhdZ0iwx6qnNraRQpDGki4KNyJdI462CQLGL4UCqSwVcFE3HtRyEohAoLibZ1sbq559n3Hn/xf6FH4MqHRYNhMcTsDALxfNmEf6f6ygpKwWfV3o8c2wP1e/czxrToWtAFSNGVXDtkXWMOlpD1/lIvuxtC96mo2ADdiyMEIJYt8PTle9RWlnMUHXwdstKKVnz0kueaHgggJPzhP8l4DpOX2ae6uigfMSIHXiLXwbsC4R7AtIE6z1wE6CPBnV7foKU3rCG4Q6gVFTQo3Rguz4U4aIoNqX2eMaFvB+GJb0eoyG8EmtOep0XRUJnvhXzVAL+EIPCwZBaD4ECz9m+SoNQGo6Z8uHty+G52m9fr30lZhLevBqR6s7XViUFXU10NI6nyQrSuO2PMJeGVBCKRrF08RoWLU0RDoVQFAPXtlj57HMEJzQwaf+8sLLrwOsPwsSp0DDcOwjrF0PTcoiUsDHRD9N0mD+/BcvyiJa55gRW/0oCaiEFhTaRVIz20ipikSK0TJIpuV5+WFNA3SekaOwAx8bpbuPF7/+Q1qXLMcJh2pcswclmidTVYQQN4rGtREghQFUV2tZsoOX996nZWybq3E74sIGv2NJQ2gxqPQSmM7X6JB5ottloD6ZY0bGlQ5wU39AfoFrZDIyFPo17r78HWfwyyVT9DU71PbLNB9iARk74eTJYS69vMEG7jTJrWd+YjEABXHxBLwsxIg6JNnBMkK5E0XWMiCd4K1SVeFPTbj1MewN0xZukPqMQXp/9Eus/1O33+VQOrmjh1qtrqTvgk3mWbWg36QivhnQIPT9Bp0mVRFTndX0RQ8u3D4SubZNsbUUoCkYkggyHcWwbO5PxJoWTSXLxOJVjx3L0L3/5pezf7guEuxv2Wohd6Ykdb7mo+M+F0JUgBC0WfKfd6/cpaKhiGseULsXWV4P0MUmM5lAGsHAFFBVAaRUUKB4nz9ymNOoCSv65v0c9XdCCU2HR77yJUU2DFgsml8Plp+ffZC2C5G1gL/NqpoEzIHgRCC+SRFpWYds26Ft7C65jIbtbCAwayPZckACES8ByWLxMwe+PoKga2BaKphEwbJYsa2e/yQO9H5Kiek3LZbOgZhD8/XpY/Jb3nKIySgnTtvFwLFfD52bBsUnV1eM8swTb7UfTmi7S6+JoER3/sUO44u1fcMkTjxP4rANAs5+Cp36P1dbMpM2baarox5KY2ecWEG9q2kruyotDm7qBVVxK1JTc+sCT1NaNp9EnODYMpV/kL0sfB7mXPLrClq9ImiAMb5ozj5Aa4K91cH8UXk1BWFG5uriCk7JPglQ4UJuFhkNW+vALE7DIST+acJmiz2OblecfDquManrVCBGlP6p4B0cY6DKDxEWiIBCUDRIUNyhEmyUl/TWS7S65dBikJFxVBXiZSOW4j1FE+JKhoLYW195eSHZLOTJQ+sktW5ZutKDkQ00WAdJR6czsOEodb2rCiETIxeOg6wgh0HQd1zQpHz6caffcgy8cprB+7xHM39XYFwh3J6SE+PdBJkDNT3xKGztzH2uMOlao8FxaZY0cziZzAEFFoVrz8UzXeP5WM54xAXjkOTj2oa3TYWOHwajpktfFlsuQVxxVgaAQvJb0uEnFClADE26AtlkQ3+iQKrc5mqW0NVdRW5pBxC4DFFCqAcvrK8kohK8H4LQF/2DOyLOxTRtNuh7hPFTOxNYFVKQKoWib/o2iwPGXwiM/J5vJoft0cCyPZG8JVDND2lSQHU2IivqttUvdgDnPwMLXobSm7/mN67KU0UtTNoRUXRDC6586LrGHl6EX6WhhHTtt0/vwUtq/cuZn90lb/DY8dAuEi0nbCrYUDJSbyFBEBx7NwDFNT6dRUZCOS7SmgVh1A2YwzIwLryNTVEpZe4ZgKMjfop4qzOAvSJoxqd2M1fMBuZwkYFgUhJII4ULgElC3VxgoUeGaUu/hoQgyt0L0bCqULn7s/xk/y/6YXte7VKhC4Yf+u6kW3ZDP8LZCslmvRJMeRcIWfhJqOQE3juHGESKAkCBEimNu1nnpJpdoaymoKq7ZQ1H//ghFId7SQsmAAQw46r9eWfFTYchJJ7HwwQfJxmKey4OUpDo6qBgzhpJBn9yiqiIYJNdTQLA4jZvdehOrB7MUx4ftsLwWCBCsqMCVEiuZ3Gb5ILX770/58OGfb8f+C7AvEO4OSBfMtyHzMFhzQemX74YLXKHxQmg4/4zbzIwfRMzWKfb1MKhgPivjk4iZUK3CkwnIrITb74HSYtB1l7hM8vryLFWPbWD4BTrrU4OQjgIOGI5JrZtmZrSEQhFgdr6Cp/uhdHKanuo4vvYY//zLPB5/FI4+rJ1bv2+j+rb4HPtAqYLsUxC8HJQiDktu4qJVz3Lv0BMQUuIKhSHxZm55789w3FRvV/Nl3RU5KBl3CuMzFuLZK2lq6iHsVygKKajBMJl0jupCFaVjg5eeRkpA1WHsEXDvjRAIb9fYi2UFhpuiwLXosSMgFII9UZKmiuK6uEWlSGEhIhpVuLy5SJJKmYRCnyElfPFvHh3ECKAZflwUTHSGhqLMIT/iLiWqrmNlMjiqSrawGM0yWXbgV0iVVhDpbifRVcCAwga6bE/J556PU+jZ1bDXsXz5DK789cHUV/yNc46+k5qSTXQnq2nofxpawfc+4YpkvpQa5nhjBpP0s5ltTUZicaA2k0olgxcAPyySLQm7SRxhg/kOzapNlYySED40oaJK8G7cIhTUlnLqg2fR1XI6ZiJB7/r1rHrmGcxUihGnncboM89ED3x4IuvLjYLaWo77/e9565ZbSLS0ANBw6KEc+qMffapy5IThgsTth+P72rP4IkmkpeKqNmYswrR+O2bZkepqaidNomX+fDS/HyeXQ9F1nGyW0Weeucv2b2/GvkC4qyElJH8K2ec9zzc3DnIZyFrQBtCqBngodQJzYgfgKhKfkiVuFRG3CglrUVJ2ER0O9Nrw2Eug66Drkg46yJAiVJKmeVkVX9GfZUjpSt7rPJAC0lRH00QVg45Fy2kachQy35GxHZfVLUkMXWXk/HUUVkdwXckrr25g6sHVHHHoNnf0QvUoEG47KEWIw6Zz+X0/5rTW91hZ1EBJLs6wjfMRYw6HUAG2hIte3MyL/16KHc1QsF89U996muGRapSERNhxUhkbJ5sgEAxy4NA8h6NlLTQacNZPoGzHaJHu7qG0YzmGqKNQc4i4SVwHAokAy+xqlOoCHJ9KRvqo9MGIIMS603R0pOjf/zMEwp4W8HkuGqGKcqKbNmLZNiFNECqOEOvo8S4MjoOi62Q1A8sXQLVN2oeNJdzZhpbLIFuaSJYXUxqJsDjnCat/kqGkXYLsG8jE9fzoDz/HtBL0xHTufOJm/IH+bGoNcePlOicd+QnX5fbClillCRWiiWlGT14z1I8XzHZuijgutYjNejUpJUynWsZa3wD6mRvJ4cPPlgAaAOkirBmUD5oG+mRqJ09m1PRPyOn5EqN6wgSmP/EEybY29EAAf9F/0Aj8CKgq/Oabddx033TaK5YSKI9CRy1XHTSchuKdUyiO+NnPeOm736V75cp81cNh0re+Rd2BB37eXfqvwL5AuKthL/Z0FpXKvP9Qy1YhYlnFOlHJu4n9KNAs4lJDARRhYkkdW1qoQFJ6epfPRMGnQ5YsWZlFkzYIF11xcOM+ahuakL0GJgEyWjlCaKxXQqhz0/iXhkj2B1ltoi5qIbi0mcJEAgBFEei+AK+8FeGIQ2Nbt11aOFLwaFbhhcxSigaXcva505n42L+Y0rXB24/+o+F0T4P/unuW8NgdszB0FUVXic5fw+vdEQITBzKhpobazTOYUzWeTFUthzUEKFM7IdkL8W742fMQzv/IDzgJHrkVggUgPDpCkc/mW42z+dHyQ7BcH0hIpQWDgr2YPQl6tDIsE/rXg3C8YL6trc2nwsDxsPhNKKpE9fmoHDWa2MqlJLIWvpJyavsNwsnl6Fy+nGBVFV16CM3MEulowUglyBSWgKUgXJeexYvQJ05E1fw7KP3sNkgTmbyZRXNKia1OUFznA+EHN0E2l8FnFPHsW3yyQCglZJ9gK9U7L863RTibIBBjx2zQQ5gMJ0efpUMtw1QMurVSVhtD2S+9HE91Zgjo+V6Tm4DYNVDyNCif/oL/ZYUQgkj1x3tE/ifUVMBfflBMa+fBmCY01PAfJ6lD5eWcfP/9dK9aRTYapXTIEALFxZ9rG/6bsC8Q7mpY7wNO3wSm1Ibi2MtRpIVib8SkFomGqghUB1yxxZ7NJumEQUKRAseFITkZ5qyAjJFDKBKkgZrVMAI5Dqj8gDWyipxfpzVZSTRUTnUqxmx3DM4KA6MFApsg1xLHXDwPc0QItpnydyjFb2jgtHs6ozKH6aQ4IPUYS7u2ZmkPFo/gJzeN5OqmAtSCCqgbAkKQTJo88Me5GMVBfD4v7fElbOJmkKWdBQweYvD76+5nQ0V/bKnwnM/HuMxm7nrnNsLDB28NggAHnAjLZnq9OumiZWJYmp9NoQgXhZ5iuTWAuBukgU0cObyEK5Yeh9WdQeghcG3a2zOcf/44wuHPOClz3CWwfBb0tkOwAAOT8kH9yJx8PWePOYxgflChY+lSFtx7L22dKez57xJIJxjx2hPMPucaFAGaz0BaOZp745zS34//I6TadinM98i2/g8t786mc5bOxJWbcJZqtIy/kETteHC7kFTzsW5RMgvoYK/0pNEoxAt4W/xDtqjVpsgbh/FRwdCHTa3Thu3oNFrN+flVLb+OnFdxEGEvWDvrIXU3hK7Ocx5VEPsuS7sK1Z+ChimEoGzo0N23MXsx9p1xuxqigC3E4l6ZpJMEjlZDgRujyX8M43xnoMV1TGkTUMB2QRcOKTeE5gYo0eCmcvCr0HAwbHwWaDcoCPlxLBUhXKZ9+wWChk0/p4kJ2VXcwo1YUmeTUUYqEoBTBWYKjKXgzirDWRYk1ZRhlT/AAB/guLiuxvEnXwXG42DOBqWUG3O/Z0m2Fr9i9w1HZhw/t3UcyNdGRBkgtkq1rFnTkx9531r7swIhUhUVzOo3gWWXHUROMdA2tJNyVFRV0lkzkD8MOoHrDj94+2Om6XDxb/roEyv/9y+sak6zNuPHJ4sYHVhDwMogtQCPcw0dFQfiT68jSDd+w8/FF+3POeeM+ezfWc0g+P798Mq9sG4hNI5AfOV8goO2V8+oGDmSqbfdxtESfvncDKKXn8ewN56mq98Q1kw5BkcIrECIYW0b+cF+Owoc73Jk/oFM3EJi0yoChTbjTzPpN2Ul91w/lNr5f2JdwU1kwyVkMhmmHbIATAX0/bwS+BZYC/KTwyu9QCQGgr0cSLM1KxR4PMEAnovmx182vHdYbGW65oOmuwpchW3pHVbyT7wXm0VcVjNeX0ulrwiMI8E4FrTBO658H/ZhF2NfINzVMA6H1G+Ju120kMSRCkFpkiDMv3wDOUARnBcK8JeEg6FmMYRNzAqTcwoZbqhcUQJnF3pB6KoYcCUYH2iUNncTLEsw8LDV6I0JOlUDpMuP3e+yUfQHv8CRKmQEKOD6ITMIhKmgtE5FXfECze1JehTor8Oll+7HhMnjQWx1V39stYkq3L4SihBgYNNjhZlrrmOAsTUQFhX5KRSSDinRhMCVEIuUIBMuoRB0hUsx10XRCRFWciiqINol+WP/M7hu8E6ChBAwYAwMGEM/fz+WXvkt/FmbuOtjjV2K5ti8Vn4TazPHIfw2U4+q5p7bKymMiF3Da6oeAOfe8okWVQScXx3gnt52pM/guIfvJPHKP+kqryEc7ea8a6+mUJ30uTcpQZzF7mza5GpKKGaMMpUSkb/Fd5KQuJFcbD3pbhfNB9koFFVbHD5tDS/e0wjLF9A5aDgnTPodU4f+GnoLvEBY9BdQSj2B9NgVeIR7G+w1wFy8bG9L8NsSxFzQRnvZnNuBF8hcdq5yuy129rrEC6iQdf1sdMPkZJpNjkqD6AJnDRXmHET6QQh/FwKn72Qd+7APuw77AuGuhlIChXfS0XsBQZIIFFIizFOBM4jmSvjdky4fPFOANUDgP8KktkHh9BKN84oEY7QNaOaLkOplgTuVZmsCwYBAnaJj6VUMLltMRE8AEiyFOZ1HsVn2w5EqrqL2XXMUhMfsMoEhEAiXUXvZmZQO3kx30uS2I6o4rHFH0VxHKnilsA9DoMvtl29sLOTQCVU8PaeNeGEIWxE4WRNV16nZv4w1CQscF8fvRxE6is+PoUBP0qWzM/WR/bzlObi/cRLL/voC5luvUnLX3ZhRi+VDjqG7SMOffpF6x+SPt5xKUcGeqD3uHM2zZxMoKsJMJFAEFLY1EW5ahxCCkgGfX+Q0Knv4t3s3ltuBhkMnKqvdeZwgzqBanQC5x8HZjLS2V+xzLagflaWxqonBI57liO8+w5C6VoTQwI2C+RYkfgmRmyF6BViL8S4DWWDb70Sy9VyQbJFHQwTAdxC4Jtgz8TLHT4Otw1lSwhJ3JFnpp5+ykfHqB6xyR9AlK9DFGkq0IKTuAN+RW+lH+7APuwH7AuHugD6RO4PfoYaNSHx0KNU4KMx/cASbXm4kGHShXSPzd4N2G867DeYry/lpogvkCE4y3qZI+QcGg8hSAFLQa5XxWuvpoCSwXIXJvjCzspAV3qAn0GcF79oeX13YeQ61H2qPVAlVN+DYYH8EN3eiofB8SkVIC4FAFWBKhbCa5Rjf9mTaZlsw7PijeeaNN6BjM26xgIBG6fRJ1I50aVrRjNu/GpHIIR0L8GOH/BTNW0/bwPqdBsK/z+/g2m6NTNbCkJLiKSfhHHkK0Z+9iL2qHaXbpMxXyymnjqO5K0JlpUdf/CKQ6uigoK4O17ZJtLTg2jYFNTVoebfvz4v3nCcxZTth6X2pBjZZmWaG+xtO7W1GYALgC3uEUul6bWlF8zJDv2Yz9RtL6FcfwNP5zOu7SAsyz0DuRXDyZrrsbHu3cAS3iGUDbhsSlXeyVTyUO5FO5woO0d7km757KFbin3of0wTJyACGMJGAX+TQcFCQtLmllOB622DNB/XYT73+fdiHT4p9gXA3wXQa6PCncF1PMiqR0Gh6vR6jOIftev2RYAiSMThjjUuov0qxEgKh8Mfs6QxQ2gjLKN0ygszf86sIhFNASIENljddukPhyQ/YoMS9S5+egdEXQKjaG8px8WybHox6fw8Jem4SM1Kw0VLQhcR0vWEIIQSacLmzyiKSN+5NuvCjDni7B9Y8GEBMOo5+SpLsyAzdIzWSKZ3WXBMlm1azORzBrSzAdBQUw4+/J0XFa8upv3JHV/eFi9r5/tIUdkGAsO3iOC5dKzopHVLGxXedyMQFPfz2b2EcqfPUOy73Pp3AcFs5dsI6Ljh/DBMn1uyW7/GjUHfQQax+/nnCVVV9E36OZZGNRikbtiNp+dOiidUEpINXnnTAjWMAXVohtuhEt1cCAlWXFDVA70ZQfd5N0IqXBXX7BWiYFMMbbtkWLsgN4Kh4J0uOrWdRlu3VYmBrQFRB1PJIbgq3Z68mQAxD/j/2zjtOrrJs/9/n1Onbe7LpjVQSAoTei4DSRUApIigKFlTEAigCvr4oIEVBEVF4AREB6b0EAgkBkkAaKZvN7mZ7m9lppz2/P57ZbEJRwIQAv70+n/ns7uyU55w5c65z3/d1X3cLdzin8IR7KHdET6ZU6/tQ27il90lQIFshACnV3yKs7P/EfzMvbBjD+M8YJsLthP202cwP1hHSBgikjZvU0ZDkZTFIgVuwRwtGQ2soYB+tA6GpL3xY62GdX0OPLMLEJcDGZ0i/lw9gffA+1Rmh+tTrq6CnDCpNCJtqkkSfDzUGfL+9EB9IuFrAT8rh9n41KmofQ6fJhR7pohOwS8ji1ISySOnw4OxN8FoOihtB98GMwSYZY0xvlG4jjQxDv5OgcrcKOq96Brs6QkldjIQXhjc2ctqXZ1Bc/O5eppv+ugxn/1lEsirS0XUNIQS9jb28Vh6m6ckyDBMStssba9vwvICkVsZTrzSxaOFDXHHFgRx88Mc34XzsAQewYsYM2pYswYxECDyPwPPY7bzztonsPBp4DKArSYlUZBUIE1N66DKPUm72AzqJ6oBQAtJdsO55mHt6GaP3KEXTB1setkqeoo4kc4hotoJ8x89BZahFxl/DjdmrKbcSWCICTgMRMUBrUMW9zvF8LfSnD7WNEdIYwsVDx5carrQUCUqoNCjk9kNgfjCPzWEM46NimAi3E74YK6at70QWyjcotltwYiUMBCWInE3eUFMhgjqQZWCHQYhCeFeYgNInoxSJNFW6S3tQQSA3SxYYY8JK593vOTjhqViDQ2JwRgk8n4Yn0moQ8OdiajBwhTE0zT4fwOVdypS73ihoVmwYi4kvobVQJno9I7myfR0bHZd0MIG8lWfy55YyZkYj6f44GxfNJPREMflZAW6tgdQjnH1UD/3/WsjDzbsys76IU3+0J8ccM2TXJCWsdaAvgFWbUgQhg4F4CN3xsAfy6J6PpwtC3QHrmzQqy2DNmiS+HxAKGXiBR0YbQ12smd/85mUOOGDM5lmB2xu6ZXHEjTey5pFHWP/UU9hFRUw55hjqdt11m7z+TLkTz2qvYUiJjk+ARkbYzMm8SeCvICd12vx6QCMhBgiH05TUZ9jlKzXKIUi2QqCjjpp3XjIVFJtCA6mxtVXaOxvmbSACmkGTW40vfSzNBr8X8LHJcYJ5Dzsbr3/ILRRoIsQ0/W2agmrSMs7v8idzpPkg9XovFYat1p34DWgfsT90GMP4gBgmwu0ETcB3S4ro8/ejyYWSMlh7BCz9B7AnyLmo85MN/RGdVzumMSW+nljEx5M6AzKEgcMII8xYXU2dWJQDXapJ1glNDeXNSnUai1I4nQnYPwo31Kg17BqGH5SrNf2tDxBs1ehtaxAEEBaQloowB5EOoN4E393AQM8PudRsJGMIekUJD+12GNkxBm7aJlLbT+XcRvJr92PDCxM5p+chDsg8Q+OImTz1x8vZubia+0ZtvX+6PBWZrsgXRBMXHInr+UjLQASSXHGESHsSdMGpZRrXFJ7X35fDNAo9mgVpfjRq0d4+QE9P9qM31X8EGKEQU449linHHrvNX3uS9SUGMm/whuUhsQiETkhm0cjRQoK1/gQM6WNg0CsHcEQRO8VPpkisVCOVvLXK2EG2MUSGJmApU3XpFCIun8L8c/VTVKlIMdiofuKqBUmHUq0PH5PAa0EL1mPgcK59A7VaK/b7OM28PxQBxzSLOq2Yp9zdKAnPw01cQL3+OkIzwJxTWMMwhrF9MUyE2xnFuroBfOlI2FgMvUVAstC2FYUAQUe0mGzDJMaP3sgao5oWlKQAACAASURBVJ4AgU+UZXmTMgPGWQUhu4SNnpoy4UlFgD5KNOJK2C0M19fwng3UQ51basZe4HlYsRgCk8/F4F8pdXqKaIoEByRcUuyR6/8mRfSSFhX0BYKuaJRR5lpWFM0gj0UubGFIj7kTFtAgJnFr7BSWhk/BBFp9+Fr83Wv5WYciwUodWjzQwwb5jMRKZvGjNgGCdEWcb/R2ctJojSfGweoNEAoZJFN5hAZ+YFAdbcZ1fUxTIx7fQS7X2wFChNgl8htmOM+Tcl9goVjJRjNBtdtOozcajUClwcnjizKKZCf3+8dyWmlCvUD+OXAWQJBWrRGbRyt7ED4L3PlqijwU7rdUa4VWrBxf6ANtrHKUkX0gwlToFRxoLeEJZzeqtBAz9NXUapvoCsqZZSz9kFtogYgAORKGzrH6SxCuhehhwH7bYA8OYxgfHDtOf/7/IcI6JKZCvAZCI8EoB8MG3QXdFzglUd7yJxPTTHYPm1QaJghVm2t2lTLQBXKFCE5HidfLdRhrwg/L4IlRUGUoklyThyZ389Qg9o4CrkPnopdJLXqJjreWs/b1pfT29rJ/BH5aDpaATZ56/csq4AD7NQzZRZIyQBDVoNsqwZAuJXY3VChCl4aBZXnEi/pJBuq9O3zYLQSnv8M9q9VVdcZKXaViO32wNY1Y2MQydaLrOijb1MMYEy7YfwSaBr84D0qLIFJUQt4LkfNsKiOtlFjNdHamOeGEqYRCn7HrOmFiWQcStj9PROaYnl1JRkYwcdFQJu4eGrrM4mHzfHaL6MnaG0JHgV6rpMPkVMYz+j1IXAGlD6q0o3UkiFow5yoSlAMgkxA6A2RakZVWBaIGZJafFa/i8/Z8umWCctoQwBR9FTGRZuta5H+CM+T55bcDEuxDttWeG8YwPhQ+Y2eOTzYOjMLVXYUOLV9FcVKoobqRJNQvFaydKpg90UYImKpBu6ciJiFhWlhNeZAoQrQ1KBEwIwS3j4CSQsi3MAMXd0K/ryo/O9lwZSWMSLVx7ZUnkPB7kUJw985f5IY9v0Wkp4dvN5jUFsW4pkrVEHOBEtrgpLCAmKYUo7aAeODSJ+zNdU1TA0GAJsAijAFMtuHXVTDVHjrfSSl5++1ulva6+KWVCEOniA5Gi246RJRAt/DKypk+YiRBYdBwhABeuIf6lx/gvimlvHLY+Ty+Ksqzj72Kl+lkICU45ZQZfPOb/30D+ycOUkL6d5i5vzA16EAjjxYEWMKlJajFlRYSn2jQw8N8jVJ7i4nEQof4LyF8HDiLQBSBfeBQP54og8hpEP4K5O6HgUvAWVLow6hU0WLQCVLVApVtYDkRs55LIjdzQeR+XPcVikXPFoYGgy4y7/z9ndDU6wUp9TiRgegPwPgv3IGGMYz/AsNE+DFiJxvOLYVLO0GaKs2pAeF+0HyINIG5xblAF1BrqlTlThYszsPuYegvDOCNahAVhfmDBRJsKQz6tYQiNClVk/p5rQG333g6E5teJxMqYk3NJB7Z6Qimb1rKQEYQwqEvNpljm3Kcb/0Pc/TnaSBOPHw4MwiYbHq85RjkJFRm++izqzA0Gy1QJBg20jQPTMQNIiBgrAXTthCHdndnuOCCJ1ixohNhaKz97qFU1m/iRyO+z2JvIr/oPB+7uZVwIOkoH4E3ZiK7xU0qLz4EVr4MUmIB+2h/YZ+vXIb3/fPp6spQVGQTDn/UkfSfcHjLIXs7ulZFVncRMoUmfXTpEiJPTKTRpM+dmz7PHdpp3PlOHhECzNnq9n4QQqU+pQ7aBAjWg+wE535UOnXQYSai5mqmrwIRJybXqx4d4N1KU4ES2XhsZa8GqDyGQCX1A9AqIfpdMKaqSFTqyutUqwRtC3PcYQxjO2KYCD9G5AI4NqGiqiuaoTsFZqBIcNx8yG+AGTHo9qG88MkEEjISjk5AWy8kAzVMdRCpAEZtwQOPDqhaYdmgMFAoKXrTQJq3RJRKLMzA57WRc5QSVTdx4mE0X+L6DtLfQCnr0YRJRCaxcjfTbY2ijCbm2CbpQEPKHPVOPf8KFRMjTQA0pCaxsGsfnEJbyP6Rrbf95z9/nhUrOqmsjKoI4tnlnHnuH8i5LrmLH2fnkXFeO+hUDBkw4HrULXqRnxVtUCRoR4eKnp4Lt1+CceCpVFeXb58P6pMC50VAogmTYsrowcUTOWzD5S73OF7O7k1XRyntvZVUP/4a0/93Xz70V1o6kPkLaOXgLQE0ZYYtB0cmeUAUNEulL6RT+CwihfcqNL1vjgAL/xMGyMG1CDY39JNTzxMFFwApYeC3hekWDmgJ0OsL71OtzLmNsRA+Fcx3958OYxjbAsNE+DHhnn64rhecADIBRNRINpIp8B1wM/CtL8GBM+HcNpUSHTy9HBlT7RAS+Fln4Xq6IGhJBfDjLfig3Xvvwq/wXJLhIspcj/6czqpwPWsrxiM1DSnBME0izgBhQtya/wrT/VXsbS6ihE5CbjNlpb9Ac+bTsjrP7/8ygSVv2hTVxdlwwlxap46n1w9vNbhntaPWF9VUNPjyy81UVkZJuwmaU6Ox0h4Tet+mZWkYZ02KI/ru4JANj9FWMZpoNkV8ZZ7UGJ8a2Fr5Y5iQc2DB/XD4Wdvls/rEQAx6fUKCBKYwWd/XjBlymLFhCRsfqMLoq2Fszwp6WpP09e1GdfW7rfP+LYJ+VP2woBwVljowgSHyckGGCj2HGfD7wdgd5CjlWRqklMoUFzDBmKyeK7Pqp30oOM8RiDg5fHQ5gClB8xtUFCi8wntlIShYtgU5YDVQBM5TkL4R7KNUXdOoBmDB4iz3PCHJuyH2natx5H4QfccF2DCG8UEwTITbEd0e/LkP/pFUxDDSgDoD1jiQlzAyDnNKoNcBJsPR9bDOURFcq6d+fr1ERZEAh8fU6eLGnsL/dbigQpHkIHYPw30pdaE9WLrxJOQdj+rXn2FTPk6/jPDIbseSsyNoQYBE4Ok6GRKYRIgEDg1OPY+6B/Cd0M2Uil7QKlnT/n3O/Pb9SAlFRRb9m9J0PLSJVO14QomhzjMT+J9uVau8dQRksx6aBn35clZ0qzRdRCZxPYv+1S5uAB6CaLqHiekOECFSsob+AUe92HvB/P/AbcQ6ADI3qehIWIQJE+oL0ZEMeOD3nyPvhIiTJJfzCIcNyso+QquBVqKky9JBzR2UDLY2KBIuXI7JFMpvNKpUqO6L6u9g3RaPAzVzsKzQoyhBdkDoKHLu8/TSjqMps/hIkCSiRYgGeVQatX/oPYN2hnxPOwq9jj4yexebFj5K8/qz+eMDNfyj6fN4ehRdJHl8fhH/fCrMLZdBbLjtcBgfEsOq0e2EdABnboK7kwXFp4SNLizPKzKLaNDmqWxTWUidYm7sga+3wls5ldpMB/CrLng1O/S6n4vBI/Xw4mh4uB6OeEdrwj5RmBVSys8+X6VZ2x2f6f/8C909JkWmz/z9jyGZKMVyc0ghCBCFOQI6MQYo1/oo13ox8PhT7iRiIgC9hltvXYLvB5SXRzBNnUTCRu42GtmWxJMSAyjRIKarA2utC7f2Qm1tnIqKKKu7JiGEj6Xn6c7ZPP3mXlRPcJEe5AJJKpBIPNBqEZpG2SHHq40KhmpMfj5HXto8IY4nObBdP8IdD2MMRC9UaUNfTX2orQ1zw19PpLnFx3V9ksk8PT1Zzj13LuYWI7E+MIQBkW+gJtIXnGZkvtC/Nxhe+SgrtsE5hMo7dih1Ovj5SCCjzL2lhKAJZI68+wYLwrPwtBDRIENU5mixJrHEnkiAV3hvZ4vXCgrvl9v8t5fX+deFgv/7ZpZ7LrwF47nfc1zTl6nhbSzTo7utg6Ur8/zr2Y+0p4fx/zmGiXA74YkBRUY1RsHDX6jTRHegGtjfMaWNQMIfe1VEuN6F13OKyEwB13XDH3pg7w0wZz2c0wqN7ntPnDYFXF+tbNN2slWE+JPu5ez19L3c2L4Pf27dhZcn74cWBBjZPFrWRfOUX5uyd/bRUCbIEZFmgCgp+wug1/HWWx1b9eq1C53ukhhOZRwnkHioGqZfMMkp1uDpNGia4PsX7k/Oj+A7OTJ5H6ckwq8WXUxDdHfKplh4HWmCfI4Bt4pUR0BRfT3137gI9vsS5LPI7ADOQJp0VuO78ma+eF2EkWfAyc+rVo3PLMLHQelDEP8FxK8kXPsUP/zJd9h99xFksy7V1VGuvPIAjj9+p4/+HqHjIX4lmLuqVgvNAn0UGBMh9EXQagG70Ibho6K1Ad6tCo2o/3srwH0L/A2AJMjeyrTsM2yyZrI4cQrPF5/PW/GTKfc7kOR47wkWhWkXAAgW/0NjwYMJ1ndW0p4tJeWbaNlW5jb/Ck2oYyzZn+a5RR99N3yS0NMHTa3gv/fs42FsYwynRrcT3syrWlmyUMfLyyEjKx8wAtX+YBYm1PcFql0grqmSmASaPfUaTznwWFqJbQSwyYU3cnBXHeRb+7j0h08yf/5GQiGDL31pKhdfvB91pkEqgFdzsDQ0gvFz9kcs7Of+dTuRai0iv5OGwELzfDQfNFvHRaPYKMIXMSyZRKBhaGVUJr4HwNixJSxc2EI4bOIZGku/MBdZHoOQgdQGo0pVAy3RIdbeTP2/7uCWJa+wbFMIzf0VvgZa3ISqBANC46w7bmGnccuYNfUvRJ58jSpXsNvRh3HQuedg2DZ8909w9Hdo/9c/uePpBPdWf4XlshRTgMjCQzdCYzXcOVa54HwmoZVD6LDNf06aFOWaaw77N0/4kBACQoeom5TgrVJtE8YklebMz4fkt5SS030V5KDzLWxt0u0DYSV4EWHQJ4GwcEmieb3MTt1D0qgiEAad5nhKvf7N7kD/Hh5P/aWYjGdg2gFCk4BOxg8TT68k7HXha2Xk85Ky/97mdYeiPwWX/R7mvwahTDMTm37PVDGfyroE008+maknnohmDJ+2tzWG9+g2xoq8sjJ7Ng1NniIvW6g63aD4JUApQcfqqo7ooqIogWqNMAWEhEpENbrKRi0sIFyI3/MSluVg77c9ur7zKMGaDiKBJNOT5ZprFvL80k6c351ESBNUafB2uJgXz7qY2Il9OEvaiS9dgzF7Am5xDKkbaJpAEwITGGWFsLVZSOnT4Qv2CGuUFwjmjDNmsWBBE8lknr45o/EqE9DShx0xcUPWZiL0gWndTUz87pepGkiytCmDLh12id/G66VnkU25yM4Ufnkx5GDVnJmsnH418iSo1uFxExZ6cIks2MGNmcYdiWn8nw3rC+fMjFTiRi0JvevhjjK4aAeISKWEZt9lg9yAZzRRLIqZwERifEjRyicFQoA5Zev7rHn4cjzNLy0nUa0RLh7Ajg1mJAaVooXeQDTQxxT6DxUiwQB20Esg1OMcEaU+9yqBBGnMAu91JFu4yAsKcymGCHJTZ5yQkUbqAZoICKSPFDrS9wikxPGguMjmhEO34775GPDja2DhUqiLdTNuwRmIbB9rzDLscIYFV11Ff1MTe1144Y5e5mcOw0S4DbEgrXr4BKoHsM9XfqDFQnmDpgtRYQLYNQrTbXVdPcWGb24aIsm8VH2CEaGeL1I5eGUd6bYU5qRKcjuPwjV1el9txN/QjahOkENQ1NyD6Xi8uryTyavaqJ5ew6o8dEmNiGWSD0dIjDTprdqF0PNvIfachl4cpag0wggz4HP08JBbQjcGfb5OQoNZtiJxQ8DMmdVcc82h/Pa3r7CqPAGeT3RkMeHiMB4q8vVQvY0Vf/s95W4G17XxdB/dijAzdw/5gVLeSHwRWh2IBWhHachpW3eiVerwyABMtOArBVcax4OOPujfpP4WxSBKlMAx46m66seNhjz8pNNjkdOPR4JKawRHVS6i1lrMURxNJZ+NYbKZ7j4e+oZD/4YkpaOy7H2ej24Kikdr6Lq6/HGxWGOPYoM9nqiMMyW7jkq3EUQcQzrKLxUHD4krJHlhUyz70bUq8plyAq8TgUQz1fQUTdcQwmRQgpWqriOybgW+1ImGHVIZDRuXDq2Obq8Kyzb4+fkms/+LDPGORnMbLHoTqiugZNUDGPk+3Fg10oGutMnYuhAr//lPZn/1q0TKP+OtQx8zhonwI2IggFcyyv5s17Byf/leqyKCEaZKb0614I28IkALRRBxTUWIk224rBLaffhZuxKY5AIlkswWJk2kJNiNPeQufRA/7SAE5B6UBKPL4ZIj8Nd3ISTogSQwNJyYTag/wNd13PXd5KfWKPsyQJgmMlHEhNEe3VJj2tgZTE6EiJZH2GXJv9jz0d8A8Ob+l5Kqm8mkolJCmsaNvdDgweWFc/oee9Qzb95IrukI+HWfRlaqwbAGkBBq+t23y6F29SLCFSW80daFoRfSZ4bBnn2/xZhQzPx2A+vrh+PUF22VGOv1lUyiRIO/JxURSgnLVkNHV6E1TRS8pPtVtq5zJIz4GI9kV8JNPcq9By1DiZkiLgx6nGLubj2Ar498lOe1ZzmeE7eYuPfpxctXX03fxjbitXNwXMniO9dRP2cNesiiuE7iIvhX0cF0GFUYwiRAstI6lP0GXmey0wAyjUEMKWwsrRoLg6hWjO6/jpcb4OkrHPY+D0KF/nkhIPADNMNGFEy/DzwtyZNX1lObb8LEwbaz9LlFNI0+iu+e3Ms5Z4xhZN2n22u2px+Mgu1guOdNAl1tj6ZBLg+ariM0jf6NG4eJcBtjmAg/Al5KwzmbVOrTk+rEHddUJGehnF5mhJSzS5GramaDLcV9hRP9qxk4qQUaHOX8Ei2kPYUASw7JB8J/eoFczsMvjylxuZTI9Z2Ih94kVhJm81xwCX5BNah5PlTFyRfSraJQhzSFIFJezuocPCNhnQViUw9vpG1mFVXxevkkGotHMqF1BSKoh8rRRDUl/DmzWBl/qzUKji3RuTOt2jhShZyoD+wSgovL4Z/VlaRaWojHLDo6Mug6COkTaBYWPhHLQVZvrXMf9BxZnVdCn0xBK7FsNaxthGgVOF0MaSjSEOwPngVzP8b+sR+1wR39KlqvNJP46KQkFOl5Un6Yxkw9RmwNWbJE+HQ3tgW+z/onnyRaUVG4R9DfOo437vZ47fZ2TrtvCm+bpXRY44mJKoS/FrDxpMmLsZmM69lY8KfJIfSdKRGlhdfpA3MOzS910vKGy8JbI8z7WgYjpCG0AN8FAwfNMOkNooz5fJz5K84g+vwSRmTXEgiTmpk2f//ZrozbbeqO2TnbGGNHqO+q40K+aCKx1hcBJZgpSYAMAqTvEysMgh7GtsMwEX5I9PvwzTY1AcISBdWnVKIYEzB1yAYqVRegWiDyFOS5hd6+Oh0WZaHeV2bZG4Wqedmaiqo2BooUQuk83tpOtJLoZv8OhIB4GPPFtez066NY+LdF+H1ZKI2iZRwGBvLUVcWIzxpJvqDedAPwBUywlAAnGcB4E6oNoHMdb5eM5Zopx1HiDCjiNC3oaoHKUWhCoAFv5lSqtsJQadJRFlxdBb/oUiOVXBQJ/qpKLXHm6afz9EUXUVNZREdnnkxOJxKkaK7em+7uHKeeuSt/tw2EVM/1CvsopilibfPgi4X+ybc3gB9AuAb6EygC1MDI5RCGgYahtmUbYlFWtbOsdWCs7vGNjkXMa3iJ20bszU3FuxJIjTzgSA1L+EgEeQmBFAz4FgIxOFb3MwihWjuogvjlbDA2YGg5xGDbhABDGDgiQY89mypnDeoS0YFgQLVLCAGJ68kFa0FcSufach64MsTL2RgL11ZiGD6HH9jCyV/byJv2JP4qv8zk70dpPX4s7S09XGZfxrRQE899p4miv4ygfNKkHbxP/nvEonDul+Ca28ApO5qEuAuZ7CKcKKU84ZFq62L84YcTHybCbY5hIvyQWFBIh+pC3Zxg69bjfCHP1+ZDHPUYq3Bfnakis00uZFEq0XpTkcoqBwZ8yIqhgMcLm3gHTkG8thFdG4zqwJcBWDpmWZTJv/o8q37/EvLNFvzONNOnV3HXXcfTXaVxfY8ilL5ADd0t1eCVrBLe1AwqLD2HCtnPY9WzuWDVP9V9QoPAASkJEJv9SwXKIHxOGE4qgkNjqpex0VUkWW1IXn65mZufaUDXw0w/7gxW3HE3BiaBH/DS2G/y+l6nU1LuMzdWyqQBWBuHMaKBnJR4bRrpVXEcLU7RbIuvjVLLKS9RKSNDKAV/ZW4F85b/mvLutwg22qxvPA7th+dCZAtzU9RFS5OrjAkqdBXB2eK9R1RtiYUZ+Fab+t3xPF7paOcNp5LLN7ZyVXU5ei6DbYdwMUi7cUyrC9BwJWhCUmq3MZox2Hy6U3Wg0nFjDzmE9Y8/TqxaObpI3yPb1Mj0w/eD7BQixT4+K8m7MZxslIjtoulZpLCwApcNwTT+JH/L4rRBtWjizNha9ineE4xJVM4oQWpx3KCYG54ZR2fWJm66pJ08f88dwB/XVjHlytFYegpTOtSPcmgbVc96bV8iv38VKQPeuOUWDv71r3fsjtpGOOVIGFMHdz9aQ1/pzUxt/A2xrtcI8hFmnXYac845Z0cv8T+ia/Vq3n7wQTLd3YzaZx/GHngguvXJNsAYJsIPCRc2+2mCIsBBEhxsKG8pKMs1IRmpJ2n1TaQUNDgWDhoSgUSlSZfkYOeQIqoVDoQKghpbgKFr5I6YBsta0P1AqUmReOk80ZPnUqxDeEolp/3xC+zd00ujYXO7FeMYF0Z2wleL4ZYaeGgA7kiq+lu5DiNNFc0CECtBpHoIQgJNBjRFylkZr8OWPiWORo8HSQkhX8kWJNCcgmV5lR68paYwK1FKfvrzF/jr/avpR7mKRD2LcOjXTDu0lrdHVbBmL2XI3QP8zYbYq7DX7su4ourr3PHo0fzlwVMg8Cmjk8xTpSw4J8wXDoQ9doaKEujqhjJaOPy1syEsSU2oxrRcpj14Ox2iB678pfpMpIrm/tavAo++gnFBRECRrsZCfbno/Qnxuh4Vybd6ELg+WAmCUAnf2/MC4l4ODYmWz2CGEqT9GML1iehpXCymRFuYYxvsw77b4ejbMZj3ne/QvXo1/Y2NyMwAoruZiiKL2ZnFNN56Kmu/si8tcRfX08kna8l1WlTVt1MfaWZJWnJB9lcIYzylumCdHMN3U/vwYxuOS0DpuHFMPvpo7r/teTpSOkVWBun5VI4zCJU1IhpaOGbt/bw15RjezpWjGRCSed4IdmbX0vnY8TI6V6zY0btom0EI2HO2usEk4GYCz0Po+hZTPj65WPPoozx90UXk+voIPI8V99xD/d57c+Qf/qDaoT6hGCbCD4m5IVUP7PFVtLc5ZclQDdAEaoyAydpyNNlLP1PoIoGLwCBAom/2z0gFqqZoF1omZoWUaGZ5vqCXq4jDvDH4DyzFl+BLib73BPIHTeE3VWrKgyM1vuOV8dd+0FxFpGkv4Hevvkprw2L2rC3hzwcdRLSykqu64M5+SBSydm6imvZcQGWqgyumHI+UkrxukdENegrDySOoC4BBkbyLcslpdGHXBriwHGZsaOcP/1pDfkQZoZyLQNLXFqBvfJGaqXN4Zq8qtJwyGA8CsCKQngD75O9j3aY67n3oaOoSrZhGQEJz8Lx+rvzjTuw5W0WEf7gULroeGl64Fy2aJ1NWjWFBoFmkymtofPIJBr79LWLV1TyQgj/3K/VpOlDDgT2pLjbKdLi2RwleznqfnrMVeRW1mwI0JwNIAunTFK1gQqqFqlwfraESIgQYQmPAK0YPIvyoqpeTi2uoFTM+EyKZQUTKyzn+zjtpfnE+yavPp3jWOGrH1NFRE+axo2rZtMmlzy4jXtdLpCqFEbVYu2QCCcfh7kn74UrI+0lqzCJCgB3A9T1wVFxdkO190UUs7B+DfutKrGiIknoXKzRAICycwCS3Kcu5s67icnkBHflq0nqEUX4DkZIszsAAtVOm/Nv1Z7q6aF+2DDMapWb2bHTz09VwuiP7BrNZl3/8YwUPP7wG09Q59tgpHHXURAzj3V4sbjbL0xddRN+GDXiYNNp70BzMJP54J6V3PsE+px+1A7bgg2GYCD8kakzVr/bDDkV6Q94Xisx8VNQ0yuhHD3pBhJigb6LVU4NtVbAoEYVTpSNVqtUWUKSpyRKpQto1kBAYOvKLc2HWSERXmujYMsTIEqIaXN4Jt9QpBeM/kip1aAvA85j8Pz9gzGvzeVvX8aImr95wA4ddey1f23kXFudgbcajr6UZL50mnuxjVTRO3AnIl0Qp0TXQBamCsCfPUAQ8SPwZCXGh0q7XdUPm7Tx9Pz0CLWTi5Fxiz67gR5mbObx4IcsiB/CSXokRSN5mCgGqNqhrks5InPCy3TGkRpEVYAkQmOhmD34WFi2Dz+0LI2vg9svha19ZB/22GmeXBcuCkKXRLnUG2tqIVVdzR7+6WDGESo0KCpZ2Poy1oULAX/uVItV6D76KaupztAeVRoUUcch3yeo2YwbaCbkZWsITEVKlXe8babF7pGp7HXY7HJphUF8dgdFRKFbCmSUzS5BSo6O/AtPSSeerEXYfmJJVt+1MT8locuNtYiKNJ3vp84so0VU/bKenbnUmCE1jl8Pncc9zaaoqLXAXAjZCSsJanul1GxgpWjij5HZ+0n0xuvQ42rufjrd1At9n5zPPfN91L/3rX3n1hhs2K5PDJSUcft11lE2YsP132qccnhdw3nmP8sYbrcTjNlLCZZc9z+LFm7j88gPe9fjOlSsVCeox7i+6ijZjMpoMCALJsmtt7t4dZk7eARvyATBMhB8BZ5fC7DActVFFHDEBFaaKIGzUTMBe30EjRFhAo696DwQBOgGgIxGba4u1BpxfCjf2KlJscVWDfaTgMJPVBO6UGiTK2CoiYbKp0pPtrvIzDQSYhW/7yAVPUrPkFRac+A2adt6DsGlQ1drIqtvu5SczZvG3OoOrfvk7VvUOUCsd4ukkf/7C1+kUYaRmIHQVLg5KPQZPIlu6PQkUGRpSmWGtr69ANnSjeQHS1Dl0fANfaHidls442SCKJ03KRDujWM9aJiJ1yPiCjGvQkU8hHPDpZgAAIABJREFUC/U7hQCwVAS6xYVn1oGVNTOYtmABZqEc6OXAdT2yvrJlA5UCHiS4jBxSowaoiwur0OPZ7yvxzztxVExZ3HkSdNPGd3J4mk5Fro9jmxcwv3QSZijKaEt9hj8qh90/3eLQDwbf2+rPvmITzSn4dAOetBAZCy3uES7KkkzFGKU1s9wbj4dBuuA4pGqpULyFlmjPPeupry9iQ0MrFcUagYTubo/pE7vYbWYrEphjLeb8kluo7XqbosaNbOg+gMOu/T7Vs2a953Lbli5l4XXXESkr2xwFZnt6eOJ73+OL99+Ppn9WxUzbBq+80sySJW3U1MRVWjboJxZq5YlHN3L6Sc1MmHocaEPK73x/P4HnsTx2FG3GFOJBu7JFCHw8p5JLrof7rntva8gdjWGv0Y+IXcLK4Hp2GOZElOilxlApxyoDvp9YhYHPpqCMDCFKSWEVxtoMuq8MphvnhuHouHJVWZyDTYVILCmVtTGok7mGIkddwLpC2jIvVXrVQkWXAKNeeJSFX/w6a+YdCICr6yydMY/fnf5T9l7r8j8N/ZjPP044HuPNCTuzfPwMXNNGCwJ81928jYIhAnmn5eHggZMv1NKsiIUIJNIPEK7Piavvo6+oDGydCa0bCTtZeu1iykUH4XBACgg8aHpuNLvWvojreHT0GQRBANIl643CMGD3mUPv+coSWDP9aNxEMZHeNnTPwc4PYHa103voFwmXKmn+nhFFhqAiQx918o2qQJdsoNSpJe9zHjynFEYZatvzRghdNxnb10RJro/zXruNv626nf1qyplkwbklW0//AEW2i7JwQw/c0acubj4TGDsTdB0c5QJf3ZYjsCV9VgmPhA7lHv1I7jePY3l+Ol1tpcyatoKjrUfwMeiWFdhCHaOdPpyQGGoZArAsnZtvPorPf34yfUmDgQGf449czfVXvkTMDBHTAhIanBx9gP12OpoxR6/l2Dv+wch58953uWseeQQhxFap0HBpKQMdHZ+puuL2wvLlHYUpNgL8VvCWodEJwQCrl9wMPUeA37n58eVTpqDbNquM/bD8JMJ3wXORQUAiEtDaCS3tO3CD/g0+dEQohDgYOBG4QUq5RAhxtpTy5m2/tE8+vpSAyzqHyElKVe87OgGnlk7kFO04NgW17NV7PQOECpfOEr+QaBQo55aXM3BKMzyZgSKhortUIefaU4hoAgrT7DX1vC4f5oTgvqQS52QDpYrM+bBq94NYs9sBSKGRFeCaNpabRwQ+OQzuyFn0/egPhH0Xw/PwDIN0NI7u5JCB3Gya7TIUFQZbbzo+Q5GWI8GyDUITyulf14VwfCK5NK5tUTotwfRYwK8X38JP5p1Ff7yInFCMbT4J87uOZFysma8eeSt/fuh0OrrLMa0iihIJ/vcHUJwYes+V6yC2pozHLvoLs+67idrlL5KPF7P4mG9y7CHHbn7c2SVK3dvmKePv9gIRTTEh6cOAVKbkxvtcmRbpcFMtXNgOA4EgpcXos8dznrMe56zfcLY+kZQrNs9dvDcFf65Vw5Q9CWe9Ao8/Dk43RCfANfvA9RNht0971BiOwamXwm0/g1Qfs57r5YWRh7CgeB9cRydMGjR4tXd3yvfq4aSD/k5CzzDKcOn0bTxUBuWMYjVe7J0oLQ1z8SWHcvH31yFTP8f3c7ieheO4GEYY3ZpNzk+xyN8f3ytjnqnq6u8HN5N5z6hPCIHvONtst2wvNL/yCstuv510ezsj5s1jxqmnEq38+NyKqqpiaJpQY7f89YABMo0QAf1vtXLvfY347hgCfWf0UB1jDziAsQcdhPXyAIEQyEB9zw1NUKTlGPA8bOuTmYQUcjCv8UGfIMSdwDeAnwKPAMdLKc/dDmv7yNhll13k4sWLt/v7BBKu6Ya7kooUfGCPiHJhiWrQmnmdr7TAcm8EKRlBQ5AmgkBgChUBTg2pNOgLmaFaFqj7koEiIwv1+oYYapBHwrdK4KG0im5W5AtkCCqFJQRCKktjRcACy3OoiocQUtKayjF53ZtohdxjMpbAdz1k3Ui6w0UIqV5LoFK+TqHf770QESq9O86CdbmAtv485790NaeuuJdx1bVYhW3KpVO8MukQztvtx3TfCbk1YA6OkRrIkWwE6ZrUVuiUJuAn58AxBw+9zwNPw2U3gb8HbNwJfBMMB4oXwP8dD7tOH3pslwf3JlX6OCrUhcN6V63zq8Vw4AewAn0wqWrBXiGaNFCRpSGgdgu9RaunIpwLy+F/n4NLrwZTA90CP6s8qKd+H56ZWfBO/bSjqwWWPI3MZTkidiyL4iYxO4mbN8j3FFEajZAvzrJXaBWN7MQe0RAXlEJIUxdy71WX3RKuI7n5tqtZvi6E49rsMXUhU0Y3khhvgNbBhe4/6ZAjKdLhhiqPidqr4DeBPqIwQUOdbBuefZYnf/ADYlVViMJx7maz+I7Dl594AiMU+nfL2KFYed99zL/8cgzbRrdtnFSKUGkpx/7tbx8bGSaTeY4++i5y2X5K46uRMqCrW2L0pjgldg+hqIf0JekuHTdfSay6mlhFOSvWR/kbFxOXHegEFBWFcYwyZk0zuemWHVskFEK8JqXc5V33fwQivFlKeXbh918BB0op526bZW4bfFxEOIguDxpclRKtN9VV731JuLoHNjiSOiNPm2fioDEQqLNAiaYUoqHC/NKn0uoEEd4iXSSlEqNUGCpy3DzJIlAndh91kq4xoUqH13LqRO0hEUGA8H18w0DzVQwqDI0qq5deL0bgSca89RpmIBFCww98MlV1LNy1iqf9GD/uhEZPRYYaigQG7Ty3TJkKYP+QGqtqC9WjmAygPN3F3/9+JrG+VtANlQe1o3D+TXzLmMRdvygMOAirf/WtAqGr+ufcqaqVsT8F914LI1T7Gqk0HHseDGShpARcE/raYWwt/N//qqzdtkI6gMMaC9ukqUhyVR46A0X8ZTpMspQJQj5Q7RmPjYBxZ6hBy6EtZuTme6BoX3j4XJj2yT33fmi8sBi+sB5EQmL6AIK8AxWlUDoS7h6hxDAflvzPvhjuesRlbM0qdOGTzseYMWkZF55/BQ32ftwmrwKgx/OokEu4L/ENNVFT6KCPh6IbQSsm8Dye/vGPWf/0M7zmH0SjP5kp1ut8+6pjGLP//tt8f3wY5HIeTU39lJSEKS/fOlXgOw5/PfhgDMvaiqxTra3MPO00djvvvI9tnWvWdHPpJQ/z9so3QDrsVN/KtHXPUluTRgYugacR+IKOVVHKp0zH6+9ht5o891kXcF/7fliGQNM1RlktXHf4U1T94IqPbe3vhfcjwo8Spz48+IuU8kdCiI/vU/mEotxQN1BtBSc3w9KcEmqAIOWEKNJgnKmG9PYGKnoaFIekpVKLpgNFZDpKTOBIVeMq0uDFjIpEYpoatjvCVJMrNFTqL1UQiIQF9AUCW9NxNA0hQeoGCNXtWGzm6PNiGAboMyoItQaQ9zFKS4lXVGFEdH6zcag1ZLA2ueXIv0FyDKGIsMKEX5XDrX1q+3eyIRcq58gv3cHeK5/giM6lzB49Gmv3o6C4gq/n4OGZ0PEwREOQS6noOgrYJti2ino9H158HU76nHrfeBRu+jlccTMsXa3ee585cNHZ25YEQTnpOFIJOhwJb+YlgeuB0PGlTz86b+UDZhsNeDJPTDNpb7dxBkajvWNYsh6G1MrPSDS4Be56MsfI3VrpqIkQFVm8dATRWUJHv0nlCBU1f9ht3rgJHngWdN2kLz2G6tK11EQ3sbxhMje8fj4tM07AKvBGiWyg1Q+xJtiZiWarutN7G9I3QvzHaIbBhPN/xUnPefRmVdH3ydxp/ONSl6fqkkyemHj/hWxH3HvvCq69diGO4xMEkgMPHMNPf7oP0ahqOk+2tODn83jRWlYOTCMbhBljr6MimqJl4UL4GIlwwoQybr/9VDrXzcfwHqH95X7mX5tXYmogcHWELpFBgDMwgB2L09LWzaVfeJiz8wtZlRpBqZViRrAQbeyJH9u6Pyz+IxEKIW4DvialdACklA9s+X8p5XXbaW2fSlzRqSIHs+Ao46EOmMF+wbxUBLMsp4Q1tYZ67M8q4OedSkgwCBOYaavaX52hCLS30Kc4wVJktMlTqdPBmqKP+lBtDQwpSAOy0PZfaqTJ+TYRzUEXEhkOISaEKZOVtPlwSgL+0KvSrBTqhB6KDLfMGwxGhC5qW97Mq0kRN9aoyOnEZtVnWR6Ns2iX43jYO459onBNYZLEtBDceQKcuhTaW0DLg+mDYcDE0Vuryrx3qHTGjoQ/XQbJAdA1iG6jultb2wC3376MhQtbqKuLM/uEGVBTq/7n+OQzOfR8Dj0cw0NiplOk4wb9IkWaGGfatxP3XqZUf5CNvo0vAoQADQ3XFZSXwCigvQtKi+BT1sr2Lkgkxv4Ps0tFmsdyhzJAlHAkiz6yG6+9klMs7T+mQN8Lb61Rn78QkHVjNLTPQhM+PYZg7Zs7secMdbwDCNkBlOGLLa6CtHLIPwSxi0AIDjlLozdtYRiA9HHdPC3tBnM/38Lx857hiisOpKYm/h4r2T5YuLCZK6+YT53RQUVyFRJY+MBGrtAk371wJCKQRIqraHAn8XL7Kexlv0Sd6GRJblcyzlS+XrfxY1vrIISmUTnuSuhpIFXyIkJTigUvqyE0iZs1kFLHsG08XxIZNZ7FL77F2tY8tmUwYlwCMaoY9jr+Y1/7B8UHiQibgJeFEMdJKTcM3imEmAF8R0r5/k08/58hHSi1oCtVyhOtMLEdRSjNhQiuQqgIss1XBHNrHTycUhZodkEoM9iK0eqpVJwtYJqtTLrbCtL/kaaKDtOBij5VJVA9LxUMCVxCIiBu9jHSTlJjDbB7UQNLBkbwanIEDhoBcFAUjk3APhsU8W6JdypGBwlyEA0OfKlZ2a09MaDWpLw/JXrQRy0dvJSKsCa6iQmxuSA0di+DZb+Fx1+C5xaqKKC+BooK5yTXU0S31+z33teJbTjur7U1xamn/pNkMk88btPS1MuL8xvwzjuI5L4TSPYnCaSOruuYvoft5MgbBoGj0aFXcGL4JU6KLMWQGY7c/XH+9OKBpGMmQgPpgu4aHFyhc/BXlaFyJATfOAlOOOyTKSX/IGinjfLx7eQ3RDnYXsCbYiIdfhlRI8WEVodz9q3/SK9bFIeSOGzqVK05yjBeR8up0VvhLVLOySBMiTbABH3TFq9QKKADfUlY16yOIwjwXKfw34A0Y1i+/CW+/e3HuPvu47era0tjCzzwjNqmZQvbqWxfTl3v8+qY0nzmaAuo+/tf8XpBMwRNlSNpj53Fb7XvYWo+ATqHycd4w5xJbtYl222d/xZaOZQ9yog9zyVacReZ7gAj4uJmA3oaLXTTVLXMbJb5mTjaOpdiP4MIJK1daTrGH8ReVaN2zNo/AP4jEUopfyqEeAV4SgjxbVRA8h2Ulea123l9nyoIVEpz8Ks49JUcggT6pZpJODekTJ3/0KPm79WbQ5JyX6p0aEaqdChSGX3HhCLaLh/G6co9ZWWBUAfVpYWADgOVWq3VYM+aB5gWghLdQCIZEephTvESSt2d+ZxRTp0JP+9QEanYYu1bqkVNthbMCFRTvQE8n1F1zrcd9cQeH/BbScj1GEKiyRKak39mAg+RDP+C3/cJHkyBHAeHzYRfzoPrboLWjsK2aPCtU2B03Tb7eN4TK1Z0csYZ9/PWWx1EQhpT9TXMc+YjCei65EEa/u9PdIQS5EIR8lJSlO5nVGsDOZEhqRdz5p9OZ1K0k8YT6jBCGrX2tcyuM1nduiuubmCYPmN2a+blV3aipsSkOAHJ7hQ/uzxNwwNPcdLJ9Yzcc89PXU9bkiSlxdBtCcin2F1/TTkGhVPsvtccdP2jEeHcaeozz+QUkQmhDNdtHb6wL6zwVRpdCAhr5VwZuQhDbHGUyk6wvwBCMJBlcx4/8AtWF0KAVO5OlZVRNmzo4803O5gxY/uYISxcCt+5UmU2TCsgneljn/yzZCOleI6Kbcfpm3Dak7S7Yykrt2hqiHOOeT2uFqPf0Tdvw+zYal57az3w8Q9dlFKSzUK4/CaOvPkUnv/F5Wxa0kiqNQl4xOrqMCMR1u57JOn7/w4T59AdBPhBgI8gePYVZrS0kKjbzl/oj4gPWiN8AXgMeBDoAE6UUr6w3Vb1KUVEg/0j0Oqqup/7DhYcrLd5QJur0pzZglCm1VN/Ty9YuLV5Q1HXoGNNf4DqvwPWOyoq7A+GRCvw7hRmACTRaUruSoX5HJ7m0+Xn8fExhcZ+IZdaoWj78YHC1AttKNU6iLCAMaZyaknLoftycshN5yftcGRMGYjrBAhiCGYwUWuhnxgr/RlUDjzP//T2ssQtxS/so9v7YdoEuOtaWPSGGjuzx87bnwQbG/s4++wHWbu2l5CtExpoYUUQJ1M1j51qNhHpa2XiRV+j8afXIorK8U2LrGWzsWIE4c5NzHj4Dqp6GuluD7j3rNcxEgbZ0hgH6N9kj0QNoZ+fScUuIf73G/sRKxrAskpINjfT29AAMsLfF9RQsvh7jDvkEA745S83Kxs/DSiiGMOAWVMk7V2C3uT/Y++84+wqq/X/fXc7/ZzpPZn0TgiEEEIRECmCNLEjYlcs3HvBdn+KekWs13oBRcR2RaVJE0QERHoSkpCE9J5MZjJ95vRd398f7z45M6GIIiDcLD7DZM45e+93l/M+71rrWc+CWBRqWgRHRv7xXnmWBVd/ET77Hdi0E0pl1YLoh59XrOCnyrCqrMpiTohnqMv74Pcqir8wQJ8CiY8D0N6k8sq5AghZWZoCGMS04f0h2JGRl6arcxDAl69WOe+W1iwdb78Tcf8DJLeNkGiSlAbj0CWJag4FaTI6XKK+LUrM9IlgU7SaiMajIAOEpuE4MG3ofuBNL3gMvh+wcmUPPT15Jk+uYd68pr/b+73rrs1ceeVy+vsL1NXF+OhHj+Dc6+6lPDyMDMKxFQrkG1q567Jv0W4oCUmpaWiaEqMfRmNo69ZXLxAKIa4GzgB+C8wGvgRcLIR4UkpZfN6NX+NWCGCzrTy2aZb6Un22AbY58EhpfHgyAmE5vQINB9UeKa7BJFN5eHYAW2wlwt3vh4QUofKK5THAVBeGVvd4illaAb8Da/1A5VOyPqzNzaU9OkIu/iRFP0UgI1gi4FZjOREjyQJtDo5UJBmkOvbY/XWaStfUlaqYX6BAUAvPSQe2OPCTURXWDaSHiYcjTZb7M6gny8/Kp3Bl8TT6pOpozxjveciDLe3w9jf+U27NC7Lf/vZpHMcnnY5QGBzExMZPxNgx0si0hj78dAPxoT4mbV9HOlNPrr6VbCpDomcPx1/3dRZu+APWrAwDW0o4eQ+75KFPakZP1ZAp9rBg09domZrh2vdew5+3ncbNT1xEcShLJhIlKiV52UKyuZlt997LrHPOof3II1++k3+R1kQT7bTTZXbR0hqjtVVQokSGDJ28uBDY5A644buwqxtcV+WFKw7zYTH1oywDNb8G94kx5RNL9pdPCAE/+E/4wBch8A0UQ15D4HNoywo8Ty0hZ89+aZrc7u2FoRFoqofWN92PkclS9NLEAwPfMYjXFxAjAaIk0XQNw1QLoYmpbnLCw/MCLEt9u6QEXXpMnHxATsDvAvseCEbBWgLmYsWeBYaGSnziE3ezdesQoOaKRYva+O53TyUafWE+0P33b+eLX3yQTCZCa2uKYtHlq199CMPQOOusauurWF0du8rgNLcrYY4x+9CkxPYC4vX1AGzbDbt7oKMZpnX+a6QHXsjVWA1cKqUshX+/SwhxKfCEEOItUsrNL93w/nXt94M+n/3ZWgbuXodfcpn0uilc/28LOaQ9wY0dcEkv/HJEhTYDqqSZSt89H9VJos0qMiByNJgxuuwUo4HGaFBlkDboCggrKjQ6imyS0pXnuHYMnfPAMGyl5tAChnyBYe3C8xPERWQ/AmU9uIvlLLDmcHhMeaW7vWpot+KjdLtKU3OyBTvd8TlCVejvMSXexS47TWfMIe9aDLgGxVAbRyLwpI6By5CMkAwZrhXLBSo8fMpLxFuQEn6fg1/vK+OsXsE03adr1xCxmElHR4rNffsIdFUk7wFFx6QmLdFLPv7ICMmps9D7uqkZ6ObQn3yDqdvWUT9tKn0bt5PrcVSppi9wV/QgZnic9plRalpcdpTaKYkYZ825ldktT/O+/E8ZtH3qd/Uxw9yI0DSEEOx+9NFXFRAKBKdxOitZwXrW4eMxmzks4khMXjwTSIgXGBEQBljHPufbF56rCFif/jasWpNHd7uZXrsBUc7RN+rzoQ8dTmNj4jm3fzEWj6kwrpbIE+vowc0mKB86A0wDrWwjTQOtUeLvDDAtnZaJCuRkVFCfGMZ34gw7CaQEA5emGp+mc86pHsD+K+Q+C7IEGFD6LVjHU7K+yc9/L/nmD3cyNDyTSc0FOtK70IXH0qVd/OIXT/HRjz6jguBZ7ZprVpBMWvsZrfG4iZSSa65ZMQ4IQS2W+49/I5Nuug59dBg/XQNSYgz0kZk9i9S0uVzyTXh4hZJODAI4egF84xIVTXgl7YXkCK95lte+I4RYhSqon/ZSDGysCSFOQ+UjdeCnUspvvNTHfD5bb8PHL3uQ/ENbiWRiGJbBtj9u5LSVXWy89S2s1CweLao6wdW2IsxUvKuKvqiOpMkaBCOHjYZhlGkUebJOM+XAwAlDjgN+NaATE0oWLKWr8OtWZ3xZw4EmUHWGdnhwXc+iBbFxH9CkSb/M86MhyRRT8LBQZRgVj1CTUGeocWx1FCV+ZgS22wowDMAUPm9ovgsnMNnjLMLFJmqVmKiVWVeeAUiKWOwMGkNdSkFRgh0uGyPh8XY9V8X+32GFQOVWc4Fi3E4PO7/8aBhuengFs792KUGpRBmoC2CvcQKNExbQMbmJwR3D+IFESqhJesRiBkFBZ3jCVDpnzKRmQgflQgF3+lwaigPYRSiPRhE6SD8AJJoT0Kh1UVujkd8jIZ7DbcjQs6eFqTVbWThhJY+tP5pyy0TOqbkRRlX+JZJ+Zaj8L8ZMTBZzFIs56pUeyvPaksPgkd9olMsZ/vCHfdx7b4xkMsOb3zybY46Z8JIdt75GTfQrdvlMQ4F7pD5B/8feSdNPb8QYKeC7OqNaLWcs8Yj6JWQJIkUbuXges0pJCl09+IEgHpNEz7sYZixUO5c2ZD8JXldYGgWIFqT9F8798FIeuX8TpWIZAWwcqGdo0jwOa1tLbW2M227b+IKBsFLrONbicZPu7hy+H6Dr1XB+Rod3TGvihv+6mrlXfoV41048oLToWM7/xmVce5PgnoehpQESMTUPPrICrrkR/v09L/pyvyj7h/VupJQPCCFe8qpUIYQOXAWcDHQBy4UQd0gpXzGxwF+sGyH7yHYSTSlE2NQu2ZyiI7GR9U9/mmCSxfHGiewQb2C6ZfFUuZpHs1DF9Gg2RamTkQaaUB5LIC0OS69j+eghCKlhMB7oChKm6aqAf0sozG0w3jsba2lNFeQHQE1/D/Y9q6jbsY1g7mTcMw5DNqRwsOkv1/PwiNifU6wAU0xTq7waTeUGJ1twdgqKAdyRU/matAaTE110xvcyaDdC2JvdQCOrRdFxCdCJ4mAQUCCNROwPE1fOywCmvUhHYl1ZNdTNB+FKXCgm7Mdr4Dc9BQ674hKKCPz6JnXsUon5e+5iqWygfWIrgwN9eKNl5rX2URfx0AeG6Jo0i55Dj6JDgh9NkLUSHPee9xL51F/ZYwc8feYFFJMZWp98mAlrl6L7Ppk2gdAkvhGhZs9OyrMy2J1pjLLPxKbdLJNHop0Q8Ed5NtOuXUHUMJh6yilIqbz/iPjXCBe91iwaNXjLW+bylrfMfdmO+aWPwaf/O01uX4ZIJo+mx6g7aRrFRZdi7N4Mm+bztjPOoin/G4JHb0b3Azj1LBqP+yjCiJDZ+TQUs9A5B1J11R2XbgZvO4ioCoVKCbKbm/90Mg//+Smi8RS2E1GBH3uQ/p1PM1xXS1z04fvPlkR5dps9u5HNmwfHgWEu5zB1at04EKzYRbXQeew8fj3vBvb09XNUTYQPdGbYvha+fJXK/3f3KyCcMwUa6uD3f4Z/u+CVfeZflPCblHLPP2sgz2NHAlullNsBhBC/A84GXjEg3LVzBE0T+0EQ4MxFT3DRmXdh1SfRvTgX6g+wU97BN/khCc3CQk1y8yLKq9vgF0kZo2S9FBqqzc/EyDATE1t4fHQOUV2j7KsbVGl9JFGh0EpIsTks0K/Iu8H43N5gCIL1Ozdz5pc+jF7OIZJZoo9tJXLjY2SvuQDZkWHbyNH7Wyq5qJDsgogi4mx11HFjwGlJuG5EnYcvQ6JMAAsj+/CRRPQSx9Zs5qnsDCRQDHwMTSIDDUNPAAaarwY/liNZadf5YlRXfFmVQ2sxQJRL1Nx5Izvv/wNfnz4dOfsovHIJv765SiyKxTAyFnXpPoaGaqmfN4foJEnr8EP4vdB99rvZ9pYPcF6twXZX5XPfWwPvnDyH239wLVf0BLiectnXnnwe7Sse4dRvX0q2WxJ4AjeeQPg+yYF92J1p/IRO95smEMnaBBIKXS4PRup5fWyEGx9dxQ2LOxiRgokG/Ec9HPfSROwO2stodTXw08sFK/a9gUdSd2BEc1TaC7YcsYQzODMMJV8ER1/0zB1Mmf/sO7bvUb8rNZRCgIxwyx9aQYJhmJhmgOv6CCOCXx5mJFdP2Svx7ncfMm5XTqFAaXCQRFPTM2TnPv7xRVx00V0MDZVIpSzyeQfb9rj44mf3gYSAM1JwRkrABCUFd/9ueNcVMOoq2cGYBsUSrN0Kh82G8vOFtV4m+9dUQB1v7ahaxop1AYsP/JAQ4sPAhwEmTvzHqNvPZ4UAlhZVjdwh7Sn+ICVSSoQQJKNFPnz2vQxkOjCMOIbQKQSSiWI588VfWc/Jqr+dBrVGJdencXTNRubE8vQ6KdJGmWZrlEeLqgjbGcsID39XHlFbwixmF16aAAAgAElEQVQLOi01roEQMCteZyW3VwHHY37+3xiOTa6+jYJopjkzgNXfj331Cv74yR8wXG7HRnkiFV3R5WXlUVUeEE2Drw+orgwpVy1Aj4jCRhe6Si10pgWTI1Af38qi5ABPF1rY7boIdyrr8i3YUqccVHsbVn5iQh0nrVV1Vv8R2+yo69Ckg+965O/4PasnzCb376/DTSTpWPkofiDxZFWzVQKGEKTfOJPWCz7IVhciEh703kNMKOD/TK3qMDLWHAlXtcynLZEjt3olumVRGh1lz2HHsP3oU7CW3cvgPp3MVJv8iIHp2bRoPewIJvOEv4RoQiPRvYd8YysPfuIrPJEbJb1zM5P+dAcdCxczYsW4xMvwo1bV5eSgvbpNCDiitZk5nM82tpInTyutTGAiOv9g2UxQAKIQ2EihlpICSeDZCF2FVhJxk2w2wJeSQIszOlpi3qF1vP/9hyElPLLc495vXIW26gZqEgE1dRYLP/xhDjn//P3M0oUL27jmmjdxzTUr2LRpgJkz6/nIR47gyCP/dgJ39Ub47s1w82PgjoCVBLcARRPiBti2qrE8ZAZc/Rslpfj6oxTT9+W2VwMQviALO2D8BJTW6D9z33dk4d/3KWUYACNdhzG3jfyaLiL1CTon7UWmLHwsMpamVDEQrPXmsMaJkwsUMB0SgT5XeV5pI8agG+ea4UPwpc785F4W1Q6SoYmo0MnJqhdYORkLpfcZABscRcSZYFaJNT7Ku6qAmQ9onkfL+pUUGpQn5EudnNOMHa/DeKiP4Q+146LG50gFTKYIPSsdkrryYHUJT5SgsBtKgJMGdKiLQmOsnRmWgaSMJEJHdJj66F525FNcv+koaopQjkI5rliwulBC2AWp8gqzI4qk0/piORZSAfTWnj5GZx2O7vsU6hpBCHpnL0AKgXAdXNNSYOj76LpG/2FL6HYrIgAwwVDlLEfGnwmCoJjCxQAaMymMqdMY2roVggDDKbP96FM4ZN0yrvv5dNre1sCRc3dR31jiVvdN/I99MRKTSGEU17IoJTO09+zEy2UZbu0kX8rzpkvey4zsEH2Lj+e6S77AEbMyL/KivDZtzZpebr55Pb29eY47rpOzz55JKhX52xu+ghYnziE8h4f395h0QPgM5yJcedvHuHvp6YDg9MX3cvLxq7nrQclIViKlRmA04KfmIYwk8YlpaLdYs0Xj+7+C4Xt+xiG9v8KLNdJXNmkJbJzvfY94YyPTTj11/+EOO6yVH//4b5dsFIrw5DpFgvF9VQLTI8EvK/F56YRRXBcKAiwP9g2CuQO271ELhqt+C9f+F3S+zFUWrwYg3AuMzWh3hK+9LLbRhk/3Km+j4r3YgUB86mQm37CM4fs3Qb+PHjWpMQ00IcgFsNabwy+c9zAc1GKiQGm1rcAsocGQH2HP4BJqrT40XB7PTmR3qZXLa+u4RcCwHF++UBG99oAkqkHvLlexOVMaDIZo6YafreQXpa7jRWNonodvKuZXPgDDcSknU5R6Qd4DYjOQgtwJEDlKeU2BUDlCgJIPtgteAmQURBivHXDg8bzBV7xzWGP8ibzMM+JLHuyey/3rj8ar0RFJkHnQcjC9BYY05dXGhCrtGPRgogWLQ9AJPI8Nt93Ghltuwbdtpp9+OvPe+U6sxHMvFadbSq+114MhYZAZ7ifb1AGA5TmUaxtY8+b3cegtPwMUGFtIut/0DkanzaFzzMJcCAXQt2ThwppnHisiwkWKhHR7O7pl0b9uHVoiiSHg1n/7OrvnLCQQAsdOUpKTiJUN8qjrKYf72NU0Ad3z6E/WENc8Mj17yKWa2LjkDRz65F9oeOJBCl/JIq//8UuqevJqtLvu2syXv/xXNE0QieisWNHDbbdt5Oc/P/tfHgz/KZa7As/exUe//z9s655EY6afQApueuhcSvb7CczlOLkh0CPQcDhoBtFYnI62KOu2wukfhcDzeVfP9WRFPZpnkkxA70iExlSSp37+83FAmCvAsjVKFGDRPBXuPdAeXQmf+65STirZsGmHmis9E7RICICAGQUrA3YRokVoq1eKUhXrH4KvXws//vJLfhXH2asBCJcD04UQk1EA+A7gXS/Xwe/IqXCoEYIghB0HYhbah4/l6cuPJkXAsr511NBFVjbgBILb3LOJUqYk0mRCoWwLpS060YKVJXB9i3rZRkJ3CITOkGPS7YGhKY+pLKsdHyp5wqSm2i1V/vZQrNB0qDgTCQHMlmH4TwjWnfpWDr3tl+QaWkDTMAKfWHaYx0+9FHkV4IBMAiXwboE6C4YWKoDaiOog4BVAOODFwXQBJDKkwApb8FC2iY/VvZvrcyNc2W+yYVcC4QmMPggMIAn6KrAnQe185RWXAjX+JXGltVoRaH7wS19iyx//SCSdRmgay6++ml0PPcRZ1103rsnqWDMEfLMZzt8Lnm4iEQRC7A8TB8CeY08luXARPLmMhsBh4gkn8KmjD+Vj+wQHQk3FQ342m2YpQNvrQr0BsdoayukaSnWNlGbMpbuhHemUcZJp8o2t+LpBAJwQhxkR+FM0hkQgNJ9ooyTQGzCmeyx2V7Gnr4VgpYbT0EzDhlWM7NhB7ZQpf88j+5o22/b41rceI5OJEIupZyGTibJjxzC33baRCy449G/s4VVufh/Yf+SJjSezfd8cmjI9+NJAEx6aptE/2gQtp2HUe3geoJtEIxpeYLJ2iwpHFsuKaPpI7aVMKT9EvbMJN0x3FLwohd5q99xHVsDFX4M9PWo7w4D3nQv//Wn25zpHc/CZ70DEUiSYrU9XJRKNaNjHWaqOMm4erDow4tAaVwzSsVZfo7zKQvGfpyP8QuxfHgillJ4Q4hPAn1Aci59JKde9XMcf8UPQkUoFpjI3lqVSd3lPj8YHajXyse9TW7iUDm0HJZFiSNYyFNQjtSReuJGG6jpfDKqgus/TCNwow2F488v9CuyOTigw63VVAbsnFVDYQdXrC8b8jITMS0cqUNSoSqKteNtHiA/1M+3RPxHoOobvs/60t/F09O3KbawJ84o6pKPQfxvIsBl5v6c6rGckxAag3CSRlo9meAgEiaBIxImythzDk4LrhmoZyANl0EKXVnhAHpzjYU8O4n61JvKkBPywpcoYG9q6la333kuqtXW/0ooZj9O/YQN7Hn2USSec8Jz3an4Urm2FC7w0hmVQmx+mXN+opOICSTTw0ecfzughR3DDhGrHkEUxWFGCpjHfhuEALjwgKrnXhQcK6v69vwauGlYe6B7XZHTqPCJDfaw5ZAlSaOiBT5SAmlhU3X+hymg+Uw8PD9djFm0aMwNoQrWP7PNbmFyT4oglayndAqYQNFk6fV3D3LEaHlimxLrfeiosWfB/l1W6c+cItu2RTo+PDiQSFg8/vPu1D4RBDwidDZvi7OlOscVpJkAQsRxqUsNkC0riIp0o47gmJcei7AgMHYazKmSpFsgag9Z0BqyZLMj9L53uaiwTKPTT9rpGyH+d0fJx/NvXj2bbHg1dg2RceYXX3gQ1KfjyJ9SQHlsFjqOez7296hiGrkKkhg+upcKhVlq1K4sdAu87GTbcANnC+NOTIdv75VYc/JcHQgAp5d2omsWX3Y6LwVVDKp8lGR+ulBL2OPDJHhB04MjfUcsWIhTZHMwAdCJhrzqJmryiQnltEpAB9IbkDYtQh9RX3mO7oT6b1qE+UJJrZVmVUwsOGAtU3xsK3zBRbE/PivDQxZez5p0fI9rfQ65lAn5dI/JqwKqqw0QEFADfhcwARFsV+Jck5HWQLWCYDjX6MFogMKWHLjz8lEOr5THspygG4OlUFQAq480QdrataqI6qHZSYyf1wS1bEEKMkxsToWfX9/TTzwuEoMKrr0/pLJ1/GJFtm4kP9jMS5kf7Wjsp2Rrfaq6CIMDnGuBD3eoaVxY60y0VFl1vw/Uj8EQRtrqqLZOB+rKeXe7D27KRu8sGh86YSdCY4jFHp6QbBMIiZqhi+QqrVwhYWoJYLEaDKCJ0ge8J0CRCSrqKE5gTewx/Rozp23P40uL/3TCPnb1qErrn/kG+/xMbBp8gYq/n4x9fxHe+c+ozrsFr2dLpCEEgCQKpuqeHZts+jY0vowvxSpnegVPIct+jU8mWaohaJZAatmexa98kpFTfl3wpgaZVWAbgB2Lc90zXBCWrhXR5G5vjpzF55DGiwQg16e0sfJcLpVt5fGk35WIHujYRM1S9MQzQPfj1nXDJe5X4vTOm/rdUVs+5aSgPslBU2wQ6+BrMuwj+4zg4Cfh8Gp5YDa2N0NygtukfhpOOgujLHOF+VQDhK2kZo3qRDgSeBLAjLOKTEjRNMCBn7Ce6gCKWaOHfjoQOI2RIAv3hexVyS0RTep5rbVUTNxhUSS8SNQ455udAOzCn2KRDd7gCNICRxlZEYysSBbJWG7i7FVtzYVQ1F+6y1ZhkOgzLShW+FEBM98FwGQ7qaBCDiCDA1WLEDJ/pmZXU6McT1SBtQbYGggHQzHABEQfdhc56QFcU6npd1SIGvs+qn/6Utb/9LYW+PkpDQ1iJBFayKiclgVRb29+8X0LAd1vgaivKr6z5lLJFJvR1kbbL0NKGG29QwuBjbKIJt0yAB/JKtm6WpUoXlhXh0l4VDdgcho6KASyIQcN9d9J/5RVEfJ9jAd3Q2X3xF2Hx6aomFOUB6qh8ZH1Y6tVslvGkSUtM0C8FGIZaJJUlDdldGHGPtkQZfaCP0ROuYNfuCG1NsHJlN309OUCD2kXYezbx3e8+gecF/OAHL6Mu3Stsra0pFi9u54knumhqSqJpgnLZIwgC3vrWl68+8BUzrZ6Vf+5ky8AcUrEsBSeFaThoUuL6BiAwdRfPN/A8ncpqVIZEsoqlElAo1TLETCxvFC/ZweIzspx20RwyE5QUmhs0ookCtcl+RgrNeL5ij1esd0AB4aJDwrCnC6mkkk+znbCiQ6rXceG4dqj7K3zjF/CJPuVVahps3gk7upS03pyp8JlXoJ/RQSD8G/ZYESZHVL5sj1cFIQ11E8tB2MQ25OPrGiRl1SsDNRnGwxzeoA87HRgIqpqjTqBuRBJVF2hL2OOPB7tkSPkvyme2RXo2kyiWa6VMIaopdmllWGUJkWPBWgFTbAgiMFwGfRT0YyCaVOHYocq5AqmoT8wYpM+ppyjjpCIeUxNDnNKwGssMsID3ZeA7QxCrh6IPXg6kqcbQImF2Y3WMdgCykON/TzmHvU88AaEn6JXL7F22jLZFi7CSSUqDg0RSKSafdNILumcJDT7dAHv+cCcnff+rWL4SZZWGwb4LPs69517Ip+uVt73/+mpwVhooF+Evv0EuvZNvHPsFYnUd5NNNSHRi4f3esbuH2T/8KnYqg2NFMATglGn8/uXEf7QYJ1OPi7pPPiofM+BJMuYgT0VuRlpvYMhJY2kqMu0FOpgGnel9RDSD9kgbR3zzYr738BL8bD/ZcpmB7hHACllKQiVayvv40Y+e/D8FhACXX/56Pv/5B1i+fO9+wswXv3g8Cxa0vNJDe1ls6e1TsLMGU1u20z3SRt9IE7YbQSCVlKGvYZkOZSeCEjcUGIaq+ZWBAq1AqnZgrpGiri7Fj279LJHyB0Cr33+cI+dsQddgNG+SLYTzUTgpRSLKkwNoa4JPng//82uVG7RdtX9QIU4ZqN+PrFRgVyqr/QxnobMVZk6Grn1Khed/vjAebF8uOwiEB1h2714e+dE1PPngw/THM2w48930n/xmMoZGUqsWkgPjCBaBBFOregJQbWib0VTN3XZXtScaDsaDWaUjRbdf3UagwqWVCKOPmqzLfrWs4rmssm2lTjVATeCpkK0ZFXBiapg3TV1F79eL3PeLQ9m0oZnAtJhwBux7HeT96hgr4Fn0DeIWpI0CTZF+LulYhS4kBfK0oXIzF9Yob+/KIdjaBkEJZkmYlIZ14QqxslIc9OFtv7+G7iefRI9G0Y3q4xj4PoObNpHq6KBx9mxed9llRDMvvJQg3z9A7AdXUE5lcCNhnMVzafnVlexZeBz5CVPGASGgkhtXfxK2rWKovpN9ZopM9xbyhQL5+qnkUSHaiSsew/M8CmaECOpc9EgUf3iYyase4ekTzh7XrkoiqTWzjAYB63JzubB5Jdf3LqDbSaCjYQnJqY3LmJ/WmKOdyilXnMqOBx5g353XMeychCcHaJEOOVIMy9qwSZ+iUbnuC1cJea1YTU2Uq646ne7uHKOjZSZPrn3BItKvBZu9aBLlGzW6RmfjY2AHETQRIAUITRIEmsrBaz5SapiGIJNSIcxsHiImHDoD8iXwPPjcByESjapJovIFBZrrRnjdoU9xzR3Tn9EgO5tT5JiKXXAWHDVf9RX9wa8gV4R8QRH/IjGVQ3Q9GBhSc2jEgsBX3mNzPTTUwopXTCLlIBCOs+LAALe+972s7xshl6kjkh9l/o+/htO1my3vvwQZsjIrQBiEgGZLlY9zAuXhVXJ1FV1RG+jxVUeJ0hiQPBBoJAoQI1QBsJK/c8fEQyvC0M9lFTANxozBRQG0CcxPZDm59WZGpEvvxAiTL7uLTk/y0MCp5J0pSF+NOThgnyWpkXNr0bQSzZF+AuFQwiZChENQahVCwNsz8La02l4PVwtZHy7ep3JuFTsiBvX33ka/lONA0IhG8W2bhjlzeNtNNxGrGyMt9TxWEdb+6TDI+5Yxy/cpmRH20yoMkyAI6Fj1KM3HPwsTc9My2LEG6tuIawJbSrpSLYyaCQgkUhMqIhDGmCr3xxQqZCok5H1JMXy9irMBo77FRKPEilwnx9Xs4KONj/PnBxz2PJYgXhpg4tkJXn/sccxlHqX8KPdc9lnmpyexcvQUfFGDxyBJ8uSNFlx7n6pQhnF5sv9r1taWoq3t5esu/69iR33gnRx/9/Xckn0/PjoGDq6MoGuSVCxP0Y4Si9gYhkciplNy4jjhhNFQF0aWbNUE+YPnwTvOAJgC+mTwd4MIqZzSZShbS1NdQM9AGAkLiSy9g/D5H8A3L63m+KdPgn97N9z5F9iwHWrSVdJLsQymWfUUC0UFzL4Pf1mmyDVCQOMxMHMSnH+mYqfGXyZBiYNAOMY23n47/YPD5BpaiQjAspCRKIf/6QbWn/NeknV1jIQqLhqqN6AJNGoqZFnpNFEBr0q5gy5hq61ybpXJ8fnW8TZVbzAaAq9AgaxgfHPcZ7NKWQXh5+MhOceTCpg+0bqM3dJlXTlJKQANE1Nz6Ehu5sH+yaRDckqFnANVL3XITZHWTV6fykNgIJ0plMuHs85KsyhWBT4hxsuopXX4eZuqpdzjwkRDXZs/Ch2h6/v7mlVMBgG1U6a8YBAE+N0ofHtQCQA0GxoCgR2uCKyw9i8q4eyMtn+cFRv14YHBEgMzz2S+P8rsgS1EnRJbmudgeTZ64OFpagm8e8ESfF0n6TkYloUvQXccpK7TPf8odbV8n8hQP048SWBZlE2LfjdBTHcJfMlvPzXIxseK1MaS+EGKPzzoU/eeMsUP5/nho3fSWHSgsZfjar/N0tGP4EUacH2fuuJqegc37l8UnXrq5Bd8fQ7aa8PS7e1876Z3UX/ZUn785HEE6MRjHp60KLspjpzby3VffJxf3/sG7no4RX2NytmZBgyOKA/wjOMVIaXKzhTI1H+zZsV36OmXTG7rJtKmMcAhZMsRajMq11cIvUgJXP07RXK55MIqGJomfPAtqtuHHU5UpXI1P6lp6jP9heprgVSftUwVvt20E669GdZthasue3kY0geBcIz1rl6NbUXHhx11A1PXmTewmyMn6QzZ69nmNbDWm4YhBYfFqn0JDRRYmSgqfkX8OSsrHSeqAPV8oU2otmsSsgpCf2ub/UNGeZsxFDBpVBmrzQYM6fvodaLYIVVZA3xpMew0IAgoSH1/eUNGKMZsJd8FgiMiMea7Z/Lt3vAcw7EeGYPvNivSz7OZEKojR6sBn+mFDTZMPfZMOrfvIp4bIhaJqBxhqYSZSrHwQx96gWesQP7aEUVKiWpQPHwxhmmQckpo0RgJDaKeQ11M5+STj6OffvroJUqMgj2Ri3tMCrULCea1ouk6U0a7GI2kaCgMkI2mx30bS83trH7vpRz3y+/g+L5a5eoG6z/0GZymFur2bCefqSNf10ikmMcq5HFNg+FEgjZ6WPmIzdbHbTKtGg0iSZw4RS/gv362huKCaWj1b6L5PzpYcN/vqNu7g3MaLybTMI3tj65ndaGFXhYjBBx99ATuvvuCF3yNDtprxzITOrjo8g7c/4X/vTP0uAyYOkHn259rY/qs8/jUBOgdhpXrFAAVSnDeyXDeKc8sTxjNwcVfm8CGbd/Dl0X2uCbM0om06mQNiBjgjaoSKwLlwRka/O4uOPUY1VgblIeXiKm84ZpN4BcUb8I0QjDWVYh0rFUAUYYhU8dR+cvlT8PTW5QE20ttB4FwjNVNm4b1yOOIsSVKQQCeh9Oos8T9ONONbVimz6eK3+Ah73WsLhu4UoVHK+agQoHTLUV+KUtVSF0IVC+/5zNxwL99ocCoTlcAuyX426BY6ewwyVTC3COhIEAyJPXUijhFCkhp7PeMNHy0sFN9JGQ59oUhUl0ohqwbpg8GPXh3tyrx6DQVUAoJjxWUAMFbD0jl7XHh+lHFhJ1hwQpbtZFq0qH47g+TXb+KYOVjaMMDaDIgkk7zxiuv/Lv68+UD9dMcPtF+ppa9n7qc1m9/AZEfZbIFmqZx1Oc+w4oJm9nCZiQSpOC6vtPRZTstyST0ZJFFh2WNsxnVIujSR0iJ1PX9hfk6sPP0t5NbeDTGk4/hA12HH0uuuR3Ldajt28X8hk106VMoRqOM+mmEJ4h6eQbcJE8uK2DjUydixIgjgQ2uRi4AtvRjLumgnMrw+Jsv4g0//yqDBZ9mYyOLZut8/kdfY1BrYs6cpv9TebH/a1Yowj2PwGNPQWsDnHOSamJbsV/dDj/4NWzdpepQLQOmTIRMEi75Jtz6Q9UQ+OLz4es/gTWbobNNMTx396hpbXJHlZjy3V8oD6ylQbDJSVAuQ/kvoMdAc8DOsZ+soOnKe2uoVQv9R1fBnMkee5ct4/s313Lf5slkaqPEYyExBsVSnVKnQqrDWQWkmqa8S1CeYCUPKVHjE6jmzPNmSJ4pefHPtYPfpDE2+7zzWHPjjcRGhyila4n4LsbQAN3HnUhnw04axSBF0UARyQJ9Oev9aXQFE3GphgErhe6jUpVBVN4zURN/l6uA8rmsAnARlLc2KewM3+/B2vLzM0Y1oE4L85IB7PSUVmiHqUCsz4OL6mCROJyl4i4MzcULTHThE9WLyCACaERQnSdcWWXJ2ii1m8mhlNlWVwFcr69KPColGr8dHQ+EW2x4f7daKMQ1WGPDNgcOj4acj1SaPd/7Jc6KZRw3uIO3Tqpn8utfj2FZzzi/57OUpspSSoEi6wDkjjmJrpmHceT6Jzg+49N+5JF0N4+wmT+TJIVAMOjFGXBj1Ol9INpg8nxE91ZKmkk2mibqlSmZMYKwRXEl5A2wuXEC3hvfPu76a7hcNvN/mFe/g5yfYL02m37ZwBN7D2H96KGUJrVi1SUoeoIG2YgQgmxQFWswkiZC17EntKH3DrJv4kxm/vUPlCJlTvzEf9J5xIIX2f/9oP2rW64AH7wMtu1RHpLrwk1/gm99Co5fpFRerrxeeVeagGhCAceOLiWBVijCfY/D5Hb4QBhabG9WIPT2S6E+oxRcmurgin+H2VPgnkehsU7NL/0eyCEIPMCG2lkw1AeyFwJHlVch1fZlB2RhiBve/F66ejxuKX2bFE8ymm1H1yZRVyOwbZjYplimmRSsWq/CskKoc614hIJqDjGZ9Ikf+hRPTH6Kv+4u4/e2cqx2LOce3vSShEoPAuEYS7e3c85PfsKfv/ltNjy1lqwZZc/Z72b2e1o5P3413UHlcgmWmGu4xz2Z3bQThDA4lukpUX0HpVBkrI2OmigtUQUYgyqxZSw4CkKdUKkeumIA6+znb8JLeDylharEuD1C4JWqUe2CiGpWuyM3iZW5E+lIP05ELyDRWD96GJuzc6kRavyBVLlFDxV2DFB9CScYVe83LyEVEoikVJP5srI6nhU+rFcOqfNt1GG3qzziolT1gzNMReWu0QyKRxzNcOxopv+DDHhdwMfq4Ip+Nea4UGDu1dTx3reczsywdccmlmJg7l9hakg0NNzwP9OKUeo8hHxBnWRJT47L50ZQPQ432zBsV79AHuoanRB5kAXxjez1JqAHPjO9jRyi2byx7W7eOnQ7WlCHvuB8PP0GBrMuLZkIdiBxh0uYDQmYq2olvVSacjzNzrPOozx/Bu964xQO7XjuTuwH7bVjt/4Ztu5W4cWKFYrw1R/DMYfB0jXKgxJadeGsaeo7uK9fhUmvvUkRVvJFFZbsHVDfaRnASE6VLAxn4ayPQzoO2/cqD7E11P10RsIOT1JV7Ogjah5QfVOhUIbVm1TYc/2ttzG/t5d85gRMT8fUTAqD/ThGO4G0kBJGsmpuGhpVtYe2o/Zj6Own8iBUWHRCM2SOeZzmY55icDiGThKtpo8HvFvZfdvb+Pdza//p1/wgEB5gjXPm8K5f/hyvXCYwTHRdRx8+j5xv02Mb+/N19dooX4ldwbn53+Ci72eKulQ9BjPMyW0OO0VUSDSVfQQ8s7FupQS24vlt8xTgVLRDjTHvVfKOFfXNCmjFNJhgqX8Pe6qRry1heQnO2q1qHE3mIEZnYmhFykEUT5osjirNz8/2qQnf0BT4bbBVdwibaq5RhHnDYgB2+JomlNe4tFjtpbe8rMgrK8qKNVsZd07CKkfVTgpNgfjx1RKmf8jOTanjXzsC+2yPM7uX8Q57NzXZdn7dcRQ9PiTowQx2QmCix2uZu2k3F21Zz+r6OYzMeiMYFnkfymHz4IqpekzJvPgob26/j4u3vBFDxHCkhjbmvrzO/Cu2H6VmoJtyTQO+YVD2IG6V6azvwzMnYTUkmHjZaaR+9AD9/QVsX5S28QYAACAASURBVBJpr6X1029gsxbB9iUCiYZkpHMSRx4aZ3HTtBd3cQ7aq8b+skwpCY21RBz6BlVYUw8ZlqkE+zuuCBSIbNuj/t7bp/J1yThYlgpRlspKBs1xYXAYntqkAMkKawzXb1XHMCeqNAM+RGuhOKDCryIK0odAA+Gr/czqdLlj22La2laS0UYI0PADge1IfL9M2VORnZ0l6BtS4c+alPII0ykYLAKB6lBRW6fa1MVryrQsWctobxKBhmVBXMSx0nkeL67l3cOvo+GfjIUHgfA5bFyDyshJJIvX0aAn6A9r/RKM4tJEWrPwg2pebiyo9fkqdDiWeVkpTh/7WqU7BTyzRrAkYZc3fttUWBBrS1Wj+Nt2VZpQkCr82mgoD6nLVWFIKyzGHwzGH8dCB5kirakidE3AtwZVreNMSwH5sK/e6/er/RAL/ngxcD8cS6uumKDLy5DS1T5i4Tj6feXdClE99wCVA9VCj3W6qXKHphgvgfZCTQg4NQWnaqNw5cdg7xZKnsdO36ClbiK/OOFTmM2dzO8cZOGGDRxz48+JOILZmsHpboqeh2/iivN/QrdV84wQtESi4zIkiwwyArKAXiiTyuYJdJNybSPSMMmRhohOvrYWzXOxXBvXMNFNgwlWhmGhnonTjmrjW2e+i23bhrEsnZ/GMnx/SKBLCJAEKoPJ/IjFTxqmo/MKVBkftFfEatPjZctAhT4DqYgoxxymwqKgevh17VO5NsdV5QZmCGy2o+r56s2qx+i4iqyyOgRBUOBkGOq9viGUika46hb1UNoFUgOtIjEVrrxlAMM5DbB4onAsb6u7nonmdlbkFmLrUaTQ94c6CY/d3qQ83Z3dMGJB2VJA3HYKxE6C6RoEv87jeQKBhtAUwPs+1ERNku39bNvNQSB8RSx2PsK5n5nsplkzGPU9PCJcW76Mw6OCPh82OVCWlZW8jyV8XGkh0fZP/BqKsJKVVTA8MNxdeW7GEmIqvyseZDQkvYgAZkXg2wOwz1eruBG/CjYDvgK3mKZqCA8k2LhSgU7Wh5wPqbDkIR/A48XweQ8LEd0w3Dngw25HAZ5HKMcWAm0ugFEH/ncYrhlS4JcS0OWEX+RyuL+oSrjL8ORmRlUB/3t6que9MApfbqr2CPy77A8/hj0bkbWtPG0LnCCgc+863v/gD/nNkRewJ9PBeY/9HsN2yKfiaNEkU2hh4sB2rlx9HWctunT/rir3RyIJ0EhoAdEynPlfH6fXT7P8LR/FNaOY2VGGJ0zhdu1szjFvR48ZOEGUvKxnVGvlCSay1p1KVMBpKaVvqmsaM2YoNzjWp8hHZQk+grQQNBrgBIl/tHXrQXsVWq6gcml7e1VusLlBfZ/7huDIQ9TfoASvv3wVxEKFlz29KuQ4vVMxQPuGFChmcyqPZ+hh4wA7nB/G5OU07ZnAWylqLuxS3p/wQDeVZmhgV0lj+ZKGTTu7C3sR9TAruoal2SPVDjQDQ68Kfbse9AyosRpJSFwECxNQ3A32KFhrA3blV5BZt532c3OU9AhC6ghN7UMaLtndTTRO/edf94NA+EJMy0DmV2j2vdS5K6jTO3AiZ7FvdxsNAtpMqNF9VthFNGET1W2G3HoMyvhY+NLYX9KQrdTThLueYam8WSDH5wkr4dMDzUMBF6jGuZttFTq1hMrVlSRsLMN/NMANWZUvDFBh0rH7q3iY5UCFcy2U51lJWBepyr5JoTrTZzT4VhNcPqBWnDvDsGtFRDsn1efSOuyw1Qp0oBuECdSi6jlcYAuwAyhBajbUHAYbPWhBeZESWFaC87tU7eHEv483A8vvhnQDNoJyABG7wKiVZOHelfyWC+l7egKNawaJ6pJEqUiscQJ63IKaOmavvYeBwy/dT3ran4MhQAjJvMReCjcvp239CoK6qbzh2i9RbpqM4/v89cJL6YnVcW3NRXyw7lr2iha+YX+GIVlP2Uija8McHUvw5ca4kmUbY0876jlKHuD4Dfiqw0Xn33sNDtqrzvb0wDsuVb38skXo7lMhxBmdcPhcuPyT6nPbdiuv7wv/Cffthb2DkPoL9O+qeoS9g2DpantDV4oyoDzHIKiyRSsqTweaIAy5+iCWgLEGonUwurH6edOCSETguhFKQZr8vn0sKy2gzt2BZlmUM3PwwpxkEBJs3BBwyzaU74HV+0AUwfDynLj2k9QX12FYPpFfT6LlnZMYtKfj+1Gi6RJ20aChfz6TO/751/4gED6POVJ5RoM+zLASzI2ei4idC6iHaaql8m4dJrREd1IjDVxpElVOPZom0KWLL3UqBOCxIdF6MSbvhgKj/JiHsgKGlZCoN2b7uKYApzdszAshGEo17ommEvjudVXoEp5ZmjGWBKIJ5QHqlTFJBXDTLZXjM4SakAd8mGoq9ucsC/rDsGkuUHnFWZZilErAklAYhmQERmOAAdpSCG4PtVktyC+DVY+A/z6YEKpI7HBVh/jNDrxxN5yehK80VdmgL9SUMyvZnOlk85SpNBT6cTQTTzOQvkZmcASicVVjEt5UYZgkw1WsI8d74xoBk6NDFO5eTaQmwqT4EMNeBLNnO/uMOLHRYazebu7XpvFo6goGZ8wmRwqbKJP1IaJakXuKGnfmopybHn8yUy3V1mssELrhwf+RMPFBe/XZ//s+rN6sgKwmpUKWxSK0NcO1X1Fe26XfgoeehOIE2HUyaJvBf0D1+fMGVG6wpUExQPf1K8DThAo/zp2uiDSbd1bLFioANdZE5X8SpAOx0yE6GYLH1KKYAIShSihsG5IJnUz7fI571xf42Y9mI0aT5PwaLF1gaGNYoeEEJCVIF3gMChmob4P5639KU2ENo3orhhCYNziYA5tIvjNKsWkC+9Z10NS1hO9+JHOQNfpy2v15+OQ+NcEnhcqTnZiArzUr9uNFPcobGvLVpD1ZwKTYDjbkDyFulBSASXDRsISHJ018FFjoqK4UNYby2OxATdouVUm0A8OiUCXLHJ9Qvx8uPnM1p4dhj6UlxW7c60FfuAqr5C91VO6uLNVvPVwVVmoKfVkV6x721PmWwqT8o0WlJ/qJfWr7ZhPqDHi6rJL4O8KyCg9FpAlq1YqvZhBG6iC4D4X4EYVBATC8GcxVkv7jyniBZK8bJSoEUgiSQvUAbBiCzx7QxPM5bfGb4MHfoTe08+vD38VDrYehBz4lK8ZwrJaWwgC+EcHTYbSlBjsygiUL1OSKGG/6JO+vCIejPGMvvGpRylzfewTn0khzMIwQDp1mhGDbVsy8uudB3EQXDsWyzrCTwrMi6EKiiQBdaJiazY25Muemx7Mh3p2B+woqtJ3RFAgP+nBBjXr2Dtpr2xxHlTwYmgIYUICYiCuxaseFX9wKf1kKLc2w/GQwi5C9C7QoyDQQgDcIXb1Ql1aSZycuglsfgPkzlbfoeQoIhaaO5fnPnEPGRqNiOkRdKM2GY86AZZ8DuwxmCayScggkkIwbzDjzbKY/DmvuVeFYrQQRU6Lj40vFz96vWRoALvhRKA3DhK13UtDr0Q2BKKtIVXCPj3n7fSw9+jHmz9S59hsvnSD3wa/Ys9ivRuAde2GXq4CwJ8y/3ZeH20fh//Wpv9tNODquShKybhOTI0UWJPooBFFimoMtDXQ8pBTjCCIZHWaZcHYK5kSUB1aiyjg9MFIRPjNIVPgzFyjPMaZVX69YOaxJ+0sBfjmicmy6pkCuAqQWynucG4GbO5QguIsCwKxflYqzUeFPJ1DbANxTgG4PLm9U++zz1ISd0JRM2VZ3jN6qBrIGnJSSmUtvgkQvxEdgWlldh7KEICKxn4KNZYOtromHTQEHT0qSIfnntlzVQwIY9CSrugfpGxp95g08/SPQOZuHYm2saDuUltw+Uk6OXKwGTzPwhEY6kmXHgsl4msQYGSIY7mHj3Hp6TjqJLzXBO1NjQVCdvxBRXClYfuLbKOVc4kGcGq2WfJvEOjTFkkd+g53OUI6lsRMpfKlhCoeMMUpMqyZhAgJ2OfDDIfhsL9wyqspdrmxR0YV9vrqGH62DT7xwhbmD9io23w/IF3xs26FYcPBCxKh4PwPDcMu9UFcDpRrwLJA7VOTGI+wG3wBWB0QtlYO/8gvw2Q9BQw3EQqZbvqg6R+hh6UU6qQD3QKuAY0MGOm+DiQMwDNQfCykP4o4qhRjJqrE99hSceCH8/s9VEo7hDBEd3UJj+Wmm5e8h6g08E3X7Ib8bAjdA2iBLkIiquscAgZSS978Zfvyll7YrxUGP8ADb58H3B9XkXwlTSakmp2km/CanWJBNIYNBAPUG+H6EraWJfKD1cXqdevrsBJtLaf46Mhcn5PylhLrgBQkZFIj+ckTVvJXl+GL5tFCA5DHeOyxIWFNWocpKIXs5UH97UgFkBKVkE0glci2AIyIK/EpSsVlPjMO1beqLNt2C1+1SoU8f9Tldqia9HmpsllDF9A06/GQY/jgRTk4qj/hLfao8IhqWemio/XioBHuxDko+JIcU4MYlpBthuwe6kOAHyCj46GEvP4GOjykcdrkRZloKBCvn+61lm9nx9ctJbt8ICBqXHM0nv/oFUo2hy5hIw6W/5J5N+7DcCMmaGuRQP9OyXSTdIiNalOvPv5D4UY1M2JMjPjhMvqWRfRNrqRMrOI8pHJOAh0vqeYhqIBAE0qDo1ZM94a0YW7dgbX6QXZ9qw4624iZStKfTLH76L2wZnoKHhq+bBDMaqTXzQIRAyv/P3nnH2VVXa//72+X0M71m0ia9E0IgFCkRFBQUKQLiq3LtIjb0gnJVxMYVuWLXl6oIitIFpBdpEiAQCOl1kslMppczp+32e/9Y++RMChBQcrn3zfp85jPtnL332e3Za63neRZOYHNANMZZ2+TBw1LwcBb+NAzXjIE/tggDNxLKUfbH//7wvIBvfOMBdOEAHF2H57oUCh6JZIQAi6ZaEcAXQusx7UuJMlDl0qbrgTalp2eGT29bOuGw+TC+WXqGVRXS27cMUFEpoVZXwOpNYBTB9SXDK4VpiDfpwDL4+Xvh0ath2WrIjsD2PnYM4I3H5Fx94gUB4epKiA+toWbkWaJ+H/Xuep6pOo/64ioGI61k7LE79SZjQEftiUzsu4N19uFgVdFQv5U51hNMefdxHPuxt54uth8Id4kX8uyYJl8KpYQqPOzLiVf6l6sFCKR/FKGJMTjKoTraQTpi8Er2SFw/jg41aUOl92rYrmFjUcAuGRJdhoIym3S0cXZJo1YqmfpaeoS+Fvu1iFEuRaaUeH4ayMmZCyS7qDDLVmnVpvT4SiSZ1qiQYD7ZKeVAkGwuHl4UMQUHxOTBoPRQUBok3OvB3SOyX1zk/VrJtnmEotwgnMwwE9QsKPaUreJKJqaRRT5FZLBogIFBQMLM0utHSbgyDDdpwJWbBuj40meodIv49Y1oren+x9Nc+ZnPc/4tfyobd5smVm0LOiv9jIrKBoKBLvo2Z+k1FG0NURqWrsIfO47KQ+aDghiabrrw8Xm+YJIwIJLPEhkZAtPEq6qhaNj0EWP9F79NvCqJ5/bToxvJW0mMwKX50CIbX05z2K9/Qfqoau6adQFZP0ZeRzGU5p0JkyezFhGgYtTVt8mF+9Zt4MwNDxHzPZhzJLTO3TeOw/vjvy0cBy77dQ83PjyOikSeYk6jtQ1IhlhTZfLVcxSxqExuv/sxaUfE+qBnnMgacEFHCHsZoU43K04zSsG3z4Vzvys9Q61FwJ5OwORxIq9QiIPNjLEi5HdDXoFpSDk1nYBLr4LOHvlfvuTsoYW1Go2ErM6wfWIYcETPpaRzGxihihG7OZxLaFHpbiVjtex0Xns+LK3+DC+bxxMoUVJv7NBMqzyOm85duE+Ow34g3CVihkgKUoaAiD3qPuRoOLMCbskIW3ObF5Yyw5v+YfEaPso5dNPFw5kKOrJVaNRuukGQLOv6ofCpSElmkAgztiBcF5QPUCnLgnA6hRars8m2vP7yJvhGN3jBzqQSFTa3s+FniakyUSYY1Qw4JimZYcnppsaEdY70rGJGOTse0ULzjyjpHX60Q8qjNqGgH6gg7D8EkgVnDEjFZf35j4FxLQz3hv14Bda7PZypFgaaQIORDQiKJv12mkha3n9hreyrh+55kPG5EfyGpvDzKfz6RrZv3Mz2ZctoXrBgx2c/MQ33Z8OL2rQY6B+hV0Wp8oo053rQiQhDm9uw4wkS9XX4+MSIY2Aw1grIdXVQtXFt+GRk4imbwRnzGIinGIo+RX8ij+c3UnAqsPwigWWgDZPK1gI3f/mXXDTtET5XeR9bcxOo86dzdLSBVqOS09rFlH10vO/Fm1lw33+SGezCKxaJJBJY7/0E0Y+8tv1+QEA//WgCaqjF3C+2+B8TngdfuhTufCBBQdfieSYRMxfyVEyMIMvnTzf5zJkyAfdzZ8HzrwigqRtAnQrGyRDcgwh7u0GPQCQm4PTE85BOONx6bx4r8JgxNUZLc4LFixSPLIHeQdkG1xNZRkON6PviUQG7MQ0waRwsWyUZ5WhJF5QBt+QbWvqb70M8t4U80gdPuL2owMMlRpQRFD5aly8Aw4CsThOpnk1KDRIUckyYGqOXGTzwksGH3gKW6K6xHwh3iUVxybZaLBmkW9RyI9XA4pQQUGZEYXFbmB0q+Wow4Kk8bCrE8JjAlb2w1dmzLZqBZGc+Ag7FsJwZN8AMpPw5y4a1rmxL6TZYGugbVzA3Kj02HZY6F8SEzHPbMIz2DE8pGNRiaWaEmWe9KcAXGwWYTZaUPvs8WS6IoXavL9vgafCNPmpTa3hvushmNZGvb5/AqqKBpiyw1whYmmGJtBh+xtIDhVEHY78O29ZDLgux8eBVCmgGPuAo1NU+jFME9RZeET76Ppgdk/0dbO/YDReMsCyc6+3d6e+HxYWEcuMQaN+nr+CSVIoP/+060pOH6T2zAVW0GN62jVh9DXlyLOIwAGrbbiTfOxuzIo3lZNFKkYtXMe3Ru7BOPI1J6dWgFYOeZHrasmVOoYJ4xKFQUcVvBhbzlYr7+GZqEXXIzazfl30U6HLpsyLTy+n3X8ZA3iPS2ScViKEcyRsux51+BKlFx+7xXO2llwe4jwwZAGLEeBfvZgwte3z9/nh7xa0Pwu0PQSYXwwlMor6D60fwsUmYwyTNXhbOLntr1lbB1z8FDz8Df7oHam6F1Awxsfe72PG0HbFh1iR48gWfx58eRmmHAIPnlgecsKidW66exor1ir89LtZtr6yTr/VbZJhuaTbv9l7o6Ze+IgCqLKsoRaBFHpGICSC6LvQPQqc9j5b8EnJWHQYuU4fuZFXF6XjE0KMe1iK2PCQPZCBqG+SNGioba6hsAiMrsw0/dOJbfyz2A+EukTTgp01w/nYxvM6Ejg5frhHyghGSVaaE2ZOjpexYYYgrykXd0O6JLVn2VdZRAsI6UzKrra6AX8kuaVEMPlQFl/aI7jAKO+b7Bci6q8Jzqc8XycKZ7fBEFgYD6DBFn5gPBDyj4YUUaFmPo+ETVTtvk1JwcT2c1yl9MV8LGzaihNWat9ZwSN0jNFgaz1Tco1fyRPEMYtQRN9Rugv2UIdmtr0PyTCAlYICUBeMmuzRvWMK3r/8mRcPklrmncN38j+M8aRF1fIL1Ju5ahTEIqgqYJPu+ee5cvL9SvloBxw+oVQE1U6fu9pm+XCvWa8/2ZHnmL79i7nAXUd9F94CfNhl8ZyWO5VOgwHwWMJ8D2U4nmfv+wvuXmzx2+pcYrqhHBwEzVj3Cgpv/ROfMadAqBXETD0dHUUrLMTICOgqTsRSYQZJE5nTqqsqPJjUmHJGAJ3PQGFplTWx7Hr9YINXbhUVoI+QLdb39uiuYsQcgdHG5m7/i4pIgIbMXKfI37uFs/g8JEru9Z3S0bROvySnjIZV8zZfuj7cgOnvgP34qIGNZJrmiwvPiEBpy5L0keTWdj3/X4oxn4bNnwXd+CVu3y/uHRqR02fU4BLvwxbI5eHGVxnE8quw8piX1JD/wuW9JFXfc3UGqtoUFM+GQefDrP8EzL0k/0A/BNB6RSfb9w4g2XpUf/HcNP5ART+OaJMvM5uH56s/Qkl9CwuuhaFYyNXsvdc4aHqn/PihFNCLA2VAj70FLFlpXBdMmynK13n1c1FsV+4FwDzE/JmSQZQUp9c2P7Uxh1wgo7VreymkhjcyJima8BFy7skCjSsBtIID3JOHsSrhpGJblQ5YmQqKxlJRAcyFIGgh42ghzc9CXTOvejDAco2iihkPByBJPL2WCbmZ4aBYTbYOBsDwaC/t3LxWl77br5755rEyQ+EW/ZJNzohAoh5m1j5FSUZpM+dA9vqbC7qc/SKF0nEpTALYYyM3+xCTcMSLZrof0UwfDcm61CvB62zj/hd9REYsy4gV8+plrOeCR5XzJ/gWuiqK1OLO2VsJLzwMfk2387HuP5Oc3zSSxbgV+uhId+MSzGQ48+SSqW1v3eDwnRGD8mDR+ZjuFgX6oqEAB1Q8OYPx5NVPOOZ0jZ/wbUaIAbGMbAOM2Ledj159PLlGFVyyg3RF8T67MrDMR08iTtHI7ataGCtieH0tHbioVyqReRejYw9iti+vh37vk/DKB/kCR7O8mlh2U5mwYSnv0rly5x8/UTjtFCiRJlc8rooyQYRMbmc2cPb5vcBi+/hN4YWXZs/K8s+Hsk/b48v3xFsXN95VBJ1cwUEqFmZYiQAw4qipMiq7i8aVw5yMCRPGoaAQnj4clL4V6wF1qlr4PfuhXKyAoYRrgBxE++8ME48dC3xAMDElpdNJYAZ58UYbkFh05P3aAkZaKza6kT8sUP9PaqrKZ97o28NIzucu4lgV9/5d6ZwV9kem8UP0pMpHxmKbIQpyivK91LCxdKcnBgtmh800gDwkf2HMx5F8e+4HwVSJqwKJXeaheEBOQGj3yx9MiqUgpARyN7Nxd5Q0g/UGtxQd0ow1TowKGD2VFCF+a3GApee9RcemlnZYW8P23DtHtRZWA6ZAW0+qYNUzU6qcQRFkzMpkJtS/jZBpRqoY606QufLrq9iTb22mbAnGiuSMjwm6NbJepIBHpIqICOlyL8bZsl4FiemoNTzlNqCAuZVfAVSLHWObI962eWLLlkOs1pqA3m+H4jY9znNeDmRJhRnt0DAuXv8CBVat5JTIPrWTSRX1RKN6lmJ2OcOG1v+Yvv7uJwQf/RkU8xtFnns5hp77/NY+nUoojL7qIe7/4RdyuLqxoFDefJ93QwGEfKIMgQIQI0eNn4d3xCvgBydwgvobMSAE/UU9++ly8TJaovQ3TyIFfxcZCDdvzLbRnZ2Erk1lRsbWbtQdHmCoTrmwWgkyfD5M6PZyBXpQy8EtTMZSWSQE6vvsCgCKF3WdShufg9ZkCfTk4NgnvT+/8EHfxL+H5FcIYVEr0aT/5nRAnFh3wmrtwf/wL4+W10FQrVmog5ydal52MLAtfiwnHYEaE8sm4COJ7B8Rr0zTDafG7nAgaMA2FH9gEgcIw5AV+YOCSImJl6R2QbM/3IO9Ib9DzpMSZTgrAVaXFqDvwX71NHQQwa4qUUIshq9X3RWg/aM/kvqaf7sgmLTM06/fL0yjWtQkhp7keqlJi+l0iJB53GJz8zrdg5+8h9gPhm4i0CT+oh4t6YHCUIfahMRF/d4Q6MBdCHqSUCgdDu7NE2DNrtWGVA6dtlZtivyeWSJEQsAoa1jtifL0wLqD0QkH0eovicnI+FtZfM0FA2hwg0CY2PhmvgrQBHgHDOkuVqgDkovERL89SaC2l4KfzAu5trmz7YLiemLZFYB+Cf9qUm/nEZDu9hU5WZyplRAsy0PesSskoLUP6jq0RAdqNjsg6vtf2EAc9+2tUTfOObRgbV3hpxaxsGyOJudSaWeJBlq5cBWe9J8ZoX5xJ1Sm+/pVPwlc+udNx8QoF3FyOWHW13Fh2iZaDD+a0G29k5S23MNjWRsvBBzP95JOJV+/s4DuJyfxj4UT8sw+m8Kfn0IEmMCAajfD8BVfQb0RQxQiDPWfy6cYVJJJdPOrWs604lcl2lCpDHlAaLDghtdtmQLgvJ0VgEkB1NS+b9dT5vUStYMcxeW6bydSP7PmRuAkhCwUhxxZgq6fp8DXPjsTpdzt4pBDj5pEIf2xOYblFXn54GY8+PoMxLXGUEoSO2GDb8Od79wPhvoxpEzxeXpUhYiVwPBOljR0zL0GytJFs+WcjlCrYNqAFDP3Q6V/p3R+2bQt8R5EpJqmMj6A15LwkCo/a2jibO0Tq4CgBwpKcoeBA0pKrrX9I7keOK8vLFdhjDA7L91gkZJZ6O8swdMhA1br8d0PJsjVS5p3YAo9eJ1Ppewcks5wxad+RpvcD4ZuMY1JwZ0x6PYVAssdrBuGerEgIIqHA3EN28gQbPBdmRoSkEldSSlyaF/ZlrSkl0JcKAhaeFnBEQ60Bmxz4RIcsq6DL0gcoC+4dbYZjnJSYfxsjTEhsZFVmFnVKhOkjgQjpjx7VF1pWEKu4IV9GNJnIdmfCUu8hqgHHT2GpHFEVF3+koJdJlsMIfTi2xtGKiCFm0ofG4Yr+MiHEwGOcsQ1tpfhEVS0LqxLs5qSqNWMbYGq8nuUbN1FUWfKB4uPH/54T5m4GfTmoPafovuOw5Oc/Z9VttxF4HqnmZo644ALGH3HEbq+tnjSJIy644DWPbYoUx6sTePh8i8j7Z+O+2E40keLEd5zLZ5JTeT4vWfuh8TRp81AAjq+EuVqy6ryGk1LSU07vTY9j6kJmLZrLU0+sxO0bIGpptgxbTJtUx7Rz/32Pb6mimrnM42VewsTA14pO36Wr2EghSJIyfdwgwzOFOCe8uJlDv/xlzGyaTPF7bN0ySHp8K/WtLaAEDPv24EuwPxDRXvE+KNwOhRxE3gXVHwIVe/33vuoyXd6z4DvcfM8HMYxJJCIBKMVIPokfzjwtAYChBFh28gQNfy6ZWe9WGQAcT6EIKPoRhnM2hgKLQaqqa3H92A7GeKn8mS+EdmwG5PJlkI1FYOIYf4rYKgAAIABJREFU0Q2WgFCFKy2RvjZsleG+URs2bYPKlADqDpINks1q5G/pJCyaJ+txPSmTjmSlt/iOg978bv1nYj8Q/hNRb8EpFeXfn8yKBKHNLc8F9LVkTxfXwSW9ouErWZltccou7goBOBAwtFU49kiJl+hWT4Ax0FKa3O7BGKvsI6pR9BXriBgFTKWZlNzEQBBwYNWLGH6K5Zm52Aq+WgunVZTLryBuMJlAPEMtBKjdkKGa09DtGwz3nMixTffg0oXrtaEIOC63kfMif6UtdiJrI99mkm1w7RB8r0ey2N68w0k8zMcqryDKMJFowER9FN7srzFc0Yjb04VfUUedqYkM9WBNmMXXP/0QZ7YtoXd4CpNauqitGAavA7LXQuq8PR6Hpy67jNW3306ivh7DsigODfHA+eez+IZfUpgax8RkPBNel0AyOibSKlKYKV0YUwwaaNwhTTgxvfvrIwr+rVq+3nDYEayP/4CjO07DH1b4ARxhgXHKR6Bh3Ku+7XCOYCxjWcMatnk+Lww0MuLWYITGBFltoHVA0dlCoegwlAxwci6ulyS/diMdmQpmzkwzkoPFh7yJ7f7/IUZ+CF03w197YV0GuBMm/Rg+dg80lHvSWsODT8N1t2nWbChAYRsTKjdwyolNnHbaTJLJco28v/txIv79fP4Dg/zi9k+xYtMMbNMlGini+QaOa4j/cFitz+ZhwhiZKlEsSm/RcWXOYImpWdylF20agGni+zEMy+CoA0eYOqmOa+9IMNwhvUDPl/cqZJm+L8sb1ySl87VtUi5t7955OkXJlSbwy3ZsX/6IgN83fy7LLC2nf1gs2VwfKpLy3mkTRd4RjZT3XTa3+2fYl7EfCP+F4SEyhEZLsitDSc9wKID3VcBTBbE+awz3+lDYS4wbIlMoTbcPCK3HFEwPs7h2TzJNzyiXYtc5o4f9Kjxt4vlJ4maBaan1RA0HQylq1BgWxQXoFid3964c8WFLOLzXAAiZqgXkb+uKcE5VDV+3zmRw+IO4+DRok5i2wGhgUnA3VeYxHL71GLZ4YAc+9vZtmPkMa5e9xEsvLWf+l2YwbUYC232Mx3JFfnT6VZz98BUsXPsoA4ZJ3WEn03jaF6DwHlpbKmkduz7cOiWai+LtewTCwuAga+66i2RjI0ZIMYum0wwcW8Vt8TtIMQYFmJgcx7tplWLkXoWNTQv7QMQE8OB1UN2E2diKiQY7Bs//DQ59H0w/eI9vUSgmMJEJTGRVEHBJvpsqU+5sJT2q9iGVypJpGMeKOe9AR3yGHp5IbGgABoZYujLN4kPgtHfvm4/5Pyq8jTByB/yuGwYK+PEo+UEX48WN2B2nYv/n0xCVHu4f74af/B6GBzNs7xxCGxVs7DyA9Wsf4v77N3DNNe8nFpML/+UVS3j4mQ/y0AvHYhkBxx30OCs2TWNL9ziUEkKMH0gZMR6V7401MKFZnF0cFymJKqivFkDr6gsnOygBoVIZMhlTKCPK6q1RlqwWl5n27eUsECRbsy3haTXWwL+dAjMmw9lfC5eRkHtM6fV+sHNfUiP+po11ML4JGmols1RIb3PTNvn7qe+SZf3pb2XSt9YydcMP4M6H4YQjpUy6r2M/EL5OOIH4Qd40LOA0LwpXNMGCPXAYjkvCPSPQbEm2CJK5HZ8Kqfw18EpR/gajCDU67CWG9X6HMtOyzQ2H/ioZYlsXzvHr8wU0VbiMlAFFbeHh4gQRXhqayYLqF1k1cBw1QW1oEybgOc4ub7Or4dpBWS+UC5Y5BBTjSj7Pk3n4S/9aTtNdbPDqaQ8CEgZMsA2qlc3fB5ew1TuGlNIk1r2ClRnCivisOejdbH3xHswLX2bm7xbSm26iMXiGppqAW864jNsCnwFfURcxuDURoAo+u1vgKinH7iHy/f0opXaAIEBxbJTs6WOI9uVJj5X0zcXlIR7ko5yzEzHmbRF9nbBuKVQ37twUKdjw5C2vCoSjY0bEYGw0y7ZimkqzANqDwMJQPq29L/G3j36VQiJFfKifWT03k3p5G95gI13xj3PpV5Iy7Xx/7BzeatiUg4ECOV/Ru2xIAEAHxLtWMHT5RUz9jysoFOH//hnSCZ8NawaIx0xczyXnRNnizyb37HPce99G3nHUNO75O1zyy0/guS4N1f2Yps/KzdPpz1QzobGNrsEJOG4ZyDQyVb6qAl5YJaCXSsCMVli72ae732Nu6yamjfVZsmIKuYJYzARaSpWxqJQfs3kBylRSQMq2ylleriDuMQtmynp/doOQcVxP1ucMS4m0VIbdlZxTdOAHV8HcKQJ6/aEs55V1IrgvWbGt2wKXfVXAfM0mSRS2dGpct8BR81dy+/01XHNbKz/8Mrzr8H16pPcD4evFqVvhkZxkSIaC5wvw7jZYMklG54yOL9RIWXObVy4ZNFvwxdA4udkWecKjWZkO8cCIWJ0VRzeWw+8WZTAsICXRlJLRSmlTSpnL8qILTCvJGn0MfKKAZvXIbNpG5mAbBtsVzA8b0/Wjela+hi9vhxcLQmwh2HkChoWI6VvDi+DmYZMFUU0GKc9mAwH2A2x4utiCCZj5LGZmmCASw1QZAmXQMfNAxmxcztoHuuCUcdjKQulh2twWPEwqDTEfaPcNxkUWQ/FRMBtH7ZQ+iJ6yx+OTHjMGw7bxikWsqABcdk4SrQPcmgbW9GRJdnRBOklkfJIOa9sbygr3SRRG5HHc92CoG4p5iKfAisBw314tQin4r0b4alcH2wpVKCCusryv4V5qNvZSjCepbVvHkVd+n/jwIIFhgIowd9V95LqvhQmNr7uO/+/CqIEhj8DT9K7LoUyFaQqV28Ri29/uoPq0z5BNz8D1wfAclFLkC2C6eaLaJetVku83ueiHHdTfOo2+QejorSNiZck7caa0bAalcTybWMQh0MYOtykdgll1Gg47ULKr6grpsRnKYWrLWpatn8SKzWOZ0Lidic0bGc5W4fjNjOTEA9TzymBoGJINKiVlyr6hcmaWK8IrayVjyxcgEZXXRw0B5GhEhv6+tHr3nqQCXAeikYAJlW2sXbmZrrVRdHIaNfV1NNXL+pe8BNfdDtd+X6Zo3PqAw4GtN5OIDPHosiNRKkO+uIkvfr+FpbdGSOyZMP2WxH4gfI1YU4THciIEL7mApELQubQHrt4lha+z4KaxwuTc6Aor9JjkzpZnCQMOS8B/hZZFcyLwfLF8cpXs2ErqhlH9ZhwtbMR0qNmzQ1rygC4DLxBOPjQpAjEtwv42T3SDc0YlQ3dlRLJhIVKMiCEAW5ImxZVkwKUhskvcaQxE6qgxBslRFTq6+HT7Ln3GfCnZOuEIbAUeFoqARGEY01IMbs1Tj8NSdx53FaZJf1NBh5YLTgMkvwLeK+BvB8Kr1BgPyc/u8RhZsRgHn3su/7j8cuxEAjMaxRkephhvYvxzmzn8wSdxiGAHLl2TGln/ycNord2bo78Po3GinGBrnpW7zmge0bEf3evFLDLHcG3qQzxQNZ0eVUE1XUzNdtAyfQOPvvwKDffcQmx4kHxFDdqM4EZT1G/ooufOX8HB3/2Xf6z/8WEvhOYWnNxWtA4wDbOcDimbQT/K5sceY8ZHZoR/M7C8AtPMTuy4z0BQwwJrCVsL63iy4wCMBojHIRox8bwoBcegd7AaQ/kEgWIwN4mIrYhHxGnJ9wXE2jrhJ9cJSDXXC0D5TicdvTUYSuH7Fr3DNUwe08GV53+WT/7kzwyNxHAcsCxhX27tFEZnviB/y48ivpQYnY4rz2Hjm8WIuzSzUGvJ7GqrhbXq+SHPQYU/GxBozZqlT1LoXY3hGxQcTSy3mmLyFDZsrQ4zabj8WhjbKIB++Mw/0Nm5hesf+AjV6UEsMyAVH2FjZ5Jf/rGBCz6x7w71fiB8jXg+L/ekXacAmMALeZ+BF/7O7aqX9nF5KtI2h9hTWGDM44R0DEfLlIrcLt6fAPePiMas2ZJeTtwJRfPsPCx313AIp0loIc80WRD3Ra5RMuWGcg9RE1q8aZgXg5/tUnn7yzDUG9CjQj9OJS45I6FAfHZs523PaZPfej/mwsh5VNGFIkArxV3uWZxSNZubc5CPJ4lrjdaakWg1CTfP7A2PkXHSvGgeT8+DM7l6zJkELSbRMDst2dgtL8D4dBNU3wzFx8DfDNYUiBz1miy9OWedRaqpiZf/8AdGurqYUXUgK7pzHHrv03SlGgnC7n7d+h42XHMdXHDga+zl/4awbBnH4S6Xu5QyhIlgmHJn2ttwlzLN7WSa78ucR1+0qoFlsXB6O8X1q8hVNBJYMQLDRmsYP7mWzY8+DPzvBkIdsrBHAiG07dWQZ2XBnBsoNh1BomOr+CYoE1tH6MlbrNs4wKobnuaqdadhqHpeWm0zw+zBxGfQr8TDZLF1F7NaV3HawCfJFmQyg2FAPBYlm4vQ3juOdNJFE6WuWjG0FbQp95yCJ2VLy5TRSbkCvLhKGJft2w0GMhUYRsDklu2Mre9je381d/3jFP74n1v47A+m0dMvhBW0ZHTxmEyq17rM6LZMATNUeN8woLu/LJTPZOXZzA8kY2ysg44uIb+UwvOBoMhwbzvpZArfVxQ16OQ4BoZ8aup8DNPEcWX9X7sMJrZonMLhDI28i8bqPixT7nxKGYypaefeJ/YD4dsmZkbLptqjAcTXAU2bXuDzY3qpH78F3zfpypts032sj6yjNvtB/qsvIoNpkazwW3VlKv1mhx1ue7mgTFJ5LRCEkN2F9APH2zK7ztfCyuoedWJao147Liyl/jaU7I0EZQPtXCCZ2LQIrHGEKVqayFttyGBhws/f5wt7tZ1ZfM+/ixnqH8TJsMI/gGFjEl+ugG848OO+GIPjJmEP9ZNwCnz4jqvZtHEufzIuoWfZDPyXohSUiXEIJE8X8I0Z0rd8MCujnZ7JJRgI3suc6O7l5z3uF6VoXbyY1sWLw+MDA5d+jLwdA0smy6OgN91E67pncIf6sCvfRmmhU4D+DrKz3sHDFa2sS49lar6XYwfXklz++N4vR5ddEgxEklMTh0EXGn3orkih7ChFZaNNGGNDjeUTeG+znum/OLo9cfJZWZRzI2bAf9Ttmf27W1hjiX1jKctOOYSJxhBKmSxry/Fye5ZMwSD3wjPEV76PrU3XMC4WIzATdHs1NKp2TrF/T6PRQSSW4l2pZ3jEn0FVWsqOuSIkEoqIZTOuyaZnADa2S1bmjcrWgnA00pTxQpx5YRVs2AKdPXVo7VORytNcOwBAfeUAD7+wkO98rYb7roQ//BXuf0r6e2bIEbAs6O0Py522gGM2L8tOxCRTzIbs9NGhlMgdEvGyI85O4QxTzOeI2NJsrq2EXnMcKnDwXYdAxxkegYgl2W5tFfjFAdZubaU6PUQ0Ig3LIFDEooUdMw33VewHwteIBWEpcXkREsjNJa/B8hwSfh9149qhaGFqKaQPk2CNOchL2TUYzKXekhPqkdDI9seigWZ2FG4Vn2R6vPJ0eij3BvcUpXPzlaKURZst2BxmnaNjFJ7R74ue7fzQ0kshXpcX1QnoXDsoMoxUyFwd9kXH+IM6+Fav3EQ0Iv4+rxp+MgD9QZKXjOPIBWIScFE13JMRkL2wFupqG4k9+QrmzdfgZApcXvcr+qqaiKcj+AhNOlgCwVyYMley4a2uuOWcsFl0ldmw9HtGBVzW+MZm85kKolkPx4gTaEFSrU2C0CG9L5uj6e0EhIZJZ6qRT77zYrpjVSit0UpxZa6Pa5b+mr3u3tkLABN0YUcGrbRPlQHzKo7mD8e7NN55I25DI3UWTLVNctv7OPDjH3+rPtl/e2gNX90O/8iLYYUO2wmf64S7bamUvF5Ea+qY8N3reOgb32Bg0yaKvXlcohQijcT8AYzBrZw1vJihlhP5aOtKOoNxRHUOpQIMqwWMTirNDJYNKJg7HdZuElcZ0wxZnsgwWteFrV3SLtYasgXReUYjUJEWK7SjFsJ9T5ikY2001w5gGOKBpnAIGAOqjso0vH+xkGzWbYbbH4ZYDNxeWVahZKXmCiB6PnT1S+ZZ2EU4H3Y6SMSlxBqxJCPciTRTaEehyeahsVZ0hU+1aYo+ZHJKZicG0iuU0r8iHqsiEc3T1V9PMrYFgIjtkClM4Ih9bO6wHwhfJ+4fDx/dBo/m5QIaY8Gl913Mb445mfFBgF8aJ2IY2G6BPjdCdaydQWeu/FmJufLfc/BcTnp81SbUGHLDHx7l/7knX9I9hRcyS1cXoS8cr2SEfcKde4UQN+GGQQGHpvCCezoH53bC/22WfuYmV04EW8G4CPykCWZF4a6EuMHYSgwBlIIqC37VLxZtdSZcUCu2bCsdkX8UNaAsLn/X8bzzlON5cSX0fQvMlHy+EfGqBgOyL8LyaSIL8YElOSnlxgzJmHMB/HZA1v2FN4hbHbPfSesjvyYXrdjBdksWMoyk6kg17B0/e7MDNw3JfjyrUjxL35KwbH5+3DdwC0VOX3MziWKWDY3TeWrMAn75zq/xvb1djlEBqW/DyMUQDFAqkqv4WRySHEPbN1J099VR8+wq6Blm82CeRKqKkc5Ocn19JGrfRg8H/6LY6IpZRLcnfXBDCRj2+fDNbvjr+L1bzsSjj+ZDd93FdQsPJFUZZa07nURuA0pDYESJ6CwVPU+yQWdomVpNwbcJtAlanlKHxx7K4hmwfK1cR8NZqKsRwFjXJvKD0jgjhYBfSVfneeK4cth8AbGPnwqTxkW44c6xKDWMXDWK3swk3rGwkUgErvoLfPuXUor0A1mGZUm5MxGXzDMIq0iuLzMKLVPmI+4aWku1PhyugmmFvvDhDSvQYCQnYI4sxbI8ZkyyiMdgbHojbdn5mJEo9qi+pO+Lm0x99SSa67YzkotQdJPEY3myhQbi8UbOO/tNH/I3FfuB8HWiyoK/ThD3mEIgvzsdz/Ir9/RQc7ALTUUF+F7lbsvZ5okzTF6LfCKiIKZctPaIAy12nK2hHqJkVP1qkUXGHG3xhMhTemIL8QUfmYbhawHIlQ4cGCu/rtGSDGy9A9e3wMMj8GJRyq3vTZWlH6YSv9F+H57Ni2H2e1PylddCprlpWJbfaIhdXEc4o/GkrfDTRjgYAW6FsExByDeu7K4dk+wn2JLdliq8kTClLQTw4z4ZNhw3JJsuGRK8Vrzj+DPY/vJ9NPVuomhFiAQeyjB55YwfcfQoI+JXix/1wPf7yiWi7/fBt2rhgvrXX/cbDa1huza44rZziTh5QKEVLJp2DL866xdvbGGxE8CeC8VHQBchchibgpl8s285bc5RTPz+fFrPv5jYP14iaE3g9hZZ8rOfsfxPf+KMW26h+cC3Wf/0n4xMMOphMTxvFPL70rycm9ZenE8Aidpaqipj+K5FtDAEOkArO/xukjfr2difZ77agh9J4XgmyYjLlqbjuOT7BzGuBda3ifD+qltgbJNkhf2h72bJLSYIzSxKVR2lJFNcugI+eDzMnymm288tT7Gu7WAC7WEaBvV1Jl/7OGzcCv9+uQCVYUgW5gfgO6IZzObL2ZxphM4uufJr9xRBIL1DMyQflOQdO3ZdtJaWWUfRt+FJMsNFRoY1kxsVg/1xnEBeVRram4hDRzfUVMZIJMbxwy8s4eU1Lmu2TmTO9EbOPinK+DFv5mi/+dgPhHsZMaM8vy+y6ESOXPUCbeMaqEt04TlR8aOM21QYBstzMxltMNLmiMB+vCngFSXA0wWi9NGgshxtP8Isu4frzf9ghWNR1K9unVSKktquGJJcbOS5sHQejwQyUzERImObK5loKQKgyxdwOakC9jR8QGvJyH4XZkVBSLr5cWN5WY9kBYyXhyBYEvgXtZRjr2+GpgrYkAU/Fn6uQIA8OFB8Vw0kMy7NbiwGkskS7oNOT0pZtpLt/Vy1GAPsOv1jdBxYm+aRr/2Ovz5xPxM3LqG3qoX04e/nnBkTX2OvSqwqwvd7ZZ/a4TF3A/heL5xcAdP30FIbCaTMfFdGNvrEtIy62iuLNdfh83d9i2KygoGq2pAoY7Bgy7Ms3PAUTDlmLxYyKswWSHwEgJcL8OlOn+26iZjStPdX8o+Tv8NpvReQenopnmEQiyfIdnVx69lnc8Ytt9Awe/YbW9/bOKZGRIJkjnpe1WH1JK7K3rl7G3OOX8wz195Amn6yWkqShvYo2PU4KoZWJs86CylWTWdea4FZHzqOibPewRPLDO55Unpjo63HOnukNOm6oWWaKmv1TFNkTaXMsFAUU+rOHhma+/tL4ellivVtNi2NcPTB0vP75s/CXqO/O7+hZGtW8vwMNGCCNR6KI8DOIz13Cz8INc3h+0v3qGQMup3pfOv7rSye3008blPfVM/7P2+QiMt6h0ZgS6fYqY3kxInmPUeZHHvU4Zz8nr0/Bm9F7AfCNxMnfpavXv0Nzn9uMd3zDaqrukGZ1FhJzjSPZSXVdHoitShq2OaLlKLTL80izGEERfp1NVPsbp72F/OZ2Cd4wZ3CSj7MBEuyxiI7s0FHh4GASMmFJmlACugNT1Af6PIk8/IRMEk7oSZQi/ziiawMrR1vywDbA3bplzyag6sHocEMJ2Fo0Ul+t0dMBUCG+BYCWX4pIy1tnwZ+PABXXQjvvhhy4dwxFBhHQWQKFENedd4N0IaYzblIxmn4PhmtMLyA3vYBvPoKhmNRPlOAKbZM7PhK7c4X+uh4Z12Soz9wKn3+qaSNvWQKAtcPhqQkt4CRG0HbNiQrKGrFtYNwRBzuy0pJ+wNp+f0LnaIJrQnX8YchMUi/dszrZxyqfTWN3hBr4jVYykeZlrBuDYMPrr8Tjj9m7zZ8D3F5nxyLCiuPiUkkN0R/JM4zB36A4559EWWaKMSQwHccXrjqKk746U/f9PrebpE04LgE3JuVkWaBlutKa9Chh++8N6BXm3vBJQw+eT/LlnfgonF1jKJVTTY6luZYL/XeALOmxoie/x/MnwGX/Ap++gUBAo0IyxtqBKjGNIDjimo+lVAMjSiMEosTiNkCFqYhdmpBAI8+CweeBl87Bz5+moDf0bv4LXT1ljO70uR4I9QDFsLS5w6/0GkwfCao0j7oAG4E+l99H+TDvmVpUK9lyfZVpOCgOREWLRpLWwf89HrxIQWRTPQOCLg7rmTA2Rw8vxz+z4WiLaypevV1vtWxHwjfTMRTVJ73c67e8BIrtw/Q7i6gZexYZkXTGBhc3wI3D4kbS6MFXtFloH2E7kQcI2JiR4uANANMJVdkZzCGD0f/TLv1YZSCQr7s/bmr6btCQLDCkLJlSXBf4sxYlKUTo/uFW10BzYSSDOyxLKRMyVj/noXLGsRMvBS3LF3D/Kt/Qc3y5/Gqa+k77aPo936Qp3IG/b6USk9Pwz3DZckGyHZYSnqGW1ywJ0LrxbDx5VDH1ApWIxR0gB9oZm1fwblP/5bOdBOXL/4avmGRVSaBp8EEezhPd08erztP5cxGgmSUiIIbhmFGFN77Guw/U7125rinKGpNtLebxOa1O3ZgEEuQmz6HxzJDPJQxSKgovpHi7znFMQlYUYRmswzKzUoyy+fyoht9zTBMYuYQCTNF3o/uePKJqyLRVMcb2/jRnyMQpmSTaeITJ08eFbdJZHvZMvNgMA2s0i1AKeLV1XSvWPGm1/d2jcub4OUHoa1DskMGITYDYrPgk51wRSMcsZfOOkZtE0ffdD+z/+sr3Hn9feS9AlbCxrRXE3gu82dXseiMQ2E2/PYmuOL30n9TYWnW82SqAwQ8+JQrWRsmptJUVxpMGmeyeqOUL4Mwc43Y5d6d64qM4tu/hGtug59cAO89Wv6nw5Ll4kPkf6VyK+w8DaL0WmJA6OCiS6brzcC/AT/hVUtSGvk8DbUwd1o56+zpl0zxiz+Aq28R0FXhugaGBYxNJVluVYUAfiYr/dHf3wlf+djeHYO3IvYD4ZsNpVBT5jMb2LWQVG3Cp2vg08CLL3Zy8y2b6DliKngFClVxYlaAb9hUqBwFbVNpZIgyQko5aGBbSKLxgCoEWEb3sBWS0ZXKp6Xsq8RDgXKPgfB/EYSAMuDBvKR4lzaFRz9lSGnvsj44KikX0NCWLaS/9gliro9XVYNRzNP0mx9hDvTRe/a55EO38EVxOLcavtJdZqtaKpxQHwjb9M/DEIvDYYcLc3XIF7DxNEzq38Svbv08CS9Pf7yaTDTN7fNOZTiIkbHTGKZBqjfDkGWiXJ/8tkGi0xrRSMZ90/CegTDTNUBm9RpqWyqJTp7x6mnjHuLIl57gj8VavFgMM7yTKCdPVddasmNjTDQ7xbRAV+Gbs7gzY5JQIjsbdXrga+l7HvY66wvGTSNbFWXySAeZZCVOYBLVDpXFEZ44tPVVRuy+fthKHnpcoJY6uujCrdB4lWniG7dieGAaBp5bxIrHMSMRqiZMeJNre/vGqiUQvRZiJgQRMAtgPgU154ExRbLmwxNv4BQZN526n/6Ns+ZczItXX0Vbd4FozGLu5GqmzWmFwz+A78NvbgJCtmQpM1MGFF1N4PloDBQajUGgNdnMMI5bwbxpJs0N8NizQK5stbaDqBIIOPb0wyW/hgNnwq0PCfAOZcLyJWFfcBQAmmY4VaIUUeABoBU4IlxBBqgDxgFbXn0XeL5keBvbRSphGCL+v+oW+PtzAoJ2yJpHy+uDACJRIewoQ87PoREx4X7w6f1A+L82tNb86EdP0dxXwJndQrY+jaMUGZ0iFjjUWBlyOsZF8T9TrXp5Xp/KBhc6XQEtDZS5f+WMT4U/DwRltmck7Hc4SG/LY2cWqRm+xwYez8kJ2ufDZFtKhilDmHX9vjjkLP/jH6n0HbbWNBJTEMSTBHaEqttuoPm0jzLGktRRKfhCnSzz7pHythRCoL64Hm4YEsbeSCBZiq0A38PD4KCtz3PZsRfSXjmWyX0bOeWV2zl54530ZmL8Z81nWHvMUbi+DCw1bAM37xBFhP+uFjLE6HAdzQPfu54JL/wGDQyqgPjEKUz87hWomr0TIli33cgRFRN5/J2nEYSPG7ZYJztzAAAgAElEQVT2OOjuP9Bz5sn49TG5cIIBFNtxgxY6tbARG8ywL4s8EIzZiyvMMG2WferDHPKr66ka7tvxRLH2XYdSnPZmYVAeaM6shGsGoMkyGaOayVLEn9LI8c88iFEM8E2HZGMjifp6fMdhwac+9abX93YMreFXN4pfZ0cUKgtg2uBmYPM9cMBXxJwiq+XB6o1E8hPf4R2zF/COR/8IIwMw92jWzfwUj99fQy4PvYO7+3IqwmG6QNTwqIz1E2hF3k3geXDQpE5++I2xVKbggp/AjXdJFaVUUixFxJbyZ7EIn70EHn5GwKjohGObCGcSBgJAtiUlTKfE8jTATwBpYD0wE6hFNqyN1+wVJuOynnxBNI1bLGGzThoL960Q+YXWsm6zRNozBMBL5B+Q7YhYApI1u/ML92nsB8K3MHI5l/Xr+2lqSlH9l2fon9TAwLhaggaYOL2dhcluzo4/xCxzDc8UG7nN+6QwSoERXWaN+ZTHNUGZWelpeaizkQzSCv9XYGfg1MBI6QcgruU1GVfcb6ZFRNBuqrLYvmflShpSCfqU6PwMQJs2Ua35ctCFUjtPnP39WBnue2dG+qJNJvywAY5LSVa0vCBZqBmWTB0CDK15YtJRNGa2E3MLrG+YzM+O+yK/ffJzHDzwDLMOWsEZQ79jdXo+QaDxtcayTSbaAra9noyUGh13/PpZZi/9BblIHYFhE/iaYON6tv3oIsb+6Jq9Om5uLsfBr9yH2b6FjqlzUWjGrH2GupXL2XLy+xgODMm2sFnl2PQEAnobHWjTwoC1DZgY2YuyaBhTx72Pu78bY9zq7cTyLt2TmumvT3As/9x8pE9XSwZ+ZwYMFKgYn6+Fz158IStnTuSFq6+mMDhIoq6OQ7/0JcYuWvRPre/tFnkHXp4A2YWQMYRIm1gKkXWQ2ybZcsKQfu8bDqXgsJPlC/HR/M0PJcvp6BLRfBCCQSkrLBF1FBAx8wAYSpOws2QDm9a6DgxjLB++ENq2yWqKbrnsaSLCd3mfgMmD/xDRfMk6zTIFhOIxWW82nC9YAicIM8MRpOSkgR4ECIvAvezejwkjEStZqsnyxzVDTQWs2gSbt4XAZpXlGUbYRzRCQEwnBURtS0rGE8eIxOOLH34T+/9fGPuB8C2MaNQiGrVw3YCIUtSv2079uu0MDxeZMNnn0l9a4E8G6/10R97D2p40nhYwK2V2UAY+Hzlgo0HOQU7KmBKCSalcumt5f/TvxVGvCRDZQ68P/15bZsbWzZhB7+rVHJhO0+3LtPpo4FEdgcPGN+z2WRMG/HYM/NiXLK3RKsscTq+Am4dhtSPAPajBN6T735+oJVAGEwfaGON00h+t5pppn+DHmy+kx4/zuzGf4Zx7f8iGyukUhwvY81qIqRo6PdH1fXjUk6TrgvvYrWDaBIbsPcNUDAUNxNcuh552qH/9sUrj3/VunrrsCpq7t9LS0042CEhk2sk0NGJOtplSsZy+kXraR1rpCSpIGzArJnT8EeAVV5x5Pla589zH14pZzMaP+Cyd9zxFisSIcTSLmMrUvVvAq4St4KJ6MVXo9oRJLCxJxZyzzmL2GWeIYXkshtpX48D3YdyYhYFDwcpCEgHD7BEQDEGFIw9Tn67eewnFq0XbNvjNn0S0vq5NSoCmKXrBUmnTCMo9v8B1iIZACCKHV/gcfFA9/3Wd9DOb6qUPt3KD/F7KLjVlsk0x7MPZ9s7Zp0IAMBkPZxZ6Zd3gjsggWiwbqESyww6EKPMaNlf5oiw/FpUssFCUbK8Y+o7Gw6n2mjLL1DQERMc2Sk8wm5cJF5EInHosnHzsP7P3//nYD4RvYViWwQc/OIvrr3+JpqYUpmnguj6ZTJEPnLoYEtN2vPaUuNygzumArC/U7kCXHWdK4bHzkCKNvMYNL4KSgbany3KEUpRAMtjld4CMvzNrdM6HPsS6e+6h2N9HU3U1dW6RfH8/8885h2j61dkpaXN3OnqVKezJIzdJOVYBFYRZqoJcJMlgspr6kR4qixnWp6czcsgYgsmNmLktHBN/iM35iVQcP4ugPk2zJSzX49M7z1YsOBDzhgjMnU9r01Q4ngHFHHsTtSefxvAdD1G9fgWBYWJ6AV7cZPmXz8XXNhWRASpq+1nrTiFaUMyIwbpCOXMGGd/17V6RWpywF1ZeCsU8DmA2c3BwiBLF2G0c1ZuPGlO+dluvYWDH96HN/z6Mv4/Ahd2SoWRHJDuJ2PLwmJ8HE+vhnCr4xJsZprxLPLtcSnx9A3JNWaFjTNIXkMrmpXwYi0AkohgYtMkXIWKJEKrgRZg7vpMjjpjNN6+SOYMgxJjBYQGXYmh9ls2xw6pt9hTYdK+wUmNRybRKukEz7Nu57qvrA9mCAKACVgEPIwD5KlEIGXiphDjkGEYZ7AwE6LZsl/UWRmXE1RVw/aVw+IGwvVdIMtk8TBonRuL/3bEfCN/iOPfcg8lkitx991oMQ6GU4nOfW8iJJ+7+pH9cCs6uEPp+MRCAK5VRRmd0r0LmAkKg1JKJFMMX7polasrZZoA4u0QMMeF+V1jxrG5t5aQrr+SZK65g+4svYlTXkDj3fO456Wy+0ybLPDENH63cOx1Wiy3T2y/rFbarCi0AfGVjBS496UamDa8jF0sxO9JO9wmTiRoGHmN4vPE8ImocCSNLzrdY68QZ0bsPGE4lYFP9sczufh6Myh3NCNPNYleloKn19TcUaEgnWHnpVbQsfYL0y8+xpqqGwePmM1LdwCxrBbPVGoaDJAEGRWyWFcq9SjvcpzFD+qTf7907ICyFiUmc/53AtC9jvQNf64ZCDtzukLzkQd6ViSyT58Gz8/ZeUvN6YVnlHp0I4H2KBQ/XV9RWeHhejIktBuObBRxWb7TpG2zEUsMEgeakw4tc+Z8zsa3wYgoz1K2d8j0eFyH6lPEyhDfQYoC9cqOsO5uXsmhpOgRIhpgZCd1qwqzM9/dw/8gAv9u7z2lbcOgBst5lK0P2aI2ssyIFE1pk5uG2bhjOwMLZ8G+nwinHsWOs0uS9dPPZl7EfCN/iiERMvvWto/n85w+hpydLS0sFqVTZq2vID9l9hkwKWOmEzvOU2Z4xJc38UrwWEEJ5juGuADgaUEuZpYmsr9qUbG10NMyezfuuupqrBjRXDijWOTDcH8o38Hl5IM+DFLhxWpLkXgwP+2otXDkgfqaGgggKbSiMWApfa4anNVMkyacq/0K9Mhh0szxYPJYtwQQcbHwM0lY/tVYNvx2Ic2pFuacJclEe+fn3sf6Se5jkrMA3bJQvjjKpT/5YpjzsRSQN+FBdhGsPPZbaw4+lw++nQJ5AR2hI9rIumMLjg0fR59bjaY2lQYf7zkcyAVOBreWY7o99H7cPQ+CA1yYaOTPMiHwtNqyTi/86EAQ44kCwTXFp6ej2cIsO4hBksL2zHy+I01hdgVI2pgmzpyq291hccl4N7zhIQKQURy2Ex58XoBvJCYC5jvTkVq4PtZAOZLNw8FwZqrt0pQjVC8WywXY0KmBYlZZqya7EnTcasQhMa5UJGDv6nVpKnfGoTLfv6pX/NdTASUfDT78u5c+3e+wHwn0UNTVxamrKYLGuCD/oFf0ZQJ0hw3o9RPc224BVrvzu67Jjy65l/j1FCfxK17mF9OZGdnqNBjxsPKLKJKlsjozv3ih5rgBXDipcDcPhdkQzg1RtWIkRBDxfVcMlv7qS8875IOOPOOI1tythSD/m1mEB3hjibrPegaShqI2M4Xv2xRxsLCWvoUL7POgfx7yapUxLr8ZQAYNODd3ZuejiXNocGRU1Og5ZGGfN5Vey5PqHSW/5BxVjG5j94ffRNGfijtcUHaFrP/assNVOfifMDhP0nn645+/Q1Q6HjYXlM8COxIjaPUxOrcM1FVvy49iUnUzazFDIpMhsNWGM7Gg/IyUrquXYNb0B15L98a+LDg90N0Qeh+JJ8P/YO+94Ocqy/X+fKTtbz57e03shhDRAulRpIqAggiA2FMEX7GLlFQX5WV5QXkUsiIJERHqHAKEIhBASkpCek3J627N9d2ae3x/3npwTaniBJEAuPoeTbGZnd2dn5n7ucl2Xb4EqgnZAeRB5EngH54Jqq+DSC+G7v/Qp5F00NobSBK0Mng5hDCylr62ehrFTtj1HKcnmyrafO+Prn4HVG6WEqLUEsYAtXLxB9RnXg/6k9CMr41Je3FKiKMzdSwZXkqUgmsqUplX9odd9q0HRUDC6WYK968HU0UOZqGNLcL7iYiHQ9yZEF/XAWbLNewHvkbf5/kKvB19oE0WWOlMMgJe5EuTKSy4QNkJt2FhqEkZLWaJPaQT6DfY/mPlFlVhJbS6Kqg1IhllJF92UoTGoUL1UqRRNCs6KT2SYeiAAtw/ISbKxNJJteS5Va5ejlULZAQqmTVvDaB785jf55O23E66ufsPPfn6lqNO0FGEAyZwOC8PVDTDCrkd534H8gyxKJ7miOI3yqn6aQi1kvTAag5g9QFXlY6zobKbaeu3mzqRJDpMuOxY49lX/li/A+ZfCCy9Lz8b1RJn/++dB44Rerl70Ak5jOzmrgpZHZtH3l3pqyx26rQh9B5YTPSxBwQpgKg+3YJFba2MYEEhAoRpUBDKdoCPC+Tr//adj/Z7AvBA8HABrA9gPQG4v8MrAboGxz8Hoye/8ax75IQj4nXzhq0vxAk3kvDABM0fY38KGzs30thW3BcLBAZrxr1EmrK+Bf/4a7lsItz8MD/0HevqHTHC1lqqD74uuaGePZGG5AqAlKxzTLEM2uZz0GW17SCR7EIMEd3/YNOlrwVDSsxzXDBu2IspQhgy7VJcuwc4e2ccXT3uHDuZOxp5AuAtwf0o4dfWW9JG6Sg4UyVKAc0r9vXKrn5mhVtoKZfh+hAYVptp0WJIT7cssry6TRpHeonTgRPos6Q/TBFQFJpsvk9IxNnpNlKsEF4bv52OBO6nQVyAy2UPIarlYBgn6TqIX5fv4gUHBTU1VLomXz9OycCFTPvaxN/zslSbc2CzybuuLMNqGg8JD06pYo8H6PMvymnVqE4dHbqOvWMZgVzHnBQkYKabFluPpA9/4QPvdkHsQ/DawZ0LgQB56OsCSVRCthJbSsbGLcNkd3Rz81X9RvpeHch2iNS1ExrfwwM+OI9cxkpqGGl6+PUTn0hoO+u5jeP0W3WuroaDwgWKHSNepBNAPTha+eIAMY+zBzsfxUZjfCK0jIb8Kgu1ybY1U4KTh5CPendcd0ehQZrRQV9W9bQrX96Ftk0dBR0UxxhfKwHGHyLDIa+GZpfDTP0AmIyXRbGnUW5kl+oSSHpxGAmMmN6Qes+RlmRatrYSeEm9w78nCOVy1Ua7jTF76fYOcxkEqRsCWYJvND8myOQHZflO7BGCFGAwPYrBEOnrHTF12S+wJhLsALcWhsmWuRDK1DCnZDKrhVwU62afuNgKGi6ctAoYLOkB79xFUmyvwDXiRuWx2yyluYxgaGErKcRVKSqtFbyjHM4CsVuR1gKAqUG92sY+5inMjj4A3AIWnIbB9IDwyIhZSlUomrYumjVZy8WSjZYSyafZatwzX9ylmdmwqM6Dg8Ci80cT0wWHF3zNygTnKpaBtfC1Bp+DZ9Og+Jq2F2SH4S6Nw9rZD8SUYOB/8DKAgexPY03j0mT/gWTZLSyVpW0HRhspDnyWZ93EKUQY6XDY/51Pwikw/egFPX/NpJo9xyNkBWlbG6bu2md71EfQ+CPcKyQDpg8gfodqGz38Svlu/Q4djD94FxEz4SxP88atw1S8h2yZCEREDLvg0zPm/6xS8IcaMKWfGjFqWLeuktjaCUopstsDYZsUJZ0xi8TrptX3lDDjpdS6ARBI+c4kY6CpVItKX+IfhYKnnpodKnKns9qo4xVJw29Ihf58yrmSE68tjPf0lb8CiBLtIsBRIPQmMhdK+nYD0H9MZGcRZvhZqK2DyWFGVqYrLPnsT0tecOPrdOaY7A3sC4S7ADAduKf05aJQmQ0uTnhrJBj9U8QTgEyVKvS1cv3KzlePKLmGaXs1t3nGszo9gktPOgBEjVQyRcKspYNLniTBElSFcOxchtBcAD0WPX0GN2U9B2xzirB56Y8arxxuPiIrA9CMpCHigTZNsWSVYNtVbN/CJBfMJZlOkTZOmuXNf9fz/K6Y5cFqkjtVYJdsclyIWplZYyqMnX4+jxB7qxE3wwOghyTi5O/xIJljM+qHHisuoDC+jLTcLAjIpC+JMUD+2lXQqyMaFaV74U78Yo/pgBtIUvRfo6ptFqEuRyVr03GbJwVyK8K+OASMJagGQgVQAjn/nDsUe/B8RN+HicXDRb6TnNlCS84q/hSnetwqlFD//+ZF8//sLWLSoFcNQRKMBrrzycA49dMfq5NfcBO1dkgkm0xJsBis6iZRke6YpMmWGlmCVzUPJ7Wgbeb62SgJYviD0CpA/O4EhMW/bkn0NTpqaFugSgT+Xk2CXNWWwyPWgqV6C50GzZEDHCcCXToezThwKxlrLsM9f/i1OGfvOgM+ewk63Vnor2BMIdwE+HIE/94sSSYUJcUMEthssEW5uKXo0hNoYY0apt+QEm2sU8AsbSQQrqcnFuDtxPHE1QNDPkVJRPAIo5ZHTBgaKmAkJH1KelAAHSfQ+Jpt1I0k3glYBbs4fwUajihMiDzIhcCArMmJIO8YWVRRbwS/r4OkyEdd+NFiGff/TTLrjBsoLGcot6Hc9UqMP5FtXrGTylG4+8YlpjBz59jSTlIILK4LcWZzNUmsRSjtsKvhoM0feC7EpNQ1TCfVjqyvUjwsrS0/228HbBKp2+x2qGCcdcCOXPzQLqwiUpuqKCcjno3ixPp67IUGg2sC2FOQ02oXuF5/nxZFj8WaUw7MMrcYzSJr8J7kpaUTLMhAQw9U92D2gFEzaMebMO4KqqjDXXHMcbW1JUqkCY8ZUYO2AB+YgHnhSglShOMTDU0qyNaP0eH1cVFq6e+V8y5YqHH7p3ByUQEuWLI82binxkkvTs1VxmfRMpISnCEMKOINDBhrJ9gYVaspiMpTT2SMB8NHrX/v933I/XH4dOEaegOFyz2NBHltk8vefQ9OOqRzudOwJhLsAQQOua4S/JqRfODck3Lq1ebmvnlRmMClgEzM8VOkrCug+CgrK0RhmlrwOE1NpsWPyM7R6jVi4aCwMFM0WLMtLeVQzpF3qoSgQJg1MsDaw2YywKncUt7mHUuhz6MwXKWihGYyx4ZYR4vBwYBj6PHggA86Zn6Vvzlwyjz/IhkKR3pUOya1Rwv19LF3WyW23vcy1157A1Klvnyl7vL0v44mzyH+RFrJsSE5hzcBs8r5olw1WhJYOl4RSgZIkvh62BYDHlDG9zPoUPH2TVE0NDbFGWBaeTdNLd2JoH9Oy0Epjhj0SGyrQZZrc9M0QKBclgBjoDqB3yFXAtuWVpo6TG8d7ZVpuD949NDS8eerZ2gn/vA9uvhda2iS45fJyXuUKQ1mWQs6zSaMls73o07D/PvDR82Vyc9AjcPg0aHef/J4xSfiI3f2lfSkpj1qmvF44VCLde1IFGQ7XlUzQUDCu1M+MReClta/9eQoFuOqvLn77WtKJLtJKoQyDnpop/O2OCr61m0rZ7rlcdxHiJlxQKT+DGDyJlVI8wwwWs4gIUQwMfHzyhsXcXB8FbTDOWMsadxw1RhfFEj3ex8RCk0eCoMuQI8WgQ4VYNBkoI0zejuAoCJqKtZlm8r6JZXhElInCYEMRTtsCT44R893f9kr2GjEU+Wl7k5+2N8+v7cMd186+9y4BoKzMoacnwy9/+TTXXXfiDh0L33VZeeutvHTzzRTTacYeeST7fOYzhCorUSimMIXJagq39sLTKVHdUUpjplN4rku0LMb4wLBT2agCew4UF4NZygq1BzrDZutMkrPBmADWVuGYpepgjTEeJzcd01yEZXr4WpEqlJNMxiGSgryBKkgZVQFuHQSyUMyXFhoGTBgjMlPnHvuWzC724H2I9Zth/n2wYYu4Q5xyFNRUbr/NvQvhO7+EleskCBmlAOV5UrKEkst8qRcYi0h5sbcfTjwMrvxzqaLhDtkdDaJQKm+Gg9DeI8HMMoRCMah3WnChd2BoUGYw8wTJAH1fFGKqK2BUwxAhPp2F6a+j/NfRA13rtxBIdWI5DiiF9j0KbWt56unx8Pl3QMbnXcA7SCndg7cLpYZOxNnMYTJTyZIhQ4asEWTvfDsjst205Mo5yf43tnLp8qvZWmzE0wZag4uxjUc4mA8NJ9b7yARoRmtcLJSycbVB3rfQKCzl4ysPpWSSdWtRvAozWmgdr1RzyXSn8Jq2P7krK0MsXtyG90Yz2cOw8Gc/44nLLyfX24v2PF666SZu+8xnKKTT2x2bn9RAlQmZQgF7xYs4y5cQ3LIJc9UKZiy4bfudxn4M1ijwOsDrxPe76LFP5X+Sh6KAfeIQGw9mY0l1A0XlXgeR92tp76ijo38EyVwF2vHAUthTRmKMkoOpSwLK46eImahlQagJ1rjQOwfu3w8WZ9mlyJEjQT/eDjFP9+CdxAsrxGz21gdh5Xq47l9wxjdga8fQNv0DcOk1wlt1S+4QRkn5JehIYKyKi4i1ZYqyzMTRIuZ9+H4SBJ97aci1YdDFHkpOE6X5uWweevuEU+i+xuU4GDwHg+EglUIDVZWSTSYGZBJ12Wrp+bkefOZ1hsPtfCeFVBIVCG27mSnDpEiIUPfSt3Vc303sFhmhUupK4ARkBGEd8BmtdX/p374DfBa5f1+otb5/l73RnQgLi8P4MPPYlxQpylQZIbOR9e6V1CmPOmsLvwxfzI9zl7COsZh+gSJBNAoD+WLN0k9x2z4lEEobwKeofYKIK7ze9v/t4SNizWFVCkL+kM0QgBEJ4rT2bfecfN4jHncwjDdPiwa2bmXVHXcQa2hAlUzbYg0NpFpbWf/AA0weRseYFoSFozUXXnUTi8fOwK2tZ3TnZo59eD5bXlpE69gRNM6eLRubtVD+D3CX8Fyql/+XmESLP4KVeenLTnFgTkgu/IQHL+TBiIeY9I0P8/ylC9CpDL4BylXosw5Gl8dQcXD2guzSkpWPD2ObIHYitDVAQzUE49DrwwXtcHMzNO+YmM1bRpIkq1nFAAM00shYxmFjU6DAQh5jLWsARZAgB3IQ4xj/7ryRPdgOWkt/zDSgqkSpjcdk+OWP/4IffFkee365BJyBtAQhkCqHrzUuHqalmDknTaYnzsp10vNbvlbI9yvXC3E9YENrlzyWSpfEsJUMvwz2AgczRv91CPR2yULJ8+X87eqTIDqyQQLemk2iTNOTkH8bSMEvvyV0jNfcX6Gf2fYCFvnHElMJTDxyOgSGyYcijwCHvGPH+p3EbhEIgQeB72itXaXUFcB3gG8ppaYCpyPet43AQ0qpiVrrD8wyN1L6D4DQx/lW1yQOD96KZeS4J38Ez7oHUNQW/jbtGQleg8FvUGt3eFZoAQEMiig8rcn5Jo7hUvRNsXpRspwsaqixJAApBV+qgP/ukv1HlFg/VdRFsP/0OK7rY1kGnufT05Phy1+eu0NuBn3r12OYJsow6ItVsnbkRJTWNOefon3p0u0CIUCstYVj5/+WU+rq8SyLgCufNG1ZrLz1Vir3mc3fEnBHEnwMZjqzuCcl/MUaCzYVRUpudQGmOnITChtS8u3wILbfWEb+bwPrH9+CuVYTLDSTnhfGzYjIgXkAVMyEeB/8VyPsNRcu8mDMMHf6uAntrlgfnf+Kctg7gXbauYs7cHFRKFaxkhdZwkf5GI+xgHWsI0IEA4MCBR7iAaLEqGM3nVR4HyGVkbJo3St0JSri8MTiob8PGvU62wZdND5+qWrjowyFPetZYt3V5FbuQ9GFEXUwfpSUWze1lnrRg+edGuL95fISDAezvDdC0ZUpVKWkumEYEpwHUj7dfT5jmjxGNgbQKHxfMtmnXoBzXicjLB89mqPLbsfJGzyX/zAuFuVmL8cbf+DgYw7+vx3UnYDdIhBqrR8Y9tf/AKeW/vxR4B9a6zywQSm1FpgHPL2T3+JugfWsY2Tdk7ysazGUos+HoJnBdeMMEs4HVwgWpV4WErj0sMdDQJlhkijUgpWk3unjqKpnuKn9UDJeBK0NsVkx4IgIzCnJmH00JlOk1/aJt+CkAPxscojFhzbz978v28ZrOv306Zxzzswd+kzR+nq07/P09A9xzyGn4BlKZlwO+Chf89o59BXbFzMZlGFgojHdoXqQYVnkEgkuaodncmKDZLkF5vdk6A/GaQxLlBphiR1Ulwv5gByPHg++XgVjAnBXEnp6QxhPTsDrggxgXAXG+WA0QVMEqkPwizqYGxb6htX26p6grUTR552GRrOAhwGoSBvUvrQaM19g07gqHsl7rN28iNjeEzDK5U4bIECRIi+xbE8g3AlwbMnU3GF9PpCMrnHY7Njc6bJtfbVkWZ4PepB+4BnEqtNUjOwiGcphmDOI2ibtPSJo3VQnGWE6O6Tj6QSGqBH5wpAUW8CWYJl5HX9Bq2T4O6IRlqwcVLzx6VFy1wiaq8B1UdYUTDNEvOz1B2UAzECAQ771NfQPf8iB9k14gThWpoPysaPeVGxjV2K3CISvwLnAzaU/NyGBcRBbSo+9CkqpLwBfABg5cjeUN3+bcHF5RC8gSoBNrk1Awfr0eEJmCh8L5cewtChJwPY9Qkq/a0p9A7ekG3pEOMSvG4v0mXlQI7k47HJdt8ljWYgZ4ixxatnw0o04ThwXKw1lKtnzARfsy3FnzuTlthRjiz00VkcwzTfPBgEqx4/HOvhw7vzQCcQySSzfwysWcAMO1+91KKe7UGW9YvtQiGImgx2WyVGtNflcjlVHncpdKajA5dDnb+SUJ66lWMjTGqvjzmO+yebJB1NjiVTd+qKUfB1DzH2/UiU8zsouuOF6IA5WFfh5IAcV90H4Iojaogr0jU45PsdHZaExKIQwiKIeWkC8k0iRIkGCEWu6mPO7GzELBXA9xt1vYDkAACAASURBVBfyvDRxNMl/d5DGpPwbxxI9RciMFiYDJN75N/MBRa8HdyZhRV4WgyfGhKwPEpg+dgTcdI8EOZC+Wns3TBgJ3/s1LFsjHL9PHgd/v0u2a+3SaE9hWD5N4xLM+/STxOIuXeuDYLkoTGxL+oyNtVBXJdSGXK7kb+jLYMxgSXTQbcIJsM0VftAlfniWGAqCVwWbysBuALvPR/siTprNO7y8aQwhZzXR8EuowBwyWcWINxGKmHDssZQ1N7P8n/8k3dHByANPZ/JJJ+GUlb3xE3chdlogVEo9BLzWIbxEa317aZtLkCTm7291/1rra4FrAebMmfM2ddZ3P6x2e1npFUm7ETHd9EHyA4OYmaaoY5QeAuTkN0u/BxeD+WFHRSMXcINZxsKBGcxPyEDM4RG4Y8TQhf16GMyA0j78pAvu63JJbe6CfI5D/3A9R3WtZc6XvkTj7Nlv6F+Y3LqVwr4HUdvdSs52cJUiECujfvx4ei2b/2Ql8A7CDAQ45Ac/4OFvf5v8wACGZVF0XRZ86Yc8N+lgulwwkt38s24WkQkfZvbmxYRzKb78r6/z+7OvY2PzDGpNCfS/bxB7qPJhwtg/ulcOjhEsTdta4JWoI8kc1NvQaIn6xm/6RILurHL4c5+UWG0lPcdRgbdmv7SjsLAwii6zrrsZbRrkKuLk+hNgO8xYsZ62qjjtbpD+K+4mML2ZwKQGiriM4HW0vPbgLWFzEc7dCv2+LJweSfv8LaH5Y6PJaFtcYr54hmR5dz8u4tf5gpQdb74P0DB1vPTcFq8QhZmmOng+uZbO8uWMaCriZ5MsWacwlEVlcz9Kq20yaEVXFqEVZfDtz8Plf5DXqqkU54t1myQg5gsy5WkYUioNByXzcykN5QHVtaA/CYmRMv1sWMBqj+htefy8RTqrGMhEeH71ZAKWi2UXSWYCTBwFv7sZzv3Y6ztL1M2YQd2MGTvte3m72GmBUGv9hup+SqlzgOOBw7XeNgi8Fba7gptLj33g8Mc+m2JM4xgaB4XWMC68meXJKRjIjRmGrJUqBqfGSnXRqIIpASHZB0vCvdUW/LQLbktKX8tCnOSfzMDfm6DTkxLiuID017ZhxVPw0A3Q18ZTzfvy/Iwz8NZtJQJ4jsP9R52OuuJi1p98MlUTJ7L32Wcz57zztusZat/nySuvZOUtt9CnDfYvalJNo7n/279moLaRDgPiwxQ1hmPMYYdxyk03serOO8l0d9Nz6LEsGbEv3a6B9l1Gdq0la4f408xPMqNzFS6KYjrPh566gUUnX0kR+H71q50rAFZ3Q9Aa0nFViNZqYRaU6aHj4CioVXBjAh4cCRNsuHlAju8pZXBGfHuLqHcKIUJM3ehj5LIUy+P4RRetfXAsjJTPFO3RGlD42iN57wsEJkWJEmUq75Km2AcMV/fKd1xvuuBtAL+DLjfOjzZ00790PC/lR1AxEc44A06LwV9uFerBlnbRBjVM2NQGs6dJcPrTrfDgH2GeU8NPn9zEH8/NkEtCJg2Vkw32+2I98bBNb0kWraJMuIeHzRO1llOOhDseERH5+iq46V4pwa5pEd7g4CU3qhFuuxpOPF/4ieNHwuZ9YONoKJSmWX0FxckGmf1CZO5y0FpIV0XXIp1zUMpg2gShU1w7XzLdH39lV30T7yx2i9KoUuoY4JvAIVrr4YKVdwA3KqV+iQzLTEC0PT5QyPvweLKceaEaHLsbzw+jlGJydAMJt4xifioVVsmZHrF4yvjy5bpaguOkgAS+aqRs11tSsrkzJb8Hy58NBmwpwilboN8DO5smsuoljqoKctEB0zGfuQP+cRnYDq4dpPGZW/npc3fzzdlfo7+iCS+VRFkOy47/FGOXPwdKsfi664g1NDD5pJO2faa199/P8vnzidbVYSqT1VkIbN7IvN9fxuM/uIpez6fbVfSpdlzdgKW29zOqGDuW/b76VQC+2wGtvRLgDbeI1hrT93BRrKwYzdzOFdSEQxT7NnJgBE4vE43S10LdNEguFvHyNKX+atHHqPAhkeO5hE99pUFTPIytDDygz4ejY/KzMzBHzyGNRRYPrXy0bWD3FDHSHo7hUEcdPUYeK63Zh1nsxQzChHfOm3uf44mMTE/jrQK/B3Dwkyb/7N2XUX/bRJ8u0G0EuOo4CC+WEqZliWWSacpPNi/ZmRMQXdFnl0KZo3jwaxZ+CCI14FRqulbAnV9yGD9XruF0VjRSzzhOrMOUkiGcsz8GZyNk9jsfk9LnlHGSCRaK8jNlLEwYDXf8Fi64TPiKa5vA7xWFmXxBAq2XMEjPDsKd8nmDATHUNA2fkGNgm1JObQjAvY/DeaftHg7zbxe7RSAEfoNY5j1Yyhr+o7U+T2u9XCk1H1iB3OPP/yBNjA5CShmKLf1HM6LiHhyrD60VyoRJNny3KsCagmQr+4bg1gRc3iPOE5NsmByAlwrQ4Q5lWF+ukLKmqYaC4CA6XJl6PPTJu6m/5qfkXY81vubC6moOPnAUn4iWowIhXB96y8JUd7dw7IaHubHiLNxcDjtokC6v2qYK7MRiLP3b37YLhCtuuYVAOIxhmphasthsZQ01S5/B7W2nEK8FBRe2xrk21MHVNXXMDL62ud+mogQsO5si2rKWvu4UqBSFOodoIcfMIAQTKZr3OoJ5bzIvcsEh8PXHwd0IUQdcV+P25fC3GKSqHKy0T6IXeqrTTBoRxVFq+2x5JyA4Zh6O00wsW6AQsOlZuQzTVygF7aqGoO8Q98r4yGFfZQT779w39z5H1ICin0Yn20j3uPh+js2ZWkzbo76qC1OH6HBH0X8XDFRDvCiLqoAtSi9uSQf0ycWi2uJp+NwPINe+mkKXzcy9RuJTxDAVDQ0Wa9YnGVXVzVkn1HDKUdJbfD0EAvCJY+D624R8n81LT9D14VMniM/mP++TjK6yHJYFpec92M7PF8B3kZFwwLaKxMIZUpkQWpv42tjWgzQMCepb2vcEwncMWuvXJTlprS8DLtuJb2e3g6k8Dq98mS7zJRQeicwkkvnRbMg2cm48yPQgTA/KSnBpHsotkUYbV6rfay0Guw+nhQJwdBT2Ckrm6Ovhgy+SQfb7sHfrOhquupT+cBluTGqI4b5uHn0gRfLzH+VzmxbgKOmTpMNxpvWsFHKuhlykjClPPQC+TyAWwzAMsr29230mN5tFmRLY8iXB8ZAJnuHhFPPklMbAQ2lFmwufbytyR7NJw2vw8sYEJBN01qzELORxQxF8yyTQ3UX0zn9jzq1CB+Oow89602N9Vg20fhv+sgCSSyFMmvyKAtUdOTapSihTWHmfzgGHUNblxw0yuLRTYQdQn/05gWsvJjCQxqyqINnezqpcBZsKObSfZdxRR9G83347+Y29/3F6LM//W7cJc3UeQ2s8rbDK+9j/ubsp6L1xyGBYsuC0qyGxBoK2lDNzhaH9DJ/i3LAF4rkUuYzJqg0G0yeIxZkdgsZaxYVn5Ni/tJ7p9YQa9EIOxtpwchmMGHZNfOk0uP8JEb0GCVhjmuCqG+CxRUM9RtcD6qC4D5g5CZwBGxIBBcs1agR4UZO+RBkqb+C7CvSQYLnvy2Tqmw3OvFewWwTCPXhjPMajNMZXUsg75HxFJLwCbfUw0x/J2SW/u14PvrgWXlgEbi8ER8FJc+AnDTLNOC8kP8MxPiCTjQuzcnH4uiTJpmH8E/dS8H1cJ7hNni0Xr6KyYyO3eM2cYTqEvTzjA9CaLLKlbgopwyRbVUuoq42p86/FKxZJtbVhBYOM/8hHtnvtcUcfzTNXXUUgGiVYisJmqo90YwPJmkZs5eFiEraKhM0CSbfA3akgn3sNhaaTYvDg3QvYVFGPHy1DoYmku/j4375Lx6Yunj59HLlPns6hNXW8Tm9/GywFlzTBeafD1lPhjgXdXLs+TDDtMerhPrqnR8jUBDAHfA5uG+D0ybVvssd3CZPmwo/vhJcWEipkKQSqCSxZzbRUipEHHUTT3LnbBAr24J3DqZmb+Pf9W1k+6yhM7eEaNlOXLuDI+Zfy1MTL6YwPyQqGR8KZk+CK34nyyyDPbzhsSx5L+c3Yai29CU0urwg60Jfw6OmH1kQ12RwkLDhnqyg8BRU8lZGe/jUNMKPU7174vEyoThsvjhPRsPQK739SSrHRUoU8k4H07eCOh3wclA+FSlBFCL6oKJaDWzTxK0AVQOfAdyAakpJrT0nmrf59kA3CnkC426OPXlazinIVY6aj6Pch59uYkU4+Gm0hpMaxoQDHPwcvXw2qlPUFDLj2fpj8fTjndVZtg3SIO1MSSEF0PKst8FNJvBI3USOZolKKtB2mQ4VYGaln9kALVW6asOWx8LgvMrFL4dz8Zybd909i+Qym49C3YQNlzc3M/sIXtnvtqaeeyoZHHqFr+XKUYdBQ8GkPhFh04bfQSuGhMPCpsESrzEax6XV4ebOCcPQLj9C++Hm6p+9FGW00ti7FDHokRtXz8v4H4tW5hHmGAzloh457lSU/i6IBlJKCspNwaXpSaAh9XQH2O1/vWk3RaDnsdwIgblBz9vvwLnwzHwz0LL6dE299iI8s+RM95XWU97dhbBnA9Qo0DDzNU2Wfo+iJoMU5B8AXR8OvrpMAopE+36CzA5QGshQQGYtdXE4+1UVvr017t09/wqN67Fyu+FOI3/8TGs+EZf8BOwGVU6FuHiQduLwb/jcu2qWXXyeTo4NyjYNOFIWiBMFBpRkUqD7QV8LokyFVDlvGQl0RCiHIt4MVAq8oGvbVc0HVQ9s6qIrClz8JZ39013wH7wb2BMLdHD30oEr/oUrToKYihaKDdsrdcZzTCqtuBCMHqqI0Ig34m+DqO+Dk47ay9IYbaHv+eeKjRrH3pz9N3YwZ5Hy4okcGaRxjKCtcUYCnjzqNCT29xFs3YuVy4glYKIAyaJm2Hz/rCXPd/d9lS7CC20+8nIaJsznnt2eT2rgec8Y0kq2tFDIZouEwgbIyovXbR+NAJMKJf/gDLQsX0r54MXMbGlh5wJE8ror4WUWZmaMmkMZULj5gEWKf1+HlKQUnz5vOg0/eR8PmB0EVZELV93G9AvlRDmWEWckKDuDAUnjfMZw0q4afh3pIpQzssEFXIUoyFcBE402w8fWre6x78P6FGawApalIpahIdoPOkw8ZJBKK9txkMj02lgmf/Tj8YG9RgAk6EnxMU6yRhvP4BheapmXRNON4Cv2rqIitZ3PCYe9DplLZ0IxSQqBfcIUMtlgB6F8BbQthxkWw0oXTfg7dbbB+kwRb0xDpNcuS19RaSpnJ9CBpXl7YSBcpWziAHakkO12xVw20fAUy94P/IhhhsI+BuUdBF/DftdJaeb9hTyDczSHTfq9Ni4wR464U9A2A3gRG+ZDIdhGww9D/yGZuvfHTFDJpnGiUgS1baHn8cY688kp65h1CQQt1gtJzluZFOi06YgwvnnkBZkcbB1x3OfHOLRgoFn32mzTU1vB81aEcPfJenGgcwzRJ9sJhLe00NjZh1oQwP94MTUHYkMW9cy1uNotpb9/gMwMBxh5+OGMPF6vumcAx9HFB9zIeT4ykoDW+b2J6tUwIWBz1BhfgpOOPZ8Hffo3V1o0XBAo+ZIoYp04nWQ9lJQmrt4rKiM0fL4lw3pU51rSXoVE4Ec2Ec01+nzfJ98KFO+a3+q7hnqT4MdaZ8N0aiA90wKaVkjGOmTGk57UHbxuN+3+ZQPgW8gMFnLIgEMTUWUJBh9PO+ySnVYkR7aiS7EdTneh2LlsjA2GWJRnaIAbdJZQBhmXzja9O54Y7p2Omoa8IdlpKkVvaQVeCGQfbBKKQ7YDWJ2Bgb1Cd0FQDazYOLcwyWQmGji0ZYTo75DJh+C5j7LXYpuYj7sN8a+IjfLPwPZ5TswiXgfNRcE4SWlaFIRxD5UL1a8+rveexJxDu5qingQoq6aOXcElzNEuWAAHGM4F/5GXIxBy0UlFDNitZH6Zs+BNLiymSVeLo3hSPUJtL8dSVVzJ9/kFobeCVnre5WKJdKIiair6GZnLRchae9z3GPPco6w46FmPiFKZZ8HTRxAxVknbBc6HJ6WTrXz9OvqKHSCiLlzeItCaxxjtYh8wlG/NwduDzVlDB9VWzuNlJ8K+BCK4f5Kgyg0/FX+18MRzB8nKCVxzPwG/ugkVb0VEbjhuPd9hIVLZAOpRmApPeUjY4iAMmxfj0T2L8ablPtakpG2VimFIuvjEBZ5cPLSZ2JjwPZm2AZYM3Vq3J3vFbfvjiX6mySil+7Uj40lVQ1bDz3+D7EFZsDsf86jLuv/j7pLrSKDTKjHDoj3/FxBPHvHp7Cy77L/jSpdDWKZJmrilZmWNL38404dC5cO4p8Ju/w/J1QoVIZUT02rFLgzbtkDPBrgVduu5aHoW6AlSV5IhtG2xXJlIL7pDuaG2l9PW0lvN2mvUyVVYvrhnh9u5DuKhnPj+45QI+ddZNZCtHorQIbNhKhnE6XBhlw8x3QS1pd8CeQLibw8DgOE7gURawhU1ooJpqDuUwIVcH4YEQ1EyFthXgDRrDazGeLS88S28kjq8hp2EgD61mlEntHTzd1s/aYiXLtSit5EoB0aNk4qsM7FgZHdNmU5g2G2XIhOeAJyvFDld6kREzwcya2wAPmzz4PqajSTRUULV0E+G9xvCcepYjOXqHPrOpFGfEyjnjLfDytO+TW7EJ86IPYaV8cuTQaLTv47b1ER07lv3eBpVglQ+1Yw2ixpDHo1VS6Ghzd00g/EaXBEGz9H4OWvc45/7nD3R4RSrzvSjLglQf/OW78LU/7/w3+B5AjysE+WabHZ7+rZtzAWc8cBZtz92JV7Spn3UMwfLy193+0HlCZp9/H6xtgQNmwQmHSpDrG4CFi+Dx5+HLl0oAG1kvhPhcHtBDfoOGBrog6UGxF3wPQgnoa4FApWSXBVeCbMgR2sS4kRL8jtgfFjwjlArTzVLd3UfOD1J0FZ72WbQhzozaDq7Z8G+ubPoqYUMkCC1EPWluCH5YM0S1eL9hTyB8DyBChOM4nhw5PDzChLdlNsdF4fo+WHcSmF3gdjFUSZ0BGaeBio0v4wdD22yYcoUi67TNwkKEyQ6sKQinMKMhCExwRIi61I7E1FBQciFmtHASPeTGYSkYE1uOqVzSXoRAGLRrg+fjBItYM/YiHgvTQsu7eoxyiQSBf60nM3MmfszASQVxAxovbFB2Syunf/MXBEozoykfbh0QOkncgI+XwYHhNzbTHW3BvUn5/BpZOIy25XjW76Kr6MZE6QZZ+vupS25mRP8WTM8lb9sE3QJkkvDCQ9DTClWNu+aN7oZI+eKksiAtxy9kwDeq4NgdXHxZwXJGHPQKOo6fBN0PRp1MmAzDpDHw/S9tv3lFHD5xEbR1Q00FbNwiwW8gLWXMQX2tQVtP2wJLg9spHFdfwYwx0NkDL28QFwnLkgVtKguVZZKBNtfBN86VMuntj4BTyOP5CkPBgFfG0ZEFuC6sbwswvauFq0rFA62hS+w4qXyflkQHsScQvocQROoSvuvSn83z13yY21OKLheK5SIKnV4P9gAEGiFZBy8sPpujL/8v3FwIgkGU6xLs7eL5E8+iIuQQNGCuKavi9XkoKtHSzGvRzPQQxwYNbCoR8iuVrBITGso1xAM9uHrQ19DAsETpVOPhB208PIKvKIx2pftZumUjdTUxplSOxuTtXWmBaJRQlybwqw0kTm4gPyZEIFEk9I9NjOqt3xYEcz58oRVW5YUc7QJPZuEL5XKx35qUz35sFD45rBy7pijHyEIWAEkPlnjwtarttUp3JtxXtI4ndK4i4BbIBMIEDETPS5eywu4tewLhMFzaBY+kpa9qKGkj/KgLGm2YabVA4TGgCIEDwZr0xjvTBdau/AV+9nYKRY1phrHiFzFh4om0d0kmuGi5lEHn7QVHfghGNwnXr617SJzbCUAiJf28VyIYEO7ftv6ihrEjRHKtvUv6fn7JfT4UFE7gqEa48huw3wxYo6HmeIishtWLolR5lfjKZIyxng9b95PNQ0zluHbJHD5flBKrUlD7AYkQH5CP+f6AVyyy+NprWfaPf7BuIEtXw2jKz/smAzPm4WtRiTAmyrbFErm9ZfZBPPGFS5j796sJ9nbhK8Xm409nySfP56BSBmSUplEnObDVFY1RR0m2E1RC3H0mK0EwoESHc5BbmPKhM1dPbXAzRSDnxohYCTQmoNFGlj7yHMCBgDhF/Pdf7uT6PyyTXqYHs46A34zdQkPag/1OhDnHgPnWTk3Ttpl5zjk8e/XV1G4Q6kYxnSafSjH7t5cAopbzzXa4JwVlhpR1q005Vpd2Q7UBFRYoDdf0wuMZ+GOj9E5X5IVzuaEoggO2kiGC5l14BR0dhptSQ5qoiWAcpSV98G2HpDKI5FMYpgXFwhvu64OEbhceHRYEQTLClA8396wi1HEJf7z7BJatn8yY+mV89pRlzJ5z6uvu7+WXfo3O3EIiXYsyLAyVJepfylPP1PC9/92fnn7Y0iF9vpvuhpGNcObxQ2oz6Yxka7VVsh1sE2Xahlyh9BgSMJ2AaH3WVYlMWygo7hahoCjMhBzhE86aCpcn4K6UqNio88H/rcVeS1s4ybuBZmMLnmETV/20ek38eevx1D8KJx/5rh3+3RJ7AuF7CM/8z/+w7MYb8atq6LPjlPV1UvbjC0j/+ibsurGkfQleWT0UqACWH3kyaw85nureDrx4BTXxKFW+9PmCw8qBaS1i0Z+Kyw0/48MDKViYEV++sBoKkBYyZRpREC9ORetlRKwURTdGURWwrRSub+ObffiEWMISqqnhufv7uO43SymrtrADBn4qxaI7fL5+bIgb/OcwVi+ClxbCuZe/ca3yNTDz7LNRSrHkL38h199PpK6Og773PZrmzaPbhbO3wtLSKHnKl+A21pZFQMKDaAqWb5UVeTgE2RHwRLl8ZguImTBjWPbX7UqWvKtwTRM8sAZ6SqWzhyYewX4tT7GlcjR5K4hCNFenuwliFW+gLbfmebjn97B1LTRPhGO/COP32TkfYhcg4ZecRV5xejmqyMvdbXzm8kvxPJOySIYX103hvMsUv/j2Zg7e99UOHtrPoLP/JpGtlQUHoAlR8LJs3fJXkun96UlIthYNSw+wr1/slzI56OyVkmdrp2Rh2/b7GoPigwpQpiHBrlgUAW/TAl2EqgoJriDDNuEgLCpZRtUN0xPmWLhr7THkegxOD91IGQnuzZ/AbfosqhvLuOORPYFwD3ZT9Cc7eTb1BOrivcm2FOA/aZbN+ygvzzqYjHZwtQS2Qfslt+RIqEr/LwQCdNSPIKbkRjA7KL3BSlPEqvs9yfbOLhfJspLKEyeVwW0DcOZWKSPmXsFAcIEeL0L9wClMiD9Ln7mRgldNwaticrBIWAUwMSlS5FEe4R83eDhRhR0wwHMx/ALxCoP/PBJm61n1jOjJwJJHYMMyGPvWbFyUYTDznHOYceaZFLNZAtHoNseLfw5I37PKlDKYreQ4tRTld9GFra0Q1ML7KhRgU6vmQTfPBVMdPNS26drhn32vHRmFfZcQN2HrBPhhF9yXhq55H6NryY0EfA8MhcIgVkjx8Mj9mVczniofXspLz2eaU/JPfPkZuOYrQk4LRWHjMrjqi3D+b0W95n2IEbac81lfMsFBpL001uIQ2jeorRDhhGCgyEDa4FfXRzho3qvXZvl8Cs/zMdT2t1LPDxIOtBEOQSo9ZFdklUS327sloA3aIwUsoTu8ESxTguGgma7nSSBtrIFISDOhcRH7jHuMXMHhoeeP4mNHTOKBrJzrw4N+w0To/oji/r8ezYLU0cIRtmH6BAnUu1QkYhdhTyB8D6CXXv5lzSf5iWZMZeHOVWw9ZR+eSh6LLkAwPYCtJWsLKYiZWXI6j4dJ2MxgGUUShVrSfoCchumWOMwrJTfTpC/DIudVSBB8JVQpaGT10AlTqrxydAT+1AxRIw7IMnIBj7CaVURKdA8AG5sCBXq6s1hO6UrzRM7GMBVeUZHWphCqfBc2rdguEOZKtA5rBy5Sw7Je5YH4fK6UvVpS/h000i1q8Rr0cxIETRPQmlPWz+f0lX+geaCPhsnNnHTyL7klMm6bXVWfL73UY3YxuThgws/q4WfAomwDPz/9Gr50339T27MRlGLp1CP4zVHf5YA+xVNZMK02mmKLqGQLI2yfox6/gzEBn1CkAgMFsUoY6IF//xq+/ZZtQd8TCCjp7V7aJUNijoKUhnqrSN9Ch3g0vd32sVCGze1RsjmpFAyH41SRK1ZgG2mK/tD5HrIHWNFyuNAehp2zg2T6VIZtYgxuiWxvma8uiQ6H58nra2B0gzhajGqEv12u6Wv7KY5/27b9n3LgDdQ0X8wP06e/ioWsFNQfAxPbYetiEeCOl87j9iSc+AEUKNoTCN8DeJKF+AGF1edimJqnnMN5JvIh7LBP1gswUCtDEDZQY6U4oekfzG/9KEEjiQsYyqc80E4m10wegxZXtAk7XNgnCD+uHRLm/lGnZJZHRuHgsGRA0wMyNBMEhneaHCCby7L+xn+x5p57sIJBppxyCtYxEbT52lfznP3qeOCeFoL1bHMIzaQVVY0eYwdKDlyGBVERFV1XgCu6YXFOAtfxUfivqrfu9TfKlrJonQlTAzL8kteS1e0XgJ7N4JaDkYWT1tzMl1+4kn6ngp58A2QSfPO6Mxh33g3Mj00k6cMnyuDccimX7i5I+rC2eQZXfHE+ZakeirZDNhgjUYS/JWDv6CYmVN2FZaQxjQzdaB4/chTFwgANXZ3UZSOo1rWQGYD1S+H678Op34DI7uss/n/FCTFosuCmAbkODgjBx2MOF8Y62NRRTzw6aHLjk3cdysoiBF8j+1eGSc7+OhXqOwTI4vohAlaSVDZGvO5sMpmSOXZe+nrZnJz2hWIp8zJkpsnzNLHQAA0VvXQmqigUAxTcAEMzwRIAfV/2tWGrBMZ9pkBV7EVGBm7Do5aCa8h0qVGA3K84dMci9gAAIABJREFUMXIE96art6tmJDyRD7zq8/CVS8Xpvr1bgvKH9xVqxwcNewLhbg4Xly1sIWpG8UeNpGVDL0sis3FUDjPgkdMVaMvGQLhQFaF1tOUryfsOGh/LKOBrA1N5OCpHXofpdmXEusyQTAngd31wXV9J7gl4IC1SSj+pEe3RGlMyJwvQJSpFPS4jv/8Vnl7zAk5ZGdr3WfD97zPihaPQ3xtBnjwWFiYmObKECHHJFz7Cc09cT297BiekKAxYGAZcMreVgOvLhGOkDKYfSK8Hn2+VEladKb3J25LQWoTfNry1Es4nyuDulHAgK0yYZUhWfFQUrqyBw38B6+bBgNcJN93LzzKz2Cua4yOjEhCKYXkup913Oadd/Kd3/Dt+pzCtdKN2UQzEZBRRa1EKChowKv4UWitMI4dG5OE802LV7NFU3LOMbEcL4VxRmk6BECy6F/o64Ku/f1/Wy2aF5GcIZXzuE4187Uob28wTDubJFwP0pCZx0TlBjJJGwcIM/Dsp5+XRUTh23yN4+ulqetr/RtTZTHviWCZO/hSG1UA6K4Lb+bzImykF2gDCoE0hxVsujKzbjKGKJLNxtFZEQhlCfpZEOs7wYJjNDw3LjG6CzW3wo6u6eGntr+lNVTNv8mou/PidTBjRBr5mrvUCnyo7kpsG5No2kEnoK2thfBBu+41YQvX0i4fh9Anvy6/6TbEnEO7mMDCwMPHxiTU1U1DjMXpBKR8wicbjKNMkryHjQXdqAi8OTMFDdJgclaHS6UdrhS6xD4tIabCoodaAX3TDlT2SCfoICaLWzvB4Ls3iXBWzgib7h+HlvDxHI7025+knqVu9lFhT47ZenBOLsfH2+9BnnkLraFlVOzhUU81HOI6apiruvfFz/GH+kzzzwmZGNCg+bzzB7P6V8oHrRsM5l0Ewwj39kuUM8vQMoN6ERTlYWxjqY+4IJjnwqzoRKG5zZXX8qXK4qFJ6NR87VfPPB29l678Xs1BXopTBc+3VLMpXceO4DYSDEeh4d7mQbwcaTcJaw14VvTzYNxYHg7iK4fsOo23odz0cq7s0zTuEXLSMvjoXM5MhYyt01sXEQ40YhVNRD+uXwJbVMOJNKATvExx6wF78KJfh6r9n6UiYhEIRLjjT5lPHy79f3QvXJ6ScaiJVlAdTcNWHZmKpmXielDkXvQS/uxnGjRC+YFcvbG4HXQWcDtwOREqC9kmfxorNpDMRNA4D6Si+ryi4zjYtUiykJHMwjKmG0S1SIVm7Cf5+/z5MHrmOqrIBnnt5Auf+7Kvc9KMraa7uRCmHi6ul1/9iTiopHwoP0YKCDhy+x7JyTyDc3WFgMJmpvMQyoipKZWUYOx8mYKboK1TgGcLAK5bI7qYK4hg5tPbIaZucDpMo+oTMLAxzk1AlZZg+T+xcujwp4YSNPJWBTvp9h4Dq5D7zbhrV0Xy3upHz2oa8A9Maxq15kRqTbUEQwDN8+lU/Zau6qRo9gQESFCkKCV2Xo4GGujJ+cMEwWyb9OeG5aQ01I7YtSdeXBlmGQykJYu3uWwuEAPuH4bYRMjQTMYYGJR5OQb7hl+Qf2khNOQRCHhYevjZY3FnN77L1XKyXwoTZb+0FdyJW8TILeIT9ygM0Oz08m2wgoxWHRArErDR/bJtLwQ9jq9x2z8tXRAn6Pp7v43Ym6e0rsKLXZsvS9Rz+YYfRFQb0d3xgAiHACYeHOfbQMImk+PQNTnO2lkrMdcU0Zsd6SPUTt2yerZ3A07FyDoqZlCw2uetRGbhavLKk81mq+tMMlCE1+QKyuovDgFUO2mCf8YupjDWxsmUChvIJBvOkjaj0JmLAi7D1v0BXw+hnhUNomRHCwQJKWdSUD9DRG+fG+/fl4H0idLn7MWksTBgFY9/Mg+wDjD1qvO8B7Mt+jGIUadJUBzcTtxPgVTDKKCNoQN4XaoMBxJTCViaG0pglsbSCF6DKSoAO4hSyfGTNwxy8+BYmdaykwdTbhLotfOJOB0r5hIw8Hfl6Em6Qu7mLccEs85ulL3ZASIYNzptYT9oXk9D/ZIWk3qdToCBfZdBHLz4+nlZs8Lfy5dS/OazF59q+V5DBlZIAWDtyu7rMdEfuF8Pha+FDjX6di3pLEa7qhYva4fp+WJsXHuDgEEFGy+NHt8D+G+CSDvhNdxvTWu8iW3CwwgpfWeRxsP8/e+8dH1d5pv1/n+eU6aMZ9WLJtuSKG8aNalNtmiEhJCGQ9qYsy7upn+xuEvYNm81ukt1UwibZJJssyaaREEIMhG6KKTaYYlxwt2XJKiNpJE2fOe35/XHGsk3N733JAkGXPmM+SFPOc+acc5/7vq/7uoRNLFBm3XCSUlCDi/76NftOX0soFE/yBAECBEWAGeE072h6inObbyMUu59EcB9d0Z1szc7CES6ukihcAtID4eHG6vn5Vau5/3CYR0eSjHoxDF1n/QO7KJcq0NL1ei/xfxya5pNIjh1p2FEB7DKFBx5l5PGDlLMeQinE2CBPPbXhuNcXStCbhnIISrWgavCb+EPAbmA6PrvNAzzBoN2A5WisXPgQn37Hd0jGMuia41d27OrzRoER8DbBYCfkAVtzcZs0DqjZVDwXT1XQZIXv/uHt/NWvv8Hn/mDynmvhH673STmTeGlMZoRvApiYXMjFjDJKTmQ5uyXJ11I1bK34A92NQV8J5e9SfnamK5OipzjS7ncw2V+cybz0fr5/yzXMd8YRnkuvI9k2bzXXnv8lQMPUSkg8XDRfER+PIDEcRjlEN3OMuVxT679nzoWblp/LAf37eOkxSvEEGaUop4eJTa2htDiBgY6rfGaeBOoCfYyZffxgrJ2MC39X/8rrXh2Fn45DvwO1VRWYcc8nzBzryp0ixTaeo9sZ5/5sG4fzi7DdKL8chwrQZfiKIf9YDz8e90ur9RpE8PuGGVtHj+i43tFZLQAXDWE7ROpNBj/5Raa3zH8tv9bXDDY2RQpEOcqUTTOKQKJQBEWQdzXsYmsxTHGsnZpkH7oxiicqWHiMaRV0U6fSWk9oxwBCMzA0qBQt+moX0lXf9jqu7o0DNVpg/yduxeseAiFRClpXNWK87yQ2ZFx+t88mqBtcHofwHMjcDyJWNeON4meAHrAPOBe/NNMNSglGzVpOuGwHic4xaoujXL7yd9y+cS29xQ4oc7Q04kJhM4SWljjnqm8z/u1LSMYyeMpkt5qDISSj5SjlDxtkWwVC+aa7v10PSx6Ad6x+PfbcGx+TgfBNhNrqDzr8uM0vD1Y8PygMuXDdsP//CoGHQMcPHmEBIRRfuvNaFqg8sbomMi6kSx5LdtzN4oWXM9S8GE14gKq6VwhCUtAWyOOhqOCX1Ppt+JcRX2lmp1VL/Av/wVnfvY54v98/61u0iNavzCMuBY7yCQUo38C25EmGRIrDVjs3pGFzyS9/TtF9MsvpYZ/wc2TmKSrhv1rhP8f98mVU+hnpFTVH90k3B7mHuxBKsMfRaY6O0BTeza2H34ul/LSxqPzA/dEB/zVT9KPBrkWHw1aI7vp5dM0psn9XiES9T+mzLYmjh1n8gSb0ljduadDAIEQYGxsDAw8PGwuBwKhKy9nPHKTxK+uwe9KEzTjld06ncvU8jGAAx61gz4hy6CdriHzsARp2DxOygNoE6vR3vb6LewPh1n99CK8njaqNEFAuyrPpeWgQtaiMmNdAU+U5PG0RPxozSNdWnSaOZHPg06yLgImfHZ6N76isg2dr7I7P4+vxz/DPxeuY0XaIwdEWbMdkQp1QAXGQJqx44gku/evfM7iqnocePJtkfJzmwAG6c1MpvE8RmVLCyARBSVwNUufCTzdOBsKXw2QgfBPjWLHnZh0+VAPfH/P7eB7+wxR+b6xt7DBdowdx6xsBXzTakJKCEeI9m3/Ko5cupuwFcZWBgQFK58zkXsJahQKSFtVAxRng6oFGhhyNeHUGL9U1l5u+9VuiI4N4uoFVG+dtNTfT7vRgChcXhRSKjB3HUTq2F8ZSMOLBxiLMCcBTJbiv4NPZZ5pwXQOsCPvrqtfh8/X+44Xw8HiEDRgYaATIuRAUAbKuRkmVMYWJwu8JzjR9koyljs8mdQEBGWG7N4/PX/sz/vlfFnBob9hn2Wo65/1dlFmLY7Tw+toYKRRDpChSop46YhwdaRAIlqllPOLcj9IjGMJAVafHEiSwDw6T+th/YxsONJlk3RLezzfD4AjuV1f5qup5G+Im1qXTGfqNoqFci8hVaDtlkkkBkE4XeWpzH4ujRXY7UDCCIAUq6KDfs42WaQ1YTSGC6gC1cja7k5DsgNFD4Bw53hz87G4mkACq0q96EVSfYKzQSDrXwJW7b2b2oxqmCRW3mlFWe4laCxhOBT3tMkwTl7ztj9TGi6x7eA258Qh1K4axZ+kkw1mcUJjyQCOaK8CAnkknrpfFZCD8C8JXmmBBEL4w5GeLjbrPlgwIJqZ0j1jNSAHzgnCwKJCVEm06pF2DhAwQMYdZFt/H3EgfeeUxx7FpyFzFQ9Y8UuWP0WREcGQH7pFJYSHIN7Rw5GNuH7iMFbWPs6J2E0pppK04rtKQQqe/0Em5KgGX9eD5qvNFEH9IvaDgUyn4zRToMF5ikcegRIkiBSJEJ6jhCii4cTRZ8V25VXX9+GIDeXV8+ROgRRcUjcsYTT7O17+5mUM9AQrlMNZJy1DJFi7gIvTX8VQpUOBO7mCUND6nV7GAhZzKaT79afujzL31ejpTOxmLCbadv5zcysVYwsbEZPzmJ7DdMqo+4j9fB9EcgQe68VLLEE0RREj3rbfaIsi+UcYMm0uu+yah2trXbd1vJFQqLkIIwolGTtr/LAUsHF2jRzSTKwSwawKUtSCaO4xuzkIIQcv7IfsN35TC8+nY0AbyDyAPg/seEGmI65CtsrFFSeBM8YPg3E7fU9kW0C0hXR3AD+Ud5s3Yg0LiepKFK5+j9qoeIl6R5yvz+G3/FbiWjh4uoYUquKUgng3tMzwee+wwY2NlTjihgc7O5Ou8V984mAyEf0HQBLw34ZcY39PnV2ACwieKbI+3U6lvJ1wcREX9i1tMeMwXRYxVl/C1Jt90s1avp5ciewii6GRmZZSO7DcQspaUmoalAgzZORw5iqDuONUKVf1XIdiVnU+tOc7s2A406ZKxEmwcPotRT2HjYOLhYZDzBEHhB6aK8su4BQ9uy8HHXuUabGIikHh4SCFpMmDAhlpjFKU0vOrAfKd+dP8sDMCA6/cIJTDi+v3D706po6JuJGv3s6o5x5huoguTJppf0RnDrQb1P9fslYPDH/g9aUZIkERW17uVLTTRzIy9GfjhpxHBMMHa6bRYZZp/u4Vz3DN5+Ow2drGTTPchVMC/TRAI/3uSVd2t4SKqKYIW0vE8CNabRP/2JOJnrWFWy0V/nkW9CdHSEqWtLc7I4DhRM4yu8gQKLqgyodNbqTSEEYCjFGEUESHwmmD+tfD8w6DZUG4FeS8IF4JboXxhlaSmQSxcVZvRQfb5bhNf+1tfg/TLP4RWs6ohXAQtGGDt2fdwxx2rufeBs6l4QfRaizVX3k1keYWQAYXhME5Z4pYUygI94XDwlw/ziWcPTLQeLr10NtdeewbyhaKrb0FMBsK/QEwz4fom+OcR31xTAasigukf/Qr9//6/edoO8+v5b2dP/UyCUjB72iL+MeCrTYCgg6l0MNV/s8Llfsdf+BZQ+90WZHXa0JvwPTgCRWNgkDpjiKJTg+3UUrbayNsGdw2sJaiVqTdTjNp1VNwAiDIBggjh63ga1dEIQ/i9yFeDgcHc6mhJhAidhsT2HPKUqNM9Djn+IH5E+u83JwD/3uwTcNblfDLe+VH4eK0/ShECEppfrzpCO7GV7114axbCEt6fgFNCfo/0vzM+Y1Yqj+bn+8j8xyNQtFizpourr15KbW3o5Tb9T8JWtvBH7qBECYFgnHFaaSNIEB2DHWyj66576J1Zz/75rWiuYua+HM09Au3uG1mx6g90awcxTmrHe7IXlfDLyf7CqvMyHX6J1cNXt2uaHcKaO5c2fQ4A6b172X/PPTjlMlNXrqR12bLjxmXeKhBC8E/XncHfvO3bpCoe6YYO4olxZk7PML42yahKUM8wYzSQcSRrY34Zfnct1J7mMJqF5udcanImcprg6ndBcQH8ax+URn0ujBkH14BrWuBffgQPPwW/vB3GszCeBzcB+hKYdbHO9zf9LbvuqSNWkyViFhmz49z6vctYG9yB8ZQiOyfu35IKDTeokE8PcmhdN4YRpaMFpjQpbr11J0uXtnD++TNf7937ukOolxO2exNj6dKl6qmnnnq9N+N1h6f8vlhY+moq3xqBX6QKDJQtXNfB0k3ywTiGEHTqcPtLzBp5I6eR9eI4mFyV+QJbnekUCaKUR55oVdYbQlqB85vvIGmkAdCFS0goPC/OnkIHW7JzCEkL0Kh4BiNWEqUEAaERQMMRMMv0e50DDnyhHi79E5S9HBweYQN72F0VHNeY6ZxC1FpArwt35/yh/POi8I74/z9ptrIHb+/17ZjgqJtHneYr1DhArabwDoww5ihme3tY6m7CNSxkT5Jvfvh91AT+78RIBxnkx/xwYo1Hen4SSSedWNjEqaH+vjvZNTfhu5cLgRKw9Kk0S9fvZPNXv8LT4T0YKZu+938Xb7QIiQBYLuQt1NWLUX/lu0xIpWhyC0jlomntXKb9FYM3r+exr38dpRRCCJRSzLr4YlZdd91bMhiyezNDX/80d43M4Mb4Ei446zEuOnk7Y3qSG9VHeNw7BaFN48NJk2tqIYxiZ+a/GC1txBx2SB2sJZkIs/zkj2EGm/EU3JiG7xyGkRIkFXyuGd7XCTffNcq//qdGxVIcHo7hOhpKQug00OZD4Q7FtHiaKWYfAos+PU4+G8PQJeWijjmnQm56hHKphuJmF3dTL8nyRgSCigUndIFGifnzG/nBDy5+vffs/xiEEE8rpZa+8PeTGeFfMKSAtmqfLevCb7MwakQYEZGJ2UGBP1x/2IEfjsIXG32G5pNlX5szVfouzbKXGbKHw26M+foBhtwEB2lDU2KClLOy/gGSRpqC67+3ISrUh3uw1RiylETiIoWGo8CUNi3mOGk7ji5cXBWiTfdFsfttmBHwA9cLUc5kOLxpE8p1aVu+nHB9PTo6Z3E2p3AqJYpEiWHoBuhwEnDpn+g4/lK4KePLaYWqmaqr/D5mv+OXcCMCxm2PSjTEScZmFs3ZSnDIRpYVbleKn2Z+xV83foAAf9rkv0IxwjAWFk/yBB4eAQK41UEYgcDDI0sOiaSBBnYtaiY6PIbQ/TsYT8DTi5PMer6RXYU9jPbuYzQfZOeXriPbJ2h7fANd6afRr5iNWN1JwLMIejZCgKlsWlUNy+QHCY3CY9/8OpWkoGhaKDxCXohdd6xj1sUX07rkjSsu8GdDfozGsMUHFqW5IPAEn2i7mkOFHtq0A0QNg7fVtvOlZtM3RQYo38sJzvch0ATtGrSnwR2C8hAEf4QU8OF6+ECdz66OSr8D7GR/wn/8YhGJsIcbsKiN6hxMLSA1GsV+ELTHwA4K9ofq6RH1xDRFh7JRoVEO7Y3R3DlGbFTSMRTmqS2CsFJkvQiKIFJU0DToS8G0FoFte6+45LcKJgPhWwSpqttEQR3VHAQ/EKpqRHy0CO/rg4MWdNv+RV9nITNVkOfs+USCebTgEOliC4brUXQ9FJKYLDE13EvBiaALF7Oqb+oqHV24zI3vIFVpxnOOOqQraSFFjr9re5Z5zhp+m/XFgM+NwjtifhZ7LLoffph7P/95ipaDRBHWNE6/9vPMufRSAILVn9cKhYLFfx72QDPRpL+DKtV+oItfMg1LkK5CBXU6pvRRtoJIy8MsVaiUFZm6cXazi4UsetXPy5DhLv5IhgwCwQgjE1mggYlFBVX9KVKgky6CBFE19YihUXAs0Ayk66KUZNuaczhY2Muhphncq6/FUwIZd9i/6Cy2B9O8p/UB6mSAsApSIsUcN8IqcSYYC0AIDmxZT8YbxzYDaGgIJAVZpKDyHHz04bdmIGyfAyjwXBorGX7x+PU8nehiyFHMXDOT2S2h43vF5d+BCIM4pscsG8DZAu4waA2Az1yeEG+3n2H7wAPsLLydUsxESJdmNYTysnhuBKkJnLXgPgteXqEnLcY8xVgFGgoB2up0Em4ztZ7Ovj6/7wg6SmmUikWiEX9G2LIV+bzFRRdNlkVhMhC+ZdBi+EHv2JGkI3JrflnNo+TBfksSFL4otwZ4aOz2ZrEw/hQz4rt5aGg1pnBIBIYJOEEGrGYQrn/JFn62d6SIqAm3mh26LE1u4nCxE9tNkCq1k3FjzAj3cHaggzkBWBV54RYfRTmT4bd/fy29WgivJowCIk4F68tfoXXJEuJTprym++rmm5/n+us3svPqsynOa8P1PKLRAB4vLgfqmkCTDhVlElAVpF3N3gSEdJNeel41ECoUd/FHsmQIE0YgiBAhTQkXFw2NIEFs/MbpSlaxhGU8zw5EIAydiyDVDcUcmEGob2ajPoaX83h47Dw0bEKa5X+hqsRYpZFD2aVMTRykIioILcF87Z3A0RmVfLBMRViYhDnSB9bRsXDJhAsvWsML0dOToacnw5QpcaZNS7zs8zw89rKHXezEQzGLWcxmzuvK0n1ZNHbAqZfBI7+DYBhdSlaMPAxdi+GExbzo8FBlXlq8S3DEx6W3N8PWrUMMjgYwY22EIgf5TuNnyBshDNvG1QQ9og0nqCM1FxHXsU+sGrc86lLJKcywwsrr5EMaZ77ncbb8ciW5DAwMHbF1EpihIJUhB9wiKN/E97TT2rn44ll/5p325sAb8GibxJ8DUQnvrYEb0qC7PlHEP28VrnDwlMsOW6IhMIWGh/Tp9Pin8gk123k+Nw9H6ZjS9v+rlakRFlk3wpiVIGHkcJTAxSCm5QCB7YXRhE1dYIywtgdLaUyJ7MaypnJVjWQWZ73qtj/y8Eb6yzayoXbigC3qAXrLDt0bNrDwyitfs/30zDMDfO1rj5JMhpj53CGenj8F24N8vkIgFpzwZIxKn+WqaRKkTk1gDC3roFVsKpaLaWpEa4zj5v1eDiMMkzkmCALUkmScMSwsdAyoZoOzmM0K/Nm+TrrYxEassI45fQEAZcooLJzhUYpjUSo1AUKBapNTAOMVQtEKWwsNLElsI0yElaymjuMHNQNLpiFjQbxMCVnjD3Wqsg2axDxvzsuuxbJcrrvuQdavP4CmSVxXsXJlB1/+8jkEg8dfbhSq6l25CxyD9FCAnfpGTmzs5gIumtgXbyi867PQdSI89nuwK7DsAjj17b5jxwsRWAOFb4OKHaUVqwzINpRo4YbvbOLnP99G7wDkS6AZIWKLTiPfWE/7ycP0PVgPIQddcyjpQTTAjYByQZ/hoSIe7lYdXJvoSRVmXjTCiiU7SWY7+eENU7Fsf1TI80DTI4Q6r8ArHKSrtcg3vtDEuWe3TTJGq5gMhG8hXJOERs13m9hqgaUUmrAReESkhYOOrQQVFLYyJjIgXdgI4VF2I0jhc0WDAsbsBDYaIQRb0udwevM6dGER10tEtBKuF66+g4flRujU6nGo4JoZTowozuWcP2m7NxY8XyrqmN9pQMGFZ/MuC14wF/j/gltueR5NkwQCOg17Buh4Yh89K2b4hUnPw0DSqPkKOIcdGHMF00Im0eEQhneQii2pq4vQ3hVGaIp5zHvVz7SqKjDHXvgFkiaaEUCFCho6J3HSRBAEiBJlNWtYz/0UKFQz/iDz1ALursnTr9VSKoaQto3pVpBSwbiFFzfokAGu5L1EiCJfImtJBOqJXX85hU/dijuc9X8pJZHrLqBt2suv6cYbt3D3vftobAliShMUPPRQNz/60dN84hMrjntumhH2sJuBnhBpu4DnWgip2Pl8lqZ5h1nS0P6q++5/HFL6wW/ZBa/+3NA7wFoP9jb820nPL5XGvshjjx/m5z/fik2EsieJxsEqlxjfvRERuoRcd4gZlw9w6N56yuM6WsLFHdCRo37MFaZCn+HgTdVJtpXQGz1mJYbQhGTeab3wnamEg75WqvKqPohGkI5Zc3nkJqj5f+id/yViMhC+hSAEXF7jP5SCh9yt3F7ez62pldToJTJOkJRVgy4cHPQJRqgmHUxpU2cOkSq3oBTkXUnWDWGi0CR0aU2MpK/EMXfSGNmOMDK4boR4sI+MHSNVnMmwViasCYTUKMpBzjKrJBRceullkAEiROhiBmHCE9udXbAMghrGRXH0c+pRAUlpax7rx+P8x4zTuK8fbmiG2tfAJDedLmEY/hsJoGbdFsIP7aU8o5Hai+ZxYmuMtXHfx/EUCZfH4ZSQpCLexQPqfnroQQhBkAArWfWiTOul0EAjAoG/1/1T8kg/8FxWM4OX7+NMYzrv54MMkUIiaaSJ68ezjAafIVip0KANMOy24BkFgk4BLRrFkyaz488QY83Lvm8jjbTPX0LfnQ3ozw6D5eGeWE80VksnXdjYdHOQIYaooYYZzEQi+e7ND1CqK1KSCokkKWqpr09wyy3P8/GPLz+ObTrEECNpwaiTwyqEqn9TxDuGuOGJ7fz0ovY3tzeeCEHNj8B6FOxnQDZDcA3Iem6//X4MQ6M/5RvpAhiBIJViASEy5A7FOeFD+2g5rR/X1djVt5jUZ01MwNoI1hkSiUCTHqOVMB1yjOXxHn/G9L4ZBAP++a5rgAa6DsUSnLViMgi+FCYD4VsUQoDQR5FCoklfC7RGL1NWBuNOCF34rBoHKLthck4NMyL7OVyaSsZOgvJwlaSoNNr0EoNiBMtzKBemojvTmF27iT6VZsPw2ezNz0FVGTnTw70sSzxNj23wmzK8s8bmTu5ggP6JbXuSJ7iYS2iiCYAzOxpY94P30BjcgzdWxBuD4CwT5weradSa2F2BLw/DN5shS4Y8eRIkJ4LpiOMzW33LqFfeL2eeOY3Nm/tIJIKMu3DQBr1vnFjPKCd+cBmjCjaW4GcYGwuiAAAgAElEQVStx79XkCAXcDGDXgFDWiRF/GUH8R3lsqmSI2UHmGGEWBgwOV2cwcM8BPgjEi4ubUyhnXYOcIASRQy3gazVSJMujpOJMzBoYwql0VE23HgDB26/j9B72witbefk5KM8Pr6KMa8eSwsRrddZldzFrHDqlY8PBOdzIU+Ym9i9YicuHp1M5xROQ6H4PbcwxujE8zfzJDo644UCoYRAVgW/04wgNUml+OJLTYgw6ZyLXTKOCZACu2gSm3WQrfttFs14aXmh7u5x+vtzTJ+eoKXlDXxlFwYEzvIfx8CyHKQUfg/vyFOFwJAClIFDGFdMIXsoSOpwLSviOjun+Mzl8rPgSEF6icQOQMNwhg+d/DCaniZGnNJgA1NbobvPt4LSNHBc/3i95NU7EW9JTAbCtzCaaabO7MFTAq8q39Rs5kjoORynhavi8K20AqHYPHQRSxvuYWX9g/QWp9JTnEaplCSiFZHGMBlXw0On5EFefx7KS1mkWrg3nyUkRwENpeBAsYMafZQ4TfyyBAtqdtJPHyEVI+tWVU+0EuvFfbyHqxAIlkZHeWC+Ryo/G93MohRUEkliMZdkfhduYQkbih63uPczou2bcF1otpdx29AStlX8i+wME/6p4ZV9DNeuncXtt+9h164RUppOpeL6NomfOZuCrlEvfYPiQ7YvXFD2fFHvZ4vw7TFIORHCMsJ7a3yBcO0FgXe7d4CPDVboLtcCFUzKnBKM8t2mE3iHVs8udlGmzHSmkyTJb7mJgioy5CjSLgxXpvLMwGnMDWf4UqPHPDkNicQuFrntIx8h1X0IGU6ifrIP+dhBoh9cwtLFz7E3Nw/HreWjbQ+BlmYOy1/1+AhUs9ozWIlCTZRQN/I4o6SJHeN2kSfHIQbpPEtn/30Qa2ai1Ns7kuYdZyx/0exhO+0IwHUlR1tVCpREah6jOQdfnfooCgWLa69dz8aNh5FSkE4XiUZN5s5tYO3aWVx22VwCgTf+ZW3Nmi42bDhEY22QA4eFL9BtVTDNAC1GPZV2ydZfdVDaDUkB+02fARoJQ1t9hfQzfdRtyzJeruHcDz7MSI9g2fSFLNVPorRA47mdsGgOHB6EYhkSMf9xxluQ7Pun4I1/xEziz4ZOuphmPMMJ0f1sy3UR0iqAi/ISnGDqvLu+h2LkGe4dm85wpZG7+tcytfs56nr3Mq0ujD5vGqNamYpnoKOwvACm8JgZ6Wanm2XHyFratQh7vQKGrPg6iRTZll3MIiNB1oO97KHsmmyr+MEYAYIg08wsWT1DDQm65W4SRoZQPMZh01cONqSLJ2wMYxgB5MhzQPXSjK+naSn4PwN1SKdEu+Znht02XDMAt7YfQ1d/ASIRk//8z7Xcdfc+rrmjB7cmQnD1HHLT69nnZjitZjNLAn1sEA38euwkbhtvJuP6s4WdBjQZDnlV4YYxg7yn06FLDtkwNwALIyN8aayfg6U5JPUSQoCjSjxahu+PJflsfSMNNE5sy+/5HSXKFN0ofRZIYVEX3kOHFeWZ/Al8buQgn2zcy3msZv/995Pp6SHR2opXAleAd8gj+IkHOPCjd5BqaGVJfC9CG6OJFhZx4nHrViiyZNAxiHA8hfeF/ct97H3RqIpZFc079W80Bp7xyA0opAGeA8GEx6c+dfKL9rWOzkxmsSO8G6so/SoFgmBAURpOMH/2i8dhbrjhCR59tJeWlgi7d4+SShUYGMhTLjvs2jXCI4/08O//foFPYnoD49xzu1i//iAPPthNQEI+C0LqTF28mvZ6yeqT4Gd/gKYmj3S6SCZdxnVDWJrOSKUbQ6/geDpXX/Az3j7rDv7jV/9A+tQziJwHl50Hv78fhkaho9XPCksV+PQH/EA6iRdjMhC+hWFi8jYuY0rDs9wU2MHmbCeGl+TSeIS1iSE2yD8yO6wTNzL0lyzyn9tA5LEdmGiYUjK/uZld3/ggz4ZOwVEGzYFhFtfsJKy56DJNnwPNuoZ0mskqi4B0cT2DsjIYlXBGGDxP56CtkIBRvXa5yg8sI0qjxoAM4wihiOklGk2NETuCFL7ZVN5OMup5xMwUjZo2ccE+WGokZ0ep0TMI4Z/9cQl9DtxVcHlb3MXAeElmYjhs0Hr+XOIL5lJ2fPm1qJHm7JbfoAubGj3Lc94wldBBplQuoD/biVKKvbZDQUsRkA62lHxtPMh0LYjj+czRxe4OtuS6iOuVibKqLjSCWpZbcjH+vk6f+H2ePEMMESHCbhukULjCRXgmbeFDDJZmsKvQyS7v98yX/Qxt3YrUdQzhq/P022AKCVIjcbCI3Rjn6poEc7mYKUw5jiDTTx8PsJ4CBUDRTCsnqsX0pzLE3QQntLYfl83p6BOjHEfgzxoKom0eV/1GY9ddHsO7IDHT5bQLG+hI1vBSuKL1VL7a3Y8MF8AxkLqD50oW5VbRkDz+u3Ecj9tu20NjY4RCwWF4uEAoZOA4HmNjZbq6atm8uY+nnupnxYrXdqTmtYauS/7t387j6af7eeaZAcYLIepap9M1Pcypi+F9n4VIyGXrcykKBRshBZ5XIFbj8r1PfZaCPYW2+kFCgQr5UpL3nXM9P7rvQi47L0BtAv77X315tg1PQ30Srrp4Mht8JUwGwrcISi78KuNf1N8d8/sGACFCnCFO5YwafG+0Ku7jWZ+FKAJMM8G97RAjTpTBlefTeWAv0iqjDwyw8HvfpfUbo0SFMXERl7JCqdxOkwajLnSagufKAQquH+TCEsJC8TdJeKJyAkL2Vb0Q/YuzKYuMWU08pWJ01qiJHkqFCnFDkXcNhHAouyH25+cQFIrzGp5CVjeg6BrclZ7LgJVgyPZICX/d466HkDl+UtnETnc7XbKBleJMOuh40f56rAgJCZYGWU+xLPEIhqxQcGLENI8xO4YpyzTXbKCcmYYmFDYuGTtGSyCLgz8jN7/ubjKFk6k4dRTJUfY0gpp73GcJwFbHK3yoCaqS36c9amrn/1WiUEpgKY1++qmZOhXl+u/bafouI32WwnY9pre28O3WEHOMFxsL58hyJ3dMzC66uDw9tp31I89ARQMB7pNJPrXoSmZ2+sSfecznMR6ZuJFQKEqUmUI7Awyg1SgWXOH3OQWSS16BlFNrxvhcx7v53b4d7Cn2oxeSnNswn3PPfbHiuuN42LaLpgnyeWvi91IKHMdDCIHnwfbtQ2/4QAj+di9b1sayZS82PlYKhoeK5Av2xNiJUhAOFTDUAF2t7sT5ZrtBwoEcTXV9QCfgB79Pvt9/TOLVMRkI3wL4lyH40og/E6iAjwi4sQXe/fJzzowxho6BUnDv6GweqlmCukzhCsF2S3H6LT+m3vWoe+IQidI4diSOwECTFQRwMLuYj9cqfpUV5D2/PzfogKs8PvDILUz/1Q+4NzOOtfAkRj+/msapwwAoT2HZUXaPnsvpNYqbrE3sE314uj8/4YgyzcEhRsut9OaW8JF4jMviggd1QYYyQRXiptRJDFTiSFxMJcgpxYgLbcHD2Gg0h3spU2CfKjAu/sA7eTdNNFW3D1p1qJE+uWBBEFKuRXNwENsLUCXhIQR4GEiZpV4vc8jWESgspVEbGGB2cBet4T5CWpF4YBClDPJWA1NCPQyWuqjRS/56UeTdEO+KyOPIN1Gi1FHPKKPUaWEO2wKEQhc2qdJUSp5Bg5EnIisECNB14YU8+1//RXFkhFBdHVOkRzI7RPPyxaw9Y9bLkoT2sAcHlyi+pt1weYzRoQqlEUXICKAhME4Y44u/v4mffOyvCQZ15jGfIVLsY181o1Y00MCFXMx2tvE4j1GkSBP1nMN5dDGDHFm2sIU+DhMjxokspg0/WNUFw1w9f9nENuXI8hRPkiFDCy3MZBYmJsGgzoknNrNjxzCmebS2bVkuLS3+9kspaGh489f/LjkL7n/EQT+mxGt7AdrremmsGaZYqT1mLtMFpTj75Fc4oSfxipgMhH/h2FSEfxyp2r3gB8KKgvf3w/kxqHmZXlkrbWxnK33lZh7LdBHJ70JY/kA1ZpJtl32QT//4i5AP0eat5A5vN4aWo+CE0aTDaU1/wDAi/FN8BTuzc9hlCeaa0PbLH3D4xh8TqK1Fb24ms2sHtVfuZP/136EsU5RyLmPjCUYbx/lRk2IoOB3BDJoCaVY13oshS9yZOofR8jRqRYKfIgkIWJM4kz+yjr3jRfZmwjTru4mHowzbTQSETdAzKHkmU8KHaQkModCxlEuWDPfZO1g/1MSOir/2dsOfuZRAxYOE5mG5IcJGDiE8YnqZITuGUh6ekjTrBodshaM0OoK9zK15HEMrEtTKSOFhahmKViths59F8QJFu55xJ1b18FBM1QN8pu74U1EgOJtzuI111Op5MsqjpFwGy63syc1FCsn5DU9jCJ1OugjXRVj7wx/yyFe/ytC2bTiax5QLV3L2336BPnGYXezExqaLGXQxY4LRmiM3UR5WKMaKOUpp0I7UqZFQMoidPM6mTYc588xpaGico84jtqeL4cooC+e00m62IhAsYznN9nKeLftarM1hyMkst3AzZcqYBMiSpZcezuIcZnP8cP4gg9zBuolsch972MpzvI3LCBHis589jY9+9HbyeQsp/cwwEjGYMj9MpnMYuVCybn4dNQVYGf7z2WP9ufHO8+Gr3xmndziK42gI4WFKi7bQPoIBD00vkS/FkMKlNpZi3LqQM06e9I78v8Wk+8RfONZ0w33FF3LvfGWZv62FrzW/9OuyZPgdN3PbyHy2ZjsJ9vfjdQ9jBsPoGGQjcS7/wT+yxPS4/Le/ZdwT3FHqpTt4O3FhUCtNHOFQpsQZrGI+CyhnMvxizRpCySRSP3rh7+4b5PHFq9h22YeRho6HoGgGiWVShBck0DSTvBskrpdIGhn2lhro1KLERBRb+Z6C1zeDftd/8av0bu5bfiVnTH8YW+ocyM2k15pGQCszNXyAWdEDRPTShHdiRLrc3f9OnMpcGqo3BeOer//4yVr4VhrKykUEtnJy/UPU6RWCmseoHSLnCYbzixjLnUWPWyLtWqxt+x1RWaAmMIKolns9T8d2ayi6IabKBCuNmdxVzJO2kqwwmrg80kDoGG7HiF8LpV73h+0P0c2IV2BzIcLdBZuYOcqJsYO0GA7nsXoiswI/kNyV+z2W4WAHFVmyeHiECFfHSRQdTOV8LkRDYy97WM99RKoZ4fMjBxl83sUICoIEEUhkyEGrr1AXTNCcTDBtaD7f+9/d9PZm/ZnJoM6XvnQmZ5wxje+NwX+PH5Xwi0j4X81PUwxuIlplmSoUFW+IWmsfJ1uSFjkNEXwnSp/Ob/g1eXIEOWphlSPHSSxhxWAzjKcY1lu4fUOaLVsG2bo1hX7COJ3XZBBBnVBTFEyDveOnc0VwIe97EydJ9967n09+dguBWBMhwyIRGCI9kuMDVwxzzfsfpFjM+mMRwfOpaf6sP6w/iVfEpPvEWxRp9+X/NuC8/N/i1PAOLuemSoFBqwaZjFE7rKCQx3NyvpNPKsVIaYx1H/oQq7/+dWL1m2lGI1RVNDWq4fcpNnMC88gPDoIQxwVBgKRns/Te3xHtmEZaSYpmiIMnnU4gn0OlDUSTSUwvM2aHSVlxao1xNOFfVA3hmw//YsRi2Td/zNzmNvaefhA0gW0HaQ/2MCXSR30oRUgrMWbVE6GEUn4freJpFJ0YU47ZpKTm7xtPwT1TYUdF44AIcNh0KQkLG0VUtwhaUymUT6Mo4X0Rk7rE3aSNXiQSS/nOf7ZX9bWSZRJEaDdcThaLOSkCvaZfgj0SBA9Z8MVh2FHxA8mCAHyx0WSmMYuZEk6JwdrYAR6nmyw5krQeN6toYXEnd6BiOiAYZxgbC4XCwSFLFhODIiVOYD7Tmc50OmmgkSGVQrMlmqMRTDpQMhBIhOkRmVUGobC1HL0qy85YH+lPLKD8wHxq9vYyOtDLhz77a668aRnr3JW0aiZ6NRPLuPDNVAsf6wj6/UYcRrzDmO4gfZpgXUin3d7OmvHbsWq+RsYYJ/wC1mrANdh/6Pes+PaDoOs0uC4fWvVuuP7T5GWRb5Z+xkCllYAZQAkQymV28lFuTHVwWTxB5I1NIH1ZnHdeJ9fsGuEXv9iKsgTpnGLOnHqu+MBVyNq/J5ocBBEH+Sf4lU3iFTEZCP/CsSYKz1SO3qHDUdrFO1/l/NlVTNBbSqArCGpQmrsIq7+X0GAfQU2wtD6OSZThHTtY/w//wOgPl+FrX/jwSRRFxhnnZn7DrNbpgMJznOOCoVUooHkuiQ33UAdsXXkxtqewKi569xjkLfSmBF4ggqsUYa3E1MiTRAOHcdwoMn8iw+kESikSdpmZ5V3kSPiuGtVV5504Ib1Y1WvxqsPLNkWnBuE2YgQyRAIHEbgUrQ6U08Cw6xN7loVgKXPpJcp2tlEgT5eYwYLAQoyqz5WNx7O0cC9bAdCERkDpBKXEwSWsDKJ6hRks5O4cfC3tW++4+OzZjyTKfHJsL2awj8U1w9jWNHZnlnN1f4Bb2yEo/bGF+7gXA4MwYVKkWMcfuJS300wzvfRiYxEhwhCHj/suVfUI8PAokGcjjzGd6bi41I6E2DPejRVwCAyWMUUSL+SCtAg026ApZEXHNEwOl+KUPWhbNMqz3qlYKzpovutB1OM93LYzRGbWGA0ihl4VM6jRIOWG6KnEmR0skWYU4Y0T8Yq4QiOIzpAeoU+UaB99PwvDC+kPnUpJPzpG4o30EBhKQbKp2px14YFfQlMnh0+fQ0kDaQYmDnCFhkARD/bQYyeY+6e5YL3hIITgE59YwRVXzGfPnjR1dSHmzKk/yuDV3viEoDcL3qT3SpP4U/GP9VAnfebhkYcLzDXgklcJhD8Zh1rp62pagKUkjtCohCK8d/2vCQjfsDXS2MjgM89Qkw9TodpoQ5EiRZo0AkGOHBtjT2FcuZh8KoVdLOK5LoXhYcyaBGPxWkzl+fT/3n1olRLCqqBKCmcgQ/G5HlQ2R6M5xtK6x0iGn0dKi6AxxNTknZxcvwvXsRn2hqnf9ChBt4BQHkJ5BFyHICFsN0RQupgCItIhKiJcpN5OU6ibafW/oj62gURsAy11v6Aptp62YHpiXwgEHXRwvnchXc+sZPDOOHufH0cpRS89fJtv8kfuwMKiQgUXF1vYOKKMEha6VCRIoJUX8uXRDC2xTSysu4tZsS3cnS9xzuEczxUaeWpsCetSaxhWeU5suI206/FY0Q9km9hIgABBgmhoEyLdm3kC4Jjsz0XhIauMzqMr8JmsEkmKFCWK3Or8jsdH78cbL2BkPGQyRNvmgwR/2YfYXY/u6ehKJxIOkHcD5L0griupMceJ6WmkZTF03kpUQMcpaUh8otWxiKgwrlJUqFCiSMzL4AmJjk7YK7I6eydRtxfNSzG3vI3Txr9Pwu4GwPMcbCvDgt3low0/qUE4DhtuAvyKgPcSHR5PCepeA9m91xuNjRFOP72DuXMb3pqGyP8DmMwI/8JharBvJlzTB/cUfaWT98ThW42v/toe2+/xJDVo0v0yV/OG25jy0B+ZGpETd+BCCISmccLIdDZEn6VEEQ9FiZKvN0ktQYIECFD4m3ksbFhM98/XUUynaVu+nMarP86jP/olrQ/cjhcMMfWRu9jfPpO+RadgSEFIE1Q0jeW3r+OST87jaWlRcGJ+cqAMItIh3riNyhkNZDdsRz5aJnbeIKoeLEsjEg2haWX6sqfywdAJFNQQtSLE6WY7rumwvP6nDNg6TnUWTinFrJotHDJ2kuaKCb3Q8fEyH//4nWzd3U9BFXCFw8xTQqz6RomKUUT4HTU8PBRqgvARJIDAJyDdWhjjxMbbMKWLQsM0exnPNiGEQ0D4jFRXSZ4cW0pT893EAodJux04OOTJTfTZjiBAgCGGAGihhSNZn4OLy9Hat6pSc45sY4AAu9lFqtCDSBXRAwEoKlTJwTmnha5PbmbN6it5sMGkr5pd5tygP78pPFylY7sSzXaxIyZWcz1LW1I8zAzKqkgZxYAjGHH8EZzlLGeU+/HwcNEI4qFj0FneSsCrUJZh8GxiWheO18Pc/DrWJz6CUg4LN/czc3/leOaLrkMxRzsd1GqSYeHgKN0vySoHWwlOkB00Tl7hJvEnYPIweQugRoNfvXhU7lVxYhAeLECjDlHhWw+Fk0lqRgYxokdNdu1iESMcZlbrEhJM5Ume4AAH0NCoo25CqUQgQEpqrziDU6/4q4nXpx3Y+7Ev4J60gtr71hHd/Chn/vq77BgbZmjBUuqVzfLtG5m6aT3a2y5n1vwa0sKXN0to0KTrZESW3JeXYd8bg4cOEfo/f8T+8Cr0U7twpEGguILpzkK+MCjRqMcDZgXgM82HaNZdLJEn64RRShI3SkT1AhXhsoUtnMO5AHzrWxvZsvMwqjlHRGigNLY/kmN+2iLazMSQ+pFgCH5wihKlRIlNbMSteRLT03HcWkDSV2rGA8JaBdvza3ia8FAIeksthPVxZgU60NEJEiJHtupWIYkSxcMlQRKFooYEC1jIwzyIh3tMNujDn00UJKlFItnBDkqygjqGSSUUoBR2W5C96ecoILGrP4oIAoOQabF3tIv8sMC1XRxb0bCgzLIlh8mMd/N0djqbLL8Erwuo1+ArA9P4t6b/RSJ8KylboGm9IBWNTj9lESDqlUG2oGFQJzqJO710qAR9Mkfrnn4K+RLh2BTkkbuv3DisejcRIpwvz8IJPES/U8JRoIQkUV7J5+peeoh/EpN4ISYD4SReFh9J+q71Q45P6igqGDr7Es549A7y+3ehB4O4loWQkrO/8hU006SFVi7l7exiJw/x4EvKdR3rLAFQp8PqGo07z7iAxjMvoP2GLxFd/0c6t23isr2biElQSlFQippoIwVZoNMMUPZ09hfrSVVsIuFuCLmIS2fDRTMRtkeDGcLV8pyrTmNL6US+mYWW6owgwJ4K/GxcMKvOI6aXSOhHh7QdQMdguJpt2bbLPffsQzaUQWh+0BMQqoXSCERfgn17ZCxhhBHGGcPDQ9cESugYWoWcVU9F2XhKIPBLtvYRuykFttKYp8dZFPDZoOOMMcYYEv/zxxhDAEWK/IL/5mROIUSIKFFsHLJkjguGOvpEmdqiwgD9eFEPTqrDGygTSDkTBstursTuOcNE6MSjjlHGiOjjWEpwuDiD/6+9846z6yzv/Pc57da5c6d3SVa3bNmW5N4LLmDHxsEBExuIYSkJCZAlleySzYZ8YAkhYbMLLHGCgYUFYqpjbLBxwb1gy0W2ZHVpJE1vt597znn3j3Pu1YyKsWzLI8+83/nMZ+ae2573njv3N8/zPmXr5HJijeCacVoa81x1xT5GbOHC1gfI+Au53w/Dkq1WmJSUD+D/fO9O3nrr15gYfImJNpv4R1ZQPM8hq6bIkEFZiyhTpkyOiuTYLvtISJb111/BGf/8Tbyxl8hareC50NwJl90EwApW0mP2ssvYxWSgOE766IzrBBLNK0cLoeawLHXglh64eRzWl2GVA+/vSLHuGzez9c472fXgg6Q6Olh57bW0LJs5Kug4FvMoj1CkQCISviJFGmiYke5f469awxmHt+XBveTtrL37No6nQoMRQynFyNAwexYs5/9Zl3FK5Ud41RR3DZ9DRZmckH2EPqsRx6yQtXOIZaKsMEnF9WP87cBxPJEPPcgRP+wYsyoWTqN4dKqXFU0OgRFEHp3gUSWIvuLEuZM7KKgS+aAIRhVnWjGKYcCOew3aTgzq+281b1AQqrgzpjSAwjQ8vAAMI0fWDvchfWWSMCrYKkbZFwzxOTVZ4L+n+tgiL3EHtzPJJCYmPj4BYTqwgYGFzT72civfp4lmEiSx8SgRDuQNbx/UPdYSxcjDjFOmTGCGHqCMTUHcxNg8RqnXxj8xRplRsjTSSJaCWaB/agmbJs6hHMuRWiK0mCUu77wD2wmfp1lamKy2ssQJE41qdD10F51f/DRuWyMLuk8gVxij/N834/7TObQe/xSYXQwzQlEVSASTPJ9YyYQUcWigsOg4Hv7Ux2h79FFOH+wgsfhMOO1tkNovdmnSrJJVHGbgh0bzsug6Qs1RY4wx7uWXDDNc3yO7kItedmp7IQi/h3/8Ax794j+ggoCyH/Bc9zI2/dU/kOzoxLS381Qpjm34pMwKF7T/B2U/hodFW2yIuOGilEFVmdwzeAWj5eMwjDyesql6WYpBgj4LFtkw7MOXF+ziAevfmWIKPxKY6UXmtT6aP/u40P8oNLfH6qIyus9l1Y1VzvxEEGWkhvcShGaayZGLworT/s5UmLDk+klKfpqhcgePjV6AD5G3B9dkR/ls80IcHL7FN5hgnBIlTEwUiiLFenszQaJSFSHAJ0aMNA11b7a2DgcnLO3ArTfNDghwlYuvfMxJj8xjOaTqU7iiC8O26nMRW2glTZo8eUyvi8cro8QNRWd8BEtUvURjMUu4f++NbKiEe8s1jvvwdTA2wmltDfXSCjefx8k08O7vvI1S6atMMomFsN1ZzC8bziKQGCZG/R+nAgXewmUsYcmrfk9q5je6jlDzhtNMM+/gd+of2olpRdKHI2WE3+3XvYNlV1zOyKZN3Fxt4Om2ZXTY4SfoeOU48m5ANr4XpQw8ZSLigzIpeCmKpCj5MZJWif5yF3EjhyWCwsV2BlFuOwNekmYTuizFcWYDrVzHT/gRE0xgIFjYFCnUk0ssLM7/M58f/ifF+ICLqSwQoX2xzTk3WWRJ4OFRxaWPBZzAau7nnoNFECBKiklYRRJWkbbYKIuS+9hbWMHxwRm8NdnM2niYzbSPvRTII1ESjIlJUJfb/Y9bG+prYOLiUqRQF0kAM/pTD/cXw2MBQWifhPHYjqYF9F2xgH76gRIFCpjRHuEwQ/V/EorWFrqsiejxBHCwsAhQLGQR78nCxwcgEYRlH74CY+8umtrbMCXMahUM7FSKXP8eVPLD3JdopOC/CEYbw6bgM4YVzWWsUo0GFqt6K7gaSsHGbfDSDmhrhtdRSgAAACAASURBVNNXh3k0Gs2RoN8ymqPOgXuCrxQnnaZ73Tpe7IfUtOJ/BdhmBdsokY0NkPPStMWGmajaCAEFP03cKKMCE8EE5eMCBoKvAixrnEIlgWkUuKr7Tv5dhghQTDJBI40kSTLBeH38kE84qaKpx+T6W6uM3puhsMsku9xn7fktnOuczULC/S1FQBWPu/lFfTjty2FghOUQVgE78zi/3Luan0y0cGEKrmlbz3PGQ9FeYDjB3sSkinvQY9Q8sjQNWFjkydXDtEZ9VzFMsClSxMfHjR6nJpglymxkIy20EMOhQJ4S+3uiDjJQ90L3n4uwLMLFJU2aLFkWJQv8RUuKfx6HnBeer6ZlK2gZeJH+uFcP6zpT0LtsJSBUaGSP0cPGYicjXpyMs48Fyd1YoggIyJOnk07ap42pqlbhU/8E9z0RvQ4C3e3wlU9DZ1soktv7w3FEi3uhveVVvQ018wAthJpjmseK8HxZscPzaLIKLI2P0yTtZGWYs1rux1MmaStHzCjTFnMRFIYo9pYW0hsbpSc2RH+5k4RZJmlAgMFE1eKSlM91PXdSMIdIkmKKSTw8xhit/14LC9bCoIIQSwkXXNXJO7keH78eNp0eBs6Rw8OjnfYZe4YHUvPMKqpKMQAfE8vZiu2necrLId5DrLKTpCVNgQIGRl2YplOzEyBJgioevfRSokQ88lQhHLtVoECcOEMMzfAYEyRooYW97KFEiTTpSEDNejmIiYWPNyNsXMPAIEWaB3mAh3iAcxrP4xcNJ7LXC0OkO//0ndz+h3+IMWYTNNh4+QKVssemP+jk+sGtbKmcwnCwimrgREN6T6TFGeHtXXfgmR4rWMnZnFN/boAf3gW/fDQUv1plxZ4h+Nuvwmc+WuFv/vgB+p/diJtewHDbxVz3W2n++H3hvq5GMx0thJpjlidK8NEBhW9M0hKbwFMWG8opTsv+ipZqEz8fuKo+rGhp+kXOaH4YA3hg+GJ25E/kht4fsS67nrHhiyj4CVQQzvXrihX4fNcE95rDJEnh4jKhJqLhRj5uNGuv1pimJjRB1J1lLesQJArXheOhfsqPcaOuLqOMoIDhSGwORc3bVCgqyscN4hgoelNb2a5s+uLDFHyDgmnSYraiCKcy1O5rYmJiUaE8Q9DCBgYGoKhSJRF9KRQ5pqI5gmG4149KLDJk6KATQUiTxsVliqnIA7VQVLGwEWoTTFR9r9LBoUIFQchEe78+Hg/wKzCg6lQJSNN/BjR+5d3kvnof3uYBjBVtBB88ncJprXR7d7Bx6BIq1SYc0yVmuJgEjLhtjExezV83r5jRSq7GD++GTHpmeWFbEzz91CT/cNEHkV3bWRzlNS3Z8WV+WPoXli3s4+qLX+07UjNX0UKoOWb56jiIuFjmOA3KoqQUgfJ4cuIURBQZK0eAUFUWW/Mr6Y3voTfZz8bcKnotxXjhNHqzt3N5+13sLi0gKwmaY8PckFxAg+HUxahIgaoSKsrBMWqhwBBRoERRpoyFxTpOYwlL62FKQdjONsqU6/tXBkaUMVqtZ3keKIiC4OBQxSNQQtwsA5C2JlnV+ASgKPhJ1pfhOEfotJooSD5KwmlhgnFUfc9wf8aqQ4w22ggIGGSAPewhTpwECZKkGGG4Llw18SxSJCCoi/sCenmW9ZFoelEyzsEh0YCAMqHdHh5DDNJIFgeHcca5kztIkMDAYIpJMqcuQN18JbHo9ZnwQfBRShhyG0kYoUj7gUPKVFiG4sFcI2bztH6q+Ty7H3kEv1KBqXU4FZvm/p/hFPZQbDmFqe4LWTT+PynnNkO2m6oK3T+rOMTx2z7Pd+/4Zy2EmoPQQqg5Ztnighh5QLAEGgTS4vNSqY3uRD8JsXBxsfAQFM9MrSVhulzR+hhxw6PkN/LSxNn0Nazn/KZnEbNMvJpispLES7RSpEiJIqXAo6z2d2NVTAv9KQtTAtKkuZH3UaHC9/ku44yTIMEa1kYJMfvDnynS5Jiqe2o17wv27+c5OPhR9xfLCIU3UBZuECPvJ2hxhmgQH4sCOXOSUuSLCUKFCiry+KZjYNBAAx4ewwzXyz8Eg0mmGGOMKlVsbBwcylRQBPiE46hMLBawkN3soo8FDLCPCpWwDOWAfclDhXtz5ChSIkaMCmUSxOteYo4phhisC7alwikj4b5tHJFQZg2oHw9QxCJ3r0yZHc89zkP/+dMEk0WUUpw0VGZs1MVIZkAMmsq30/fZu7HNIphnwc487lcHcZ928BKtZEcfZWiyDFHGrEZTQwuh5phlqQOPVyxkWlSsHJigDEwCwCRBAhcXx/CoBnFOTo+QaCjgBSaFQGFhcbIs5wV/PQPbYGpsgu2tD/JA1wM0mCkKdgmPIMxmVCb79VAAwVMxYpFnF+BzB7djY5EmjYfHwzzIUpYjkcAJQpYsFcqUKEUtxcK9xBhxQM3YW6xdXw1iKCDjjNCgDEypYkkkNgIBCiPyTqeHSA/MHB1kEDPa2zOjTE6XUhTYDW9by2Q1kHqoM0+epSzDizxAE5MUKYoUgZdP+KnZEu4lVnEjjzNPHp+ABIlIUMO9SoXCEw9TDCbcFkwjR3dskP5yD3Fxw/xQBfnA4V2NFe7nPjb6GxgxNiFfOJGWOydIPzjJ5K4nSLt5xhJdeGYDTd9ZhrHQQp6roDxQ3Smc/7YI/6M78HeZeIHBRWfqDULNweh3heaY5SNNYKoEJd9BKSgHFuXAoTc+QhCkMRDKlPHxKfsJFiT2IhikVQPNRoo+K03WUjxpPMiO5/NMDPhhIXmLwlc+g3vLjJS6qXoZfGVQDRwCTMIPfoUf2FiRCJiYPMLDUZ/OeD20aOOwg+100kmeHC4uVaqkSLOKE+imBxMTGzuSvNCGAgVKlEJPUcAxKlhSxTFcalososJvFEpZONHz1kKhNeGdnkCiCKIyjmpUzFHBo1r3RGtf4fVeXRzLlBlikE1sjMQPipQij/LQjZ4lynitvR61cGnt9RKEKSYZZIAq1XrotfacpoQ/3cBGBRZxw6XkO7iBw4TvcFqiynmNO3mB55HJKjJexvANRt7RRn6Fg18pE09YLGgcoeeDWaylMYwAjKY4pu/DVBXlmNiXxyE3THnxpbzvt51DrkUzvzmmPEIR+STwBaBNKTUiYav1LwFvA4rA7ymlnppNGzVvHKcl4MudNn8z5rDRDchaed7S8iRNVpX/2HclE94khphUlUNcAip+jC9uv56Y2KxJ7+Gy5o1YpovrV3Fdi1jMxogHiKXANyDpUx33UB1Rj1DxKflxbPEwJCDsAhNKxiijUTg0nPgwyUQ0aSMM413KZSxkEZt5CUE4nlUczyommOCb3BJ5WmGT7OnTGepZpRL1GFVgS4BphHtnEomFSICgsLHrIdHpBfUwM4uzJpa121WoHDLhpEZYpB9miw4xSJEibn2SyOHuE9S71hwYKg2DvgcPvAwbkUd2i6LJVDwxejY5r42FTp53pxpwTJcT7BRrYg7fkNuihKZJUGB4ClUJyF3aCjcDjoH3307Ev6QDZRlgGgTHNWJ5YI0WCSoGRrvQsXYFN37zT2h+Ew/q1Rw9jhkhFJE+4DJg17TDbwWWRd9nAF+JfmrmCWcnhZ8nW5lQE+yTMjYr6WMBb+kd4QuTexh3m+lxBtlQ6GJPuZuEmcfG4ddTfQy4GT7Q/SuUUgQq/KBWtc9rQ+GXBUfGsA0fhYEf2FSVjWX5TLottMaGCcSv7/MpFAXyFClgYWNFxe0BAffwS27gPaxhbd12hcKlQpZGxhgnS5YcudCbUwY78ovJ+xlaYkN0xfdE3h+YUsUPrFAMCb1DE6hKFRuHOPG6NzfdMzxUQs700opDCdN08uQoU6rb/Uo4cJ/y5aiVoQSo+uuZsUu8t/M5FM+SJ8+H+QNqNZObeYkceZIEBI0GflcCSgpxFV6rg3nxUorL4/hn9zJg9tLOSPhamAbVlc0kCq1UgworM2t454c+okcYaQ7LMSOEwD8Cfwb8ZNqxa4BvqrAP3KMikhWRLqXUvlmxUDNrZCVLlv3/znc7Ja5se5IUaZ7Ld/F4biFNVoEKCoOARqvEvkqGzeU0rUErE9kxKAQoF5QnGDFFedgktbg2LEnhqiQVP44jVTrjI/iRCFpY2Nh4eLjRzL8qLn60F9dKKy4VtrONlRwPhCJ4H/eyiRepFV/sY2/Y4syPc/vAlYxU2ghUGALtjvdzWefPiBkQpotAoEwEDxFQEhZv+HhkaSVFkoFopuB0sZvObyrmP9TtDxQ2FUClEOP521eTyFRYfPYWEtmDaxkP9VgHe6y1nVeo1WdWqTLJJEUKpEgzyCApUtzGj5lkigKFsLmBKbA4TdUPoBpg5KoYf3cmfl8S14nBpMKdhFhDUH+isuORNNK8NXvDYcO7Gg0cI3uEInINsEcp9cwBV/UAu6dd7o+OHeoxPiQiT4rIk8PDw0fJUs2xQgcdQBgCHHJTVAIh55v4yqSqFFWp4FKl4DVzU/I92Ju6CZwqRoNHfqvD5Es2mRaDdFZhAAUvTclLoBDSlo8jTn1vz8FBMdOjqs0bBKL2YgEFCvXr++lnExtJkaaBDJ100U0PSZXm5/uuYazSQsYq0mgXSJl59pT62Jpbg42NIYIYHjEDLGP/3Ecg6hcqKMKOPc004xBDpo2AMl6nP2ulYHhrO7mhDJP7sjzy9XP5/sduYHhr2yu7fyR2Ng52VIdYK8yv2enhMcoIefKMMMzX+Cpf4otsZSsF8tQSdRQKtwzPfsdieKfFWCmBu7AJScSwLEV78yj25lGCHSVUJUAF4D80RvMNvyJe0J24NS/PGyaEInK3iDx/iO9rgE8Bn34tj6+U+ppS6lSl1Kltba/sD1Xz5qWBBtZxGnlVoCjDVAkQ8agGCVxl4AZhODNlD/NreZK/fevvcdrzV7Pv75ey77PLOPWZ3+La7ktplgw9dJEyTNpjkyxKDGEbYZZomoZ6NqiPd8iSAReXvexljLEZQrmdbQAzPBEHh6qfYdRtI2EW63trIhA3XbbnVs/opelFz1mrOeyhlwyNDDNMnjzNNNNABkVA4x4D81sb4IO3o97+Pbj1xUOPbT8C/KqBGAGG5dPQliPdlifwDX715Yv5Tb36p4eTFUF9bzVLloUspIuuGT1TbeyozCT0uj08ChTqHqpScPefmZTGwU5BshXckqI8VXs2hSxLoYZ91M4K7r0T+J/bh+ycpP/RR1/T66CZ+7xhoVGl1FsOdVxEVgPHAc9EMfxe4CkROR3YA/RNu3lvdEyjYR2nsqvUhe9vxsRiqNxDgzWGbdjk/ATHJ0dZGauwhc0sjy3nvb+7hvf+7pr6/QMC9rKLYXOYFWYDBQrkcWmgmWu4lrv4BcMM4+LOFEFVyyuFcGyTT5oEz8jTHM8qMmSi7Mig3ofTxMDGZpIJAlS0vxjUsz59ZeGSZ4KJGUkutdCihUWSJHvox8SkEhWyOzjI3jyFG36AXYRqiwmVKvLZh1EDedQfnjbzRasLmFH3ztKSJB/tXU4n8EwMywdlMLarFYB4psT47maKYylSLQUOQikc5ZINqvgYlIwMXcZCVnMSO9nBRl5kFzuxsUmQoJFGRhjGwqoX54dmqhn2jL4Eex4T+s4NcFLhOgxDQs+vIlgxkISFtAcEI1UKtwyyPKrD8Mrlg+3UaKYx66FRpdRzSql2pdQipdQiwvDnWqXUAPBT4L0SciYwqfcHNTUE4dlcL3ePXESh2oynDEarzYy6TSxNbeeqticxogSJ7Ww/6P4GBldxNSdwIj4eMWKcxdm8nw/SSx9v51qWsYwkibpgmZiUvCyesgiUSaAMPGUxVW3GV4qtbAFgCUuYIscA+xhjlGGG2cUu4laBxfEpXL8x6roSNgIvBSbLGp6re5W1Zty15wwiUZ1emF/D/t5mgqKL3ZHFsGxIxaA9hXx7A+SmJb1EIhgoCJTCVyYj5W7Ea6m/nta0/40NM8AwA/a90M1Ef1P9MQQwbf/g86EUa4tP01kdQVQZUxU5sfwC7yq3kCDBAAO00U4vfbTQgktlRlPvl9vTnNotiAHDz4U/p3ukpjIIfItgsEzhu4NMfnon7WNVmgMXROg+9aCpOxrNDI6lZJlD8TPC0okthOUTN82uOZpjjcfK4VT3tAFpo0rcGiTvN+AYLg1mLVSponl9BxMnzvlcwPlccNB1TTRzLe+IPEKff+NmBoJx8n6MRqMYFuBjYIjPSNXAEUXFckFgnHEsLLyot2etkD5GjGvbnuGWfWcy4cWoKBNDYElqByszG+vPXQsV1uoGjah/qIlFFZcGGvaHbPunMJKxSEQjX9WKhHJvHlbE6o9bm0AfBBZVvxHb8On3y/SajeRlCjOaU1HFo5KP8eD/uZD8SEM947I4lqLn5F3EMzO9LBOTnuouzio+T8nYQ96MEVeKRUEGU32Op5z3EDdi9TkYNjZJUhSn7avWPOBaW7rpZHoVKoDB54SRjdC1BpQXJcZYAY5lUfVMMj96klbSxMWgDJzxiU+Q7uj4TW8jzTznmBPCyCus/a6Aj86eNZpjmWIAg17YMMtV4ARxFCaOlOgv9ZExtkQfqMIylr/q53EIi7Cv4mpuUT/E9x2whQDBFJ9AWVQCmwHPZZfbx5kp2MhGGmmkldao3lAxFM30y9hTfLzvfp4tJhnwq3TGJlntWOwREzBnzDCseWgB4Ry/GDFAMcpoNPZJof7kJOyti4n/eC/FTbvCNftR/LYjdfCClBCoGH4Qp+j2kYxtJQhi2KaDIiBGjICAxmxAtnuKwU3d4U6nKLK945z3+/fWH6o2FLiZFi5wd2MTxxOL7sAJQ8Ri4as8pvcCA04ntVZ0GRpppplRRvDxo7Zx+5uR12ZAhi30LNpWBPScoeh/WHjoMwZn/omi7YTQPY0lTWyx6Fm8nPLNv03ivmFWW6ew+NJLaV2x4lWfd8384ZgTQo3mlRLW3MHqOGytQi4QfLedtD1CyvTqTarP5TzaeO0JVH0sYEn1fEbMxyn5SRJWHqUMil6atJVnuLiCbxe6eWcKanHIsNGZVe/7WQsFWhKwPDVAlkFaaMEmSwMN5KcN4a3192wkyzpOJU2ax3mMIoVIXEOcxgxussDE7y0g9k9jyK4R1GgB9c7jITuzr6ZCCJSDZRQxnQJJZw+BsigZJnEsLGJ0080+Bmgym7ji9x/npKvXM7azlWS2SOfKATACQOiggyUsYzGL6aGXJB7wPNA54zmrVMhL6FXX6h7HGMXFZRnLOY/z2cAG9rKHYvRVG4VlYWFiYYrims9bPPK1Kht+ZPCrvzRYfaNi9Y0epim00kZcEsROWUnxlD5Wc9MrGgSt0YAWQs2bmIQB5ybhoSKcEg9DpKJiDARdXN8wzmVcQSddr9sHoiD8trOOb/WvZJcaIGvlaLDHQRR7iotpV130B3m+q24HCSiQZ4zR+r6fH1UelilTpoQbtR0LRx759QbVefIkSOL5CQbdLFWzygb7eUpSpEBxhggC+EmBpU2okQLV1c3YW8bwPnw6/vtPnHE7hVANHGyjikS1igrBEK9eyhBOyoAUKQwMGslS7NlLY0+tG07oAV7JVaxh3cwXKH4FlL4DygWJWpkFE0yIMGEtxI1at4W2KKaYZDUn0UQz53LejOtKlHiRF/g1TzDMMAkSxJJxzvjEKOd9wqaTrsjDNqL903J9ALTi0E3BNZrDoYVQ86bmz1vhw3thnxfOyjOB0+MGH2tsIcHrP5I8YcCXOlJcsmMJuwOwBGyBxbEKu9QQSbvImIwgCBNM1MN8goGJFZV9nEqBAs/zHFmayDHFFJPkmKKNdn5b/Q63TTbyhfFSOAYKg+7YGGd1/ICU5R5kk48PGQcyDuqPmol/7GLiEg7fNaLyDR8fEYVthPcPlIFI6NkZKHzxsIjh45Mjx4ms5kkeJ0cu3Oscz6NQOOkGrravZTUnzbDBxWXASpJJf5DGwr8itWwWaeThxsupiFf3jGv1hQrFBBMHrUcQkiRZx6ms41QGGOA5nmEveylTooVWLCxSpJhgv0ADlKLra6Ko0bwStBBq3tR0WvDvffBoEQY8WOLA2vjMYa2vNyti4XN+aB84QNZy2RYM4SqDC5sfYZQRIBQfB4cmmsMSCTxKFKN+pOE0+RjxaFpFmEHZxwL6S318aWyKhFnBEUEp2F3JUh6+mLd13faytpmGRY5cvbg/SZI8+fr1RjhgMWpzBkR9Tn38es1eG+2czhk8xzNMjQ0SfOoujF/vA9PAi1s8/hdlVl+xXwgf5zHu4ZdUcSEBnbG3887qShqlC+w1iNxGwHP1vdbpzb93s3tGW7pD0Rl9ubh8i1vq3l6GDEUKUdmFIk8OhxgXcZHuJKM5IrQQat70OALnHyIn5NWiUGzkRZ5hPSVKLGABp3IajdNavJ2VhJu7A74yXuWRyiSN9hhnNq+nN7mT6rSpDi4uOabqnpBCsZ1teHgkSNJAmjQNxIljYzPAPu6eAqhiSZg+4kmVuFlib6mXfLQfeThqYdja8xUoHFyWIApbDs7MrFLFwuJMzqrbav35/fjrB5COMHM0KLn0f/pbbFh4BSccfyE72MEvuLNe9B+gGDDKfD22jY9xNQYGa1jHBp7Hw8PAREWl9BkaSB5B2NrB4S1czi+4kzyhh9pAhtWcRIZGMmRYwlK9N6g5YrQQajQH8DiP8RRP4hDDwmQzm9nFLn6Hd5EmTZkyd/IzdiS2sy5hspxcvfC9Mi3js0aRIk7UZqyWHalQVCjjUaVAgU668KiSoZEJHxJi4lIlLLsIEAmLC9zg8GOEbJyoXIP6uKOaMNZqEWu2HSiCENYmJknSRltYrrFzisozu5GOdL18QhIOasrl4R/8Cyf8lwt5hIfCAbpReYoR7SHmmGI721jCUhaxiGUsZyc7CfCJkSBNmoCAVZxwROdmIQu5kfeyi514ePTQQ5amI3oMjeZAtBBqNNMoU+YZ1pMkxY7tbdx2/4kM9KdpY4ShK7dz9SkWt8r36vP1ICplO0BoDiQUpDAbNEacatRGLOwSU2GKqch7Wstkqsw9lSrxKHFFoagEFjGjQtY+eE8Nai3NhBjhgN9uuhljlEkmgVqiTjhL0aVS72oz/f4ODilSuLgkSXLcZDeDJjOmNihDIad0MdZVZTe7I7vlIFsAJqft/13F1fycOxhmKLI0zObtPnTr4JclQYIVrDzi+2k0h0MLoUYzjXD6u+KBzcv5v9tPw+8WpC9g0MvwP34wxeQJX8GyqxhiRPt+oWd1qBFHtdKJChUMTBrJRGOY/PpcvlqJRI4pruS3WMJStmUepjXfxnClCUUJX0KpuaTjjmhO4gE2V5uo+Ek6YiOYEtBGOzY2TTRTjKZT1Irzazt0tcs14a6txSFGnLDk4vSlb+Xx7r/HL1cwDAOVtFEfWUvQlsDvbuF2bqNKtT5suCaA4R6esJBFdRtTpLiWdzDOGGXKtNAa1URqNLOPFkKNZhop0kx5cW4dXENQVthGKHTKgpbrRvGDAOsAEalxqGM1oYvhkCTFFFNhwXygsEerMFLEjxkYnTGOSy5GEEaM7byn4SkembLZyXE0ZMosT75Ac2yKWldEAwPfz3Dn4EWMlhei8FiR2st5rQ8TN0Mhq9XgKRQONkTt2mqiNT2Jptbb83hOQKF4kAd4Ifk88a9dR35XP+pHm6GnEdWRREo+HZmFOMSZYqpe+G5iznicFloPen2aj0Imr0bzWtFCqNFMI0mScmkdpUq4qxaiEA9M28NVDk40FeJA0bOw694eUA8/1qbQ17yzIAgwXhjFHyuBIaAU5bLPQ+Pf4fJzPoTnuYxZ/Szv81kuW8Pb+ApcCeOwdiiGj46fxFC5h4xZwBEbv7yGX49VSTavJ2WGXmqcOFWqVKJwqIPDyZzCkzxBLCqXqGVh1lrCPc6jPMajVHFRLRYJu5vKhzP4EpAkQdvyRcSsMCElTRoLk3Y62MY2HBzWso6zOPsNOV8azeuBFkKN5gCWqxOI2QFuoDDMcJp8oGz2jdcGoRxuEG4wo09mrV9oK20oFB10cDyreCx/P9WxEsSt/dmWls3GO/6DxSeeyaDRjxIFAWGHbENBzETu242s64a2BEFgsiy9kc7YIJsnLqJJdWCJwd7cmQypNXyyfYr1rGdDcRvP/uRknrqnD6XgxAt20vCOjcSTCVzcaLRw+JWliWGG2MF2SpQwI8/Rz1jYmSYchG56D5p3aGByLe84ymdFozl6zPr0CY3mWOOchElPk42nYrjVBH4Qw8ckX2hgaHcX8jIJMSbhEFgDgw466GMBSZLEiJEnz+mcQSwHth3uxcWJY5hhSDH/7DZue/5mitU80p8LPUFbwDSg6KGaw5BnF930qMUEQSPtzhQrkgP1nqSOwLAXo5U2BtQAP/zcBdz/g2X4QViP+MiPV/Cdz55OOkjTSScttNBGe5S0okiSpEgRqx4AlqgQXiEYFGvdYaKXoETxNfVx1WiOBbQQajQH0GPDf+2D3uME1SBU40IQg65yjiULC1iHmWQB1LNJQ0GUegKJS4UWWsNat2czBBmHoNnBb7LxGkzUbS8hg0WcVBozGYfxEgzkYawEuybCnz1pGuJZUqRIGmBhUA3iNCb2T60oBHBh1FRl8IUedm5sprGtiBPzsWMB2fYieza107DnJFwqxKLkmJoHuICF9Q4w01EENJLFczNsHt/D9qFNbB/ehpePcwZnvu7nQKN5I9GhUY3mEPxuFs5eBXfnYEMOlqdgSdMWtliKAnHyePXm2NNRKFpoZZKJqG+niqZEwNqoN+eFK65j8I9vwjujEzPu4D0/wNTGLdgdWdKrlzBu9qPWdIDrhy1ymhNQrIIyaE2H5QYisNSBl9xw+3Dch5IKO+tc0xDakug/AaUURIX5AL542H4Wtf4czupLs56nyZOnm27O4hxiOKRIkyMXFcAb9b3OhsJyGv/+m5yU2kehJUVyrEjj9jzP3nQ8Z5yq9wQ12cEbBAAACstJREFUb160EGo0h2GRA/+pBWqJjltpZBsGTTSRJ3/Ixs4KhYVFBx3ESZAnTzvtnMXZdBDOxWtavJi3/MGneOAzn8GvVqkEOYzeDK1fvBHTtAAFMSucKeiF45TMjIMVizEu47RG2ZgtlmKpUSaXPwUnHjYgv6oBUlGc5+T2djJGCUW5bmuaBuJmip42g1NYwymsqTcDqLGcFbzEJrzoy8YgQ4bRe/Zx2p4NTGa7iA/7QIzArOJ8529gze1g6o8TzZsT/c7VaF4hC1lIhgZy5EjTENUchtSEJPQSfd7FjTSSPUhkaiy9/HIWnn8+wy+8wFhyigdWvoAl6fpkCRFBWSaOZdfLEmoz+gqRCAsGS41e3po5GTtzsL1nrBZWdiXZvrePpiYPQ0zGx0y62uCcae09D7TvEt5CAw28wIaoe0sv53AuDz7957ix1IxGrl4sTWJqCH9gJ2bPktfy8mo0s4YWQo3mFWJhcTXX8ggP8SIvUojSSWqJKnHiJEnSTU+9L+nLNX+2Ewm6162jC8U+XLawBX9an9KwKVu4H1nr0/k2rkQwKJCnmWa66D7sc1gWfOWv4R++bnDPYw5Kwfmnwp/cBLHDd2rDxuYczuUszqY2MR7ASWYgmNk4wFOKmCjMuJ72oHnzooVQozkC0qS5lMs5l/P5JrdErdUUNjYWFnnydNJ1RI8pCJdwKUtYxhY2s4HnqeJGXVsCiIrUe+lhIYvqwvRKaG2Cz/5ncKPpTc7LCOCBHFgm0XfROzA2/YrAqyKmja8UmdwQyaUnQ8uRrVmjOZbQWaMazasgQYI1rI1q8cI/ozw54sQ5gRN/w70PxsBgMYu5jMt5NzfQQispUgihUK5kFdfxriMSwek4zpGJ4KE4ce3Z2Fd9hJbSBMncMG35IVr7ltH7wb97bQ+s0cwyotSha6LezJx66qnqySefnG0zNHMchWITG+vjmvrq45oaX/NjV6iwi11Ucemki2aaXweLXydyY7B7I6Sb2DtUYP0ttzCxcyedJ5/Mmve/n6bFi2fbQo3mkIjIr5VSpx50XAuhRqN5NWy/7z7u+tM/xbRt7GSSytQUpuNwzde/TsuyZbNtnkZzEIcTQh0a1Wg0R4xSike+8AWcVIpkSwt2IkG6owO/WuWpf/mX2TZPozkitBBqNJojxs3lKAwO4qTTM47HGxsZWL9+lqzSaF4dWgg1Gs0RYyeTWIkEfi0dNcIrlWjo7p4lqzSaV4cWQo1Gc8QYlsXJ73kPxZGRuhhWi0WqpRJrPvCBWbZOozkydB2hRqN5VZzy/vcT+D7Pfvvb+BMTxDMZLvjrv2bheefNtmkazRGhs0Y1Gs1rwnddKlNTxLNZDEv/b605djlc1qh+12o0mteE6TgkW1tn2wyN5lWj9wg1Go1GM6/RQqjRaDSaeY0WQo1Go9HMa7QQajQajWZeo4VQo9FoNPMaLYQajUajmddoIdRoNBrNvEYLoUaj0WjmNVoINRqNRjOv0UKo0Wg0mnmNFkKNRqPRzGvmZNNtERkGdk471AqMzJI5s41e+/xEr31+Ml/X/krXvVAp1XbgwTkphAciIk8equP4fECvXa99vqHXPv/W/lrXrUOjGo1Go5nXaCHUaDQazbxmvgjh12bbgFlEr31+otc+P5mva39N654Xe4QajUaj0RyO+eIRajQajUZzSLQQajQajWZeM+eFUET+SEQ2isgGEfn8tON/KSJbRGSTiFw+mzYeTUTkkyKiRKQ1uiwi8j+jtT8rImtn28bXGxH5++icPysiPxKR7LTr5vR5F5ErorVtEZG/mG17jiYi0ici94rIC9Hf98ej480icpeIbI5+Ns22rUcLETFF5GkR+Y/o8nEi8lh0/r8nIs5s23g0EJGsiNwa/Z2/KCJnvZbzPqeFUEQuAq4BTlZKnQB8ITq+CrgeOAG4AviyiJizZuhRQkT6gMuAXdMOvxVYFn1/CPjKLJh2tLkLOFEpdRLwEvCXMPfPe7SW/014jlcB747WPFfxgE8qpVYBZwIfjdb7F8AvlVLLgF9Gl+cqHwdenHb5fwD/qJRaCowDH5gVq44+XwLuVEqtBE4mfA1e9Xmf00II/D7wOaVUBUApNRQdvwb4rlKqopTaDmwBTp8lG48m/wj8GTA9I+oa4Jsq5FEgKyJds2LdUUIp9QullBddfBTojX6f6+f9dGCLUmqbUsoFvku45jmJUmqfUuqp6Pcc4YdhD+GavxHd7BvA22fHwqOLiPQCVwI3R5cFuBi4NbrJnFy7iDQC5wP/CqCUcpVSE7yG8z7XhXA5cF4UKrhfRE6LjvcAu6fdrj86NmcQkWuAPUqpZw64as6v/QDeD9wR/T7X1z7X13dYRGQRsAZ4DOhQSu2LrhoAOmbJrKPNPxH+oxtEl1uAiWn/BM7V838cMAx8PQoL3ywiKV7DebeOgpFvKCJyN9B5iKv+inB9zYRhk9OA74vI4jfQvKPKb1j7pwjDonOSl1u7Uuon0W3+ijB89u030jbNG4uIpIEfAJ9QSk2FjlGIUkqJyJyrERORq4AhpdSvReTC2bbnDcYC1gJ/pJR6TES+xAFh0CM97296IVRKveVw14nI7wM/VGGx5OMiEhA2Z90D9E27aW907E3F4dYuIqsJ/2t6JvpQ6AWeEpHTmeNrryEivwdcBVyi9hfLzom1vwxzfX0HISI2oQh+Wyn1w+jwoIh0KaX2RWH/ocM/wpuWc4CrReRtQBzIEO6bZUXEirzCuXr++4F+pdRj0eVbCYXwVZ/3uR4a/TFwEYCILAccwg7lPwWuF5GYiBxHmDjy+KxZ+TqjlHpOKdWulFqklFpE+MZZq5QaIFz7e6Ps0TOByWnhhDmBiFxBGDK6WilVnHbVnD7vwBPAsihz0CFMDPrpLNt01Ij2xP4VeFEp9cVpV/0UeF/0+/uAn7zRth1tlFJ/qZTqjf6+rwfuUUrdANwLXBfdbK6ufQDYLSIrokOXAC/wGs77m94j/A38G/BvIvI84ALvi7yDDSLyfcIXzwM+qpTyZ9HON5KfAW8jTBQpAjfNrjlHhf8FxIC7Io/4UaXUR5RSc/q8K6U8EflD4OeACfybUmrDLJt1NDkHeA/wnIisj459Cvgc4TbIBwjHsb1zluybDf4c+K6IfAZ4miihZA7yR8C3o3/4thF+jhm8yvOuW6xpNBqNZl4z10OjGo1Go9G8LFoINRqNRjOv0UKo0Wg0mnmNFkKNRqPRzGu0EGo0Go1mXqOFUKPRaDTzGi2EGo1Go5nXaCHUaOYQIvIREfnKtMufEZFvzaZNGs2xji6o12jmECKSBDYBq4Fzgb8FzlZKlWbVMI3mGEYLoUYzxxCRzwMpwgG9lyqlts6ySRrNMY0WQo1mjiEiKwmH1F6jlJqzTbc1mtcLvUeo0cw9Pk04uLTeVF9EFovIv4rIrYe/m0YzP9FCqNHMIUTkk4Tz6d4JfLx2XCm1TSn1gVkzTKM5hpnrY5g0mnmDiFxMOI7mLKVUTkQyInKKUmr9b7qvRjOf0R6hRjMHEJEFwM3A7yilctHhLwGfmD2rNJo3BzpZRqOZB4hIC/B3wKXAzUqpz86ySRrNMYMWQo1Go9HMa3RoVKPRaDTzGi2EGo1Go5nXaCHUaDQazbxGC6FGo9Fo5jVaCDUajUYzr9FCqNFoNJp5jRZCjUaj0cxrtBBqNBqNZl7z/wHHVLu5Rb8XFgAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -997,7 +1021,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/demos/node-classification/directed-graphsage/example4-directed-graph-fully-directed-graphsage.ipynb b/demos/node-classification/directed-graphsage/example4-directed-graph-fully-directed-graphsage.ipynb index 17f418247..7ca8314ac 100644 --- a/demos/node-classification/directed-graphsage/example4-directed-graph-fully-directed-graphsage.ipynb +++ b/demos/node-classification/directed-graphsage/example4-directed-graph-fully-directed-graphsage.ipynb @@ -25,7 +25,31 @@ "name": "stderr", "output_type": "stream", "text": [ - "Using TensorFlow backend.\n" + "Using TensorFlow backend.\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", + "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n" ] } ], @@ -209,13 +233,13 @@ { "data": { "text/plain": [ - "Counter({'Probabilistic_Methods': 42,\n", + "Counter({'Genetic_Algorithms': 42,\n", + " 'Neural_Networks': 81,\n", + " 'Reinforcement_Learning': 22,\n", + " 'Probabilistic_Methods': 42,\n", " 'Case_Based': 30,\n", - " 'Theory': 35,\n", " 'Rule_Learning': 18,\n", - " 'Genetic_Algorithms': 42,\n", - " 'Neural_Networks': 81,\n", - " 'Reinforcement_Learning': 22})" + " 'Theory': 35})" ] }, "execution_count": 9, @@ -398,7 +422,7 @@ "output_type": "stream", "text": [ "WARNING: Logging before flag parsing goes to stderr.\n", - "W0819 15:38:48.459014 4613264832 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", + "W0820 17:31:40.336038 4741768640 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", "\n" ] } @@ -407,7 +431,7 @@ "graphsage_model = DirectedGraphSAGE(\n", " layer_sizes=[48, 48],\n", " generator=train_gen,\n", - " bias=True,\n", + " bias=False,\n", " dropout=0.5,\n", ")" ] @@ -428,14 +452,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0819 15:38:48.474189 4613264832 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", + "W0820 17:31:40.352051 4741768640 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", "\n", - "W0819 15:38:48.479403 4613264832 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", + "W0820 17:31:40.358065 4741768640 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", "\n", - "W0819 15:38:48.487928 4613264832 deprecation.py:506] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", + "W0820 17:31:40.365343 4741768640 deprecation.py:506] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", - "W0819 15:38:48.540890 4613264832 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", + "W0820 17:31:40.417157 4741768640 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", "\n" ] } @@ -468,9 +492,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0819 15:38:48.817672 4613264832 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", + "W0820 17:31:40.771018 4741768640 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", "\n", - "W0819 15:38:48.823397 4613264832 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", + "W0820 17:31:40.777916 4741768640 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", "\n" ] } @@ -509,7 +533,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0819 15:38:48.996465 4613264832 deprecation.py:323] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", + "W0820 17:31:40.874204 4741768640 deprecation.py:323] From /anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" ] @@ -519,45 +543,45 @@ "output_type": "stream", "text": [ "Epoch 1/20\n", - " - 3s - loss: 1.8718 - acc: 0.2544 - val_loss: 1.7467 - val_acc: 0.3089\n", + " - 3s - loss: 1.9143 - acc: 0.1991 - val_loss: 1.7683 - val_acc: 0.4212\n", "Epoch 2/20\n", - " - 3s - loss: 1.6894 - acc: 0.3422 - val_loss: 1.6491 - val_acc: 0.3495\n", + " - 3s - loss: 1.7004 - acc: 0.4686 - val_loss: 1.6589 - val_acc: 0.4397\n", "Epoch 3/20\n", - " - 3s - loss: 1.5481 - acc: 0.5120 - val_loss: 1.5117 - val_acc: 0.5492\n", + " - 3s - loss: 1.5570 - acc: 0.6153 - val_loss: 1.5347 - val_acc: 0.5505\n", "Epoch 4/20\n", - " - 3s - loss: 1.3784 - acc: 0.7357 - val_loss: 1.3860 - val_acc: 0.6596\n", + " - 3s - loss: 1.4112 - acc: 0.7551 - val_loss: 1.4071 - val_acc: 0.6879\n", "Epoch 5/20\n", - " - 3s - loss: 1.2675 - acc: 0.8312 - val_loss: 1.2752 - val_acc: 0.7149\n", + " - 3s - loss: 1.2746 - acc: 0.8695 - val_loss: 1.2963 - val_acc: 0.7469\n", "Epoch 6/20\n", - " - 3s - loss: 1.1246 - acc: 0.8720 - val_loss: 1.1875 - val_acc: 0.7354\n", + " - 3s - loss: 1.1301 - acc: 0.9390 - val_loss: 1.1928 - val_acc: 0.7797\n", "Epoch 7/20\n", - " - 3s - loss: 1.0080 - acc: 0.9187 - val_loss: 1.1052 - val_acc: 0.7506\n", + " - 3s - loss: 1.0000 - acc: 0.9508 - val_loss: 1.1023 - val_acc: 0.7842\n", "Epoch 8/20\n", - " - 3s - loss: 0.9084 - acc: 0.9153 - val_loss: 1.0398 - val_acc: 0.7510\n", + " - 3s - loss: 0.8865 - acc: 0.9652 - val_loss: 1.0314 - val_acc: 0.7900\n", "Epoch 9/20\n", - " - 3s - loss: 0.8475 - acc: 0.8871 - val_loss: 0.9856 - val_acc: 0.7531\n", + " - 3s - loss: 0.7955 - acc: 0.9619 - val_loss: 0.9658 - val_acc: 0.7994\n", "Epoch 10/20\n", - " - 3s - loss: 0.7502 - acc: 0.9363 - val_loss: 0.9358 - val_acc: 0.7600\n", + " - 3s - loss: 0.7101 - acc: 0.9865 - val_loss: 0.9146 - val_acc: 0.8048\n", "Epoch 11/20\n", - " - 3s - loss: 0.6963 - acc: 0.9348 - val_loss: 0.8979 - val_acc: 0.7699\n", + " - 3s - loss: 0.6336 - acc: 0.9720 - val_loss: 0.8638 - val_acc: 0.8048\n", "Epoch 12/20\n", - " - 3s - loss: 0.6018 - acc: 0.9619 - val_loss: 0.8593 - val_acc: 0.7777\n", + " - 3s - loss: 0.5840 - acc: 0.9797 - val_loss: 0.8233 - val_acc: 0.8072\n", "Epoch 13/20\n", - " - 3s - loss: 0.5553 - acc: 0.9585 - val_loss: 0.8312 - val_acc: 0.7781\n", + " - 3s - loss: 0.5123 - acc: 0.9898 - val_loss: 0.7931 - val_acc: 0.8093\n", "Epoch 14/20\n", - " - 3s - loss: 0.5075 - acc: 0.9797 - val_loss: 0.8131 - val_acc: 0.7797\n", + " - 3s - loss: 0.4668 - acc: 0.9898 - val_loss: 0.7634 - val_acc: 0.8068\n", "Epoch 15/20\n", - " - 3s - loss: 0.4729 - acc: 0.9677 - val_loss: 0.7855 - val_acc: 0.7801\n", + " - 3s - loss: 0.4283 - acc: 0.9898 - val_loss: 0.7531 - val_acc: 0.8031\n", "Epoch 16/20\n", - " - 3s - loss: 0.4345 - acc: 0.9686 - val_loss: 0.7669 - val_acc: 0.7847\n", + " - 3s - loss: 0.4004 - acc: 0.9831 - val_loss: 0.7354 - val_acc: 0.8031\n", "Epoch 17/20\n", - " - 3s - loss: 0.3999 - acc: 0.9797 - val_loss: 0.7461 - val_acc: 0.7818\n", + " - 3s - loss: 0.3580 - acc: 0.9898 - val_loss: 0.7080 - val_acc: 0.8072\n", "Epoch 18/20\n", - " - 3s - loss: 0.3487 - acc: 0.9898 - val_loss: 0.7334 - val_acc: 0.7900\n", + " - 3s - loss: 0.3412 - acc: 0.9923 - val_loss: 0.6994 - val_acc: 0.8076\n", "Epoch 19/20\n", - " - 3s - loss: 0.3345 - acc: 0.9932 - val_loss: 0.7278 - val_acc: 0.7884\n", + " - 3s - loss: 0.2964 - acc: 0.9822 - val_loss: 0.6871 - val_acc: 0.8109\n", "Epoch 20/20\n", - " - 3s - loss: 0.3194 - acc: 0.9754 - val_loss: 0.7182 - val_acc: 0.7892\n" + " - 3s - loss: 0.2855 - acc: 0.9932 - val_loss: 0.6795 - val_acc: 0.8064\n" ] } ], @@ -601,7 +625,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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UikhpVVWVJ8HGspoDrdz08HL21Pp59LYplgSMiTGRbjX0TeB8EXkXOB/YBXR0X0hVH1LVElUtyc/PD3eMUa2uuY3PPLKc7dUHePiWEqYMgIG2jTF9y8tWQ7uAEUGvh7vzDlLV3bhnBCKSDnxCVWuPZ2WqGvU9XPZ1NV5jSzu3PrqCjXsbeOgzJcwY0//uZTDGeM/LM4KVwFgRKRaRJOA6YH7wAiKSJyKdMdwNPHI8K/L5fFRXV/f5jrI/UVWqq6vx+Xx98nnNrR189rGVrC6v4zfXn8mF4wb3yecaYwYez84IVLVdRO4AFgLxwCOqulZE7gNKVXU+cAHwQxFR4A3gS8ezruHDh1NeXk60Xz/w+XwMH37iLWz9bR3M+XMpK8tquP/Tk5g5cUgfRGeMGaiiYvB603ut7QG++OQqXllfyU8/eTqfKhlx9DcZYwa8SLUaMv1Me0eAr/31PV5ZX8kPrp5oScAYA1giiBmBgPJfz63mxQ/2cM/HxnPztFGRDskY009YIogRD7+1jb+9u4tvXnYyt593UqTDMcb0I5YIYkBlg59fv7qFi04ZzB0XjY10OMaYfsYSQQz42cKNtLR3cM/HYqMbDmPMsbFEEOVWl9fy7KpybptRzEn56ZEOxxjTD1kiiGKqyvdfWEduWhJ3XDQm0uEYY/opSwRRbP77u1m1Yz93fXQcmb7ESIdjjOmnLBFEqabWdn700gYmDsvkk2fZ/QLGmJ5ZIohSDy7exp46P9+9cgLxcdHdGZ8x5sRYIohC5fub+MPirVx5RqF1K22MOSpLBFHohy9tQATmzjol0qEYYwYASwRRZvm2al5cvYcvnD+aYdkpkQ7HGDMAWCKIIh0Bp7loYZaPz39kdKTDMcYMEJYIosizpTtZt6eeuy8fT0pSfKTDMcYMEF4OVWnCqN7fxk8XbmRKUQ5XnD400uEYE9s62qG1EVoa3OdGaDsASemQkuM8fFkQ1z8O2DxNBCIyE/gVzghlD6vqj7qVjwQeB7LdZeaq6gIvY4pWv3l1MzVNrTx+5dSoH7s5KgU6oK3ZfTQdem73B71udl63t7jPfmjzd5sXVNZ9fkcrxCdDYgokprrPvqDpzucUSEjpNs99Ts5wHr4sSM6EhKRIb7nQ2vzgr3MftYemAx2gAUCdZw2ABk2j7uvu8wLu36jJ2am3NrjPjaFftzf3Lk5f1qHE0JtHZqGz/fuYZ4lAROKBB4BLgXJgpYjMV9V1QYvdAzyjqr8XkVOBBUCRVzFFq61VjTy6pIxPl4xg4rCsSIczMAU6oHk/HNgHB6qgaZ8z3VTtvD6wzynnBEb0Uw3aoXfb6Xe0HueHirvj9rmP5K7PiSnODiQhGeKTnITQGUNTdbdYmqH1AGhH71cfnwy+TCcpJGccmvZluUkj053nJhA5gSNgDThH2ME79+ba0Dv8dv/xr+doEtMgOd05uu/8XpnDg+alQ1LG4a+TUqG1yfkd9fTYv8N59te6Saiby38GUz/X51/JyzOCqcAWVd0GICLzgKuA4ESgQKY7nQXs9jCeqPW/L64nJTGeb1w2LtKh9D+BDqj9EGq2Qs12d+fu7uwPVB+abt4f+h8PnB1pap7zfEKn8uLsFDOGHOHIO/gRdDTeuVM/+Ozu6OMSoK/PADvaQpydNDtVGy2N0FIP/npoqXN3zPVB8xrgwLZD0y31nFDy7InEO8nGlwUp2c5z5lDwZXed58s+NM+XeWh7SZzzoHO6p3nSdV5iKsSF4dJqIOBsu+6JonCyJ6vzMhEMA3YGvS4Hzu62zPeAl0Xky0AacEmoDxKROcAcgJEjR/Z5oAPZaxsr+feGSr59+XjyM5IjHU5kqELDHqjeCtVbnEfNNud5f9nhR9udO/a0fMgbCyOnO9NpeZCaGzSdB6mDID7G+mmKT3QevsyjL3s0gYBbXVLvJJGekm1viByqlkpK7/sE2J/ExTnJLCUbKPZ8dZG+WHw98Jiq/lxEpgN/FpGJql1/Lar6EPAQOIPXRyDOfqmtI8AP/rmO4rw0bjmnKNLheEvVqcro3MEf3Olvdea1HTi0bHwyDDoJ8k6GcbMgd4zzyCl2dvLxkf7Zx5C4OCeh9EVSMZ7x8j9iFxDc29lwd16w/wBmAqjqUhHxAXlApYdxRY0nlu5gW9UBHrm1hKSEAdgSONABTTVwoBIaK93qmqpD042Vbpk7P9B26L0SDzmjnB180bmQO9p9jHHqa8Nx+m5MlPAyEawExopIMU4CuA64odsyHwIXA4+JyHjAB1R5GFPUqG5s4f5XNnH+yflcOG5wpMNxdur+Orcus7aHi2E1XXfyTftCVxXEJTpH7un5kDYYCia6rwucI/3cMU4SiLUqG2M84lkiUNV2EbkDWIjTNPQRVV0rIvcBpao6H/gG8EcR+RrOFaVbVdWqfnrh54s20dzawXeuGB+e5qL7NsOa/zvUeqb7w1/HES8KJmc69Z1pgyF7JAw705lOH+zu5Ae7r/Odi3vRXP9rTD/jaWWpe0/Agm7z7g2aXgfM8DKGaLR2dx1Pr/iQ284pZszgvm9T3EXNNlj8E1j9V6eePiX7UJvm1EFOdczR2j77suzo3Zh+zK6aDTCqyn0vrCMnNYk7Lx7r3YpqP4Q3fgrvPunsxKd9EWZ81TliN8ZEFUsEA8xLa/ayfHsN/3vNRLJSPTjKrt8Nb/wM3nnCqZ6Zcjuc93Wn7bsxJipZIhhA/G0d/O+L6zllSAbXTenj+ykaKuCtX0LpI86dpZNvho98E7KG9+16jDH9jiWCAeSPb2xjV20zT39uWt8NP3lgHyz5Faz4o3Pj1aTr4SP/5bTKMcbEBEsEA8SeumZ+9/pWLj9tCNNH5574BzbVwNLfwrIHnW4ETr8Wzv+Wc/HXGBNTLBEMEA+9sY32QIC7Z40/sQ/y18HS38Gy3zm3/U/4OFwwF/KtnyJjYpUlggGgwd/Gs6XlfOy0oYwYlHp8H9Lmd84A3v6N07PhKVfAhf8NBRP6NlhjzIBjiWAAeG5VOY0t7dw24zg7n/LXwdM3wI634OSZcMHdUDipb4M0xgxYlgj6uUBAefztMs4cmc0ZI7KP/QMaK+EvH4fKDfCJP8Fpn+z7II0xA5olgn7utY2VlFU3Hd9YA/vL4M/XQMNeuGEejAnZy7cxJsZZIujnHl1SxpBMHzMnHuMNXRVr4c8fd0Zq+sx8GDHFmwCNMQOe9dXbj22qaOCtLfu4efooEuOP4U/14XJ4dJZzZ/Bn/2VJwBhzRJYI+rFHl5SRnBDHDVOP4S7iTS/DE1c5o2t9diEMPsHmpsaYqGeJoJ+qbWrl+XfLuWbyMHLSknr3ptXPwLzrIf9kJwnY3cHGmF6wRNBPPb1iJ/62ALfOKOrdG5Y9CH/7nDP+7i3/tF5CjTG9ZheL+6H2jgB/XlrGOaNzOWXIUcZ6VYXX/h+88RPnJrFP/AkSfWGJ0xgTHTw9IxCRmSKyUUS2iMjcEOW/FJH33McmEan1Mp6BYuHaCnbX+Y9+A1mgA178upMEJt8Mn3rckoAx5ph5dkYgIvHAA8ClQDmwUkTmu6OSAaCqXwta/svAZK/iGUgeXbKdkYNSueiUI4xF3N4Kz8+Btc87A8Zc8j0b3tEYc1y8PCOYCmxR1W2q2grMA646wvLXA097GM+A8EF5HaU79nPLOUU9dzXd0ghPXeskgUt/AJd+35KAMea4eZkIhgE7g16Xu/MOIyKjgGLg3z2UzxGRUhEpraqq6vNA+5NHl2wnLSmeT5X0MCBMUw08MRu2vwFX/Q5mfCW8ARpjok5/aTV0HfCcqnaEKlTVh1S1RFVL8vOjtzVMZYOfF1bv5lMlI8j0hRiGsq4cHpkJe9fAp/8Mk28Mf5DGmKjjZauhXcCIoNfD3XmhXAd8ycNYBoQnl31Ie0C55Zyiwwv374BHL3fGELj5b1B0btjjM8ZEJy/PCFYCY0WkWESScHb287svJCKnADnAUg9j6fda2jt4cvkOLhw3mOK8tMMXeO3/QXMN3PpPSwLGmD7lWSJQ1XbgDmAhsB54RlXXish9IjI7aNHrgHmqql7FMhD88/097Gts5bZQN5DVfggfPAtn3QZDzwh7bMaY6ObpDWWqugBY0G3evd1ef8/LGAYCVeXRt7czdnA6547JO3yBt3/rtAqa/sXwB2eMiXr95WJxTCvdsZ81u+q5dUYR0r0Z6IFqeOcJOP3TkNVDSyJjjDkBlgj6gUeXbCcrJZGPTw6xo1/xB2hvhhl3hj8wY0xMsEQQYbtqm1m4toLrpo4gJSm+a2FLIyz/A4z7GOQfxwhlxhjTC5YIIuyJpWUAfGZ60eGF7zwB/lo492uHlxljTB+xRBBBTa3tzFuxk49OKGBYdkrXwvZWWPpbGHWujTBmjPGUJYIIev7dXdQ1t4XuZfSDZ6F+F5z71fAHZoyJKZYIIkRVeWxJGROHZVIyKqdrYSAAS34FBRNhzCWRCdAYEzMsEUTIW1v2sbmykdvOKT68yeiml2DfRufagPUqaozxmCWCCHl0SRl56clcccbQrgWq8NYvIXsUnHp1ZIIzxsQUSwQRsH3fAf69oZIbzx5JckK3JqM73obylXDOlyHeRhI1xnjPEkEEPP52GYnxwo3TRh5e+NYvITUPJt8U/sCMMTHJEkGY1fvbeLZ0J1eeXsjgjG7jC+/9ALYsgmlfgMSU0B9gjDF9zBJBmD1bWs6B1o7QTUaX/AqS0mHK7eEPzBgTs3qVCETkGhHJCnqdLSJ2JfMYdQSUx98uo2RUDqcNz+paWLMd1vwflNwGKTmhP8AYYzzQ2zOC76pqXecLVa0FvutNSNHr3xsq+bCmKfTZwNLfQlwCTIv5gdqMMWHW20QQajlr0nKMHl2yncIsHx+dUNC1oLEK3v2L09V05tDQbzbGGI/0NhGUisgvRGS0+/gFsOpobxKRmSKyUUS2iMjcHpa5VkTWichaEXnqWIIfSDbubeDtrdXcPL2IhPhum335g9DeYl1NG2MioreJ4MtAK/BXYB7g5yiDzYtIPPAAMAs4FbheRE7ttsxY4G5ghqpOAKK2Y50/vrmNlMR4rpsyomtBSwOs/COMvxLyxkYmOGNMTOtV9Y6qHgBCHtEfwVRgi6puAxCRecBVwLqgZT4HPKCq+931VB7jOgaE3bXN/P3dXdw8fRQ5aUldC1c9Bv4661zOGBMxvW01tEhEsoNe54jIwqO8bRiwM+h1uTsv2MnAySKyRESWicjMHtY/R0RKRaS0qqqqNyH3Kw+/uR2A2887qWtBewssfQCKPwLDzopAZMYY0/uqoTy3pRAA7hH84D5YfwIwFrgAuB74Y3DCCVrfQ6paoqol+fn5fbDa8Nl/oJWnV3zIVZOGHT7mwOpnoGGPDTxjjImo3iaCgIgc7A9BRIoAPcp7dgHBFeLD3XnByoH5qtqmqtuBTTiJIWo8vrSM5rYOvnB+t7OBQIdzA9mQ0+GkCyMSmzHGQO8TwbeBt0TkzyLyF2AxzkXeI1kJjBWRYhFJAq4D5ndb5u84ZwOISB5OVdG2XsbU7zW1tvPY22VcemoBYwsyuhZueBGqN1tX08aYiOtVIlDVfwElwEbgaeAbQPNR3tMO3AEsBNYDz6jqWhG5T0Rmu4stBKpFZB3wGnCXqlYf1zfph+at2EltUxv/ecHorgWqsOR+yCmGU6+KTHDGGOPqVashEbkduBOneuc9YBqwFLjoSO9T1QXAgm7z7g2aVuDr7iOqtLYHePjNbZxdPIgzR3brMqLsTdi1Cq74JcTFh/4AY4wJk95WDd0JTAF2qOqFwGSg9shviW3z39/N7jr/4WcD4HQ1nTYYzrgh/IEZY0w3vU0EflX1A4hIsqpuAMZ5F9bAFggoDy7eyvihmZx/crdWTnveh63/hulfhERf6A8wxpgw6m1/QeVus86/A4tEZD+ww7uwBrZF6yvYUtnIr6+ffPh4xG/dD8mZUPLZyARnjDHd9PbO4mvcye+JyGtAFvAvz6IawFSV372+lZGDUrl84pCuhTXbYN3f4ZyvgC8r9AcYY0yYHXMPoqq62ItAosWybTW8v7OW/7l64uGdy739G4hLhGn/GZngjDEmBBuhrI/9fvFW8tKT+eRZw7sWNFTAu0/CpBsgY0joN23nsnEAABJ+SURBVBtjTARYIuhDa3bV8camKj57bhG+xG7NQt/8GQTa4JwvRyY4Y4zpgSWCPvTg4q1kJCdw07RRXQsq1sHKPzkXiHNDNCc1xpgIskTQR8r2HWDBB3u4cdooMn2JhwpU4V9zITkDLvx25AI0xpgeWCLoIw+9uY2E+Dg+O6Ooa8GGF2H7YicJpA6KSGzGGHMklgj6QGW9n+dKy/nkWcMZnBl0k1ibHxb+N+SPt/sGjDH9lg1A3wceWVJGeyDAnO4Dzyx7AGp3wGf+AfG2qY0x/ZOdEZygen8bTy7bwazThlKUlxZUsAfe+DmccgWcdEGkwjPGmKOyRHCC/rJsBw0t7fzn+d1aA73yPQi0w2X/E5G4jDGmtywRnAB/WwePvFXGeWPzmDgsqMuInSth9Tw45w4YVBy5AI0xphcsEZyA51aVs6+xpWtX04EAvPRfkDEUzo26YRaMMVHI00QgIjNFZKOIbBGRuSHKbxWRKhF5z33c7mU8fam9I8BDb2zjjBHZTD8p91DB6nmw+x245PuQnB65AI0xppc8a8oiIvHAA8ClOIPUrxSR+aq6rtuif1XVO7yKwysL1uzlw5om/vvy8Ye6mm5pcK4NDJ8Cp30qovEZY0xveXlGMBXYoqrbVLUVmAdExQC9qsrvX9/KSflpXHZqwaGCN34GjRUw68cQZ7VuxpiBwcu91TBgZ9Drcnded58QkdUi8pyIjAj1QSIyR0RKRaS0qqrKi1iPyeJNVazfU88Xzh9NXJx7NlC9FZb9DibdCMPOimyAxhhzDCJ92PoCUKSqpwOLgMdDLaSqD6lqiaqW5Ofnh1okrH7/+laGZPq4elJQXnv5HohPgou/G7nAjDHmOHiZCHYBwUf4w915B6lqtaq2uC8fBvr9ofSqHftZvr2G288rJinB3XxbXoWNC+Ajd0FGwZE/wBhj+hkvE8FKYKyIFItIEnAdMD94AREZGvRyNrDew3j6xIOLt5KVksj1U0c6Mzra4F93w6CTbOQxY8yA5FmrIVVtF5E7gIVAPPCIqq4VkfuAUlWdD3xFRGYD7UANcKtX8fSFzRUNLFpXwVcuHktasrvpVj4M+zbC9fMgITmyARpjzHHwtCc0VV0ALOg2796g6buBu72MoS89uHgbvsQ4bj2nyJlxYB+89kMYfRGcPDOisRljzPGK9MXiAWNXbTP/eG8X100ZyaC0JGfmv/8HWhth5o+g814CY4wZYCwR9NLz75TTHlBuP8/tO2jPalj1GEydA/njIhqbMcacCEsEvbR8ew2nDMlgeE7qoeEnUwfBBd+KdGjGGHNCLBH0QntHgFU79jO12B1qct3fYccSuOgeSMmJbHDGGHOCLBH0wtrd9TS1djClaBC0NcPL34GC0+DMWyIdmjHGnDAbP7EXVmyvAXDOCJb8Cup2wjV/gLj4CEdmjDEnzs4IemFFWQ1FuakU6D5465cw4RoomhHpsIwxpk9YIjiKQEBZWVbjVAstuhdQuPS+SIdljDF9xqqGjmJzRT0jmjdyW8si2PJ/cP5cyB4Z6bCMMabPWCIIpXk/bP03bF7EyPULeSG5Bt0icNKFMOPOSEdnjDF9yhIBOPcF7F0Nm1+Gza9A+QrQAKQMYk3ymbzQMYHvf+3LkB75LrCNMaavxW4iaK6Fba85O/4ti5yRxQAKJ8N534Sxl6GFk7njx68zdUwuYknAGBOlYicRqELFWveofxHsXA7aAb4sGH0xjL0MxlwM6YMPvmVndRMV9S2HbiQzxpgoFDuJYPGP4fUfOtNDTodzv+rs/IeVQHzozbB8ezUAU4ssERhjolfsJILxV0LmMBhzCWQOPfryODeSZacmMnZwusfBGWNM5MROIiiY4DyOQef9AwcHqDfGmCjk6Q1lIjJTRDaKyBYRmXuE5T4hIioiJV7Gcywq6v2UVTdZtZAxJup5lghEJB54AJgFnApcLyKnhlguA7gTWO5VLMejS/9CxhgTxbw8I5gKbFHVbaraCswDrgqx3A+AHwN+D2M5ZivLakhNimdCYWakQzHGGE95mQiGATuDXpe78w4SkTOBEar64pE+SETmiEipiJRWVVX1faQhrNhew1mjckiIt+6YjDHRLWJ7ORGJA34BfONoy6rqQ6paoqol+fne39hV29TKhr0Ndn3AGBMTvEwEu4ARQa+Hu/M6ZQATgddFpAyYBszvDxeMS8v2A3Z9wBgTG7xMBCuBsSJSLCJJwHXA/M5CVa1T1TxVLVLVImAZMFtVSz2MqVdWlNWQFB/HGSOyIx2KMcZ4zrNEoKrtwB3AQmA98IyqrhWR+0Rktlfr7QvLt9dwxogsfIk2ApkxJvp5ekOZqi4AFnSbd28Py17gZSy9daClnbW76vj8+SdFOhRjjAkLaxLTzbsf1tIeUGdEMmOMiQGWCLpZsb2aOIGzRuVEOhRjjAkLSwTdrCirYUJhFhm+xEiHYowxYWGJIEhLewfvflhr1ULGmJhiiSDIB+V1tLQH7P4BY0xMsUQQZEWZ09HclCK7PmCMiR2WCIKs2F7DmMHp5KYnRzoUY4wJG0sEro6Asqpsv1ULGWNijiUC1/o99TS0tHO2JQJjTIyxRODqHIjGWgwZY2KNJQLXiu01DM9JoTA7JdKhGGNMWFkiAFSVlWU1dn3AGBOTLBEAW6sOUH2g1QaiMcbEJEsE2ED1xpjYZokAZ6D6vPQkivPSIh2KMcaEnSUCnDOCqcWDEJFIh2KMMWHnaSIQkZkislFEtojI3BDlXxCRD0TkPRF5S0RO9TKeUMr3N7GrttmuDxhjYpZniUBE4oEHgFnAqcD1IXb0T6nqaao6CfgJ8Auv4unJys7+hez6gDEmRnl5RjAV2KKq21S1FZgHXBW8gKrWB71MA9TDeEJasb2GDF8CpwzJDPeqjTGmX/ByzOJhwM6g1+XA2d0XEpEvAV8HkoCLQn2QiMwB5gCMHDmyT4Ncvr2GKUWDiI+z6wPGmNgU8YvFqvqAqo4GvgXc08MyD6lqiaqW5Ofn99m69zW2sK3qgHUrYYyJaV4mgl3AiKDXw915PZkHXO1hPIdZafcPGGOMp4lgJTBWRIpFJAm4DpgfvICIjA16+TFgs4fxHGb59hp8iXGcNiwrnKs1xph+xbNrBKraLiJ3AAuBeOARVV0rIvcBpao6H7hDRC4B2oD9wC1exRPKyrIaJo/IISkh4jVkxhgTMV5eLEZVFwALus27N2j6Ti/XfyT1/jbW7annKxeNPfrCxhgTxWL2UHhV2X5UsYFojDExL2YTwYqyGhLihMkjbaB6Y0xsi91EsL2G04ZnkZIUH+lQjDEmomIyEfjbOlhdXmvNRo0xhhhNBO9+WEtbh1pHc8YYQ4wmghXbaxCBklGWCIwxJjYTQVk1pwzJJCs1MdKhGGNMxMVcImjrCPDOjlqmFllrIWOMgRhMBGt21dHc1sHU4txIh2KMMf1CzCWCzoHqpxTbGYExxkAMJoKVZTUU56UxOMMX6VCMMaZfiKlEEAioM1C9NRs1xpiDYioRbKxooN7fbjeSGWNMkJhKBJ0D1VsiMMaYQ2IqESzfXsPQLB/Dc1IiHYoxxvQbMZMIVN3rA8WDELGB6o0xppOniUBEZorIRhHZIiJzQ5R/XUTWichqEXlVREZ5FcuO6iaqGlpsoHpjjOnGs0QgIvHAA8As4FTgehE5tdti7wIlqno68BzwE6/i6bx/wAaiMcaYrrw8I5gKbFHVbaraCswDrgpeQFVfU9Um9+UyYLhXwWSnJnLpqQWMGZzu1SqMMWZA8nLM4mHAzqDX5cDZR1j+P4CXQhWIyBxgDsDIkSOPK5jLJgzhsglDjuu9xhgTzfrFxWIRuQkoAX4aqlxVH1LVElUtyc/PD29wxhgT5bw8I9gFjAh6Pdyd14WIXAJ8GzhfVVs8jMcYY0wIXp4RrATGikixiCQB1wHzgxcQkcnAH4DZqlrpYSzGGGN64FkiUNV24A5gIbAeeEZV14rIfSIy213sp0A68KyIvCci83v4OGOMMR7xsmoIVV0ALOg2796g6Uu8XL8xxpij6xcXi40xxkSOJQJjjIlxlgiMMSbGiapGOoZjIiJVwI7jfHsesK8Pw+lrFt+JsfhOXH+P0eI7fqNUNeSNWAMuEZwIESlV1ZJIx9ETi+/EWHwnrr/HaPF5w6qGjDEmxlkiMMaYGBdrieChSAdwFBbfibH4Tlx/j9Hi80BMXSMwxhhzuFg7IzDGGNONJQJjjIlxUZkIejFWcrKI/NUtXy4iRWGMbYSIvOaO1bxWRO4MscwFIlLndsT3nojcG+qzPIyxTEQ+cNddGqJcROTX7vZbLSJnhjG2cUHb5T0RqReRr3ZbJuzbT0QeEZFKEVkTNG+QiCwSkc3uc04P773FXWaziNwSpth+KiIb3L/f8yKS3cN7j/hb8DjG74nIrqC/4+U9vPeI/+8exvfXoNjKROS9Ht4blm14QlQ1qh5APLAVOAlIAt4HTu22zBeBB93p64C/hjG+ocCZ7nQGsClEfBcA/4zgNiwD8o5QfjnOaHICTAOWR/BvvRfnRpmIbj/gI8CZwJqgeT8B5rrTc4Efh3jfIGCb+5zjTueEIbbLgAR3+sehYuvNb8HjGL8HfLMXv4Ej/r97FV+38p8D90ZyG57IIxrPCI46VrL7+nF3+jngYhGRcASnqntU9R13ugGni+5h4Vh3H7oKeEIdy4BsERkagTguBraq6vHead5nVPUNoKbb7ODf2ePA1SHe+lFgkarWqOp+YBEw0+vYVPVldbqKB4/HC++NHrZfb/Tm//2EHSk+d99xLfB0X683XKIxEYQaK7n7jvbgMu4/Qx2QG5bogrhVUpOB5SGKp4vI+yLykohMCGtgoMDLIrLKHS+6u95s43C4jp7/+SK5/ToVqOoed3ovUBBimf6wLT9LD+OFc/TfgtfucKuvHumhaq0/bL/zgApV3dxDeaS34VFFYyIYEEQkHfg/4KuqWt+t+B2c6o4zgN8Afw9zeOeq6pnALOBLIvKRMK//qNxR72YDz4YojvT2O4w6dQT9rq22iHwbaAee7GGRSP4Wfg+MBiYBe3CqX/qj6zny2UC//3+KxkTQm7GSDy4jIglAFlAdluicdSbiJIEnVfVv3ctVtV5VG93pBUCiiOSFKz5V3eU+VwLP45x+B+vVeNQemwW8o6oV3Qsivf2CVHRWmbnPoYZjjdi2FJFbgSuAG91EdZhe/BY8o6oVqtqhqgHgjz2sO6K/RXf/8XHgrz0tE8lt2FvRmAiOOlay+7qzdcYngX/39I/Q19z6xD8B61X1Fz0sM6TzmoWITMX5O4UlUYlImohkdE7jXFRc022x+cBn3NZD04C6oCqQcOnxKCyS26+b4N/ZLcA/QiyzELhMRHLcqo/L3HmeEpGZwH/hjBfe1MMyvfkteBlj8HWna3pYd2/+3710CbBBVctDFUZ6G/ZapK9We/HAadWyCac1wbfdeffh/OgBfDhVCluAFcBJYYztXJwqgtXAe+7jcuALwBfcZe4A1uK0gFgGnBPG+E5y1/u+G0Pn9guOT4AH3O37AVAS5r9vGs6OPStoXkS3H05S2gO04dRT/wfOdadXgc3AK8Agd9kS4OGg937W/S1uAW4LU2xbcOrWO3+Dna3oCoEFR/othHH7/dn9fa3G2bkP7R6j+/qw//dwxOfOf6zzdxe0bES24Yk8rIsJY4yJcdFYNWSMMeYYWCIwxpgYZ4nAGGNinCUCY4yJcZYIjDEmxlkiMCaM3J5R/xnpOIwJZonAGGNinCUCY0IQkZtEZIXbh/wfRCReRBpF5JfijCPxqojku8tOEpFlQX3757jzx4jIK27nd++IyGj349NF5Dl3PIAnw9XzrTE9sURgTDciMh74NDBDVScBHcCNOHc0l6rqBGAx8F33LU8A31LV03HuhO2c/yTwgDqd352Dc2cqOD3OfhU4FefO0xmefyljjiAh0gEY0w9dDJwFrHQP1lNwOowLcKhzsb8AfxORLCBbVRe78x8HnnX7lxmmqs8DqKofwP28Fer2TeOOalUEvOX91zImNEsExhxOgMdV9e4uM0W+02254+2fpSVougP7PzQRZlVDxhzuVeCTIjIYDo49PArn/+WT7jI3AG+pah2wX0TOc+ffDCxWZ/S5chG52v2MZBFJDeu3MKaX7EjEmG5UdZ2I3IMzqlQcTo+TXwIOAFPdskqc6wjgdDH9oLuj3wbc5s6/GfiDiNznfsanwvg1jOk1633UmF4SkUZVTY90HMb0NasaMsaYGGdnBMYYE+PsjMAYY2KcJQJjjIlxlgiMMSbGWSIwxpgYZ4nAGGNi3P8HHczO2jUt4EEAAAAASUVORK5CYII=\n", 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\n", 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\n", "text/plain": [ "
" ] @@ -646,8 +670,8 @@ "text": [ "\n", "Test Set Metrics:\n", - "\tloss: 0.7124\n", - "\tacc: 0.7900\n" + "\tloss: 0.6787\n", + "\tacc: 0.8113\n" ] } ], @@ -754,7 +778,7 @@ " \n", " \n", " 13195\n", - " subject=Reinforcement_Learning\n", + " subject=Neural_Networks\n", " Reinforcement_Learning\n", " \n", " \n", @@ -796,7 +820,7 @@ "31336 subject=Neural_Networks Neural_Networks\n", "1061127 subject=Rule_Learning Rule_Learning\n", "1106406 subject=Reinforcement_Learning Reinforcement_Learning\n", - "13195 subject=Reinforcement_Learning Reinforcement_Learning\n", + "13195 subject=Neural_Networks Reinforcement_Learning\n", "37879 subject=Probabilistic_Methods Probabilistic_Methods\n", "1126012 subject=Probabilistic_Methods Probabilistic_Methods\n", "1107140 subject=Theory Theory\n", @@ -960,7 +984,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -986,12 +1010,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Observe that each class of paper is separated into three distinct regions. This can be explained by the fact that the resulting embedding looks like (node, in, out). Hence there are three distinct types of representation, namely: (node, 0, out) if there are no in-nodes; (node, in, 0) if there are no out-nodes; and (node, in, out) if there are both in-nodes and out-nodes. The fourth case, isolated nodes having no in-nodes and no out-nodes, does not occur for the CORA dataset (see the counts below)." + "Observe that each class of paper is separated into three distinct regions. This can be explained by the fact that the raw embeddings look something like (node, in, out). Hence there are three distinct types of representation, namely: (node, 0, out) if there are no in-nodes; (node, in, 0) if there are no out-nodes; and (node, in, out) if there are both in-nodes and out-nodes. The fourth case, isolated nodes having no in-nodes and no out-nodes, does not occur for the CORA dataset (see the counts below)." ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1035,7 +1059,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/demos/node-classification/directed-graphsage/experim.ipynb b/demos/node-classification/directed-graphsage/experim.ipynb new file mode 100644 index 000000000..1ff043169 --- /dev/null +++ b/demos/node-classification/directed-graphsage/experim.ipynb @@ -0,0 +1,799 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simple graph experiment to investigate inner workings\n", + "\n", + "This experiments seeks to provide a better understanding of how, and possibly if, the directed GraphSAGE algorithm works in its entirety." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import NetworkX and stellar:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "import networkx as nx\n", + "import pandas as pd\n", + "import os\n", + "\n", + "import stellargraph as sg\n", + "from stellargraph.data.explorer import DirectedBreadthFirstNeighbours\n", + "from stellargraph.mapper import DirectedGraphSAGENodeGenerator\n", + "from stellargraph.layer import DirectedGraphSAGE\n", + "\n", + "from keras import layers, optimizers, losses, metrics, Model\n", + "from sklearn import preprocessing, feature_extraction, model_selection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load a simple graph" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the graph with directed edges" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "Gnx = nx.DiGraph()\n", + "Gnx.add_edges_from([(1,2),(2,3)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the features for the nodes" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " f0\n", + "id \n", + "1 1.0\n", + "2 0.0\n", + "3 2.0\n" + ] + } + ], + "source": [ + "feature_names = [\"f0\"]\n", + "column_names = [\"id\"] + feature_names\n", + "node_data = pd.DataFrame([[1,1.0],[2,0.0],[3,2.0]], columns=column_names)\n", + "node_data = node_data.set_index(\"id\")\n", + "print(node_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " f0\n", + "id \n", + "1 1.0\n", + "2 0.0\n", + "3 2.0\n" + ] + } + ], + "source": [ + "node_features = node_data[feature_names]\n", + "print(node_features)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create the StellarGraph" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "G = sg.StellarDiGraph(Gnx, node_features=node_features)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "StellarDiGraph: Directed multigraph\n", + " Nodes: 3, Edges: 2\n", + "\n", + " Node types:\n", + " default: [3]\n", + " Edge types: default-default->default\n", + "\n", + " Edge types:\n", + " default-default->default: [2]\n", + "\n" + ] + } + ], + "source": [ + "print(G.info())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us use all nodes" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 2, 3]\n" + ] + } + ], + "source": [ + "nodes = list(G.nodes())\n", + "print(nodes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check on the features" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1.]\n", + " [0.]\n", + " [2.]]\n" + ] + } + ], + "source": [ + "features = G.get_feature_for_nodes(nodes)\n", + "print(features)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us look at directed sampling" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "sampler = DirectedBreadthFirstNeighbours(G)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Examine 1-hop sampling:" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1], [None], [2]]\n", + "[[2], [1], [3]]\n", + "[[3], [2], [None]]\n" + ] + } + ], + "source": [ + "res = sampler.run(nodes=nodes, in_size=[1], out_size=[1])\n", + "for node_sample in res:\n", + " print(node_sample)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Examine 2-hop sampling:" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1], [None], [2], [None], [None], [1], [3]]\n", + "[[2], [1], [3], [None], [2], [2], [None]]\n", + "[[3], [2], [None], [1], [3], [None], [None]]\n" + ] + } + ], + "source": [ + "res = sampler.run(nodes=nodes, in_size=[1, 1], out_size=[1, 1])\n", + "for node_sample in res:\n", + " print(node_sample)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now examine the node generator:" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "generator = DirectedGraphSAGENodeGenerator(G, batch_size=len(nodes), in_samples=[1], out_samples=[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "train_data = node_data.index\n", + "train_gen = generator.flow(train_data, shuffle=False)\n", + "print(train_gen)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us delve into the inner workings:" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "features = train_gen.generator.sample_features(nodes, train_gen._sampling_schema)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[array([[[1.]],\n", + "\n", + " [[0.]],\n", + "\n", + " [[2.]]]), array([[[0.]],\n", + "\n", + " [[1.]],\n", + "\n", + " [[0.]]]), array([[[0.]],\n", + "\n", + " [[2.]],\n", + "\n", + " [[0.]]])]\n" + ] + } + ], + "source": [ + "print(features)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "******************************************************************************" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us see what happens when we ignore in-nodes:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 3; in_samples = [0, 0]; out_samples = [2, 2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A `DirectedGraphSAGENodeGenerator` object is required to send the node features in sampled subgraphs to Keras" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "generator = DirectedGraphSAGENodeGenerator(G, batch_size, in_samples, out_samples)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the `generator.flow()` method, we can create iterators over nodes that should be used to train, validate, or evaluate the model. For training we use all nodes." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "train_data = node_data[\"id\"]\n", + "train_gen = generator.flow(train_data, shuffle=False)\n", + "print(train_gen)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can specify our machine learning model, we need a few more parameters for this:\n", + "\n", + " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use 48-dimensional hidden node features at each layer, which corresponds to 16 weights for a node, 16 for the in-nodes (unused) and 16 for the out-nodes. This corresponds to the 32 dimensions used in example 1 (where we do not distinguish between in-nodes and out-nodes).\n", + " * The `bias` and `dropout` are internal parameters of the model. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "graphsage_model = DirectedGraphSAGE(\n", + " layer_sizes=[48, 48],\n", + " generator=train_gen,\n", + " bias=False,\n", + " dropout=0.5,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we create a model to predict the 7 categories using Keras softmax layers." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x_inp, x_out = graphsage_model.build()\n", + "prediction = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's create the actual Keras model with the graph inputs `x_inp` provided by the `graph_model` and outputs being the predictions from the softmax layer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Model(inputs=x_inp, outputs=prediction)\n", + "model.compile(\n", + " optimizer=optimizers.Adam(lr=0.005),\n", + " loss=losses.categorical_crossentropy,\n", + " metrics=[\"acc\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Train the model, keeping track of its loss and accuracy on the training set, and its generalisation performance on the test set (we need to create another generator over the test data for this)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test_gen = generator.flow(test_data.index, test_targets)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "history = model.fit_generator(\n", + " train_gen,\n", + " epochs=20,\n", + " validation_data=test_gen,\n", + " verbose=2,\n", + " shuffle=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "def plot_history(history):\n", + " metrics = sorted(history.history.keys())\n", + " metrics = metrics[:len(metrics)//2]\n", + " for m in metrics:\n", + " # summarize history for metric m\n", + " plt.plot(history.history[m])\n", + " plt.plot(history.history['val_' + m])\n", + " plt.title(m)\n", + " plt.ylabel(m)\n", + " plt.xlabel('epoch')\n", + " plt.legend(['train', 'test'], loc='best')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_history(history)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have trained the model we can evaluate on the test set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test_metrics = model.evaluate_generator(test_gen)\n", + "print(\"\\nTest Set Metrics:\")\n", + "for name, val in zip(model.metrics_names, test_metrics):\n", + " print(\"\\t{}: {:0.4f}\".format(name, val))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Making predictions with the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's get the predictions themselves for all nodes using another node iterator:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "all_nodes = node_data.index\n", + "all_mapper = generator.flow(all_nodes)\n", + "all_predictions = model.predict_generator(all_mapper)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These predictions will be the output of the softmax layer, so to get final categories we'll use the `inverse_transform` method of our target attribute specifcation to turn these values back to the original categories" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "node_predictions = target_encoding.inverse_transform(all_predictions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's have a look at a few:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results = pd.DataFrame(node_predictions, index=all_nodes).idxmax(axis=1)\n", + "df = pd.DataFrame({\"Predicted\": results, \"True\": node_data['subject']})\n", + "df.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Add the predictions to the graph, and save as graphml, e.g. for visualisation in [Gephi](https://gephi.org)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for nid, pred, true in zip(df.index, df[\"Predicted\"], df[\"True\"]):\n", + " Gnx.node[nid][\"subject\"] = true\n", + " Gnx.node[nid][\"PREDICTED_subject\"] = pred.split(\"=\")[-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Also add isTrain and isCorrect node attributes:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for nid in train_data.index:\n", + " Gnx.node[nid][\"isTrain\"] = True\n", + " \n", + "for nid in test_data.index:\n", + " Gnx.node[nid][\"isTrain\"] = False" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for nid in Gnx.nodes():\n", + " Gnx.node[nid][\"isCorrect\"] = Gnx.node[nid][\"subject\"] == Gnx.node[nid][\"PREDICTED_subject\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Node embeddings\n", + "Evaluate node embeddings as activations of the output of graphsage layer stack, and visualise them, coloring nodes by their subject label.\n", + "\n", + "The GraphSAGE embeddings are the output of the GraphSAGE layers, namely the `x_out` variable. Let's create a new model with the same inputs as we used previously `x_inp` but now the output is the embeddings rather than the predicted class. Additionally note that the weights trained previously are kept in the new model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "embedding_model = Model(inputs=x_inp, outputs=x_out)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "emb = embedding_model.predict_generator(all_mapper)\n", + "emb.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Project the embeddings to 2d using either TSNE or PCA transform, and visualise, coloring nodes by their subject label" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import TSNE\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X = emb\n", + "y = np.argmax(target_encoding.transform(node_data[[\"subject\"]].to_dict('records')), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if X.shape[1] > 2:\n", + " transform = TSNE #PCA \n", + "\n", + " trans = transform(n_components=2)\n", + " emb_transformed = pd.DataFrame(trans.fit_transform(X), index=node_data.index)\n", + " emb_transformed['label'] = y\n", + "else:\n", + " emb_transformed = pd.DataFrame(X, index=node_data.index)\n", + " emb_transformed = emb_transformed.rename(columns = {'0':0, '1':1})\n", + " emb_transformed['label'] = y" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "alpha = 0.7\n", + "\n", + "fig, ax = plt.subplots(figsize=(7,7))\n", + "ax.scatter(emb_transformed[0], emb_transformed[1], c=emb_transformed['label'].astype(\"category\"), \n", + " cmap=\"jet\", alpha=alpha)\n", + "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n", + "plt.title('{} visualization of GraphSAGE embeddings for cora dataset'.format(transform.__name__))\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tests/mapper/test_directed_node_generator.py b/tests/mapper/test_directed_node_generator.py new file mode 100644 index 000000000..4237b48dd --- /dev/null +++ b/tests/mapper/test_directed_node_generator.py @@ -0,0 +1,232 @@ +# -*- coding: utf-8 -*- +# +# Copyright 2017-2019 Data61, CSIRO +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import random +import pytest +import networkx as nx +import pandas as pd +from stellargraph.mapper.node_mappers import DirectedGraphSAGENodeGenerator +from stellargraph.core.graph import StellarGraph, StellarDiGraph + + +def create_simple_graph(): + """ + Creates a simple directed graph for testing. The node ids are integers. + + Returns: + A small, directed graph with 3 nodes and 2 edges in StellarDiGraph format. + """ + + g = nx.DiGraph() + edges = [ + (1, 2), + (2, 3), + ] + g.add_edges_from(edges) + nodes = list(g.nodes()) + features = [(node, -1.0 * node) for node in nodes] + df = pd.DataFrame(features, columns=["id", "f0"]).set_index("id") + return StellarDiGraph(g, node_features=df) + + +class TestDirectedNodeGenerator(object): + """ + Test various aspects of the directed GrapohSAGE node generator, with the focus + on the sampled neighbourhoods and the extracted features. + """ + + def sample_one_hop(self, num_in_samples, num_out_samples): + g = create_simple_graph() + nodes = list(g.nodes()) + + in_samples = [num_in_samples] + out_samples = [num_out_samples] + gen = DirectedGraphSAGENodeGenerator(g, len(g), in_samples, out_samples) + flow = gen.flow(node_ids=nodes, shuffle=False) + + # Obtain tree of sampled features + features = flow.generator.sample_features(nodes, flow._sampling_schema) + num_hops = len(in_samples) + tree_len = 2 ** (num_hops + 1) - 1 + assert len(features) == tree_len + + # Check node features + node_features = features[0] + assert len(node_features) == len(nodes) + assert node_features.shape == (len(nodes), 1, 1) + for idx, node in enumerate(nodes): + assert node_features[idx, 0, 0] == -1.0 * node + + # Check in-node features + in_features = features[1] + assert in_features.shape == (len(nodes), in_samples[0], 1) + for n_idx in range(in_samples[0]): + for idx, node in enumerate(nodes): + if node == 1: + # None -> 1 + assert in_features[idx, n_idx, 0] == 0.0 + elif node == 2: + # 1 -> 2 + assert in_features[idx, n_idx, 0] == -1.0 + elif node == 3: + # 2 -> 3 + assert in_features[idx, n_idx, 0] == -2.0 + else: + assert False + + # Check out-node features + out_features = features[2] + assert out_features.shape == (len(nodes), out_samples[0], 1) + for n_idx in range(out_samples[0]): + for idx, node in enumerate(nodes): + if node == 1: + # 1 -> 2 + assert out_features[idx, n_idx, 0] == -2.0 + elif node == 2: + # 2 -> 3 + assert out_features[idx, n_idx, 0] == -3.0 + elif node == 3: + # 3 -> None + assert out_features[idx, n_idx, 0] == 0.0 + else: + assert False + + def test_one_hop(self): + # Test 1 in-node and 1 out-node sampling + self.sample_one_hop(1, 1) + # Test 0 in-nodes and 1 out-node sampling + self.sample_one_hop(0, 1) + # Test 1 in-node and 0 out-nodes sampling + self.sample_one_hop(1, 0) + # Test 0 in-nodes and 0 out-nodes sampling + self.sample_one_hop(0, 0) + # Test 2 in-nodes and 3 out-nodes sampling + self.sample_one_hop(2, 3) + + def test_two_hop(self): + g = create_simple_graph() + nodes = list(g.nodes()) + + gen = DirectedGraphSAGENodeGenerator(g, batch_size=len(g), in_samples=[1, 1], out_samples=[1, 1]) + flow = gen.flow(node_ids=nodes, shuffle=False) + + features = flow.generator.sample_features(nodes, flow._sampling_schema) + num_hops = 2 + tree_len = 2 ** (num_hops + 1) - 1 + assert len(features) == tree_len + + # Check node features + node_features = features[0] + assert len(node_features) == len(nodes) + assert node_features.shape == (len(nodes), 1, 1) + for idx, node in enumerate(nodes): + assert node_features[idx, 0, 0] == -1.0 * node + + # Check in-node features + in_features = features[1] + assert in_features.shape == (len(nodes), 1, 1) + for idx, node in enumerate(nodes): + if node == 1: + # *None -> 1 + assert in_features[idx, 0, 0] == 0.0 + elif node == 2: + # *1 -> 2 + assert in_features[idx, 0, 0] == -1.0 + elif node == 3: + # *2 -> 3 + assert in_features[idx, 0, 0] == -2.0 + else: + assert False + + # Check out-node features + out_features = features[2] + assert out_features.shape == (len(nodes), 1, 1) + for idx, node in enumerate(nodes): + if node == 1: + # 1 -> *2 + assert out_features[idx, 0, 0] == -2.0 + elif node == 2: + # 2 -> *3 + assert out_features[idx, 0, 0] == -3.0 + elif node == 3: + # 3 -> *None + assert out_features[idx, 0, 0] == 0.0 + else: + assert False + + # Check in-in-node features + in_features = features[3] + assert in_features.shape == (len(nodes), 1, 1) + for idx, node in enumerate(nodes): + if node == 1: + # *None -> None -> 1 + assert in_features[idx, 0, 0] == 0.0 + elif node == 2: + # *None -> 1 -> 2 + assert in_features[idx, 0, 0] == 0.0 + elif node == 3: + # *1 -> 2 -> 3 + assert in_features[idx, 0, 0] == -1.0 + else: + assert False + + # Check in-out-node features + in_features = features[4] + assert in_features.shape == (len(nodes), 1, 1) + for idx, node in enumerate(nodes): + if node == 1: + # *None <- None -> 1 + assert in_features[idx, 0, 0] == 0.0 + elif node == 2: + # *2 <- 1 -> 2 + assert in_features[idx, 0, 0] == -2.0 + elif node == 3: + # *3 <- 2 -> 3 + assert in_features[idx, 0, 0] == -3.0 + else: + assert False + + # Check out-in-node features + out_features = features[5] + assert out_features.shape == (len(nodes), 1, 1) + for idx, node in enumerate(nodes): + if node == 1: + # 1 -> 2 <- *1 + assert out_features[idx, 0, 0] == -1.0 + elif node == 2: + # 2 -> 3 <- *2 + assert out_features[idx, 0, 0] == -2.0 + elif node == 3: + # 3 -> None <- *None + assert out_features[idx, 0, 0] == 0.0 + else: + assert False + + # Check out-out-node features + out_features = features[6] + assert out_features.shape == (len(nodes), 1, 1) + for idx, node in enumerate(nodes): + if node == 1: + # 1 -> 2 -> *3 + assert out_features[idx, 0, 0] == -3.0 + elif node == 2: + # 2 -> 3 -> *None + assert out_features[idx, 0, 0] == 0.0 + elif node == 3: + # 3 -> None -> *None + assert out_features[idx, 0, 0] == 0.0 + else: + assert False From ca5ccb787e77ef967a7534b4c48020644500bf20 Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Wed, 21 Aug 2019 13:37:10 +0930 Subject: [PATCH 14/30] Blacked the code. --- stellargraph/layer/graphsage.py | 35 +++++++++++++------- stellargraph/mapper/node_mappers.py | 2 +- tests/mapper/test_directed_node_generator.py | 9 +++-- 3 files changed, 28 insertions(+), 18 deletions(-) diff --git a/stellargraph/layer/graphsage.py b/stellargraph/layer/graphsage.py index 2c7d25ec5..769605c9f 100644 --- a/stellargraph/layer/graphsage.py +++ b/stellargraph/layer/graphsage.py @@ -191,7 +191,7 @@ def call(self, x, **kwargs): if len(x) > 1: sources = [from_self] for i in range(1, len(x)): - sources.append(self.aggregate_neighbours(x[i], neigh_idx=i-1)) + sources.append(self.aggregate_neighbours(x[i], neigh_idx=i - 1)) h_out = K.concatenate(sources, axis=2) else: h_out = from_self @@ -840,7 +840,9 @@ def __init__( ) self.input_feature_size = feature_sizes.popitem()[1] - elif in_samples is not None and out_samples is not None and input_dim is not None: + elif ( + in_samples is not None and out_samples is not None and input_dim is not None + ): self.in_samples = in_samples self.out_samples = out_samples self.input_feature_size = input_dim @@ -852,9 +854,17 @@ def __init__( self.max_hops = max_hops = len(layer_sizes) if len(self.in_samples) != max_hops: - raise ValueError("Mismatched lengths: in-node sample sizes {} versus layer sizes {}".format(self.in_samples, layer_sizes)) + raise ValueError( + "Mismatched lengths: in-node sample sizes {} versus layer sizes {}".format( + self.in_samples, layer_sizes + ) + ) if len(self.out_samples) != max_hops: - raise ValueError("Mismatched lengths: out-node sample sizes {} versus layer sizes {}".format(self.out_samples, layer_sizes)) + raise ValueError( + "Mismatched lengths: out-node sample sizes {} versus layer sizes {}".format( + self.out_samples, layer_sizes + ) + ) # Model parameters self.max_slots = 2 ** (max_hops + 1) - 1 @@ -886,6 +896,7 @@ def __call__(self, xin: List): Returns: Output tensor """ + def aggregate_neighbours(tree: List, stage: int): # compute the number of slots with children in the binary tree num_slots = (len(tree) - 1) // 2 @@ -897,8 +908,9 @@ def aggregate_neighbours(tree: List, stage: int): # find in-nodes child_slot = 2 * slot + 1 size = ( - self.neighbourhood_sizes[child_slot] // num_head_nodes - if num_head_nodes > 0 else 0 + self.neighbourhood_sizes[child_slot] // num_head_nodes + if num_head_nodes > 0 + else 0 ) in_child = Dropout(self.dropout)( Reshape((num_head_nodes, size, self.dims[stage]))(tree[child_slot]) @@ -906,8 +918,9 @@ def aggregate_neighbours(tree: List, stage: int): # find out-nodes child_slot = child_slot + 1 size = ( - self.neighbourhood_sizes[child_slot] // num_head_nodes - if num_head_nodes > 0 else 0 + self.neighbourhood_sizes[child_slot] // num_head_nodes + if num_head_nodes > 0 + else 0 ) out_child = Dropout(self.dropout)( Reshape((num_head_nodes, size, self.dims[stage]))(tree[child_slot]) @@ -949,8 +962,7 @@ def _compute_input_sizes(self) -> List[int]: i = num_hops[parent_slot] num_hops[slot] = i + 1 num_nodes[slot] = ( - self.in_samples[i] if slot % 2 == 1 - else self.out_samples[i] + self.in_samples[i] if slot % 2 == 1 else self.out_samples[i] ) * num_nodes[parent_slot] return num_nodes @@ -966,8 +978,7 @@ def node_model(self): """ # Create tensor inputs for neighbourhood sampling; x_inp = [ - Input(shape=(s, self.input_feature_size)) - for s in self.neighbourhood_sizes + Input(shape=(s, self.input_feature_size)) for s in self.neighbourhood_sizes ] # Output from GraphSAGE model diff --git a/stellargraph/mapper/node_mappers.py b/stellargraph/mapper/node_mappers.py index dc9197c20..dea506c87 100644 --- a/stellargraph/mapper/node_mappers.py +++ b/stellargraph/mapper/node_mappers.py @@ -924,7 +924,7 @@ def sample_features(self, head_nodes, sampling_schema): features_for_slot = self.graph.get_feature_for_nodes( nodes_in_slot, node_type ) - resize = - 1 if np.size(features_for_slot) > 0 else 0 + resize = -1 if np.size(features_for_slot) > 0 else 0 features[slot] = np.reshape( features_for_slot, (len(head_nodes), resize, features_for_slot.shape[1]) ) diff --git a/tests/mapper/test_directed_node_generator.py b/tests/mapper/test_directed_node_generator.py index 4237b48dd..b0aaab0be 100644 --- a/tests/mapper/test_directed_node_generator.py +++ b/tests/mapper/test_directed_node_generator.py @@ -31,10 +31,7 @@ def create_simple_graph(): """ g = nx.DiGraph() - edges = [ - (1, 2), - (2, 3), - ] + edges = [(1, 2), (2, 3)] g.add_edges_from(edges) nodes = list(g.nodes()) features = [(node, -1.0 * node) for node in nodes] @@ -120,7 +117,9 @@ def test_two_hop(self): g = create_simple_graph() nodes = list(g.nodes()) - gen = DirectedGraphSAGENodeGenerator(g, batch_size=len(g), in_samples=[1, 1], out_samples=[1, 1]) + gen = DirectedGraphSAGENodeGenerator( + g, batch_size=len(g), in_samples=[1, 1], out_samples=[1, 1] + ) flow = gen.flow(node_ids=nodes, shuffle=False) features = flow.generator.sample_features(nodes, flow._sampling_schema) From 917bd8c1ffa445c5c6b3edad08d035c0e04260c1 Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Thu, 22 Aug 2019 11:11:12 +0930 Subject: [PATCH 15/30] Fixed bug with back-off to MLP; now example3 generates NaN. --- stellargraph/layer/graphsage.py | 102 ++++++++++++++++++++------------ 1 file changed, 63 insertions(+), 39 deletions(-) diff --git a/stellargraph/layer/graphsage.py b/stellargraph/layer/graphsage.py index 769605c9f..efc289905 100644 --- a/stellargraph/layer/graphsage.py +++ b/stellargraph/layer/graphsage.py @@ -63,8 +63,8 @@ def __init__( ): self.neigh_dim = neigh_dim self.output_dim = output_dim - self.other_output_dim = output_dim // (neigh_dim + 1) - self.self_output_dim = output_dim - neigh_dim * self.other_output_dim + self.neighbour_output_dim = output_dim // (neigh_dim + 1) + self.node_output_dim = output_dim - neigh_dim * self.neighbour_output_dim self.has_bias = bias self.act = activations.get(act) self.w_self = None @@ -98,29 +98,31 @@ def weight_output_size(self): if self._build_mlp_only: weight_dim = self.output_dim else: - weight_dim = self.self_output_dim + weight_dim = self.node_output_dim return weight_dim def build(self, input_shape): """ - Builds layer + Builds the weight tensor corresponding to the features + of the initial nodes in sampled random walks. + Optionally builds the weight tensor(s) corresponding + to sampled neighbourhoods, if required. + Optionally builds the bias tensor, if requested. Args: input_shape (list of list of int): Shape of input tensors for self and neighbour features """ - # Build a MLP model if zero neighbours - self._build_mlp_only = input_shape[1][2] == 0 - - self.w_self = self.add_weight( - name="w_self", - shape=(input_shape[0][2], self.weight_output_size()), - initializer=self._initializer, - trainable=True, - ) + if not isinstance(input_shape, list): + raise ValueError("Expected a list of inputs, not {}".format(type(input_shape))) + if len(input_shape) != self.neigh_dim + 1: + raise ValueError( + "List of inputs must be of length {}, not {}".format(self.neigh_dim + 1, len(input_shape)) + ) + # Configure bias vector, if used. if self.has_bias: self.bias = self.add_weight( name="bias", @@ -129,8 +131,39 @@ def build(self, input_shape): trainable=True, ) + # If there are zero neighbours, back off to + # using only node features. + counter = 1 + for neigh_shape in input_shape[1:]: + if neigh_shape[2] == 0: + counter = counter + 1 + self._build_mlp_only = counter == len(input_shape) + + # Configure weights for node features. + self.w_self = self.add_weight( + name="w_self", + shape=(input_shape[0][2], self.weight_output_size()), + initializer=self._initializer, + trainable=True, + ) + + # Configure weights for neighbouring nodes, if used. + self._build_neighbour_weights(input_shape) + + # Signal that the build has completed. super().build(input_shape) + def _build_neighbour_weights(self, input_shape): + """ + Builds the weight tensor(s) corresponding to the features + of the neighbouring nodes in sampled random walks. + + Args: + input_shape (list of list of int): Shape of input tensors for self + and neighbour features + """ + pass + def apply_mlp(self, x, **kwargs): """ Create MLP on input self tensor, x @@ -143,7 +176,7 @@ def apply_mlp(self, x, **kwargs): Keras Tensor representing the aggregated embeddings in the input. """ - # Weight maxtrix multiplied by self features + # Weight maxtrix multiplied by node features h_out = K.dot(x, self.w_self) # Optionally add bias if self.has_bias: @@ -177,7 +210,7 @@ def call(self, x, **kwargs): Keras Tensor representing the aggregated embeddings in the input. """ - # x[0]: self vector (batch_size, head size, feature_size) + # x[0]: node vector (batch_size, head size, feature_size) # x[1...n]: optional neighbour vectors (batch_size, head size, neighbours, feature_size) x_self = x[0] @@ -191,7 +224,8 @@ def call(self, x, **kwargs): if len(x) > 1: sources = [from_self] for i in range(1, len(x)): - sources.append(self.aggregate_neighbours(x[i], neigh_idx=i - 1)) + neigh = self.aggregate_neighbours(x[i], neigh_idx=i - 1) + sources.append(neigh) h_out = K.concatenate(sources, axis=2) else: h_out = from_self @@ -230,7 +264,7 @@ class MeanAggregator(GraphSAGEAggregator): """ - def build(self, input_shape): + def _build_neighbour_weights(self, input_shape): """ Builds layer @@ -239,24 +273,21 @@ def build(self, input_shape): and neighbour features """ - super().build(input_shape) - if self._build_mlp_only: self.w_neigh = None else: - in_size = input_shape[1][3] - out_size = self.other_output_dim - self.w_neigh = [] + self.w_neigh = [None] * self.neigh_dim + out_size = self.neighbour_output_dim for i in range(self.neigh_dim): - self.w_neigh.append( - self.add_weight( - name="w_neigh" + str(i), - shape=(in_size, out_size), - initializer=self._initializer, - trainable=True, - ) + in_size = input_shape[i + 1][3] + weights = self.add_weight( + name="w_neigh" + str(i), + shape=(in_size, out_size), + initializer=self._initializer, + trainable=True, ) + self.w_neigh[i] = weights def aggregate_neighbours(self, x_neigh, neigh_idx=0): return K.dot(K.mean(x_neigh, axis=2), self.w_neigh[neigh_idx]) @@ -283,7 +314,7 @@ def __init__(self, *args, **kwargs): self.hidden_dim = self.output_dim self.hidden_act = activations.get("relu") - def build(self, input_shape): + def _build_neighbour_weights(self, input_shape): """ Builds layer @@ -292,8 +323,6 @@ def build(self, input_shape): and neighbour features """ - super().build(input_shape) - if self._build_mlp_only: self.w_neigh = None self.w_pool = None @@ -361,7 +390,7 @@ def __init__(self, *args, **kwargs): self.hidden_dim = self.output_dim self.hidden_act = activations.get("relu") - def build(self, input_shape): + def _build_neighbour_weights(self, input_shape): """ Builds layer @@ -370,8 +399,6 @@ def build(self, input_shape): and neighbour features """ - super().build(input_shape) - if self._build_mlp_only: self.w_neigh = None self.w_pool = None @@ -441,10 +468,7 @@ def __init__(self, *args, **kwargs): def weight_output_size(self): return self.output_dim - def build(self, input_shape): - # Build the full model if non-zero neighbours - super().build(input_shape) - + def _build_neighbour_weights(self, input_shape): self.a_self = self.add_weight( name="a_self", shape=(self.output_dim, 1), From 896d67948081c10e8b4da209488348c0c378f169 Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Thu, 22 Aug 2019 14:14:34 +0930 Subject: [PATCH 16/30] Fixed tests. --- stellargraph/layer/graphsage.py | 8 ++++++-- tests/layer/test_graphsage.py | 32 ++++++++++++++++---------------- 2 files changed, 22 insertions(+), 18 deletions(-) diff --git a/stellargraph/layer/graphsage.py b/stellargraph/layer/graphsage.py index efc289905..be829e57f 100644 --- a/stellargraph/layer/graphsage.py +++ b/stellargraph/layer/graphsage.py @@ -116,10 +116,14 @@ def build(self, input_shape): """ if not isinstance(input_shape, list): - raise ValueError("Expected a list of inputs, not {}".format(type(input_shape))) + raise ValueError( + "Expected a list of inputs, not {}".format(type(input_shape)) + ) if len(input_shape) != self.neigh_dim + 1: raise ValueError( - "List of inputs must be of length {}, not {}".format(self.neigh_dim + 1, len(input_shape)) + "List of inputs must be of length {}, not {}".format( + self.neigh_dim + 1, len(input_shape) + ) ) # Configure bias vector, if used. diff --git a/tests/layer/test_graphsage.py b/tests/layer/test_graphsage.py index 67299a98d..07f0aebb4 100644 --- a/tests/layer/test_graphsage.py +++ b/tests/layer/test_graphsage.py @@ -53,8 +53,8 @@ def example_graph_1(feature_size=None): def test_maxpool_agg_constructor(): agg = MaxPoolingAggregator(2, bias=False) assert agg.output_dim == 2 - assert agg.self_output_dim == 1 - assert agg.other_output_dim == 1 + assert agg.node_output_dim == 1 + assert agg.neighbour_output_dim == 1 assert agg.hidden_dim == 2 assert not agg.has_bias assert agg.act.__name__ == "relu" @@ -70,8 +70,8 @@ def test_maxpool_agg_constructor(): def test_maxpool_agg_constructor_1(): agg = MaxPoolingAggregator(output_dim=4, bias=True, act=lambda x: x + 1) assert agg.output_dim == 4 - assert agg.self_output_dim == 2 - assert agg.other_output_dim == 2 + assert agg.node_output_dim == 2 + assert agg.neighbour_output_dim == 2 assert agg.hidden_dim == 4 assert agg.has_bias assert agg.act(2) == 3 @@ -127,8 +127,8 @@ def test_maxpool_agg_zero_neighbours(): def test_meanpool_agg_constructor(): agg = MeanPoolingAggregator(2, bias=False) assert agg.output_dim == 2 - assert agg.self_output_dim == 1 - assert agg.other_output_dim == 1 + assert agg.node_output_dim == 1 + assert agg.neighbour_output_dim == 1 assert agg.hidden_dim == 2 assert not agg.has_bias assert agg.act.__name__ == "relu" @@ -144,8 +144,8 @@ def test_meanpool_agg_constructor(): def test_meanpool_agg_constructor_1(): agg = MeanPoolingAggregator(output_dim=4, bias=True, act=lambda x: x + 1) assert agg.output_dim == 4 - assert agg.self_output_dim == 2 - assert agg.other_output_dim == 2 + assert agg.node_output_dim == 2 + assert agg.neighbour_output_dim == 2 assert agg.hidden_dim == 4 assert agg.has_bias assert agg.act(2) == 3 @@ -199,8 +199,8 @@ def test_meanpool_agg_zero_neighbours(): def test_mean_agg_constructor(): agg = MeanAggregator(2) assert agg.output_dim == 2 - assert agg.self_output_dim == 1 - assert agg.other_output_dim == 1 + assert agg.node_output_dim == 1 + assert agg.neighbour_output_dim == 1 assert not agg.has_bias # Check config @@ -213,8 +213,8 @@ def test_mean_agg_constructor(): def test_mean_agg_constructor_1(): agg = MeanAggregator(output_dim=4, bias=True, act=lambda x: x + 1) assert agg.output_dim == 4 - assert agg.self_output_dim == 2 - assert agg.other_output_dim == 2 + assert agg.node_output_dim == 2 + assert agg.neighbour_output_dim == 2 assert agg.has_bias assert agg.act(2) == 3 @@ -224,8 +224,8 @@ def test_mean_agg_constructor_2(): # (e.g. in directed GraphSAGE), we must handle odd dimensions. agg = MeanAggregator(11) assert agg.output_dim == 11 - assert agg.self_output_dim == 6 - assert agg.other_output_dim == 5 + assert agg.node_output_dim == 6 + assert agg.neighbour_output_dim == 5 assert not agg.has_bias # Check config @@ -239,8 +239,8 @@ def test_mean_agg_constructor_3(): # Ditto above, re multiple neighbour sets. agg = MeanAggregator(12, neigh_dim=3) assert agg.output_dim == 12 - assert agg.self_output_dim == 3 - assert agg.other_output_dim == 3 + assert agg.node_output_dim == 3 + assert agg.neighbour_output_dim == 3 assert not agg.has_bias # Check config From 785fb13f773f1d8c129b7bb7a67d859d5c3115ce Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Fri, 23 Aug 2019 10:13:51 +0930 Subject: [PATCH 17/30] Refactored common code between directed and undirected GraphSAGE. --- ...ndirected-graph-undirected-graphsage.ipynb | 120 ++++---- ...-directed-graph-undirected-graphsage.ipynb | 98 +++---- ...rected-graph-half-directed-graphsage.ipynb | 266 +++++------------- ...ected-graph-fully-directed-graphsage.ipynb | 112 +++----- stellargraph/layer/graphsage.py | 260 +++++++---------- tests/layer/test_graphsage.py | 2 +- 6 files changed, 292 insertions(+), 566 deletions(-) diff --git a/demos/node-classification/directed-graphsage/example1-undirected-graph-undirected-graphsage.ipynb b/demos/node-classification/directed-graphsage/example1-undirected-graph-undirected-graphsage.ipynb index 6ca711d14..afeb05628 100644 --- a/demos/node-classification/directed-graphsage/example1-undirected-graph-undirected-graphsage.ipynb +++ b/demos/node-classification/directed-graphsage/example1-undirected-graph-undirected-graphsage.ipynb @@ -25,31 +25,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Using TensorFlow backend.\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n" + "Using TensorFlow backend.\n" ] } ], @@ -233,13 +209,13 @@ { "data": { "text/plain": [ - "Counter({'Neural_Networks': 81,\n", - " 'Probabilistic_Methods': 42,\n", + "Counter({'Genetic_Algorithms': 42,\n", + " 'Neural_Networks': 81,\n", " 'Case_Based': 30,\n", - " 'Reinforcement_Learning': 22,\n", - " 'Genetic_Algorithms': 42,\n", + " 'Theory': 35,\n", " 'Rule_Learning': 18,\n", - " 'Theory': 35})" + " 'Probabilistic_Methods': 42,\n", + " 'Reinforcement_Learning': 22})" ] }, "execution_count": 9, @@ -420,7 +396,7 @@ "output_type": "stream", "text": [ "WARNING: Logging before flag parsing goes to stderr.\n", - "W0820 17:26:50.029736 4429116864 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", + "W0823 09:57:58.263214 4533151168 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", "\n" ] } @@ -450,14 +426,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0820 17:26:50.045212 4429116864 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", + "W0823 09:57:58.279142 4533151168 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", "\n", - "W0820 17:26:50.055482 4429116864 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", + "W0823 09:57:58.288129 4533151168 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", "\n", - "W0820 17:26:50.062143 4429116864 deprecation.py:506] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", + "W0823 09:57:58.295388 4533151168 deprecation.py:506] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", - "W0820 17:26:50.088773 4429116864 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", + "W0823 09:57:58.325520 4533151168 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", "\n" ] } @@ -490,9 +466,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0820 17:26:50.245335 4429116864 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", + "W0823 09:57:58.485157 4533151168 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", "\n", - "W0820 17:26:50.252226 4429116864 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", + "W0823 09:57:58.491479 4533151168 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", "\n" ] } @@ -531,7 +507,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0820 17:26:50.339275 4429116864 deprecation.py:323] From /anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", + "W0823 09:57:58.580814 4533151168 deprecation.py:323] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" ] @@ -541,45 +517,45 @@ "output_type": "stream", "text": [ "Epoch 1/20\n", - " - 2s - loss: 1.8869 - acc: 0.2670 - val_loss: 1.7227 - val_acc: 0.3462\n", + " - 2s - loss: 1.8569 - acc: 0.3104 - val_loss: 1.6733 - val_acc: 0.4098\n", "Epoch 2/20\n", - " - 2s - loss: 1.6700 - acc: 0.4314 - val_loss: 1.5809 - val_acc: 0.4262\n", + " - 2s - loss: 1.6224 - acc: 0.4668 - val_loss: 1.5172 - val_acc: 0.4590\n", "Epoch 3/20\n", - " - 2s - loss: 1.5122 - acc: 0.5406 - val_loss: 1.4226 - val_acc: 0.6300\n", + " - 2s - loss: 1.4281 - acc: 0.6122 - val_loss: 1.3768 - val_acc: 0.6030\n", "Epoch 4/20\n", - " - 2s - loss: 1.3389 - acc: 0.8018 - val_loss: 1.2970 - val_acc: 0.7699\n", + " - 2s - loss: 1.2987 - acc: 0.7228 - val_loss: 1.2662 - val_acc: 0.6813\n", "Epoch 5/20\n", - " - 2s - loss: 1.2074 - acc: 0.8628 - val_loss: 1.1941 - val_acc: 0.7908\n", + " - 2s - loss: 1.1672 - acc: 0.7831 - val_loss: 1.1717 - val_acc: 0.7326\n", "Epoch 6/20\n", - " - 2s - loss: 1.0721 - acc: 0.9120 - val_loss: 1.1014 - val_acc: 0.8097\n", + " - 2s - loss: 1.0586 - acc: 0.8661 - val_loss: 1.1002 - val_acc: 0.7502\n", "Epoch 7/20\n", - " - 2s - loss: 0.9553 - acc: 0.9560 - val_loss: 1.0335 - val_acc: 0.8080\n", + " - 2s - loss: 0.9498 - acc: 0.8720 - val_loss: 1.0377 - val_acc: 0.7670\n", "Epoch 8/20\n", - " - 2s - loss: 0.8790 - acc: 0.9415 - val_loss: 0.9720 - val_acc: 0.8142\n", + " - 2s - loss: 0.8420 - acc: 0.9390 - val_loss: 0.9815 - val_acc: 0.7810\n", "Epoch 9/20\n", - " - 2s - loss: 0.7737 - acc: 0.9585 - val_loss: 0.9096 - val_acc: 0.8232\n", + " - 2s - loss: 0.7876 - acc: 0.9449 - val_loss: 0.9316 - val_acc: 0.7867\n", "Epoch 10/20\n", - " - 2s - loss: 0.7082 - acc: 0.9619 - val_loss: 0.8611 - val_acc: 0.8253\n", + " - 2s - loss: 0.6852 - acc: 0.9652 - val_loss: 0.8940 - val_acc: 0.7974\n", "Epoch 11/20\n", - " - 2s - loss: 0.6381 - acc: 0.9686 - val_loss: 0.8174 - val_acc: 0.8269\n", + " - 2s - loss: 0.6283 - acc: 0.9628 - val_loss: 0.8631 - val_acc: 0.8002\n", "Epoch 12/20\n", - " - 2s - loss: 0.5646 - acc: 0.9865 - val_loss: 0.7880 - val_acc: 0.8290\n", + " - 2s - loss: 0.5743 - acc: 0.9695 - val_loss: 0.8339 - val_acc: 0.7949\n", "Epoch 13/20\n", - " - 2s - loss: 0.5009 - acc: 0.9932 - val_loss: 0.7499 - val_acc: 0.8249\n", + " - 2s - loss: 0.5315 - acc: 0.9567 - val_loss: 0.8181 - val_acc: 0.7884\n", "Epoch 14/20\n", - " - 2s - loss: 0.4505 - acc: 0.9898 - val_loss: 0.7308 - val_acc: 0.8310\n", + " - 2s - loss: 0.4696 - acc: 0.9831 - val_loss: 0.7919 - val_acc: 0.7945\n", "Epoch 15/20\n", - " - 2s - loss: 0.4207 - acc: 0.9831 - val_loss: 0.7203 - val_acc: 0.8232\n", + " - 2s - loss: 0.4326 - acc: 0.9729 - val_loss: 0.7753 - val_acc: 0.7904\n", "Epoch 16/20\n", - " - 2s - loss: 0.3895 - acc: 0.9865 - val_loss: 0.6956 - val_acc: 0.8277\n", + " - 2s - loss: 0.3983 - acc: 0.9720 - val_loss: 0.7551 - val_acc: 0.7953\n", "Epoch 17/20\n", - " - 2s - loss: 0.3683 - acc: 0.9754 - val_loss: 0.6828 - val_acc: 0.8249\n", + " - 2s - loss: 0.3616 - acc: 0.9932 - val_loss: 0.7626 - val_acc: 0.7818\n", "Epoch 18/20\n", - " - 2s - loss: 0.3322 - acc: 0.9932 - val_loss: 0.6686 - val_acc: 0.8220\n", + " - 2s - loss: 0.3409 - acc: 0.9932 - val_loss: 0.7542 - val_acc: 0.7957\n", "Epoch 19/20\n", - " - 2s - loss: 0.3039 - acc: 0.9822 - val_loss: 0.6568 - val_acc: 0.8290\n", + " - 2s - loss: 0.3083 - acc: 0.9898 - val_loss: 0.7452 - val_acc: 0.7904\n", "Epoch 20/20\n", - " - 2s - loss: 0.2706 - acc: 0.9966 - val_loss: 0.6618 - val_acc: 0.8224\n" + " - 2s - loss: 0.3071 - acc: 0.9797 - val_loss: 0.7265 - val_acc: 0.7875\n" ] } ], @@ -623,7 +599,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -668,8 +644,8 @@ "text": [ "\n", "Test Set Metrics:\n", - "\tloss: 0.6534\n", - "\tacc: 0.8187\n" + "\tloss: 0.7187\n", + "\tacc: 0.7974\n" ] } ], @@ -761,7 +737,7 @@ " \n", " \n", " 31336\n", - " subject=Neural_Networks\n", + " subject=Probabilistic_Methods\n", " Neural_Networks\n", " \n", " \n", @@ -786,7 +762,7 @@ " \n", " \n", " 1126012\n", - " subject=Reinforcement_Learning\n", + " subject=Probabilistic_Methods\n", " Probabilistic_Methods\n", " \n", " \n", @@ -796,12 +772,12 @@ " \n", " \n", " 1102850\n", - " subject=Neural_Networks\n", + " subject=Probabilistic_Methods\n", " Neural_Networks\n", " \n", " \n", " 31349\n", - " subject=Neural_Networks\n", + " subject=Probabilistic_Methods\n", " Neural_Networks\n", " \n", " \n", @@ -815,15 +791,15 @@ ], "text/plain": [ " Predicted True\n", - "31336 subject=Neural_Networks Neural_Networks\n", + "31336 subject=Probabilistic_Methods Neural_Networks\n", "1061127 subject=Rule_Learning Rule_Learning\n", "1106406 subject=Reinforcement_Learning Reinforcement_Learning\n", "13195 subject=Reinforcement_Learning Reinforcement_Learning\n", "37879 subject=Probabilistic_Methods Probabilistic_Methods\n", - "1126012 subject=Reinforcement_Learning Probabilistic_Methods\n", + "1126012 subject=Probabilistic_Methods Probabilistic_Methods\n", "1107140 subject=Reinforcement_Learning Theory\n", - "1102850 subject=Neural_Networks Neural_Networks\n", - "31349 subject=Neural_Networks Neural_Networks\n", + "1102850 subject=Probabilistic_Methods Neural_Networks\n", + "31349 subject=Probabilistic_Methods Neural_Networks\n", "1106418 subject=Theory Theory" ] }, @@ -980,7 +956,7 @@ "outputs": [ { "data": { - "image/png": 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UOt3Ah4H/M8YcgOZlm1OSWYvFYrE8+dhjhFaU5+5UNCM5xpiaMWYcTftxUbTZRWgqHIvFYrHshewxQgvNwD4EfE9EbhORC6PMwP3GmM3RNluYYcoKEflHEblZRG4eGhp6nKpssVgslseTPUloecCxwNeNMcegE9dNMwVG88G0HA1tjPmWMeY4Y8xx8+fvUCSlxWKxWPZw9iShtQGdJTSeX+hyVIgNiMgigOj/4G6qn8VisVh2M3uM0IqmaFgvIvGUCaej0zNcQWOuq9ej0yJYLBaLZS9kTxtc/C7gRyKSBlYBb0QF609F5M3o3C3NcwpZLBaLZS9hjxJaxpjbaT1D6+ktllksFotlL2OPMQ9aLBaLxTIbVmhZLBaL5QnDHmUetFj2Bm4rw68KMOjD03Lwik7I2NdHi2VOWKFlsewkd1Tg22PwcA0OTsN5PXB4tvW2F43Dl0ZgUx1KBr43Dh8fhEuWwNPbHt96WyxPRKzQslh2ghvL8MHVBU64/0+8ZGQVD80/kHcc9Cw+tbyNIR9GAjgqC8fnYCyA/xmFAR9qQJsDxug2b9kMv1sBS1O7+4wslj0bK7Qslp3g+49s4j++92b6CsMYEZ5pDGdd823ece6FpHsWUDbgCpyWh7M7oGz0kxHdXwRcYCyEX0/BW3t1uTFqRry9Cvuk4cQcZK0J0WKxQsti2VGMgRN+/yV6iyOMdy3curx7bBPnXP11vvj8fwEgNHBJHXodCAzINgWF9MswhXoILOSaIrxiAwwGujotcFAaLlqiWpvFsjdj390slh1EMJz40JWMtc3buiwwMJifx7Me/BOeqMABmKjDv62F1ZugMAD1ki7PmDGWyf18NvNmzuOFbBr+MGevCxgMVANzgbqB+6rwts1QCR/307RY9iis0LJYdoK+TJogDAmMal41AxIG1NwMDlAMoeBDWIPQBwyEGRgfhUK9wjxZzZvT38ITyDr9fG3qQAomRDAIqpW5gA+sr8Otld15thbL7scKLYtlRxGh9+QXcUh5iHpoGA2gFBj6SiP87MiXUg2haiCMBI1ThFwB2irgefCK4Kf8T+7dHJ16hNuDI3n1xDv5QeUsAlSLm3YoVCBWW85xYLHsPViflsWyE8jZ7yC/aRXz7ruVPhFSJuTGA07heye8mYqJ5tFxVGA5ZfBcKGYhyMLh+btBDFfUn8sVtTMJjBAghDiECJ7uigFCoMuBYxM+Ld/AvVWNSlyRguUpcLZxmFksTy6s0LJYdoZsG79/49e5+O77OWRqA0O9y1m/4EB6a8Kgr8Im3AJSh5wLFQeqecDAn2rPYVw6+HbtdVTJ4G4VV2BCNQliAIEM8JkF0OXqYe+rwrs3wz1VmAxVEzsiA19YCCfmd8+lsFgeD6zQslh2krIRVi86hOqyQwDVjo7MwIOiQRSj/VBYCwWAPlSSCfwfz+SvtVOoo4OzAkI8fMQPMbi6nQ+dA7DkCvDOAZ6lwRjv2qxaVslAXnTTu6vwzi1w2VLYN707roTFsuuxPi2LZSc5LqcPUpDwN4VA3lGzXSUPZh8wvWBqqPRyIRCXOmniIPgQhzppQj8FAWRvg66vwuL/hf4qXPgzLfvGMgwHKrDSRGO9RE2D4wH8fPJxPX2L5XHFaloWyw5gjKE8MoKbyXBARwev6oRLJ1X8GKNjrCYCuNtEilUWnCwEASrhtvqeZNp/A/pUBsBGFURTfeAfpGbAKyZVSPnReC9p8mEJsN7X77UQfjkFpRDO7IB59mm3PAmwzdhieZTccNcDnH/TI9y3YAW5SpEXjK7l/Fc8h2cs6uLPRXigAn+vaLqmWKaY6IM7x4OMQmoNlA8C/0QfJzNCf26cTw3kODSTIUW/BmiY6cEXadH8h58egi+MqHlSgHcNwEd74cMLHrvrYLHsDqx50GKZI8bAj9aO8dzJXq4+7On4uTySTnPZ0sN5+2X/x7FZw4fmQQXoczSQQmg8ZHMeFxwCv4F6ATje5+COO9iv7QGW5EZYyDruKY9ybu5Kehw1EZZC/eRE8xl+awQ+NQRToQqttIBn4IIRuLb02F8Xi+XxxAoti2WOfGcc3j+SopLNgwij3fPZtGA5XdUS1yw6kIceWgWoXyktkBLVrly2Td0UDxpuRgBJw/yz4IgTYX7bGJ2ZItlMFs/zCCRNiGFTdTXn9xU5pwMOSKtf7e096lerR8eNzSiFEDxHl/3P6K65NhbL44U1D1osMzDsa9BD3cAiT6cfCf06Lg6uCZEQirk21i9cSb4wwQOjkwyVNADjoRoscGGDD/UWZcfZLtJAt6tjrTwa2tjEQhhaCr0UcXDB0ajBkoGK8ciYcX49uZYD2w7lwiVqIvzhuPq4jJl+nBA9B1CBarE8kbFCy2Jpwa+m4IIh2OzDaKC+IwG60mlMPcR3XSrpLAYo5NspZfL8U3453Vs0AGIwUJNdbB5MmgYF1XoCIAUUAhVsUSQ8RN/X+LBcPELpJB+WQSAgjYPhOam/8X1zLn8owNntcEK+cYx+Dx6o6e+tpslIaJ3dueuumcXyeGDNgxZLE5vqKrAmA5gIVXPKiPqqJtN5TCSwxGjKCwkN7QRsNB7jvu5jjPq0HGCJp6bA+GEziU85+gBR+qbpD+WY6WKRs4kJOhg3nVSNyweyX6Hg7MOELEKAayI/1Ul53TctsLQxzEs1LeDwDLypaxdeOIvlccBqWpa9EhPNa5UW8JocTleX1Jw2EKgmZExDkJQQ3EwWCUNCEQRYSo3hXAc1A6ujcPO4yDqwxd+a2KJ1XRLfA1TAxcum6CBDho9lPwPGZ6W7hnvMSVwcfAIi31VHVLn90/C2HvjGGPSldOzWcKATS76lRz8p+5pqeYJjhZZlr+NHtwzyyTVVBnI58i68us/lc0/p2TqNSBiNrapGqZQC9Hcc3GDEIXQdsmhapWGvk0IkZQQVdEk/ViufVoyLZrQomsYxtobHR+X93ZzGzdWTyYSbmaCdCdNDuwPdDvS58Jz2Rnlv6oHT2uC6kgrjU/KwxM6GbHkSYYWWZa/iFzdv4S0bBcdNkStWqAHf9jMUbh7jwuN7AM3d5w9DLdonKURcYLEHYz6MA07Y8HfF29WZrj01I4n1HqrVtQFTNJLjxriAGPDxGDTLtu5njPralnmqSZFYvsSD13RtO/B4LtQMjPjQ49qZki17JlZoWfYqPnXjCLJiAblyDUTIAE6hws+9PJ8LdP6rH4yrSS/WfGKEyEdk1L8FamKMhVVScM2ENK2vRstqiWVxiHwQHW8k1CjDOJFGSCPr+6TRCMen5+Gn4/CxIa17TuA13fCFBeBEwqcewpdH4CdTWtY5HfDUPKytq4lxyIcfTmqUoifwui74xx6bOd6yZ2GFlmWvYoPjkgpVlzGAcR08oFipc2fJ8K8jwtq6BlM0DwZOoZ39SNAImMjQEGCzTXUV+6qSAi7ez4vW16N1WdHfhUgoxgEVAmRRjWgggKEAri9pyqg3b27sWzfwzTEY9eGipao9Hb8K1vqNY95SUdPkPikNuR8IVBAu8DTl07fGtaw39DzKi2yx7EKs0LLsVSwtlHjQ7SPoyFLuacO4DiYMSZdq/K0kDEQh7imiqUES1Iii81BBZRL/50I8RCqeIyv+Ho/Zin1fNXSQcDJc3onqk6ahncXL/ntUhYxBIx1BA0wkhJ9PwZd8eO8WDaF3E2X6qC9twIfhSEgPhzBSg0wdDkzDDybg9d07Zmq0WHYF1mpt2av42PF9+CIUFnRiBAg0CrCtO8vvipo9IjbBtSLu7F3AxZB2yrS7k6Scynb22pbkgxeggqo5ijA2EcYh60TbxUcJUeFaCnUwc+zaCkMoBjBlVCi9diP8dqp1SimDmh+TNTdAJZpgcihQrc5i2VOwmpZlr8EYcI9ZQXaNTxUh9BzcesBKCdmnM80jPpQD9eG0ivjbmpxdwCNkQWYQRHUtY4Ri0E45yFEJ27ZfD6BN1PS3PTEX1yEWNB4qMGMZkhetT6cL5RBGI2fXlGlodaDjziZNY7/YJ0bid5JYqQpQgZi2WpZlD8JqWpa9hu+MwyeHoOB5uJ6D6zqQTTGWy+IjpNCEs1Wzrckv2ZHXDIhTRKRMYFwC4xHi4krAU7pvnLkCYciKm6/ixO/+J/tf/l2yIwNzqndsHuxzmwSKUTOih6aCKgPjTQLLBVbX1Q8GKvSazZ7N55r0u7lY06Blz8JqWpa9glII3xtXjUR9SrLVt1MM4e9ljaBb4GkYezMODZNdlwOhU8MPG1KkHqbwnDoHdN7P9WMnE5imwVFhyNO/+58c/ofLCB0HMYYFD93Jn9/zbwS57WtmDpqRY5Gn5rpkuqcScH8dFruw3IV1wfQxXqAaXRxE0px6sDnFVNLflgU6HZ27q3kAtsWyu7BCy7JXMOhrhz0eTDfNxR8BDkxBt6cCIgXcWW1oJbHm4aBTfqzMDzBcnY9Bs2I4EnBk1624YlpmvkhPjrPmqacxsWgZoeux+P7bMSJkigVKUdb4ZuIIwxAVHPfVGnVtlqsTUZh6GzoxclznpG/MiT6x2TEe2Fw16itrdydYlNtM1c+xpbKUAJehAM5YCx/ohRd0WK3LsvuxQsuyVzDPa0TpuVHnPmWmByTcXIW2Gsz31Ex4RFY7+WqoHfuqeiOxbbs7xcHz7mVDeSkZp0p/dgu96VHWl5bjN2tZQKpaYeDAIyn1zMOt1xg45Bj6Vt9Pta1DnW0tpEHSbOcDrlFBE2tLqaguARo4kTKNPIbN5cTBI7HJz432VYFlWJZbw3MW/g4Twl+GnkN3eoRU2EVaMtxShnM2wnFZeGcfnNkOOetYsOwmbNOz7BW0O3BupwqjUqiBCc3aStz5DwWwoQ7v61XtJevo8mRwxl2Tx2BEOLr7Vg7ruove9AjFoI3rRk5tefxKdy8S+KoleSmceo2hfQ8lVS3PqL5sDfyI6tbuTB+8nDQT+rQWWM3nF2tcsYZZA1JSpSs1Qc3Ps6WylILfRYdXJpceZCgwZKJxX9eX4Q0b4dQ1cNnELAezWHYRVtOy7DW8s1czRbxnYPqcUzEGHQNVD1WLWZmCPyyHvxThnwemR+/VTZpfb3oJi3Mb6EmNUQjaWV9aQWBaP1JBKo3jOBT6+rdOeiV+Ha9eB5me7SIplJKMN4Wm78jUWHFuRInyHQJknBo9qRF8HOomDYBvHEQCck6VySCLj/q4PNGBzJ8d0ZyGT8/vQCUslp3AalqWvQZXNLnskRnopuHjaSZEhdfrNui8Wp8ago31bbc3OGwsL+fuyaNYU9xvRoEFbNWm3FoVt1rBrVUJMjnS7e24kV+sVQqo5OzGcx8FNkMVaCTzTY69Sjt1lufXasZ4b0LrYSQ6R6PRkkDa0fOvoX6/S6y2ZdkNWE3LslcRm9g6XZ0vqzl6LsmqAF6/SR+SKtvmIXxUY25FCFNpqp3diB9gHMG4HuOzRDY8lhMNx4EZ7VEgSnzknFsi55Up+u2k3SoHtj3E2tIBBH6KcpAhRMdqpaOgje5ofrGB5th5i+VxwGpalr2KeR6cmteouWR4dzNJX1Kn28gN2Lw+BeQexfGN4xKm0xgv1dKXlWLXvUnGUYTPy2tOwQx6DTZWlnDXxFGsLa7g3snDWJCa4pULrueIdJYUDllR02DVqLBa6MFkCKduP1IfY+CuMvx6Ch6otDbJWiyPFqtpWfY6PjlfO+DRCQ0Vb9ZmkoltQYMwkmOcYjNbDTW1bW++rB0hHkw8F3PgXLeLEeAXRZ1eZSiAtIESDjeMnkRK6ixIVfGrh1Dx0vxokcedVfjCMFxf0TFbvS7cVdXzb0eDWvItXn2nAnj5eriurKZIV+AZefjfpZB1t93eYpkrVtOy7HV0uvDVRXDjvvDpBXBKrhEOn2L6nFYGzUcYCxLQN73mXIE7Q7IPj0Pq5yqIZttO0CS7KRq5CWvAipRqnUg8/kzISZqj0x3s6+UZ9j2+P65C6vR2OK8bDs/AI1WdS2zUh48MwbkbdHB2M+/aDH8uQPF2qF8I5W/C7/8Gb9oQ1cHAzSXDjcWQys466yx7FVbTsuyVVGtQ2Qxv6oR/3gc+uAW+NNpaa2rWfOIIwljIuUSZ2dEHKpmxPR6QvD3f1K7os+PxXLEAjk2hyfm4lqWgEqh/y0EzgjkKnaQAACAASURBVMQGyzbRtFc/nWqc+90VLaPd0fyMFQN/Ler8Y2/rbRw7MHD5FIQ/B24kClcEHoKf3ANv/XCNN9+5hcLkFIQh2WyGz6/I87KDl+6CK2F5smGFluVJjzEatv6NMVhfB+8GeOB/oVQHx8DJJ8Kl74Kz2uFF61TguOi0I/HkjMmYg+Q8WD6N+asctLPvcmGzr+X0OZoXcFV9ZuG0IxpbMhCklYkw+Tuue1Lr2uxHgRSmsc2wDw9UYZPfmNxySQiHZVWbqtMIzY9TSxUN/LBJaBmgvAm4CehEo1hC/R4+BOfcuAF3cpRc4GNEqFervPvBGkd3Z9lv4bwduBqWvQlrHrQ86fn8CJy7EX5XgNvvhGt/BMMOpDsh0wl/uwZe/FXYEkBNVFgV2DaxLEwf8AvRtCGmIThKRjv9kEjwGZ0ZeK6CKdbcHg2thGFzpGPye7ursxPXDBRpaGBV4KG6nkO8/+YA7qg05veChhZZNvq5raKDju+MZsP0BNIbUIm/ClgLrAcegty+w4SVEukwAMdBREgT4ovDf191+6M8c8veiBValj0CY3Qa+8eajXX41ijUQo0YDK5G7QspNYu5LrT1wI33wr8PTvdbtQpGjwf/JtdV0I48g5rkfBrZJ0pm+nQi2yM23c3F/DGTUEqSFLBxCqdOF+a7sCloCOVOmS4okwl3DTAYNPIe+ui1LAYahAHQ7+l8Xm/bDKsi1fSF84GRqKCVwHI9sVxqDCPONpGToeOyZXB4Dmdu2dux5kHLbiWIzEs/nNCMD0dm4J/64Mjs7PvOhXuqmrLJFe0nw3EgFQkIE2UwdyHYB4q+agmhmZ6ZohXJ7BgpYIWnwmt9Qj3b3mSSrYjHUcUCc64yvNV2SSEU+7McdIzVOr/h13JR4T3T9CQxk2EimjJRbg7YL60RhAM+XDoBH5sP/7QMfr4PBK+gMSZgCuo39OKyhVAEJ4qBNyI4oc8xnY/RTbc8qbGalmW38rVR+K8x7aQXufBg0xt7kg11uKGkkxrGVEK4YhLetwU+PQR3Vabv0+U0QtgBvINJJOkzmHqN8mgRMnUq9TKlsBFJuD3i4IY0cEgGlqbVJ7SzuDSE4bYPpyFFhSWZ1cwmVuMZj2PtiKi8VfXp0X5BYtvtMdP6Y3ONkPe8Aw9E923KQOaN4KXAmQJ3Crw2CE/vY/HqtZQzeUptHZTaOyll2zjwjus47/mt8zZaLEmspmXZbRRCuHQSFriQiqREr6tv7D+agE/M12XVEM4fgj8XVWMKDDy/HT7Yp3kEb69oMIRv4IopeG8vrEhrnsGjsqoJ3FbRxt5+KozdBmbM4Hh1xn0HyLIo+xClLWk6822M9/bPquVkIo1MRIMX5jK54mwIGrhBlN+vO7ouG30tKy1Vnt3/O9aW9mFLNSCM9KnmyVDSbBuSHwB51FdnUOEYb9dM94bVHPa7n9CzYRWbDzmWe57/SipdjUiLZMLdUqiBJ0Tfj8jo98GFIIPQHQBRInvXhUIKXn7ic1l3z2+4c9Mw4tc5yAv4+GvOpnd+LxbLbOxxQktEXOBmYKMx5iwR2Qf4MdAH3AK8zhjT6lmzPMEYjFSKEBgLVBDkHc3E/kC1sd2F4/DHIsxzIjMfmmVhIgoSWOQ2XCTr6/DWLXBwSsOy+1y4YD58cFBNhXRC/t1Qv6pG/WEHr9snd2yRwYNW6AwhQGhCQpmu5yTNdVk0PHwqUEH5WDXGDmA0bMyBNRrotRAgKzXaUxMYhIHKIgSDJz5149IcHtJcH627oWAiO6AIKYF2RygaDRaJz23hfbfzgk+/HTfwqWeyLLzvNg774+X84t+/z+TCpVvLiwXXurpO5RLfv3O7tJxqChbOh5FVDR+gH0BHNxx+oMvHn3o2QXRcO8Gk5dGwxwkt4D3AfWiwLMBngS8ZY34sIt8A3gx8fXdVzvLY0eeoGXAssj256IDXBR6ckoafTKhJ8FtjMBWGrMZHCOlzXBZ7KX45BYsT2ZAKIaypGxwg6wjdrnamnxuFa1bCPTW4pwLFfvhSMEJq6SNUO3tZs2Q/QtclFAfHrxMa7UhjHxNMDy9f6GlwQtL0tqMkBzWXEsuIyi5GASQ4NfJekS5vkrxbZIxePPEJjDvNdNfKF+ZhCAOfwHHxwjpe6HPg+DoGFh5EXhwkmofLGMNJ3/ksxnEpdPcBUG3vom1kC8de/m2ufOe/bi0z1tYMmlXkaTl4R6+O/do8aLj+ijG2rOwg7YS0ZT3aci49PRC0wVOi9E+uFVaWHWCP8mmJyFLgTODC6LcAzwIujza5CHjx7qmd5bHmo0MqsGLvTB3Y4MOtFfjMMLx3iwZpPFwPGTNVOlNb6EwPMCFjrAvHts7oC+BXyqzasJnqVIH6VIHi+rWEgU+Pq76mu6twdBZe063aXOh5iIGBvoXUvVSkPRhwVLWJfT2gUYFZYKUHp+e1nG6nIdh2hth3VWX6eKo4o3xchzYn5OS+a0k7cHD7g2SdOkHoYpCWkX/JJXUDgaPxgw7QVyvQVxgiNzEAwDwXlnqQLpfoW/cQlY6uaSVUOnpYdtu1W3/Hg5YDNEDjqAx8aaF+/8r6kFd88BFu/e1mFqzZgt9eZyysUXBr+G3wui4VbBbLjrKnaVpfBj6IWkpATYLjxpj4ed4ALGm1o4j8I/CPAMuXL9/F1bTsLJMB/HhSB+OGAuVwutYSotpMOQzxpE5gHApBOx1ekbxToWgMi71JtvidSOBTu+MOKj0LCdu6yJdL1NevZWhqkv4jDkdEKCd688UpyHd0EgqUcm2ICRHM1uk4ENna+ceZJTzg3b3w9l64eAKuKk0fz9TMbNF/sUEvOQtxndaCa0UKFqaFNq9ANUjTlxnk2O7buW3sSOomtdWcuH0BKjgmpMMvU3Yz3LD0OCpeBhPCBLBfCvZvyxCkM6ptptJb93TrVYp9/ducXxq9fz+YUFPvOh86rr6P8THo6i1wwPVF+jcWGFzRQWlVlo8t7ebFvXtal2N5orHHaFoichYwaIy5ZUf2N8Z8yxhznDHmuPnz5z/GtbM81qyqa0CFJxpE4TSZigQVGCXjIxgEQzHIUTdQRzDGkEtvJCNwd8WwsW8Jrufi+QFLBteTymSpTIyzsVBhfR0uHoffTelA4JPzsKI9izn4CAh1/t8QB8eEkIpzDjVCzwX11zyzTccrfX10en7CVsymgaVphJy3SvEUazI+cG8Nri10cO3gs/Ecn8l6N7eMH0nB5Ik9RrNqfMbw9KF7OXb0ESbT7dTcFCnjk42Os6oO2YxH9cyX0zcxjBMGeg/8OpligbvOft3WyPU4arLTAc/R+/jnogbUzNu0Cc8LEYRiAH3rpzj0mk2s+Otm5m1ZN9MkzRbLnNmTXntOAs4WkReg1phO4CtAt4h4kba1FNi4G+toeYzoc7XTmwgakX9JGmHnIQZBxOBEcXKBARFhaaZAXwjtmzbxUL6DruIUXlBn7eJ96ZkYoZDOUglTLBe4owo3VOAPRfhCP3y+Hz4q3aydCikYfRA8cSk3ReLF45Xmu1rGhWM6Fqvb0ajGEjtGnemDlLfnGzPo2Kh7iwcwXF7BlBHKRs19MqvAEjABngnp8ss80tZPIEJXrUQ6nabmGMqhvgjcW4HVr3gHrxpazavu/Bm9Xo0NYZ6LX/w2Rp91FhiQKPtHvlygc3yY4oLFBK5qZY5A/+IR7jVLGD6oh+EDenEd6H14HOeGKpe4PuUpOKO95awsFsuc2GOEljHmI8BHAETkNOADxpjXiMhlwMvRCMLXA7/cbZW0PCZsqsNfC3BYBv5eMpTCMJq7Vz+xFqLaRoqsVKiFabJeiVroUTdpVuZXE/qLMAY2dC+gYhzy9SqLhzYyle+knMtjDBzt1OjLaDM3Bq4paYaMiydV61qWclhd12jAktFchCEqUON+1aBV+/qYzg+VAqqxJZFttaq5DAxOTn+SQoXWbIORBRgJ09MG/s7Jp+Y4ZGoVpF5l0s3oeaUcKilDKfDZOhRZ4NjBu3m9uZHMIf14jsf+gc8HJn7PO4deycCCfanXfNoHNnLWp99O2/gIfjbP1W94P9XTz9Qijl/BaNDD2LJuUn4dY2Di6MUsOXAz9/au4MYhDeF/Y89cKm6xbIuYPXBmtoTQOktE9kUFVi9wG/BaY0x1e/sfd9xx5uabb971FbU8KkJjOG9wI6vlATynziOF/dhSXkLGrVMMstRN7KF3pmUlT0uZ3sxgY0p64zDld+GaDgIjW0O5nTAgVa/ROz5EEBrctjxPWbJgWh021jWicN90Y1DsWAAP12C+A1tCqEf+tbppaED9LmQMbAobgQhx/ZKCJra3b0/4uGhEYNk0wtuby2jOSDHXsmcijWFJWKYWhgx4ObJuFRBKQYYwCgfJmICLL34di8c3UM53c1qbasKDI4Ncs98z+PbLPsf4gw9w+P9cQH5iBBwXr1KmqzjBHZ/+Frmjj6EYGh4pDGE215mc6sRxQvp6R5ma389BuTxtDkyF8KcVGs5v2TsRkVuMMcftyL57jKaVxBhzJXBl9H0V8NTdWR/LY8NnC7fgdN7AvkYNffu0PcTa0kquHTqZ0Dg4BKgR0BAiLHdUoxkIckzVepif2cLG6lL80FMNDIlSCgmIQRDqqTSj3fPpNT5ue/s2dShEkiA5cWGPC0tT8PIOuKasYfEb/IZwaI8G+waoKbNops+39WiIpwBZ6KqZMR2ZRkPTMDW2EkrxuKgdsaoJav7b4uZ5Sh5qfoFRP4NgojKFFHUWO8McPPwgI52LaI/GxPV60NvXzb4DN3Jy+zhXnf96/K5e1qVd6gYWduXoNkUW/ukSfnbEMYwHwpi3gJ4VRZYxScV4lGUZtdCjEOi1NkbH0x2c2YGTsez17JFCy/Lko0CB9d5NlPw2Yl1CJM2K/BqulmeqRuXoaOPQeNRMmsGwMZZnKuxkqtw5rcxkxiYXwXWjMUuuh3gqGEZ8zbIhouO4PCDflEa9bHS82DfHNPx7JBrUmxPVsJamdN4oH00LRQCTif2T5sDkZJGtiLPCP1xvBHO0O7psseiMyhPhtvN6GaArEnDF7ZTfCgcVtvtHSYL70usZ9/fFj6aFFALmp4fIuRXCTBvpoEZ/OsPWsci1KumeBfQXx3CAnrRL0rpXzWY4cHwzP1+muQe/Nw77ptp4pN7GqK9DBkQgEwVthOh1tlh2BKugWx4XBhiIzGrJJieERkMJFmY3cur8P/OCRb/g2J4baHOLVIDeWVponA8ijraLx3vNc+HiJbDIUx/KQF0F1pcXQi4KogDtRG8qqsmqGmXECIymJCqEGlV3U7kxyWMxmK7tOECPM/1BSgZYtCJDQxhXjYb/u8BBGR3DdEJu2/0NquEtS2nkXpptpzBpJQfit9KSUW1xTR22VBezJLeZZdmNzEsN4IhhImhnMOjke8e8mv7SMOnQ54Yy/H2yxiMTU9x+0v+jc8kSUvk89XJ52jFqhQLLTjqJZSlNdnxAGoYDfWnA6HCGFOonHAjgue3RrMkWyw5gm47lcSFNmjbRMUHaIUd/BfZte4in9V2HwcEPXQ7rupN92h/mio2voE22NfElaQ5KiAfrHpzW9E/x4OWCgZoPb9qoUW4C9AqsC2AKTUReMRrSXqGhEYEmf40pMT2hrouG7beLbteGamQ9rg62LZvGtgG6PhDY34NHag2Be2RGNaxVtYYfLR2VlZwx+aE6LPF022o4PVw++T0V7RcLcoAN0XQkLjmqVZcQF0/qdLhjuI6h3xN+d9J5HBEWOOKmy3FEEHH4zsnv5I9Lz+TLdeHE97+fK88/n3qxiJvJUCsU6FrWy5EvExg/j5TTyzfmvZpPjx/NLRUdE1cMVUMt+gGvTFV4d1+eHTN0Wix7aCDGzmIDMfY8AgK+HVzMTdUyxSCHIHhSJe3U8cSnFOQJjL5DhTi0ewXuHj+ae8efPk1oxCSn2wiYHqSQQpPNFkMdX3VoRrWlOjqWwhP9fnhafVeFoGEynAoSSeBbHBNUUIVo2fFUJvHg5UxkwjOJ9YbGNu2igvr4nOZNnIgESTuaFcOICsuA1mO44vFrx+fgtrLuM1vUYbL+8XZCgCdqhDQIDoaTslnGQ7irCv3VKRYVh9jSuZDJdJ6Frh7z4qWw+dZbuevSSyls3syKU57CUS+6Cpz1jIR5KqZOXuqk2/8ZkzuHEOgyAdd957s89KOL8UtFupYv58T3v5/lJ500h1pbnozsTCCGFVqWx41RRrnM/z331Sc0mMGk6K0fSX/X39hc7WDAzxFEWSnyTo2C385fN5/LRKjmuTiLRJydPCXQ76hwqkXCwkF9UROh+qVCVGAUTSPMvMeJlgErXBgItSw/gLHt1L89Su8UT1a5bxpWpqLAjchs2CMqhCYjDSsfCa4gqvdYZCoTGoltY82tQ1QL86P6tqINFaqHpNXUVzbTBVbsX4v/x8MHQtQnNradx/24jAZIDIc6ji4mjEx8IZB2NGfke/rgXb0glcuoTX2GW+uLqZtYqNbokCmyfX9k/2w7N33ta9zwne9S7OojSKfpKE2RrZY5+8IL6T/yyO1cccuTlSdd9KDlycfNZfj8cC/31V5FxvF5fkeZD/TmSbX5XMQtLMhmcYywqqZh6W2uT6HWQY3pU21k4/FRRjv3dYEKgUWuZkjPSTQLMnG4PIwk5siKTYhupHbURP1Ea2tqJtwejuixOhzV5A6IpjwZiARWt6PbpIHOMJpTytEOPy/qN4ujBeNAC0HLdCLtzxj93UrLyqBRj9VQJ8xMCdPSU7UaH+ajU5IIDXPnTIwE6vtqi+oYZ1+vRQLei8oaCuAjgzAewCcz17E5ylSS2WrxS2OM4dLRh/lI78Fc/cNLWNU+n8BJgQ+S7qS/VOP2i37Ac7/w+VmuusUyHRuIYdml1A18ZACetQZ+U4A1NWHUT/GriQ7OHxllA+tZzGKKFAglxBNwnDoiIaZyFMdk4cAM7OvCKzrgoLR2ovGU9qACbZOvE0KGkUDIJbSi5EDepPByUa2rw4Fl3twGBPtGhVLFwJ9KsDlo+J2SRgtPtJPvdeCAVON4+3j6X9CHz0UFWS3SEtNR/Zs9Ph5qVpwIdbv/7oeP9cGyhEYUH745mrGECp3Zog4HA50xerEXpZCKzJzxMIGOKAw+5+g1+OoYBNJPKahtnQ8trklaAm6sdrNleIz1FR8nlSIr+tKRFhjK5Fn10COz1Mhi2RYrtCy7lIvG4ZKJyFTmqOYxGtY5tO/X1Dsu57fhn9nIBkAoUaLdK+KIYcP4MynXl9LuaNRZ2lHhtNFvhJWnaOQHjIMXpqJO/bCM/i8ZjSCEaCJE0f2LoZr3frhEpxpJBkzMRDma8DDO1VcPtRPOEE3RYTTysB6Z00DLXprWjjotatLzUGEV+8aqkWZYNjpJY8lMT5wb171i9Br4wEcHDE/JQn9q26jBWGC5aN1gtnmOVQjtm4aPzYMeT4M90hK9CKCBKknBlHY0wnJL6iU4IqRNPMrM0M0Aj5ijGGElq9vnEWTzeLXGAAUBvOIUwwcftf0LbrG0wAotyy7l0okoLD026wFHdd1Ee2YtpSDDlAnJmpz6TMoHk596FSPDb+SGiUNYW4MHq3BLBTb7cGelMY9VSMPEFtu4c6KmrXKo0753ChyfVQF2Uk6Fn48KicMy8KtlGnq9bzRh5PaGDjnA0RnVlNZE2lUZNdPFQjQEMFqPXDTOazLUqVHaEmOUgkgLS0YGxj6u2HeXQrW0NA1fXqZW5eRLv8abXn8az3jZU/nOe96Ht3EdR6VbP8hZoNOdXRjH2l451DFp31qk0YyeQLsbBY807VOPzJN9mYNYl/kPfAydDNHNIA+ap/K56md5aQfkMinWvubteOMjuFMTSL2GNzxImEqTP/cNs9TMYtkW69Oy7DKM0U67y1F/SRmohYYVHXdTDLK4ElCUIR4hYKLWz+rSFL8d6NLIQmALOvA2jg5c0zTi1qAdf9yI68CKtI7RmgjU5PbWHnhVd7Q+1Hm1ulzYJ6Xbn7Netbf9U9ppbwpaayQGuK3aCCGXxPIaqonUgP3TKsgGAw3GuKva8FvViHxtieCJWNjFJkM/Oue0TDdl5oHTv/YvLL3uj4ysPAiDMP/mazjsgbv48Td/hzheU3Sg/qmZ6efTyu8VoCbAR+pw7Cq9ngbVjPf1YExgdR0kCm6pBaoR9rvwkvXwwo5nMZg6hbtKa6jQwZBZyKlt8M5efRkovfDlPNDTy4qffY/U4GYmTjiVB855C285dGWLK22xbB8rtCy7DBF4eg7+UtROMTTgEOBIQC10Gaou4u8jp1Dw2+hOjTFQWbzV/wQqVCZCQ0ZCalLHwxCYFMHWwO/GYOIUqmUt9/S4bY6atr41Di/vUq0g5cAxuUb9riuqwOr3IL1uFafdeQub3CzXH3ESle5ePBranEHDy93E71hwhai/qNeBD8+DT4+AGzS2i6cYiYVXnAoqQLWxtMAhGdUkA9PQrCDSUA3kh7eQX/swv/nwVyhEc1v1blnH/AfuoBgaTKRqJYNNalGwSrOAbI3Z+hLgx2PoQnjYhyMy+gKyydchAQGGTgL29SvUUm1cOCYcn0vx1SUHsN7XsVnLUrBx4yTXXLOOZ6fS/PK4U9n89NO1fgbO7NCsIAFavs36bpkrNuTdsku5sQQnr2l0/i51njH//3DE586Jp5B1ynhOnbFaH5P1LjwJcSSkbjyM8ZBIt/EcLSE0Dp3eOOUgTzXM4JPZ6hsyaNj4EVlN3QQ6OeGvl8P8Fq9nl07Afw4Zjr70a8y7/AdM9sxjaMUB1Oo+95/9Oh4+8mnA9FmEt/e0rPQ0UKFdtENuFba+0IXjcrCuBg/W1I+0T0o77fsqsMqH7kjAgmbgqAJL77qJia5eyp3dpCol8pNjmFSayZ751HN5gkxu1uzyMwutmfbSjPuLPDg8o0LrHaW1fGpTjfzwAIIhk04z79BDmMh18s1FjZeCX/3qAS644Cp8PxKhmRTPfd8pHHvGAVw8oWXFgv+4LHxxIWSts2KvwYa8W/ZYfjXVyGQuwEGd97Igu5Grh88g5xZJiYYceOIT4hAYgyOqkQU4+rpvBN94pKRGPGFjV3qCk+f9jb9uOYtJX+1/ARrEcGMZTslr4846ag5sxcoU9N5/B32XXcRNL30j955wBoLBBAGpcolMrYqf1qk8clF4eRwg0WyK28+Dg7NwXQmGmXmc1US0U39KzXFxOqnQqM+tRzQjRzVs5CZc4MKGA4/EmBA38KmkM1Q7e5g/uIEgkyXlB4RRxIVHw+cXB2IENEyT27L9l9YaMBpoEMj8SoHfXHQJpWe+FPF0Dq1KvY5/192kn/JU1tQ9jsnB8HCJf/u3q+nsypLJeoiBej3gz5+7kvEjlrIxlWOhp0Ezq6pwcVV9lhctUR+cxbI9rNCy7DQGwyADFCjSRy/diXSqV5bUJBb7pephFgeo+FnaUn40rSMExsUghLgYAlxMNG7Jw8Un75Yo+TkcgfmZAY7tvom8W+LQ7pu5d/TZBMBkEOJWq5gw4O7NI/R1tvOhfbpIS+tm/tQcHHLDn1m//+Hce8IZZKcmAEOQy1Pv6CRbKVFPZ8hFod7lVlMMo8ESeQe2+Oo3mymjBmjnf09VTYnLPFiYUv+XAU5sg1/Pg6uL8OuCJun1UNOmk8kQViu49RriCOK4jPUtxK1VcTo7yYsGmEBDOAVoXTw0KCXOTj/3qU1UFyoaHXT8tNX3MlAo4DhCmE5T6Oqjls2RLhVYUCjRv0QTGl9740Y2nn4ok6cdTOi59KweZL+r7qc+WuEPkyEHLVAhdUelUZcrCnDUI/D3ffSaWCwzYYWWZacoUeJ3/IZhhtBp3w0HchDP4DRcXPq9xkBZA6wvrWRhZh2u41ML0owFvdTCDCmpkXeKLMxtZnl+DfdPHkq5uoi01HDFkHMrdHoTHNV1C4d33sVUtYNa0WNxZj33iSEVhsxf/QBTnb1U2rvwXZdTL/wMizrBfO5zSAuniSvw0k741+NOQsIAP5NhfNFycFxCE1LP5lng6DinOLWSAyyPwu+HQ9X7fEIerusUH2Uc2pAZByobYMiHATRo4zdLYbWvQSCHZ1UzPLtTM86PRRF6pThiMp0BE5KqVjBBQJDJ4ngpPEcHmWXYdibl2OdXi6Iaq0ZNpyPBtpnkZyJEhdbEvP0wZ76Gcr6DkYXLwBhcP6Dam8d3XHLRJf7qvH42vWwFEgnR8lErmFzSy9LP/15fUowGxBgaQR+gg7Q/NgjfWTLHiln2SqzQsuwUV/E3NrGRMMpBkSfHfdzLAhZwOEfw5m41mY1HnWQ9dLl25DTanBJTYQegHX9begqXkKf3XktPeoKD2+/n9rGnMlXvxzcOx/ReSUaKhJKiHqYwBaG/OMDdziIC3yczOU52bIRsqcBURzfPvuoXPGPjfay7dYDRhx+m74ADWtb/kDOeTfaPd9AmMLh4BYLB9TXBUuhCzYxxeup27gmfSmDaWOSqGdAYuLFSYywQ0lLHdaBuXIxJkXKgPRQKTI/WE9Tf1e9phGMlhDdu1qhIR1RAvatXp1O5p6YZJlICdVGzJyL42Txk84AK0WWebrOuPl0IJUW0QU2OaaOC+rCMmuZursxN63LR8XW1VIb2wiTDy/fHOOqAClNCpjDBko4sXx2Ff+iGa9s7oFDBCdVXZxyHwrwOpo5fyRndLtf7GiCSHPSdibKN/OnRzrti2euwrk/LDlOjxj3czSSTFChQYIpBBqlS5W7uAuB57fDx+TrIVoAQlxR1psI4e7tovkCnxLE9N5J2fRanS6TEcFTPzSxx+uh2chQrHbR7JdwwIL2xwvLNa2gLSwze203ROISlEqHjUmjvIlcscMpDt6p25TiMrVo14zn0H3UUZy/upJDJqibl+/x/9s47zq6yzv/v5znl9ukzmcmkF0ISCCSU0IuAChYs4NoQu66AhUV3Xevq6qqr673iNQAAIABJREFU7rqyuuIKurKu4A9QmnQQpIMkkAbpIZPp7fZ7zznP8/vjOXfuzGQSEgz9fuY1r2n3nPvcMs/nfL/fz/fzVRr8hItDkRQjLNb382/R9/PD5o002SZSymtNS3QbrlUCYeNrm6j0iUiPMgGHRE0j8/h/sJSEY+Nm+GGLbeyl7sqZ0SRby7C5BF/qNfOoKv6I41WKUBWFCIyX4IlRU3OqGOxWoKf4mhIw2zEEEdk98JwC5kYJzNVtIZZgYN7BKGkhgoBoNk0kO0IQjZOKxrglC2fsgCIClYjiR20CpdF+gAamv+UQvjknQqs90YHeoupgsm/rquG1jFqkVcPzxjDD5Mnh4CDC1KBCkSNHTucpKDN+IyngjATclYfZjqDLd9hQrmznmjp7hJKKI5AUlY2HosH2GfIdklYe128n8+wxbI36zG/bRp2XQVjQ+H/dvPe2bzDviuO4MxqjmEiycONq3vHQTTSWcmitQWuS7e2AccHIKxPlVLKFQgg+8b63c9fTGW7yYig0JUui0Lh47NQz+FfvEv7Ty6OyNodGNXNdgSdyFEWWYpAnZY2azVcoBr0YveV6itqo4p71Q7LW5n5Hwum9OWVqYAoTRcWESTmO+GYW1SERky4bDCZGQxojma+3YIZtFIgFDc2h0mVQTZyqXBGMJIQROlzcCw/lxwszTCwoUJNmnVWRAaQChYBUPWiN1hbadlBuhJjr8IwnyIQWV+ETi3ZtpG0RV4qyLVnZWU+HAzfPgiWbQ49JaSKsitz+vLopl1BDDWOokVYNzxtDDGJjExCg0fihtq4UWNySbuRTPR5l7YxFCjYgNfSrysRcE0cobAqeyz39pzInvp2l9gbmRDJs0Tme8iMUfKChnfTahRz3xStpc4vIgSJeJseR73sfFy1MUGpXXP3OSyiNjhJvaUFpTa6vj9YlS0gesoyPdMH1GSOEaLbgW23wvrDp2BLw0/kpTt0OwwEMBiUS5CkTITCdZeRIEiPPEyXNkBJ8v93hLrGdawqzgdLYUMeI9JgbHeFot5V1ZTPXq8OC67LGaLZS3/N0NZ2X16bm1CgNmRa1ib7kpMbgCpLCuHg8VjTHeZj0XxJDfuOVi4KqEONzvUaKXzETHn8rvRc/kEqvWQTTw6WEQNgS102NjWIZDd3r4xLKqkqInhTkpRyLGHd4MMuBP86EN+yo1tY0cEocvtS6x2XUUANQ69Oq4a/ARjZyK39khGHK4aANL3DpLrYTsUqsGV3GA4OnTDhGUE1vVeXjCluU0UCLmyYh4f3T72BZJMpR3ttZVxKkpGaxGmXrNb9ny6234SQSLDn3XOafccaYyGJk+3bu/+536XrkEaRlMe+MMzjukks4P93AjTnTxGtT9fr7TacRPVRw1Sh8shvKKo9AUSAWxo9mvXWMElDPvDWPc9Sjt3NkyybuXnkaf55xJnnlUFBRlIaV8TJapcgrs5lXnDxiwpBSVk2Mnip1LxcTRdVJYxJ8b75qEVW5TeXnSuNzDNPYXPkvToakVRE5JMNQK81E0+DJrh5QrTE9V51LYNJ4Fcf9wrjRMS5Vl5DKeSxgtg3Ntjnmik6Y70Kg4NqMIbKTE6Z/rYbXBmrztCahRlovDkqU+AU/p5eecGuHwVIzWT+J1oKYXeCqHecx6tcx3kui2RkmYeXpKzdTVGankgQkrDxzY/34OmBRtMSvp80lQYINwXZ+7f2ZNCMo7TLNW8GFieU0WVOns7xCAWlZWK5Lt2dSUZLqqA0w6bnFEXh03sRjT9sGm0sZUEP06VbKVPTXghRZVv7sUubf+XtsS7LACejTw2x+3zu56c1fxhVlXCGwgmYGlaDNMim+sq4OakwJM7Jkgl/hONiYZuPfdsLJ28NRJaGtk8IQhKdNVONKc/sRVY3aKsMxo8AJMZM+7AmqzvHBuPuB6sWDJpyqjIna9tSoPJ7wlkVMKjOrxkn9tUaWSzTs2k4xkUTbDhaCaEsrc1xz3Alx+EH77ucuhz12mdBtvrMmfX/VotZcXMNLgn76yJAeIywAT7lIoRBCI1EIERCRJTzl4kqP09puoT3ajUJioVk9ciRrR49EYnFyfQ9z6+9FihJSCB5gF7OCg7jUu5lc4OCpJJYMUM6DXDIS8F9NR+NOUbh3YtVL9m7fbIaJSbdzgJ1TaL7fVQeXDSeIBl2M+mV8LAIsbHymbdrG3Dv/QKllGpYlSdswWGil4Zd303T0J8m2zmJIOWYWVRCwIx+gN/SYTX5WE7oxzqisEu1UEY2DIdQOx7hlbC5XCSunTOpQYiKZojKXAlEBtq6SzdJKYUnAyDgxR6SiQqRKmglplIQKEyWNt62ajIoNVYXkGkOrrIOj0OvDmjKIICCwHUY6ZpIc6OV13/s88x69m2z7TAbnL2bw/At5bPlRu517Ywku6jHp2cp9f6ABLmisWTzVMBE19WANzwsazb38CR8fGxuhBUrZWNJDaYkjPApBjEIQRymBLXxObr2dmfEdyMDGK9rIoTyH1z/EwsgzHJ/q5aDGO5BCU1JJbB3jiWAjP/NvoqAkgYpgCYHWNvkggR1bxX15/znXOd8xkvDJfcE+prYy9rOGVUWj7FsckZTtRdTbMUKPeqJEaH7ycYRWWFIyywlrVI6DVoKGNRsYCSRlHRAon+LOYZTW2JaEhjg66kC+MsrSYPJeLDHRxTQH/piFH7ebteeUEXAUddVZBKpOFxltohwJHOUq/jRbsyxqoqysMo9VEk5RHnv9zGdOVdONoxgj3D2hYmUlMZFcTBpRSUwaBaNQAdIrI5RxWhSBT2P3NrKNbdx3/ue456P/wDNlTWn7VvxxrKg0fL7XkGebbVoCmi345Qg8vLdO7Rpek6hFWjU8LxQosN1T9AYdNLmD+BTxtaDOTqO12VpXjazAlWXyKsmCxDoOa3gCoUFbwzQGmsb+IUbdOo5tvJFAt4JWeMol0JBTknQxSV10B54/zWy8Y1GDhabIpnKR0xLJCevaVjYOE3Md4zdYb8P76uCKUXC0IYFiuGHOss1mudSFq9KmmRdt/ineWydIWRFGArglC0+WQMQTRKRgumtMYbv8ipu6IOO6VWIcKZqdONBooRHNCaNk9BUEGmmJ3eZbuZh05UzH+CX2BvDhBNw5Gy7ugYcKhnQqAzB3S99pjSgWSN99O5fedy3Fj3ye6JwlxIREaeNfmFNGJj+sqyrEAJMW9KkOe9wTKkRXifRm2OY587SZGm2rALtcxNcukfQIsfQIj7z7U4x2zqEcSxDJZ9EaugLJ9wbgH0PRxcayiYhbQ3f+bNhULbSxATsmvm/vyRpeG6iRVg17RbdnrvoHAmN7dELcbCpf6I1wW/GNQIASPgsSGzi0fjUxO0vCyvL48DF0F2dS74zSEd3Jqa13oDEegpbvEXWLFJYkcX2fJjWI52QRtsTx4rjCIqsgJgSeconJIqMqOqZ6s4VHSbmgqmnArIJ/7IMH80YN6Gt4V8qk2bZ5ZkMcCutLSWkI4FkfdgbwsyGTJjsialJRBWVI7OqZJhq7pMXUlnpTp3Lj736E6+URTpwOG54dSiPicXYcdkxV5BAYcmLnEH4qCpZEBAos03zV4JgNOa0rSj7NbN8j5QeUEy6BsDgmfGgrYvBPbfDZHrOxZ4OJYgoAlMIuFnCLeXoWLuOHh67E8cvM27GJtnkHsaZkLBxLGGJqkXBeg5GcP1Iw0c2a0r69HyrehmAizXNS8GvjfoUW0vRwaU1iZIBIdpSuQ48mks8SyWWMTyKauv4u/pCZzccbTb+aF7Lh0+WAI8RNvEVdRaSY5Xb/jQxFP4Ch1RpqMKiRVg17xCMFs1lKmaY9sYZ1QQ9/SDfjl5bxRLGRDkuSFTkKSvBUehktkQHaY7uIWwWW121hQXwXJTSd0V0EaIS2sVTZMIMEbQElhdyZQUTjODOiaGuErlIzUkAZn7ifoqwlcStHMYjiSg9HejzUfzIFYfHxJnO6Hw7AvTmTRuv1TeTwZNGYzS6KmLTfUGCii2xg/O08DY8WTHotExivv8Njxuao24eP74IGy/Rbvbcepk+fxunf/S53f/nLlPv7adCQjqa45wvfIYhFKwM9sIQm+OUDCM+DNy3Dbk6iIjai4OH6PjJiEQizNidXYudAnl25Et1ao6IORycEJ8xtHnsdPAXrwlle43uwKrBLRaRWlJJ1eLEEvu0g0ZRyaVZ4JRa4EYYC06N2ZtIIUrZ7RhSSCnvWPHZPoU5G5b5djOS+yzcjRuokfL4PfGykVjRs24Tle5QTKaTvYZVLaNfFBhLaJxKPYwlzfIttXp+ihvPkt3hL5koyvRBowcfjT7C55zoKjX8gFq9JC2swqJFWDVPC1/CVPkg5wyxpuQYpyihl49FD4G5gln82Rb8N0CiRJ++neHjwRJbGctRJm/b6PyFljqTQKO0SBAkCBK7sNpf9lvGgC5RFd+MitBA0i1EskcYRKRzpY4mAhwdOYlepnWUNj9Ie20Xaq2fd6BG0qQVs16bPJynhhqxJDY5OSnF1BbBAm/6hdhueLpnH1imMpLw47vYFDIkd4proLKuMGOKqNNychf/phDknncR5t91Gz+rVSNvmrlnLuHLYRoVFGhufaIOkkM6herLE/+9+lqzaxpo3LycetZnR1shmz5BOp4TB797KTCnQJy1AuQ6N67sY/fMmVv3HGznqqE76fLiod+9SdGVZ+JGoeS0sCzQIrSk7ER4uQCR0gC8ok+qc4xpxx1M+7PLMNGZf7+Hk4xAHtIDDo6bmNBCYlON59SZqu3IUZGMdhUwDQSaNEhYLHrydLce9nkSpgJdMMWA7tLW1ojV0hruPI2Clu42z0leza6uLdozMsJCOMz/5DP979X/z0Q9etK9v3Rpe5aiRVg1TYmvZ1BeWNTyMxMMPbZd8BUoUaK/7MzuGzqGFVhqFT17CaLkTTwqyAh7Pz6FXD9EqXRaknqEl+Qi+SpErTgNGcSMltKfoKXYivAAdiTJQasWVZdJBkkIpyY7MYewqzsRTcG/fWdRbpg411wYpoRgYRVy/b0QH6T1svKtLJq0J5vYZBb2e6deaLDv3gKfKppF2pmMIMRk6uF8xAl9pBTsaZcbKlfx2xPR1BbqarvNxyNoWkW++Cfmbh2h7eCvumi4uPKQFcfZhbEJwetL0JNX1pfnq5l7apyUQN60aW0O3hm//Zj3vOaiTdSVDDrB7X1UFynZAK4TWCK1R0jL1J6XwbYeoNtGkD3QHUC6bc3nh79Z6e4+yxmTuwpCdpCrj/1MW3jBoojgAKSw658xneUxzUb1CRHv4AJJn2w9CS4lyXAZsm+meCg28DJbaz8BIEWElkSLs55MCLMnIzvvx/Quw7ZpurIYaadWwB0RkaP8T2YGno0BVueapKEmnF4GPxsbCpsmGUyNmLPtQAItci/fHW7lsBAZzh1If24BjpSk5DnE/BoPdeNolkBLbFpQiUZJWjod6j2ddfgUVT0JXgC0NiRwWMevS2ggVTo6b9NbPh0yj6p6QHve3iIQmTD0GJjbuVsjAx9S86sftkfXSpEvH45sD5jnyqdZ6gvCskUaXL39zOX9X9waz+U6BZ3o9bEtMcKAf8OHpQLK9p8D2AdN4W9DmvOPvYzJxCWUegRYSK1TxadtGCcmIrjZ1S0wKVRMOytTV9fvsmbwqTdkCk14taWPN9dV+Qu9Ic64yRoPyn+2CZtuCM19P42bYVtJGjBIEWIU8vVJywYPP8n8nLEQIwRHxZggUlhRjEnelTRlwYKAO31c10qoBqJHWqxYBARtYzzrW0k8fefLY2CziYE7ldUTZe41gpg3zImVGA5uIlSPQDkJLNC5z3YC+wKHHl7jCbKpzHfhRu4lKPAzZ6FCldsVIlNG+c2mOr2VeYjsnxpNwbZo/zcsTX+ajECTsHBtHD2J95hCEBTERoLSkyRJ8uslEBddljIJQaVgSqarPbsub6GvNXhTwShsyTUq4dBp8rlexwxfo0Eyq0vpcGZ5IGJFVBkgWtbmP8djlm8dobi6wAAuND8y2Y7w/EUfuZZ+dO7eBeNwhlyuTSLj4GjaUNBQ8Zp4wl07HEO6wV21OHk+uEEaKUppeOQ12uYTrl1GxOGVr4oLHz9IaH2lVSKeg2E3VWLkPC0hahrxnO3BKEv5v2KRUNSbjKzH9cL0B/CkH76g3tcJ1JZBKEShz78pxkb7HLaKB9ddcw5JzzuGYuuXcq2fRlNjOYN74azUmc/h5h5x3EtFobauqwaD2TngVQqO5jVvZymaGGaaEkYZJJI/yCJvYyCe5AIc9Ww4Ewue4tuu5eWQ2C+qeohwYSXfSHiatbRLlozg9IckoOD4Ob06FlkFUTVOFgE82wbl18Ew5SpN1BAe5RxiF3luO5NnPfYGfffCt6HqLPjmdtF+PittoFB4+gRDMiOS5oKmemBR8oAE2lY3x7JJItenUEdDqgONPPSOqXhqC6bDhmESOO3iId87awI5tf0NvuQkRflQabGc7RllYIa2iMsT8gfqJ522zYKua2G+lQvKKCkM4HXt5nRzH4mtfO5m///s7yGbL5ISknA9oXNRK++mLAJOi3OpVTWUrj6/iYTgWRErDrn4sjiY+RnIVzpwciGoIFZpVeXlCglLV81aOrfwcCR/XswH8zwisKk88Z0U272BSy2CIsAToMHUpRJhGdVy8SITHf/5zFr/zndhSsuSgy3ngjo+zYPYmhBDs6m7hyt+/jS997Y17eRZreK2hRlqvQvTSw3a2YWFTpoxAIDFX4xY2w4zwFx5jJceOHePjo9FjRLaDHQhnJ6e1bGOXB9FIHwKNI8sMlzpYMzqXuDb+fc3P8S5qtuHYSbeJNTXx8Z9div/IBr4fzGbUTSEiChsfW3hIoXFRHNZyHevUqRwh59DpTG3t844U/OdwmePjeR4qJChqcyOJIaBLp8H3BmFTOeAxr4AYOZQZsWbePO0W7hk8gU35WYCgyTIijLhl/PtK2tTLIhK+2AInTlJe/0MzfLLHbNbjffs6LYhZMGcfbIhOPmkWv/9tjOHuX5PxstxQPIN1c86hHDUHJ6WZcpzTph4Xw0R3eT11Kq+S6my3TaqxYkY7FXxggQX9GEHLPBuCMHqskFY+7FubZhk7qJ7AEPt4S6zxpB1g/n58WEPMKHN8mYnOFhpBXS5NfmAAHQQI26Z9+jxOfcsN3PrHR9mwvodp0+fyvR8eTEdH6rmfyBpeM6iR1qsQAwygUZRCE1s5wfjEDLjfwhZWciyjjHAjN/AsOxBI5jCHM3kTa3iSQQZRQlFULWzNHErKztAe2UWAz7Ed/03Wa+Xy8lyW25ooURaxiFba9nmddjTKp086nDeV4LxdPk97BSxZwkITYHFo6hlsq8T/FNewIjFnSjsfhWJRwyO8IbGajFIs0ZJVI4exObeAFEk+VBflp8NG5ZYmS9LOopTFs4WZTI/2c1rLfdgDJxENptNuG6IY8GFFFK6Ybsii3WZKu6gPNZnm2i/1G4KzgGnS9D19odkcc30afjVqBCzvaTC9YxNShrnv0Z64mvaFKXxc6krX063X8WP1KzyieNpEWzNt2OGbqM8Deryq4TBU640WRpjZaZtIKoFp/N0TerVRAlraRIYWRiG4yIGNnlEMusKcoxLllZioupxMissicJApg3J3zhBcSUgCIZHadKZZKmDu5jU0zp2LtKvbUGNjjHe/96Q9L7iG1zxqpPUyhEbTQzeb2IRGM5/5TKcTsZvxz9SIEUMgscOXV6PHjq18TZKiHBreZslihQmnZ3iaLnaSIYOvFff2v46NmYPDiUuSemeYE5vvpqwljdEdjPIsj+LSQCNreIrjOYFDWbZfj3d+BK6eneHTw4+xNjuXmPRYmNxOR6QPS1ukdZGtnpGfT8aTrGaVeIyDnAQ5pfnjSBtb8jMBj7QY5V9HPLwgyRxLYFmGxIUAC59nC510Rrt5Z/u9bBl6G08XDWkdHYMvt5gIsXn3u5yAi1vgY43wu1HjWjHNMenQZVH40E64OlO97R15uCoB/9UBd+dBBV28zb+GmNUOwrwCrW4CXdrMTHUn96s3IYHPNMN76+CCbrgjZ2pRJUwkFMHUoypaj+HAjCFpsqoik8kKSTv8vQfMcIwSsMUxdb9my4hQFEYG324bZ5GHdrNTqlDVxPekAD7fYr7/l3745/7QE1EItDbvxIbRQWKFLMsfup2Vn/3sczzDNdQwETXSehlAoVjLGlaziiIFNDDCCBYSB5c1PMl8FrKcFTTTHBLMnjGL2cSIUyCPRKJQKKXQCIoUkVgcIg/hcR4jR44IkQlrGWEEgHIQYV5iE1IHdJVmorUg5yd5bPhoVjY/SNLJIrWDCrfEGDEe4H7ms4A4++e9M13WsyjZRWd8G3JsPRpLlhkqzCc/RapNo1nFE8SIYQmL7iDD46PLiVl5pIBARYnLPD3KYVBH0cpFWNmJkYHQpJwsP5luijkCo0jcH6Qs+HATfHjc7x4tGMKKCcZmbSlt+r1O2W7qR8vFJg5xLEaxxkaJTHdgfsTmk7FVnOy8iaNjhlhuzcDjpWrUA+EASYxyMiVMCs4RhnhsAUsjRgRhUSUtm6qHoAVMt41wojl8zFqb+t9HGuHXI8ZbsNcfT02aJobIEaNMBJsyCocAe8yp449ZM2rkO4MmpekAubEhoaCEoKO7j5Ur3khy8Yr9e7JreM2jRlovIgZ9I5uWAlbGjNsCwCM8zF94nChRihTppx8gjJRyAPTRxxqepIEGzuTNTGd69bwMhmPvR+lkBotZwlt5K3dxJ2VK9HnDYBmdswokucEUV1v3MrvN3S21E4yrlIx4jVjS55CGp5Cjiu2FecRknv5SB2gYKjeBdmiwC7iyREqa2kMPPcxj0syP54BEstR/HQ9bN2OLLBqJJCDntTBSWMLClt2P0WgKFEiSJCBga6kJrQn7fMwjE0ISkQWyQRRbJ4nZaQQBARZz49uJWBmWiMXUUc9zXAvsF65Lm6/WuEBEYFJ66QAWuAAteFqR0Zo6aST+OzywrYDF9TNYPI73/5AFdLWmhAyFFAO9HHzPDTR1bWVk0TLOfttZ3CZT9PrQIGGxa0hnSJlUZkUyL4F3p+Bpz9SrxtYojPjknpyJGH8zOrGx2aXEDLmdTWohFgESRYQcOVJIJHXC9MX9ZtT0r0XDVKiloaQlxaEc5V8/iXhqO5d6AT/51Q4uvvhY3vWupQfuya/hVY0aab1IuDED3xoApQMSTj/2CHyhoYWVyQKreYIkSTSaIYbGRn14k7RwOXLkKfBb/pdPcgFx4mxnGzdzEwAONjt5lrWs4R2cwzs4h7Uj3Vw2cC0EDratCfJRlJJ0JbI0RiOIuonpQ39cpSRiFSgGUYrKZW5yCw8PHW+8A0XAzsJMpkX7EEKDr9lSnMXpdXmQ7FWVuDecE5vFvf3vpkuuJ25nGC7NZKiwgH9sdhhV0ComFvMlkjamMcQgDg6uMM+XuaavFo4soXEEWMKir9hB1MoxL7mJVrefRf5xvDFy2PNa794Qn0LqXhnpEQn/9pC3hFOtxSyy1jGi2nAtQTMj9AcxXPusCdXBQFfHighh0oJN2zZw6lc/jlPME3NdZj92K213/ZqfXfZLrrRbeKRgUq/faIPjYvCfQ/B/aaiz4DONJho6Y0fVyzCtTFP5gG+If1vJpEgfKVQEHhobj+1qNgJNnByL5Vo8ohREHTvVQbTYhqgnJ7JtAZYXULjkGqJK0d5mWi7K5YDvf/8BDj+8nYMOeq5kbA011EjrRUG3B9/sh/ZID3ObbsGSBXytuErZPBKU0NYIKVJkyU4gjcnw8ZFIMmR4jEeZyUyu4rf4eEgkSVI00kiGDE+ymmM4lqe2ltEJC1mOEYQSZSkhCATpnnrcOhcPbyzlqFBhuUKQsrPErQI5P0FRRVHhVqSQPD68klOn3U6DM4olNOvSh5AU2zixvkTHXoXee0ZUwqVtDdyZPZb78rDUgq2FIf7hqSK+5zM9ZvPNhU2c0BQdO+Y4juMGrqdEiTmxfiKyTDGI4ITE4CuJ0hGumG4204cLNn1+PbOcI3izfQTLontYzF+JDzbAvw5CSVVJyg/l8bMcUy/aWBJcKP6NL0a+zcnOvdgK+sU8LvO/wueDiZKWNyXhrtzE+zjqsn/BCXyCtg6iFkgbMj3dDFx5Od/8whd2W9PftZrP8Tg6Bo8VDMk+kg/l6Zh+uHVlsDyT1mwRUNAZLAJKRJkuuohTROGQFBkCbWELY0wcEfDuepMeTAdVAi+t7UbnyszvrMowXddCa82tt26qkVYN+4Qaab0IuC8PmhLzmm8ANCUVo0wJQUBWF4jgM8jgPgktKvWj+7mPFCl8PBwcNJAmjY9PPXVs0Vuxi8eyM1uHSFVaUqvnl5YmmZnOGSziRq4nR5aAAKUkAQIpTNttpfG3UIpRUlFA0OL0gxA8m5tDQ8Nq1o4cSSGIszk3k+/UHzYmAHk+cAWcmTKfn3hkO7f256lLDxFF0+VG+fBojuuO7mRpg8mddTCdd3AOq1lF1HqWt7ffzXU9p5HzoyYK1BafrI/w1jpz/je9SOrpGQ78qA0+22eUi2AipAW2IYHVJVODKukm/r74feqLGRxRQspmZjmCGZOC1TNTcFvO9EdlNcTzWZo3rSXb0g7auFyMKojHGnHvuIvjpyCtqfDVVvjULrg+O1GNqAgFFJW+Kw0JFCutByjqCBlRz49jF3Gt9w6eVTM5zbqHa/RyfOCbLUa88V/txuZqJHz82gtos424YyIEpdJz2fXWUINBjbReBAQaGmPbkMJ4+HmU0Gi0EGhRGKsjjZ8A/FwoUECh0Jg6VIAiwGeUETI6x3Cxjf/oVnjJJjrWzWbesm3IfBytJDKWp5SPclrHQcwnzoV8mg2s4w/+fYwGBRJ2dsxAVYbx1ajXgCPM0Pa034gjfDZmlhIpH4pFlJS2ONpqp+UAzRXdlS9z22BurBwqAAAgAElEQVSBxuwo0gl7lrRiqFTissee4UenHz522xZaOI3TATgnluM9szdyVxakauU98VZmugewWLUf+GATnF0HV6dNHemcOjMr62+7jY1U5dXWwCgphE4hAqNGbJ30n+kIMxTy1LixjyoqB23ZSBWQcuyxvqmi59PVMKkLei+YZpuBjlPF95VerUpaM0s9JWJIoYiT5yDrGb5tfwmJ4hlxBjEHzk7BwlBH85Y6WBk3wozRAE44s50LfyooFv0xhwsV2k+dfPLsfV5zDa9t1EjrRcCxcfhNybR5+vgE+ERlnoSdxZZTeTjsG0qUEAi8cPuTSLQ2vTy2leegutUM28vp2vJ60nc9zKHHrcWK+Aw9M4vXx09g4aHxseOe4il2FDoRzg7SXhNRO4MjPAJtkw/iZL06EpampCSO8Ckol1ZpkyQy1qP0rroD8GSF2LqzG+F5SGsiCbrK55n+kT0elyDB0fJwjj6Aa/lr0GjDJ5rM96OBiST/Yxoct61KChUvQY0hkI82TH0uIeCd9fD2OlhbivCNU9/EnDt/j9/WAQgIAmLZUdZ84IJ9Xl9RwXW5vd+mEqNrBE8Gh9Mmu/mYexkRSigt8WQzi5u+zuLI7se22XD+2OOJ8JWvnMTXv34Pw8MVoQycffbBHHnk9N0PrqGGKVAjrRcBc1x4a7SdJzXklY8UkLCzWMKfsmF2fzA+OhuLvLSNr1I0JVYznF9O51EOOwZPILbxeI6LaY49UtIxrrZRosQII+S9uThWL7YoMlxuRmtjbuRIj825hbiiBLqRJivCgDZy7krR/gvNcMQBHHk0LxlBaI0vLWxVTR2VbZfF2f4Dd0cvAnwN/zYI/y9tnquKJyNUTWzBRDQRYeyhtGaP7w0pzHywro98jubhPlKrHgbLQgQ+fWedS/qNb9/ntQ0Eex9LEqEabfnACI1IDfd7J1IgxVnun5hd90XikWP26f7OPHMhS5e2cccdW8jlypxwwiwOP7x9gmlwDTXsDTXSepHwsfoW/ttvZ6P9DFIE2LL8VxOWQcW4J/xJu1giIGKP4AfVAo5dB4tPELyjcfc7tbGxsJgdHeT/DZzKMc13YkkfIRSWUKwdXUq9M0TabySrIe8Ze6FAwycb4Zx603d0INExvZ23bruFaxceQ1QF2IFHIRIjOTzAhw+ff2Dv7HngySL8ZAjWlkz96mMN8LqkMYjdWDay8XkOdPfD566FOzdA80yYeQpYzRMd5ce7zHvAiduN/dTFTXBcfGrycgScPi3JzV/7MbO7tuAM9lHsnMOO5nbOTsIHuuCpIix04IutsHwPFxTNFqSkkcPvibvaHZhuwU4f+n1Bnibu0m8jx0mc3PB1GhP7t43MmlXPhz+8fMLvKkS+B0P8GmoYQ420XkQcay8ixw5KlMiHTb9TQYSi7X3DxNtJ4SO0hS3zZAtzAVP4r/SGTQUbmyUspc/exObs69mU+yAz4s/iCI++YjvtkT5OaLmPgWInBWHkywFmwOK3B+DcfS+h7Bf++X1vofnSX3DLtEXkUg0sfeJeLlw6k0OO2vdI4oXAmiJ8bJdRI9ZLM0jx831wehbuK5hXJPDB/lOBLb/yGPRiuA0BPVts+h+yWfYZSLWYCCdhmX4riVEapiS0W/BEHk7NmPtotuCSJvjspF61i5vNsMoNnfOg0/TFLXTg0kEj1vCBJ0umx+tnHfDBxt0fS0zChY3wz4Pm5/HvpmZpalIlZeT2P58OO8M5ZMsiNivibVPaW+0Pigp+OgzXpI011PEx87hmT+F+UkMNAELrfd0cXzk48sgj9WOPPfZSL2M3bGELv+MqFAFlylhYeHgTCMrCMiKN8ON5QQsUgid6/ob+wiEojKnsF1v2nHLy8Tmrq4+7RqdhrCHEWDEjIfMc13wf9w2cQZzI2DmCwCdX9vn4jz7Pih3rOPQ97+Gw88/Hcp5fn9aUD0VrRrZupTg6StOCBURSL7156qe7jeNFy7hLvh7PDKJcGTN1q/WXl9lxq0CXAUsgNLj1ZZw6m7o5Lm0XwoYyHBUzadYH8ubpXhIxta+nw1KnizmfD3yrdXfi0hpWFaHbh7kufKQL/lKsmukKzPcOsG4+zJui7qS0ufj44QBjs7dm2sZRoxL59Pnw3WlwSmL34/8aXNxjGplbbXMFPRAY4v7dTOPGUcOrE0KIx7XWRz6fY2uR1n7C9xVdXWmSSZfm5t2tijSadazlER4mR465zOMkTmKEUW7nViJEyJFFoylTne0gkbi4KNTY7/cv4hoHAY62ODGWQtlwWsL04+wtHWljszY7nbjQFLXAEh5SBCgNBRXjyeGVaO0iwjSgVgFeOo1yo2TnLETu3MCjP/kJme5uTv7KV/Z/zXt6KELQOG//3DVeaKwvmwbd8RgOR3pIAZmtMLgqQPs2WkooQ6AFaiiC25BldLPDtEDw+oRJB273zHEHuYYInyhWfQMVpn+tqOAHQ7uTlhAm9bccQz7rS2YdlZe6MlakBJy6Hf63E06YRDxSwJdb4UstpgH5lyPQMcV1x4HmkK1l0w4y3a6+N9tsMyX65iy87wWK4Gt4ZaNGWvuB++7bzje/eS/pdIlAaU4+aTZf+cpJ1NdXO1Tv4g7u589jTcJ99PIEjzObOVhYtNBCAw300kOOqmyrkiqsp4FhhlAo4iTIkmF/IYCkiPPmhgKLMK7v12UeZMPOXUxvreeUlmOYw5zdjjP2R4KkFGRVhAAV+sVJTom2cn02YG5yLXNT6yHw2OjOYX16GfN2bcaORkm2t/PM9ddz5Cc+QaJt393eX2mY65haVtO4XbygTNrUBjLbQQcKHUiUJ7FEOPywLMnujBOZpYk7gu+3GxPgooKzdlQFGR7VUSdOuJlHhBliuTcIqsM3x0vVKzWzjILP98LVM41z/G7HC3h9Ev5n1JjoVlJ/WWWI88gDKLQB6PLN45x8MWVj6oI11DAVXjbzq4UQM4UQdwsh1gkh1gohPhP+vkkIcbsQYmP4dYrM/AuPjRsHueSS20l7WcqtwxRa+7j2nr9w0T/+fuw2WbI8wP27uVqUKbOZTUSI4OExyAB58mN/l+FHkSJDDIZeb4o8z6FF3gMcHBIkqKOOAd3Pxf/9ay55w5Nc/uEh/umsLXzqq1ezurhmt+POTFYn2TZakJQSG4vDooJ/atO8ftptHN58L1E7TcTOs6L9L7yj8Wrm9Gwxj8OykLZNuqvrea37lYKPNZpNPR0Ygigos8E3SrMBO0nQvhybaqxE6GskNKogOfYIzW9mVF3roxIuDMecpAOTyqtES7FwQy/piSQ5FYSAv6kPa2pM7AOzMWk3H7hpL9dBB0XgM03GLb7bN76FvoZ/nWbqXwcSs5wwEpyUTPAxnok11DAVXjakhXmv/p3WeglwDHCBEGIJ8A/AnVrrhcCd4c8vOq65Zj0FXSCXHCYgwJEOyXbNvY9u5sFn1wKwhc14e7BhUiiGGaabXWTIVNN+WqA0KG3kWwqFhcTC2qNQ47kQI8402mmng5/dcif3/DRHos4m1WJR12Kx9kafb//4zt3O//VWONg1G3Jema/TbPjVdKiP9HNM/VZEkMTBQiqJHCyzMNhAcaFJk6ogQAUBdZ2dz2vdrxQcFYMfTjOpvO7ARKjfaINjY0aUIRZDGRctJMJR6HFz7t2U4j0LrN2GWb49Bd9tMy7v0y1DWBFh/kErM7T+rum51/aDaeY1VFQViRaG/GY65vuB54jY3tcA18+Cf2qFb7XBzbNMevlAY5YDZyQMORaUGbnS65vJ1G9MHvj7q+HVgZdNelBr3Q10h99nhBDrgU7gbOCU8Ga/Au4B/v7FXl93d4aSmyMaRkVgDFgtS3P/8GMcO3NpGGHtuQY1zFA41t0QlLmlRmlRlfpqjRRyN7PcfYWLyyEcysmcgkBw05XPEkta2I6pjwWWR6Qt4KFrR3nmM5tY5C5kmCHKeLQ6zdwxx+H3aWOSuiRirtzbbFjPAI5VYFZ8CB8NUQ0jw+D5FKc7OKsKFIaGOPjtb39VpwYrODEBJ8QNmTiYKOecOrglC3/OQ/Aui7/8VCFRRohhK5IzFKVyhNIUtRoh4PSk+QT49wFTwxoKTIR1SRN8Zgqn+8lwJKyabwQOPx8x/+BRYVKaDdK4xB+zBwIaCgxBdoRWSy+G5dXX20zEeXXapC/fkIRPNZqWgRpqmAovG9IaDyHEHExt+WFgWkhoAD3AtD0c83Hg4wCzZs064GtaeUwn193/OHGqeYtyOcAXPqNzt3El/0PmOepPGj3OsqkKKdS42xhbJht7n4grThyJRYkiPj4KxQbWMcQgp3AqxUELyzUKxZwuE2jQUlLyND8ZfJhYw8MIK0NKClqlzanWKXyocSEfmpSEzZAmTQZXOLhItAB/Rj06XSLYsRmAoy+8kMM+8IHnXPOrBULA+CxWTBq3irfXwVfeAM/cJdEjAiepkRGLICew4jD/8D2ecgyfbTGfe2sy3hMcAT/uMOq/nw5DDENm3QEsj8JJk4QYQwF8o98oGMGQ1tdaYcULEF1Nhivgo43m8/lCa43y/QOqWq3h5YuXHWkJIZLANcBntdbp8Z3yWmsthJgylNFaXwZcBkbyfqDX9ZY3L+Lffhelf0eJWNKiXA4oln1Wfi5ApjSb2TRhFtVkWNgE41KHE+1rq1Ba4OERFdF9Ii0fP5wPZe674gJvY3MTN7DimNncfON6otN8pNBIoSmlITnLYihZJCUKCL+eYS0YsDz8yB00iUaamXhZv4subCyCMH0JICyJaIzxkX+/mgS1fM54rEzBrAtg6EpBrksQFCHaBp3vh8P2Y4PeV8Ia9uFXIzCg4A0JEwl+otGQ1LUZE8WckTB1y/G9VVrD3/XAmpJx4hAY892LeuDqGeyWxnw5QSvFmt/+licuv5zC8DDNCxdy7MUX03n00S/10mp4AfGyIi0hhIMhrP/VWl8b/rpXCNGhte4WQnQAfS/F2lKpCJddcRY/+N3NbLonQDb7LPkbRedxChcXgSRgt5nkY4gRm6AErLDqWFaw4u4WcrI/wSthzyhTRob1L4nEx0ejGWCAuKqj75zFBPfsJN87ghMDvygQFhz1BUlnvIu+4nRS0lydF5TFUOCz3l7PCZw44X6y5GiljTSjFCkC4BIhToyp6fe1Ca1hq2dcMg6eAZsvhuiIGUviN8H7602d8EDitgy8d5dJ7WmMZdRJcbhxpmkOXrmXIdLPlI0ScppVJcg6y8jOb8xUfRNfjnji8st59Kc/JdbYSKqjg0xXFzdfdBFn/+IXtB1yyEu9vBpeILxsSEuYkOoXwHqt9Q/H/el64HzgO+HXP7wEywNgef0SvvHRCA989AG2stnYzuBgh6PG94ZyaG5bEWBIqnSkKkNJNGZ4oVThuBG9x+itMsbESNKD8DzVNGNAQFqkaZuzidf9opPtN/qMPlWgZV7AonMFjfM8HOmRsDMI5RKxB4nLHAUCHuVhZjGLWVSdt2cwkw2sZxrtY2sKCHBwiPEi5JFeAdhehg/uMiPuNSZyOSsJTztGCPGBejNi5EDC03B+lxEySGGEFjZwbx5+PEVf12QMBmFP2BSy8517Hu32kiMol1n9q1+RaGnBck2SNtrQQH5ggCeuuII3/OAHL/EKa3ih8LIhLeB44DzgKSHEqvB3/4ghq6uFEB8BtgPveonWB8A85jOTWfySyxlmeCzl91yxhoc3gbTG+80x9r3ADutbhb1EbWBk7ZX0odyDJZQmIBnpJ9sSY9knbOocRaBtQOBrhY1P0s6glcKWBXxtYUuNjcMt3Mw7eRfNmMF8y1nBFjaTJYNLBB8PheZUXjcmTHktw9em12qbZ+pcQpjvLx021koRaYQRHc6efQCfD+7MmpSgxsy+MhdS5h/7yvRzk9ZBrjnG14yNNwEjMDnqBRqQeSBQHBnBL5eJNky0xHcSCYY2bXqJVlXDi4GXDWlprf/Mnvf+017MtewNpoYkWMwSHgx7shwcAoK9OljsZsokzCajMOPhRfi7fc20efhYWEj2pjQUSDTD5TY641uRBGiESSQKRTlwcWUAVgalHaQIiBOhgQby5FjLGk7iZADqqecc3sWTrKaLnTTQwGEsp532fVvwqxy3ZI2zRUJWX8KCMk4UWWXk5oMBXNgDv5thpO0HAvfnzXvIonq/CjNgshxex2wtw/+OGreMgyPGaaLSI9Ziw3kNcMWwEZI4AtIKFrhwxsu4TBltbMSJx/GLRexolV1LmUytpvUqx8uGtF7uSJPmz9zHdraRDW2YZChfH98ovHcY2rLCD4RJ6VU+9gfGUy7YI0kKLdFaE7Hy1Lm7eGJ0BYtTa2h0R9BaEOgoZb+drBjBkUXKyiVQccokELag0bZJi9EJ56yjbrdaVw0G60qgc1BaDUEfiFngLQXhGtISwsi4e3y44QDWirr8aqp5/PWOwth3rS/BR3eZNGJCGqeJW0ID3UPCvf6CRtPbdVUoOz8vAefWHfhm4gMJy3E46m//lj9/5ztE6upwYjGKIyNIy+LwD33opV5eDS8gaqS1D/Dw+APXkSeHh0fJDCLHxaWZFvrpG5f22z3aGl9/AuPzl6KODOlq39Z+onJMQECUKKVwGnJ4RyamEhovcGmKDNMSGWRDZikr6p8kZReoEzEGZcCgnzIEqCUlLUkHgoyCOuVxqDPj5dV+/jJGyyAUvwcUMBcjDwCtwN9Calxflo0hmgOFqIT5LmwuV6cPa0wN7VON8INB4zhREX8kpYn4fjRkXNth9x6xVwqWnHsubirFE5dfTra7m44VKzjqggtoXrjwpV5aDS8gaqS1D9jONnJkSZBkkEEsLHx8cmTJkxsnrtjzuJHxUCiSJEkQp4++59VILBBY2Ohw8KPEGquvVZqWfeUw6jWgQ3XhQclN7MzP45D69aR1mqEgQUSCEpo6ewSNwFMuXpAi69extrCY4/YwRbeGifj15aBLoMc/X33AXfDsW0AUjQu7Bxx5AGtFb0vCwwU4PgrrijDqm7TzYglzHOP43jKpUbdRGlPe59MD9nKCEIKFZ57JwjPPfKmXUsOLiNp19D4gTTpU6ZnZwCVKY9Ly8SS1J8KaPGYkICBPHgc3lMvv384hkdjGTIlmWmhnGh100MF0UtQRKJusl2S43IoOvblFeH0iVT0WFmUtGfKS+HgMldsYLHdQUjEsGZAuN7Fx4Bzuz9VUgfuC9aPwyHqom+x0kQBWm/rSZh/uypurxANZKzo9acbOPDkKA2WTBiwBqwpw9J/MLK7SpAxySZvfv5IJq4bXLmqktQ9oohmBDFN/6vnPucKo/mxshhkiQ5oEyb2er0Jo44nNkGdAE81IJBGixImTIkUTTaR0G4Gqw9YxbOxx59C4Tg8ePjFdR2NkgIKyUWiKQZyhUgd9xelELJ98kKC1FofvEx4sMlZYskXVmR2F8XiiKkUfCEyd6UBBCjijDMVhsMsgNdiBubPVTVDMGfNbL3yLeRqGFJxfi6BreIWiRlr7gJnMpIUWcuQmDGncH0gkjTQxh7m00EqUKApFH717PW5yPawCjSZPnhhRMmQoUCBHloCAt9qn0CAtwMfSDmiBLYqMeE3MjO/AxaHDShKTPoG2sJRHZLCPxM7txEcGsEOD1r+p26+H+JqFE4G65eCPVsfGo4E8iOMMWcWlqT+VNfwufWDv/4ZtoCVoBywfpAIrNOl9sgzvqjOKwIHAfP1oA7y79trW8ApF7Vp6H2Bh8RbO5gn+wgP8GQ9vr5ZNk49VmGZhiaCLnQQElChToLBX8htv/VSplwlEGF1FyJNjGu0cHKzk+myJXaVWjnKaaUsl+ahTz1XibvpUhggQ9+dwcHwYZIIGGpFIOiywrQHUkzuxSmaAkVQRir9ey9+feyZHxl79xrcHAifEoeMdIIegtC00dFfAcpDHVedcVRR8z+Wyvr9ocJgyMS3CyG9ZFC5qNtOH22yzhhpqeKWiRlr7iAgRjuFYZjGby/k5AcE+CS8q5OYTkCOHhlB4oXebu7X7sX4ouDDCj0oTb1X4YbEt6OaqrvPo8c2LuQ74f6NwWccMPh95PzlyODhEIhH+wuM8wsMIBCVKNMgUhWdW46d83OYEWgvctTnq/rCBpsyP4Fvf+iuftdcG5rpw8Uz40achsR16ByDTCkwzqYyUhECb1yci4OS92Co9H3xwCXz1MahkKSHs/9MwLWruPyHNOmuo4ZWOGmk9Bzw8trKFIYZoCj+SJBlhZL+k6gH+mN3SvkKEsnqFnlDbqqYnNb3lJrp948xdwYAP3xmAyzsFyXFGtgezmId5kG1sHTtf8KkbqD98AamWdpyeMm5XCd3QzJY77uB1//zPiFq1fp/w/gY4OQEPtZr6VasF3+6HewtQ1EZq3mrBQhfOPsBWTs1J+EkbfLQAvjRkJTTMiUBH9IWZhVVDDS8VaqS1F2TJch3X0EfvmK1SkiQWFjOYybPs2CcRReU243u5nguVacaVlKCNvVtkFhCwLXM4TeHltUJRII9t+TxWipFRNilZ1Tvnwg+FQhVKiDUDABRvX0/LLAsndBZQSmHZtbfG/mKmM3GM/R8T8FgBrkvDqDJpxDenDIEdaJy/BOaPwqd2Qh5oiMAM10wcjtTSgTW8ilDbmfaCh3iQXXTh42OHT1WaDDIkHYnca21rb5ZO+4LKfUokUaIMMxy6uVuUKePg0lNsBtI0EaGXXhQKHyhS5GbxAG/nzVhYdLOLm7iRPHnsoYDy2l50nQv/fgbl9/yeXU88zvTDV2BHo+QHBlj67nfXoqwDgCNj5vPFwAn18Jc6eLpkalkHuTVZew2vPtRIay94mg14+Dihi7tHGT9sBB5kCP08nCz2FTY2TbSwguWMMMJqVpEM5fFFijg4tDGNFfVbuHkgSU4MYAuJ1BaFIMYRqR30iG3czI2kSZMjRy89KK0IshkzC6ukUPUu+vxD8f7lIbrXr8du76R12XKO/OTfvmCPrYYXDraApS9jo9saavhrUSOtvcCk5gyKFCY1EpsIy6gCK/OsBGXK+y2Hn2z9ZGHRQAPHcSxDDLOKJ4gRJ06cIgXKlP8/e+cdZldZ7u37fVfZdXqfSa8kJCEJRUMTQZoUFVDUT1Q4eqzYubB8luPxqOcTj3o8ilgRC8KRXqSFIlJTSS8zIWV62zO7r/p+f6w9OzNppJJE9j0XF8lkr/Kuvfd61tN+D17hXOaVb2SLrbE2NQ0dkJhMjfQwr2YxKbKsZAUaGuUETcVK+aiqECLr4ifzKMeFkxtx5zSR9wQrvvNj+qcv4u8pwX9Fg9lKJUqUKHGsUDJa+2A6M3iZl6Awxn5PeHhoaBjoBSMnCy3IY18/0r8joahsKgsNy6NfGyGCj0+KFPdzH1GiWFhkyTFAfzEkKZF004UlLBbVdTCn8kWSTg2Veo5aM1kwgkEyQ8cgRSoolxcSz1f4VSYkcxDSYVsaWzMQs2sZf/owMiFYmYObBuDbpar3EiVKHEOUUrR7IMkwi3mcdrYjCKYD78pIMYWPj4VFruCJhTADBfcCnoKcG8L2DZQS+AQGbGTb0UrtIUJFTcE8eVxc8uQLrxlrBINer53SCmVGipboVirMASysYhGHLGTgRoo5dGEgQyagoDwEORd7cQciZ5F8/yKioVaE8KnT4bE0WEcuAlqiRIkSB0zJ09qFDBnu5i4s8oSJUEU1A/SPKbjYNZxnYEDBv4oUQngjIrgv9J/NhOhW6kI9KAG+0si6MUJanrA21hja2MW+rJGy9pHR9rC7huGeGKkwtLCIEcMr/AgCoxgiRCKUwNEs1NKtyLs2Ifs8Ul+8EOfMaYCFh4+GxCVQDg8dygUtUaJEicNIyWjtwnrWkSdPnDj99BWlm0YbrV0NVphwofnXYBrTMAnxHM+S9cJsTM2mO9fI+U2PkHViuMrA8kJEVI6w1jfm2CNGaV/9X/ubL/NRePjUUksPPbiFkKJEMk1MJ1ObYfs7Dcwzp5OsaSYjy4jLNJ25ieTJk/PizA+X1BNKlChxbFEyWrvQQzc6GnlyZMgUysv3rnDq4WFgUksd/fTTRhtzmUeUGAlPY1bZapojHRjCJq9CuEpHCIXnH/ylfy0ljpGergxpJIIKKqmggglMZDtbGWSQHFlUXMeOm1T4GWxH0m/XsypxCmGlUyvhK68xqr3E64OtYMiDKm2UGG+JEm9QSkZrF6qpoZ0dWNhQqAYcafDdk5ejUAwywABBo66GxnM8C0CF4TCjfAOubyCFotpMMORUkHXjhPX9nXa8O/syWBoaIUKYmIVzV5zPBUxjOs/yd9KkiRMnTJgsWRSKqAxTZ5SxwarlxHgn7zIbuTQO1aXKwaOKUnDrEPxuOMgtRiV8oiqYKjym/8pPgbME8MA4GeTBjUX2XZehrVsxolHKmpv3ezvbhh3dUBaD+pqDOnSJEvtNyWjtwomcyFrW4JLFx8PHK+aZPLw9GozRShdhgiaZLFmEgIiWZ8CNM2jXUGP2Ua4nicgc0b0Yrb0Zx/1FH/WW6uiUU45W+N1GNhAlhkBgYFBNDQkGyZMjJvOcWP4qn+FtTKUkUncscHsSfjoItTpU6JD34T8HAi3Bi0ekoKznIPVlwC5MddQg/jUIX3pAx9r+3HM8/a1vke7qws3nqZ87l0t/8QviDQ373O7x5+F7v4RsHnwfTp8P/3Y9VBxmqaoSJUYoZSx2oZwKLuedNNNUNB5BNaDYp8Eawccf06sV0zO0RHYQ1nIM2dVI4RPVssgjFOYZUaD38CinoqAIbxZyZd4YCalyymmkiXIqOIdz+QAfZCrTjsyJlTgglILfJQJvN1R4y8ISygT8dqjwIj8JqRtBGCDrQWsAUQbpfwevY7+PNbRtG4987nP0rlpFsr2dbH8/bY8+ym9OPx0rldrrduta4Ws/Dry+uurgv+dWwFd/fAgLL1HiNSgZrV3IksXD5RIu411cWagMpKiEsS8UityoysERNOlTbRxjD9kAACAASURBVCZoiPRgSA/5GlddHMTbMjLsUaHQ0KilDgODKFGaaEZDYzwTyDLWw3NwmMdJnMAsohxm+fESB41LMKwxLIL+vn4XXs7BCzm4JwVzW+EPA9tQvg0itnNDEQLlgvXMfh9r04MPkursxM3nUUrhZLN4ts3w1q3c+6EPodSePf+/PhYYrGhBpkpKaKiFJauhY99j4kqUOGhK4cECPj4v8SKreAUPt5D7KeOtnMdzPFvsqTqcjPROQWA8RoyOjoZbUHHfX8qpIEqUXnoIEQIU5VRwIRcX+8bO5Czu5R7SpEdtV8YpnHo4l1XiMGCIQBG+04FOF9odxpQDbbLhk/2z6I2+jy+WPbmHPezeW7g3Ul1d2MkkaBpuJgNCIDUNz3VpfeQRlvz857Q//zy969dTNXUaZ33lyzQvXEhXH5jG2H0JAZoGQ0lo2XdksUSJg0Ls7SnqeOaUU05RS5cuPaBt1rGWZ3gKB5d+xpaiV1CBg7Obl3LoCDRksdR9dG7sQPJaIUJINJpoIk+ORZxJC83UULubonyePG20kiBBHXVMZgpmKYd1TPJyDq7rgFftYFaWjwKl0DwPJQRh4VOr9bOu7uPEZOHzolxQA1D5R9Bn7NdxNj70EHe95z14zs4Ige95QZKqEBbI19aTjVfiaTp+rIx3/eD79GTP5ke3QfMo1RTLhlweHvs1REoaiCX2ghBimVLqlIPZtuRpFVjNKjR0uuja7d+GGd6vcSIHjhrjvQVDHsV+zd0abdgEAg+XQQaZzGTmMrc4MHJXwoQ5kTl7/DffVziORyhU+lgcC5wWCSoFfzoIOV8hfB/p+4V3XuDlbZKhcjpcE08pHrEXERUWV1c2UrkHg2VnMmiGgWaOfUiZ+ra3UTllCn1r1yI0DeV5QVJNSlAKpRS+45GvqkUC+nCCX/3mz3z75rOZ+ARs64R4FGwHXBe+/NGSwTpqWDlY/jisfx6voo5VFeWsGm5FVzoLJ53LCSefe9xPbyjdnQrkCn1Ze+NQKvr2F4EkSoQUe09+jxD0aikE4BWEfVto4RIuo5deNrAeDY2JTGQc4/dqxABc1+e3v13Bn/60mkzGZsaMGm644XQWLGg6fIsrcVCcGApmdCUzgdaJGP05lBKVyvLdil9zdyaKrwCh85Ws5BcK3lMRvKx/40b+8f3v07t6NVLXmf72t/Pmz3+eUFlQ4qeHQrz/wQf55cKF5IeHQSmEpiF1Hc+y8AwD3coRSidx4uW4ZeXUL3uOh/IOt37X4O7H4ZmlUFsJV18Mp+z5majEkSafgR//K6p9I75u8vip5Ww+sYVUSxhXSV717uaku5dy2ZU3Hu0zPSRK4cECT/MUL/EimVH5nteHnT6cGjWheH+MpIZWKGuvwMDgrZzLUyymi67ivmLEmcUsLuaSMeXwo/mv/3qBP/5xFbW1UUxTY2goj+f53Hbbu5g+vdR4czQZcOEdO2B9Vx+JsmqE8oPEUSFMOH3ty2xfeCaGlMXG47wPpoRVU6B8qI//ffe78WybSHU1yvPI9PXRctppvP1nPxvz1L39uee486qryA0OBgNBpcQaHsaKxpG+T7ppPFZVLXgueiaNs3glvxhfauY7VrAX/5nfbnyVO2ZdRn84TFXFdt46/DdqvH5WpZuoiDtUluX5RPUXqB0/9aie66GEB0vVgwVO5mSqef1v0BqyWKF4oOjo+IVcWAMNPMPTxfDmiIJ8mhSb2MQmNu5xH6mUxZ13rqWhIU4opCOEoKoqDGGPP93+ykGvq8ThoUaHnzTCxMwgZclE0WCZuQwn3/d73EwGN59H93dOtQ5LyPpwVzKoDHQyGaI1NQghkLpOvLGRzqVLSbS1FbdxLYvc4CB1s2aBUjj5PAhBuKYW4Qc+vRcKB8ceTtB75gXMiZUM1rHEv2fL+NWcqzB8lxBJtqop/Dn6EfIyRlRzSKRMhtww67e9dLRP9ZAohQcLlFHO/+ED/IFb6WD3HpeR8Nq+1CgOFr9gekaOsy8FjtE4ONRRzyJOp5kWfsUvxpwrBH5clgyb2cRsTtxtH319QRO0rgfblC1IUXf5ANPiDhl/kBVUMp8FRyinV2J/ODkCDza7/Or/fppcJEr3UIaarRupbd/Cbb98BKUUVipFuKJyjFRGyoehrVuR+tivuRACoWmke3qonjYN17J46JOfpGPJEpI7dqB8H+X72I5L54mnMBwtp2J7G6FcFtO2yE6axuCnv8yVpQbiY4YuBx6tP4nGwR1I3QAX4qTJEGeVsZAqNqBLRdY38Di+E44lozWKKFGudD/Bi/5yNuvPMCwTRY9lRD0dApFcUZB4OlRGCjEkEg2NBhoZoL8oH7Wr4Ropkx8ZaTKHuUxjGgMMFEOCo8OMI/O6tL281Y2NcaQU2LZH1TyLlms7KatIEzGyuCLM39WTIGABCw95rSUOnqbZJxL/+ne5rTOLk86gDIMp7Zs5re0VtpywAN9W+K6DNExsH0wB56S72b56Nd2rViE1jXBlJVWTJ2PG4yjXxUql+Pt//Af969fTsXRpUHzh+0Sqq+mLV3HfDf9FtrYBLRKB5vGU72ijJZdk1hmnc0uDQdPBBQhKHAE6XNDKqpC9bWBlqUu6WBmTrirBDq2FmGpFj/k4GUlT05uP9ukeEiWjVcBW8J/98GBKIMXJKHUy51d001h9D5bIkSRZHPgokCh8woSLxuVgEQhMTOKUMcwQDjbNtDDMEIMkij5TYNwCQzTSvCwQrGQ5m9nEubwNHX03QxrM+Aoxm9l7PH40anDddQv5+c9fZuY7uxnX3EmMHAoQpHCcIZ7RHU6S8/dZzFHiyGD78LVe+N0QDIWn0Fy2g4qhAXRdsOOEhUS3rmXa1o20TpiBqyTSB4ni/Q/dynM/+Ar5oSF818VTCt91yQ8NUT5uHJVTpvD0178OQpDcsYP88DC+4wTvu2XxxI3/Ta6mnhrPgr4EJ/RtJde6g5OrZ3PJgMOklpLFOhzkyZMmRZyyogTcwTDOABeJrxTSc5FKYeQcQobLJKuV0DiFFtVwNsYYntpBlsrjVkygZLQK3DII9yahQQdDWoTMdl62PD6Yfh8XlA2i8FnLWpaxpOh1BeNIDu1GLpEFX0oRJ06SJDYOYcKMYxxllLOJjQWjNdbvUijCRFAoVrCMk1jAMpYUvbARj+sUTmUyU/Z6DtddN5/6+igb5v+UMBYuejHKZCiHjN/BYvk4iziDOPFDWm+J/cf14eQtsNbe2WbeGypDKkklUJ4aYvPE2dzwm2/wcuM0+j55IyEBU276GhV/+S2ZXA4hRFDiLgSebaOZJmXNzWS6u4nV1SE0jWx/P25vL/g+QtNI1jTQM24K8UQfhiZYcMv36cstZIn/YZYi+P0ftnPCyRP52bcjTGo5mlfo+MXH50VeYA2rCB5GFXOYx5tZdFD3lEYdLm1bzH2VM6m20+iORbuKERnOcNXtf+Dfqi+kcoLknGtjLNeXsJ41vIsrKaf8sK/tSFMyWgTThe9IBsKkZaHttFQ+ghAezUqxQgjmcxbTmM793FfMOY0YhUNVydDRkUgSJBAIIkSKkkyXcjk+Hl10MMjgXvcRJkwfvXyYf6GWWl7mJTKkqaaG8zifqey7UkgIwaWXzSCT66JH1hJWO721vAhR6SVZq62kW3Tzbq4+pCfCEvvPnUlYZ4MGhUcWsMsqyFRUYwwPEDM0fNchkRjmQyeFOeuEOPdccw2dy5aR0jSklAgp8R0HIxZDKxRhDGzcSD6VYmDzZpTrBk3FfhAtUErhy6DAQvmKiYvvxU6W8UL445h6BlO6xJxhXl0X4rPfncw9P+U1ZclK7M5qVrGSFcSJF+8pr7CCKFHms+A1tx8czHHXXet46aUOmpvLuPrqE/nqsz+hduIF/HH8OST1OLM3LOHyJ29nQr6DxBafVx8xSa9y+chvqsjFMyzhZc7jba/Dag8vJaMFOApyCqqFTUvlI8XMkKmlEXg8xiMMMUSaFCYmLu5u+oIHy4jhGzGCNdQQIkyOHE/zJNVUI5Bj8lgjGBhYWIQJIwvl72/izbyJnTHrPHlWqOUsbd/IxhezpF6u4Pw3zeLSS2cSDu98+wWC6dYW+vQa8iKEoRw8oeEJyYmZlaw230SGNBvZwEnMPyxrL7FvHkxRCNMGZb4+gBD0T5mFSvQSzg1T4zlceeMXmHbeeWR6ehjYtIlQPE5aCBAi0A0UAteyEMBAa2sxdwUUG4iRMvC0pKS8azsVvZ1kqupoXr+ClWX/gnIEUriEAM00Uel2uvsmsWazYN7Mo3SBjmNWspIIkaJXJZGEifAKK/dptCzLZWAgy0c/+gA9PRliMZPVq3t4+OHNfGXGJM5Y9itO4s94nk8m6xDTHPq1CJ2tBsr3Wf5Alu+d1821v6rCnLf1dVrt4aX0jERQInxiCHyjHSE8dJkmZPQjhYUpPVKkeJHnUSgkEhOzqOd3qHh4ODhFoVujIKkUJsxg4UdHR0MnQrRYBCIKBSIakixZZjF7tz6sNGn+lzt4oPcJXhnYhHNiB6Fr1vPTO57k+uv/huOM9RKVcSrnpp6hzg1krMq9JGenniOjlREVZWho9NBNlixttLKVrYfNeJfYnXp9JIu5yxdVSnK1jcSmzeQnZ89h+vnnI0a5O+GqqiAsGAoFBkkpfM/DtSzwffTwKE/Z35mPFVJSOWkS49/0Jq5++k5CkRBeRSU5PwL4GAJCxcMIfBGMJClx4OTI7vZ91dHJkdtj1XB3d5ovfOFRzjzztyxa9BuWLu2iujqC5/ls355k48YBPnZPI77rUGlYeL5PmWZTruX5adspuJbAyYNnQ2+bxR8/tg3j5X/Af34Aura8Xss+LJQ8rQI31MBXEz6uconqKTxfQwhBRIKPxMEdkycyMA6LgK6JSZ7gm6+ADtqpoookSSwscmRxcNCQeLjFnq7RxRhTmMqbWbTbvpezjCF7mM5NNqZpgi3QYx5zP5njxRs6+cc/tvPWt04uvn5+9Eesci/h7MwLwSqVottoYFv0ckIEcfgcOW7d/APyj65D5R2ib5nFO075JM2ilNw43HymBm5OBIrvGsGX1SEwZFeVwTfrYcooRaZYQwM1M2aQ2LKF6mnT6F27Ft/dWfWKrqPpOm4uN/ZABYEBPRLBM0wee9t7WDd+JrOExeR3XkH+1ufo1U8nLoJQejZvkapuIe0I/qDn8O98mKFnnyZaU8OsK66gYd48lIJX8314uEwONaIfqVk8xyktjKOLDqIUFPoVDCV7kKv7uO8319Fy5lsYbjoVTwsxdWoVn/jEQyQSeRoa4rS3p8hmbZYu7cRxPHRdIxo16O8Pcf2Gt/PFSS8yxeyj3SnjP7cs4qHe6SCCUWsohZvzad+gE7l9K7QtgWWPwg+egYl7LtY61igpYoxik53lT/KXeCKJJnTCQYSlMJuqnBzZYnWejz+mDP5wECJULKWXaESJUEc9HbQDgczTyIiUGmo4g7OYzGTKqdjj/n7PrQwOZNi4LjFKT1ChV7k8/9F6rrhkDjfccMZu2211H2Oldx8DMoSmn4gmdPLk8PDJ/u8SrB88XagMECh8Iu+Yz6e+diemKAnvHm5uS8DHu3YqvGsE04t/3LjL9OICiVdf5cGPf5zOpUvJD+6SBy00Ju+GlBiRCCoU5t5Pf4dXLr8GUQgZxrIpvv27f+eeJWfQ6p2E4zl4RginsYUp1xgsuPsjVLetZ0ZFBL+QG5t+w8dxz3ieZrUahKJPTaG+6tvMipXiiCP008e93IOHi47BcG8n2Y5u6v7fZuxNabZu6mZYr+ce/zKG0j4giER0pk6tYnjYors7jW17CAFSSkxTYlkenjfy/o4ElseiGT6RMoWbVty24G+8u6U1GD298Dz4twdet/WXBHMPEzPMKBdyKot5HIlbKGQXlFGGj8dkptBBOwkSh1WL0MBAobCwiklZE5MaatHQGMd4BhmgiWYqqGQqU5nKtNesMgoTQpq76ClKwBP4NtTXx/a43ST9Apr1c3iB59nIBnzy1FFPbEBj2U1PYVSVIczgo6N8n+x9K1jz9sdZuPCSw3E5Sozig1Xw3oqgKCPjBQK61aOqze10mt61azEiEernzKFi/HhOv+EG/nL55cGIET1QOVG+P9br2gXf9+k/YR7rLriKsGsH9WweZCNxbjvpHD666ec8vz5Mq3kWRthncs8KKn45hD7YT6pxHPmwoFoDK59n5Q9u4K0LppCONgJQTTvZxCfotm/D39JDqLyc6mnTjnvh1kOhljrezXtYzWq6rB0M/2UNLS/nMYZDrNnaj9IrMJId1PnrGWQmoMhkHNat66OqKoLj+IVUpEAIyOXcsc8jI9d21O+EBOkpjJSN9AXlXgpyBdm6Nc+9Xks/ZEpGaxfezCLaaKWfPgxMDAwG6MfHp5tukiTHNPgeDoUMF5cQoTH7UygcHAwMJJIIURawkElM3u9RInOZx0D8KcJRDSvnYpoSvdKj58kYujC48MK9Tyk2MXkL53AGZxaN6AMrfwJQNFgQ5EFQ0P3cy1AyWkcEU8IHKnf//cb77+cf3/9+YJA8D8+y0EMhkl1dQf5KiJ2GYR8GQkpJ1eTJ/OxrN4NpIvwg7C2AyS8/xcKbv8vgUDfjU4M0mM+jDblkWyYS6ejDj0RRjeOxCjfHvJZD8236tsYonx0cMyeqibjt3HPb9bh/2YLnudTNPIF3/PDnxOvfuEO3KqjkTM6ifcWLDP95B+G6OvqHLPKuRFMuri9poZ2N7PRQXVfR358lFNKwLG8kZbk7I++3BPygGEcJ8LwgFzkukmJ2+QBIDTwX7CxkhiG256jNsUTJaO2CRPIuruRJnqCTThKFUvMmmorVe0HBe/DUeqCzr/aEQpEnP0bFQiLpp49mWvDxSZDgMR7Fx6eCcuYxnznMIeuZJP2gT8PY5b50ArMYEkP4c1ewY3uSnOXQ+1KYob/V8d//fR6Nja/dczU6WdwSmsAqdhf2FUBtrPmQrkGJ3fGdHlLb/o4RqybaeE4hKRHQt3EDT938/whNqCKUkSRaWxnevh0jFiPe3Ey6s7M4VmSMR1PwvjTDwPd9PMsKmo8dh2x5VeHd3MlJ996KHYmSb+1DajroOkpAuLcTxwwT6e0irhvoDbWouho8ZaF8hT5Kl9BzHXzHhjqLdK2PUoKtG5fx+xs/wMd++zd08ca+DYWrqvA9nxc6Knm0bSqJtMJUFnNYwfAe+qiUgnzeQ9Mo6IUqstlRD88hHWIm5AqtK44PmkRIgXR8xoeSvKd5PeMjqWBnQkB5LWx4CU6+4HVa9cHzxv607IU4cS7nnQzQz1+4nTLKkIUqvcAT2reZOlhDNlKdGCKMXzCQwwyRJo2JSYYMeXL008dGfztf6k/Tmz6ZGDGiMigmuXiUHpxEcjpnMN9cQGLqIOluMBbFmPjeCjTtwAtH555yEc/Gvk06mUaWFyrQ8g5lsoIZ5110wPsrsReUov3vX+eZb/8PuSEX5SvGndrAOf9xH5G6uQwyyF/9O+i/aT6aYWB05HA/vwojFsNzHMxoFM00A4PkOEgjKHcGMKJRambMYGDjaoRw0cNQN1OSHehk+t8fZtVF70FXXpDTAuLd7WCYwd89Fz2fQymF7jpo+Ry4DtGBXrKJXnq6K1EGaFFFYvkgermBWWXi5fMoDfJbrOAhSICqr2Bo7SZW73iWBRPeejSv9lGnZsYMWqMLufulMMKzMX0XH8HLvInhveSrISj8tCwXXZdjva2QDrVx2FbIaTaWI5SPSFrUNcX5t+p7eUft2qIHRnUTVDXuxWU79iiVvO+DKLGCbFPw9Lm/TbWHopIRFHg4RIgW9xMjRogweXL4+Egkz/WfzfrUNGyti5ieRQDf6IPlud33GSVKixjHzKZxTJlSdVAGC8AIR7j6J7+n3mgk3OsT6VXUZMq58Jvfo3LixINe8xsRpRSbHnqIO6+6it+eeSYPX389fevXAzDU+gCPfunHOHlJrC5GrC5G+5JeHvvc5di+xQPcRyZmIxM2MuGQrxTkbzoTBwdPSDZNmMmOk8/EisTwIZBn8n3KWlqomDwZ5bRTM82lvAkaZgvO/7rgsh84XH7bN6jbuglLN8nrJrFtbbihCM0bVwYn7boYVg7DyiM9F822kLE4EV1DCUGqvR2rZxDHjrP9V5tY+a8vkd/SQ6XZz/bOatwlA8X1CxE0Prdm1h2Fq3/soJRi/fo+Huqdhev5mMrG1Hw0fIapwN+HX6EUeJ7CdX2EgLKyQtogYwUGqSIM8RBoEkyDqmtO40/3X8E73mSizX4zTFsIc98C42eCpsPM016nVR8aJU9rH0SI0EQj3fQQI4YsjBF5LaHcg81zicKPh0eWDBVUMovZbGYjQwwVlTjyXojW9HTiehpE4I01yShZH/48DAsjB3X4/aJx7jyue3gxXcuX49k2jQsWFIcJlth/1tx+Oy/88IeYZWVo8TLalyyl6yMf4V2//z2b7/kffBeiNQYKyGoGqcZqBjem8dp/QXaCRkW0ll6vBys7jPI8VE2I7GmNPDn7I8z72x0Y2TT9U2YRziSpGeimoraGCWecwaLPfpjlN1/Bqu0TWXfxxXScex6b5HI+KH/LSQv7+dI3rsH+8nfY8T8/IrZlI2WZJF4u8K6AYEBkYQ0CkFaecHUTvlI4qRSuZaHaFV48jFkv2PTTfsZ9/mpWf/nP6OVlkHPB0ECBiBhEp75xWyVc1+eb33yaRx7ZzOa2JK5Xg6lVEwpJLFfgW/t3HxFCYJqSbNZG0wJDRnWUU66t53Pb/8jUoVd5SUzn50sdvvTBVkL5K3hn5Qo+PedVon4CNAOu+TcoqzrCKz48lIzWa3AO53I/95EmjcJHIAkRwsKGPYQARwzPwYYHR7ZzcYkQYRrTaWUzbqFPDMDxNabGN5P3IiSdqmLPVlhC5+Gtwt8jmmky7s3Ht1L00cSzbZbecguqqoYNIkTSAWK11A728MLvfk84l0DqgWlIRCIMnD8D/9QWyDusN9uBMsZXj0fqOiqTCYphDI2Os8+iZukmjNQQVnkVCMjXNdIfiREd6KB/wwbKGjIs/9L/5S+ZtxH3htDxeFi7kuWcwY2zP4i2zKbx1p9ir3gBzTSD6sNIBDebHbMGBSghUb5PMpGAbDYYaQIoy8LN5/EyEWJGFadP/gyvaHcxYd0G5tYpwppie95g85evYpY57/W+/McMjz7ayiOPbKahIc7mzYNkMja2J7Ayij3dW/ZGPB6Ukw4Pe8UIX/mKTVxrPcXESRm2OhXc9kI9Eb+Vxnkn4NWN447OOJ2DZ/Djz41HzT6dJ5amuf26+xgaynPuuZN5//vnUl19BJ9+D4FSePA1qKCS9/J+LuBCzuItnMppVFFNFZVFjcARggKKIAEdqFsYBxQq3HVfAOMZTz31eKO8t6ieYW7FSk6pfoE5FSsJFRqOUz4sOj6Fm99QZAcGsPN51qoQKR9CIhglMhgp46lla2h60wV4jo8tJX2fWoR3wVTQBCqk49VGsLFJywxS1zHjcYiEsSrreP4tH6dm22byZcFcLU030HyfXLwcy/XwPY+li1tZf/NWFv7hZuo2ryPk56m3O7HahnnmrjISmzezdfFifNvGSaexksk9GywpUULgI7BtB6+QM5MjmoeahpPJkB0cpH/tOhb5ac6ZLonHJCKsM7PW5+13LWZyag9lkW8Q7r13A9GoiaZJJkwIclcHk1ayLJfhYatYUwEw3kzwP60L2brNYMn6OBnHoDbmI3u2YhgaTROqeW5rmFebz+GWO3bw5S8/webNAwwN5bn11pVce+29pFLWvg98lCgZrf3AwGAKU5nDXM7mLdRSi49frCYM5JSCnwrKMTCophrYe8mGLOgJjmbktSMGK0UKC4tzeRsxdvZUSQEhLU/WjVEf7qLeTNLlQo0G7z3+RJvfcESqqkgKA8+xMUcq0oFILkPvhOlkzrsBbWoL2xpq8VvKUd1pVNrBaKjA0E2EEvT4Q+yYMpmOmTPoO2kWrcylx59IqqEZpel4oTBqpHrQc/HyefrWrOHJL/wHtU/+ncYn/87M7/6E2seeIdrVycL/+B7Z/v07f1VocxAolJRI18UXAl8f1UBWuPsa0Sht99zJCZEUvlGBjFWgx8rxzUpCQynsJ24/vBf3OML3FBGrj1C6kwnj4pjmwUnD2bZfNHZSCjRNUGVY2EpjyWA93XYZugahSCgobSeoKNV1yZIlnXzrW0+zfn0/K1Z0s2PHMHV1UTo6Ujz00ObDt9jDyHERHhRCXAT8hEAQ4NdKqe8frXMJE+YdvJMfu79n0B5HVNqMD6UICZMMaVxcfHySpPY4xHGkedhHIQn6oQQCi51PNSMiujYWQwxRRx1RothYSNvBTCapcoepjGRI6DVMirVxvnEC76uAuuPiHX1jkidPO+0QBnX1+zB//RtkPI4KR5C5bFA1eMWH2BbNkP3Fh1HPrQRdQkhHNcVxa2MYSuIqSX+uCcc3yeTztK+rwnphmOopm1j99vdxwQ9uwA5H8TUNX2qU9XYgrDxaKISIRMnGavBEDkM4jP/f+xncPA8v76P5QX7EA5TUkN5IW8dOFARla0JgR+IoqWHkMqAEnpBIZ+zTuZPP42xZFzxpCblTsVOALyRq87Ijf+GPQYZ37GDupl9StnYthqmTtHQavbewldduHdE0ge8rRqUZiygF0ahOTpk4vuRv/VOxCDPshpB9w0Q1xZaXOqiqCpNK2Vx//cP09WWRBZmtLVuGGBrKM2NGDUuXdvLe9845Ess/JI75W5wQQgN+BpwPtANLhBD3K6WOStmRrRy+MtTKnYlLCl9mSZOZ5QONS0jp3cUvuIe724j6YHbWTpmVIHQoaKCRTjqKxmokJ5Ylyyus5CIuZipTWZF9loZXuwpy34oKUtTW2Uyunz6m1L3EsUcbrTzJExSyPvjX5ZFPRok+tRLhezi1DfR86NO4sTieBzYungAAIABJREFUvprkYC/atHq8pijURkEFVYB5lccfzmF+5zkqnlxGw7YtTDBDZMurQNP5x4e/yLIrrmPu3+5Az2fRbZvIcAIFOLkc0u4mXF6D5yvMvi50K0tVf5Z8eSWx/m7y8XIGWiZTs20zZjY95pFr5NOspMTXNKTrAg6+piOVg+7sHk7K9vTQrvKcPMkBGS7Gr3zXQzcMeifOJewG4sD/LLiuz+OPt3H//RsBuPTSGVx44TR0PQhs+a7Lw5/6FFG7j7K4SX4oQdj2uEg+yB94Hxn2/mUWgjEGSwgwDIFV6O72fUU67bBJVuKg02wOU2WkSVgGm1KVhDSfpJulry9LPG6QTjvF/UgpUUqRSOQZGMgxfvyxGbY5HsKDpwGtSqktSikb+AvwjqN1Mj/PLeeviTqiWo64niOqpemyI/ypZw4OdnG68ehZWyMCu3vyvKJEGaAfUfDAZEG9XRZ+2thML72c65xN06t95KImnqnhmTrCjCDsHDNbS1LbxzIZMizmCXQMYsQIWyb+pjYi313AwFvPxq6qQ0/00nzbf/LWz15I+7P3obIWRk6guQTVdgJQCi+ZQX1sMaFnV2N0dyAdB922iCX6cUJhLvneZ5nx1ANsn78INxylakcroWwKPC9Qe3ddajauovbVjWi2BQpCqWHq2tah2RZ9k2bimmH8vShoCMAJRRhunkS6shYlNXxdx4mV4YuxtxM9HEYzDAYTWTryUUwnje/YOLZFqrKSzgkzuH7aVVy6Hb7TB+7x0Sa0T5RSfPObT/O1rz3J6tW9rF7dyze+8RRf//qTxQrMrhUrSHV1ke7sJJbvoUyziIkcNaqfCWx7jf3v7lntiZQfwVdQaViU6xZTIolgSrUbKr4mnXaKOTDPU/h+0IMaGD6bd71r1qFciiPG8WC0WoAdo/7eXvjd606KJA8mQ4SEwBCB1ySFIKplaLeqSDrlxbElI2rssFOSaTQCQZQYddShoRMqhAlHGJmPJZDsYDtm+6tccu8WIn4YNxbHjZdjxSPMWjPApGdeft2uQYkDZzvbcHFJkWI729navw7LcNDiUD8xgWkNIRfUok+OYtYLUvctw9E9lFKEXs2jp32EaYAmidw9SH6rRIXC6MNDaJ6DpxtoroORz4HyqWnfwqTl/6Bl7VKkv3vZtFQK3bEIZ5KEchnC2RTS9/E1HV9AOJnAzGXxNB2laUEOaxRGPkfPzJMQkkAeSNPR81k0tcuxZDCV23cc2urPwDvtcuKxEFpDHdumLeRXH/wlft14ajW4OwW3DR259+D1Yt26Ph5/vI2mpjKqqiJUVUVobCxj8eJXWbOmFwArmcQaGsJOpdDDYcxICDQDXwmq9zHsdW9YeymNt5XB8lQLzyYm8kq6Ccvf3Z0dHWJ0XYXjjHhxinvuWU8mY3Osiar/0zjlQoh/Bf4VYMKECUfkGGnSWL6JLGjx2QXPCqEQKFARBC42Ds4eermCsB9oSDQ0YsQQCGJEyWOhYaFjAAofn3Iq0Aq9YegGdb0ZPnD7VraPj2GFNRp6ctRu6YD5+55MXOLo4eDQTjv9BDPKFODVGFBWAaaGunwCYnU7XZMXsHTGJXSt0xBP7qC5dYCF10omn6jQh120pEfsf9Yxu/oMHvdWQX8voBC+IpRN4+nB58aKlRNKJ4kN9o6ZlbU/KCmp7G4nPDwY7FuBGwojfA/dthEFo6Qpn/mL/4qlCoMjGRU6HLU/N5sNxqAoxabHF9PxSj2Tz7mI3137VTLjpxPWgq00AdUy6DG87vhoFdora9f24Xl+MUcEQXGE6/qsXdvH3LkNVM+cRap/ENdV+E4QMM57QSFXmnhBrHvft+a9Cfbv5dUHtAYhoKIixNe//hTf+94/WLCgiY997GTe8Y6Zx4TI8fFgtDqA8aP+Pq7wuzEopX4J/BKC0SRH4kQqqGRmbA07cvVEpFMcxZHxIaa5TDR8MrhjDNZo7ylECIUqiu1myGBiYmByGe/kdv6IhY2BTgWVhAnj4jCZKdASh+omzME+pjmF5XkuuC68+bIjsdwSh8ggg9zF/9JP39gxNlKA6wf/byljw2f+hWfsixn6wcuopA2xGhKtMTo+0835VyeZP30Yc3EnU8fNZ/aF57PpwQfpHrJQQiDwka6DdB1qtreilEI7yCdj3bFxzTCJ5klUdmxFd23MXKY4yXs0jgdyVBvGrkcs/r3QjOzmcmR6e1lzx53MfGQx6x5eATW1xdebAvoPfTzdUaeqKrxHxRlNE1RWhhkayvPxLz1PjddAda6NVE6QVFF0oIvx7GA8/h6MzK5G6kg5P1IGxTgbNwYeXyZjs337EN/+9jP4vuKKK45+yPB4CA8uAaYLISYLIUzgvcD9R+NEokT5QFk5TeFehrwwSddk0DXxVYiP1/YQEjqVBI+KI03GI5OGIXjqLqMMF684sdjB4TzexjjG8QE+SAstVFKFhoaPx7mcTznlwafpozdBpAwSvZDogeQAXPQvx438yhsFheJFXuAWbqadHeTYRVvL0CCkwY4kXm+eF2dejbN4MwzlMSsNDM2DunLExBZW3t3ItA1NXPjRr3D+TTfh2jYynyWWHAz6oaD4n/T9fRusXZ6Spa7v9ruy7nbi/d3ojg2eV5jKtP93yJFXjtmrpgWN0J5HqKyM8NAglb/84ZjtBjw4/djsZT0gzjhjAuXlIQYHc8WWg8HBHOXlIc48czzf/OZTrFzZTces9zIcbmGTmsIANaxgAStYiI+EXaaih0LyoMvhDwRNE2iaLM7kklIgpWT79iSmqXHLLUuPiVDhMe9pKaVcIcSngUcJ3s3fKqXWHq3zOVuexs3NG7gjs4b1uQoma1H+tayFqcZCniLBJjYWDZaOXpxwPFJkUU0NUaIMM8wCTuZUTqOsUC3URDPX8CG66MLHo4nmsWNImqfBt+6HzUshm4LJ86Cm6ShdiRJ7o41WlvAyeXJj1PDHYPnQGCdZOwFv0MTZNIQMBQoTQkhkSOJr1USb48z5/JVMmljOfdddx/q77sL3fXw7MCp7QmhaIJC7s3lnj6HCPc3XMhwL3bHwpVZ8ot2fgNCIuJOn6Wi+B6iilyU0LVCcHxHt1SR1Lz7NBjdorLYUxCV8tmY/DnSME40a3HzzJXz1q4vZvn0YpWD8+HK++MVFfPSjD/LAAxvRdUlPj0mleRWpXIYygpl3toyQUlW7ua0js7P2hKYJlFKvGQney0dgDL6vigbL84I/B1WFMDSUx3V98nmXSMTY946OMMe80QJQSj0MPHy0zwOCsvX5Yjbz47Nhl8keF3Ixizid3/JrUqQK+SmK4cAIEdIEQ9dO50zezKLdyuJ1dMaPiYbugmHC7NMP65pKHF7WsBq/0LoAwWdmpJIUgIwdzJHxFBEzj5ISbVoldk83WtREhHQkBjEVqHdXVoZp/dvfWH/XXWihEKauY6VSQb5oNEIEkk5CIE0zMEqeR3lLC2ZZGXYqRaqrC+V5CClRezN6UDA8+4+vaUjfAyHwpUR6HkpqoHyU44CU6OFAcFoqn/mTW5heoVj34INU3vk7Gof76HrXOxn3qU9hhPdPmPpYZfr0Gu68891FozVxYgUf/egDbNmSwDAkhqGhFPQOBZlxCIz+iAwWgGFINE2Qz3v4fvB3AM/zEUJQWRmivDxMOm3R17cHlexRBGXy+z5nXZd43u4vEiIo0MhkbKZNqyYcPvom4+ifwT8Z5VTwPj7A7fyJFMni709kDqfxJhwcGmikYh8jB0oc39jYhVIbbZRqikRR8H40AQ6gC8J+nunWGtZeNhvxfC+6MpGEcD0IDae54NLpVFdHePIPfyhIM+kowM3vbHMQhf4alNppiApejgJSnZ3o4TBuLocSglhDA5menn2uwRcS1Njsyr48LuF7uGY4OKKv8KVJrqqWSKIP6dgo3w/OwbIQUvLWL32BJZ+9hsG770ZIybAQPLV8GevvuosPP/MMmnF0n+YPFSEELS3lLFvWyfPPb2fJkg7Gj69geDhOd3eaqLSI+CkyRLAxEYCNWSjCMJFSYNs7Hxxc1ycSMQiHdSzL5aqrTmTVqh42bHjtdpfXiuiFwzrTplXR1pYgl9vpfUsZhAgty8U0dT796dNKhRj/rDTQwKf5DFtoI0mSSUymltrX3rDEPwVTmEovvWiF3ETgZRXkvnwNvz+DphkIy0MfcjnfeQLthPNZ/fGFWH/dhj+Yo0mH97xjJjfeeAawM8QGQQn5aNQeHqODMGMhNKkU0bo6Mr29+L6Pk80idD3wgPaADzjhCLqVK1YHAoX81p6RSpE3Q7zw4S+yfeEZVOWHmfPK/Ux98UW09VvwbBs7k8GMxTjvhz9Ej0bZcM89GLEYslBS7/s+3a+8wpq//IWTrrlmv6/3sUh7e5JPfvIhurvT5PMubW0JHMdn0qTKQNNvoBdT2SghsUUUfBcbA4XAwMH3JbouiyE7IQS5nEMopFFZGebFF9sJhzVyOQcpA4WMA2F0YYdSiurqCOvWjdXxCiLMilBI42c/u5iLLtr7pPPXk5LROgQSy16g9dabyQ8lmHDRZYx/z7VIY2dIcMaoMdklDgzfdelasQI7naZh7lyitceP0Z/LPNpow8UhRaoomlxOBWdpZ/OYdTuqJ41e+KxIXN7qP8z7onHOf/y7MJCmuiJEWdnORtCTPvxhNt5/P17Bk9pXeA8oTi0GwPfJDQwE4SfbxrbtfcaLnFCEnulzaVq3DH2X1+3NcAkglhrinFu+g331SZgfn4M8WcC/LMJ9vBHtpiVUNozHjMUwIxE2PfggCooGi5E/K8WmBx88ro2WUoqvfW0xvb0ZGhri+L6iszPFjh3DVFSEWLCgifaVg/T1+Vw2sQvdybC8M4auCbZZddiRGpI5hWEEHrSmSTQNbFvhOB6JRI5EIo+uS2zbP2CDFZzjzj+7rs+qVb3F/YyWiYrHdW688Swuumj64bo8h0zJaB0kbb+4iSe/9S2UHyQr1z/6JBNu+zUXPPAPpGm+9g7+ycim2+nsXEckUkFzy8kIefAfrcSrr/K3668n09cHygW3jVOvyTP/fSYY50HlLaDVH8azP7yECHEFV9JGK1vZiofHeCYwgxmECLGxYjltzmrUkI2UGm5coiccLn3LB2iMSBi3u3zO1PPPZ+7/+T+s/tOfAuMz2mDtpWlHSFn0wuxUasy/+UhEsWR9bEm79F1qt25AqQMLBUnDwHhTE+L62YikhZYWuK6NumASQmpUPxvCdxxe/NGPmHJBMNbdc5xi2HNkLdGa47sio7Mzxfr1/dTXByLXUgpmzqxh1ape2toSgMCP1zAh18NbpmRYvnQHZ5ULcq5EKo9XvHqU8nDdoN9LiJ2iuJ4Hui6wLB/H8Qsl6odWAu/7imQyj64Hxmq0TFQq5bJ48aucemoLF1xwbPSDlozWQeAMJ/j7d/6dUNjAKBgopRTbVqxh220/Z/JHPneUz/B1RClWLvsRIe92fCXIK0Hv9gYmzvoZ1dX7KCjZ6+4Uj33x8+QGthKvMcFZjee6vHwr1M+2aZ53H/S9BPWtII/dOSwGBicwixPYva/lyroP8bT/AKsjy3GcPM0dUS6a9D4YdrnjiivoXbOGaG0t8z74QRZcey16KIQQgstuuYWFH/kIT3/rW2x57DH8gjTTXqsEd80/FO5uHhp5vRrDS2OoHCO1jTuHOwry8UrKs5kDWrPvefhXzQDbg7yDLzWED/TmUG+bSP6ZLUTMGAoY2LQJN5crjj0RmoYejSKl5JRPfOKAjnus4Tj+bpe+ujrK7Nm1aJrkggumctppzWQfGWDHE0uQ6UE6VBPPuaeRC9ciEcWpxLW1EXp7s8W3WNPAMLSiCsYB9o/vESmDQo94PEQ+7xb1CEdYtaqHG298gmjU4Mwzj4xww4FQMloHQd/iB/Fcj3B0Z5WTEAJdk2y5769vKKO1afPThLw/kcw1EGgbQ8ToZdOqr/Dmt/xh9xvnazCw/mmS254mVqPAyQMumh58sTY9Lmiep4HqgewtEP/8EVjRkSdEiAsbruICrgRATBesv/de7rv2WjzbRmoa6e5u/v6d7zDY2sqFN91U3LZh3jw6Xn4ZIxpFmibW0NAeS9eB3cOHKjBNOVmF7ltkzWZibk8QgvLzSLygRN3zyJdXUtbfDbvJj+0DpVB1UbA89HAEoWm4uRxS0/CVwotIsING4033348eCuFaVrGAxEmnuejHP6Zh7twDvqbHEhMmVNDQEGdoKE9FRXCPUErhSZeP3TSFplPzLL77Hzy/sRk7dTGutZnNTMWREby8ALyi99TTM3aWmeexm1E5VKSUNDTE+f/snXecXFX5/9/ntuk721v6pkEC6ZQAIUBAagSlixQBlaKgIAgWFOyKihQxIERpUgUCSAmhmRhKOgnpfbN9d3b6zG3n98edbUmABALG7y+f12tgMzP33nPPzJznPOXzedrbs2Szdh/PTdeV7lL3GTMW7hVG63+BXLzXQQsEd0q3dF3whUM7eeX/Ltoan8Fy/N0GCyBrlRPxr6GxqX73TiYldtsfEcIs2LqexVioYHVv/F3Ivfhph/5fRxefL59IMOeGGzzybTiMHgig+nxYySTrX3qJ2IYN3cdsfvNN7GwW1edDURRUQ6dbgGEXtt0uAkOm0dwsGnlc6a1Qruip1lNdh6Kmek/NfXc2HVIi3m6AIs8z1AMBhBC4PgVSFr6MSrazk8S2bSAEvqIiAmVl+KJRjEgE1e8nXPvxrTn2diiK4Oc/PwaApqYkTU0pWhMJjrkjS/bgtcxet4DEsA3s/9MmmmsDrA1OIEMIBwXX5RPlqD4NbNulszPvyUptpyAfDhvdFYRbtsQ/13F9GPZ5Wp8AFcdOJxwNkUlmCIa9EJVjewKnIy74xn95dJ8vHCeH1Lbf+0iQNum230C4BgJng/bhlUeOhIfi8FBnjlzJdVxy8EIGDmzDibk0fwBW1jNaB10s6TZk9jLI3A+Br+22N7e3oXHRIqx0GkVVcUwTq6t1vZSkmpqIbdxISV0dQDfXCYBcGt3JYyvgFFShpFB2Wk3YBYFbCAlCKF+PRMEVOjk1imrnu3Nb4c62nbYl+Tgoj36Ae8IQrMoQTj6LLA8gkRi/XMCm2St6xiY8XpKiKCg+r+BEptMkt+2g0LZXI7ZhA2uef55MezsDjziCwVOnohoGY8ZU8c9/ns3s2etpa8tQcXycjmFraF5n0bzRy2XqYZfDfmDx1Ok7RnY/UY5KU7wvgdVFe9i1w6JRH7YtKSoyyOcVUikLVVWIRLyuyvm8DUj233/vKIbabaMlhDgOOAu4S0q5RAjxjYLu3/83ELrBCfc9zIsXnUsq4W3/BXDYpRdQeeLp/93Bfd7wnYChLSBvR/FmwSXi20RxuJ2q0hmQUiD9Zyh5EnyTd3qKP7V7Rmswa7glej1lV+n4mhT8CIYcIXnrNpj6XYjW9hIJ0vaH9F2gDgHf0Z/TzX42UDQN1e/HicVw8vlukrB0HHLxOI2LF1M3bRoAA444gkBpKdm2VgxsUFVcq9DqXoCiSsyPcLi6uFtdkAgcqWKrYRTZ4elZ9nrvbt9LLA8XPo97+n6IqUPQ2vIYz24k98YqVMNA8/uxcjmcbJZcPE6gxJM9cwsr9uCjj2bJe1t55fG5OJbNsV8+hElThnvl+44Nivpf26SsWdPO3Xe/x8KFjVRXh/ny+CzJZ+4C10XRNNa+8AI148dz4p13ovl8lJcHOfdcL9T5OI9CVmPbNk/TTwjIxSWhaghWSZINn/Ke9ILBkkCRH4oCXhxtc+wjD/MkojQ0TZLJ2JxwQh1LlzbT3JwulNvbOI4kGvVz+eWTPt0Y9xDE7mpJCSH+AVwO/AhPpeIMKeUVn8HYPjEmTZokFyxY8Jlfx81maHruH1jJOFXHTsc/aO8pC/28kE6b/Oet79Cv5F0QAk1JUVO2FSkVioJekh8kKP2hctMOC07cgeM3Q4kK53IFk9XZxGQJVg4qYxupNtpAQriq61AV9PGgVIMb84xX8b0fO84m06bVyTPSCGKoe5dnZmWzPHjccWx7913P+yjkgHAcghUVVIwaxfmzZ6OoXgh2y9y5PHbicVg50+sA7ErUgimS9Gy0Pw5ebwIFKVRUaXpZ/o8qo98NCFUlVFVF7YQJbHrrLcxUimBZGaLgYWU6OsB10cNhL4Ro2ww7/ng6J32Nv975FqIgBeWicNYJ1Vx3wAeIze9DsAimXQDHng/qJwsUmeR5kRdZzcpCZecATuRkyj6CS7lxY4zzz38a23YpLvaTz+Q4YPFv6TegmEAkjGW7hEI6SqqdqT/9Kft98Yt9jv/92vvYkKwn3ux0V+gJRRKsEDxygkYu9im+kz7NI6xHA1BVBGUhGF0LBw2Gqx+DjsyHHqooHrlY11VM02b27At44YW1PPPMKlpa0ggBRx01hB//+EjGjKn65GPcDkKIhVLKT2QFP8mnnpRSdgLfE0L8Gjjok1z4/wKUQJDasy75bw/jv4pQyOCIo27nvcVv0966iLqyB9BVB79h0ZMydcGtB3st6CP6HN9kezt6TUjGK/NJSK/cW/g10jW1RLTehMcwKMV0f22F4Rmuj0CH5XB/4/0cyoMERYbZcgAJ37WcW3XEnrj9PQI9EOCE227j79OmYedyXmGFEISqq6keO5ZMayt2NosR9nTDBh5xBN/58Tkse3UuKUtlaF0ZH7y/lWXLtpLdjRy9ECCEBNeTElKE8HJY229kt3+ua+PxYRteIZCOQ6qpidYPPsAxTRplFYsTh9PsllGiJDk0tJSazCqMUAh/SQljL7iAgad/jTOm3kq5P1fgsAkc0+SJWRuYbm1i/2E1YOVh1h2QicOXdr/gSSL5GzNpohEVDYFgAxu4n/u4gm8RYuc56QceWIppOlRVeZ9BRHQgHIvlq+KISB5ZEUHJQXlOUv7S7D5Ga+7cLTxzb4zx35cIVXpkA9clWAHb5otug+V9Hp+gIrA06Bms8gicW7ADOQtiaRheBe9s3GlZfNe1MhkbsFFVwZtvbuamm6bygx9MIZezKSsLdJPUHcflkUfeZ86cDVRVhbnsskkMGfL595L5JEbrha4/pJQ3CCG+vQfHsw//gwgENI487AjgCGj8AV7FWW9Vai9sSOdXQGigT4LgVaCPoFrzvAPbWodldKn0qThSJaKktrtSDmQa7A+8sCAaBL70kWP7W+NfmCbuIybLycgionRQZV7DnNhfmVYyZg/OwqdD7aRJjPnqV9n0+usY4TC+4mJ0nw8zlSJUWYke6ruY6od/kYmbl3mCyUKgtm0lkFCYv8Elu4uLnlBVr8KwsKJJ6SkvSCm7DZOi656eYD7ffZwRDuPatqd92Gs17OaF9SI1JxoaaLBKeV6ehGYr+BWTuBNiVuZIjjVUZqx+C19REVv/8x8e/+5PsbMqoodTjeqaOFLlvaYQ+w8XYPihpArefAyOvwSCH96afmfYKNfTkd1EZXMc1XbIRSNkSqPktCzvMJ9jOHanxy1f3kI43MO/jGckJVkLt6QYUVcOEhxXkrIET2YDTHOgWIWVK1v56lefoq0tj6hQGHm2g+u4+BRB6wrBmzf3/E6270q8y+jIeD+xYZVecvO11bBkK2gqNCa6z90bOzNiPp/KzJmLyedtbr756D73m8lYTJkyk1WrWvG+HoK//GUh9933Rc44Y9QnGPQnx8dWDwoh/l5oCQKAlPLZ3q9LKe/4LAa2D/+jUAfjCQH1aQeIF8eywFoHmXugbQx0TCcav4RztD/TbOd4wz6eiEhjSh0Fh1qld1Je9c4rHZAK2OtAqYDgeR86lHW5HJPFI3TISmx8gCBDES4qzcm/f+ytJBsaaF25so/O32eJg6+8El80iqKqKEKQ6+wk2dFK9OhxrF0xF7d3Z+CDT4bRh0GsGat+PXQ0MKgEItEg3YzTj4G0behloLY/TqgqocpKhJRdQnQgBFY67Yny6jqiQAoWO7umEEjbZoEzHoHELzMI6eCTOXxulpUVJ7BgxgxuGzyYh044gezSN7FDPtaeeDhv33ARC685n6aDD0AIQUjp5UKqmjfueOtuz3Hb4n8Qbu3EyORQLJtwczulG+rBddm2Y5u+bgwdWkom0zOGBesk7aKUYDFI00GaDko+jzAtFk46gacSnmfyve+9gmm6GIbG6geDvHx+mDdvNHjuIo3nLlY/XViwC3kb2jMwtAzeWgsLNkPED0ED0vkd3q4W0oJdIrzBoEZJiR8hBLmczUsvraOjo68I729/O4+VK1sJhQwiEV/BoEmuvPKFnQrtfpbYlZL3rcB8IcTg3k8KIcYIIe7/LAa1D//DiPwKMACn10MCReCuBWJ4RiwP+ech/yTf9v2Vbxl38rx1PPPtIxig1DNBXURAdP3gFMAPhECEQPGBUgnhm0Ap/dChNJkxNGwc+oqvZmWQUjZ8yFGQ6+zkX1deyaOnncazF1/Mg8cfz+rnnvvEU7KrqBg1ilPvu4/agw/GNk06Mi3EYg0snnEvj59zNrefN5WOtgKNQDfgm7fBFXewPBllQayEp9aEkRV+xp6lMulCSbTfLlxUUYgOHoxiGJ5x6gVV08i0tKCHQviKiqidMAE9GESoqie5pCjgOJ7h6hLt7QWhKEiglXKKogH8xcUen9Hvp9+ooRzW9hhv/OQnJLZuxcpkcBvXsN+g9TQedxiOqpIvCbPq/NNoO/dopo7oxUWzPdV4SnYzx5KMUTb3LVxF4GoqUlVwDA0tb2Kkcx+pD3rBBWOR0mvRkUzmiSfyzK45k0RlP0KJdkKpDnzZJO8ccxGt/Udxx38aOfzw+5k/v55gUCuUkkvyMZX2JQaxtb37Pe+InfHCPxbrWmHZNigpkO7XtexwDiHAMDSk9HKhoZCO60IyaZLN2rS0ZNiyJU59faLPsU89tRJNU/oI5vp8Gum0xRtvbN6Fwe05fGx4UEr5IyHE28CrQoirAR34DhAK28llAAAgAElEQVQB/vQZj28f9iK0d8Ijz8NbC6CiBL5yChwxsfCivdrjTskUBL8N2YdBduL9MAsGh607OWuS2eYk7rO+hisV7jC/yya5P9f4fo6UpldJh4Jn+NIFT0v1/nY/eqc9OlDO8ngQnSwWPR0GgyJJk/ByWqaEuRlYk/f8w8kB2PbDH9Hw7ruEq6oQQmDncrx1yy1EBw6keuzYTzmLH42KUaM48U9/4uWHf8eWK27yvJWCXEWidQmP//gyLrv7ee/Nqorc7xDeXW/irx5LRdW7HHVtHFUH14Hm5aBoENsqYDvujxoIeL6wbeMLhUgWqhW7S9Kl7PYwHdNEj0ZRdJ2BRxxB58aNxDZtItKvH7guuVgMM52mD8FHUdB8PlSfj9JEglguiBYAPRgEIajsWExRdhuO6Akn2rrByEWzWfz+IlprhiHMPHrGJDR9FO4bPq960MpBKg4nXwb+3eRENqxj0OYU4YxDOqjjL3hOmbBBKJ5hcuTwDz101KgK7rzzRH7/+/nMm7cVVVVIm36e+O5fqFy+BF8uTUv1UKxQFENKWpdtYYiTxbZdGoN+IlcegNmvlPz6JM6s9yFloqr04UX1hpSyQD/w1Ppd+TFNIPM2vLAC/JpXRZjZMbmpqoJhw0oJhw1WrGglGPSMjm139c0SBAIaiYTJnDkb+hReqB9RvGQYny/dd1ev9hbwEvAc8BfgJinlRCnlA5/ZyPZhr0IsDhfcAH9/Fto6YekauPpXnhEj+0+InQ7p33ll6PlnIHAOVHwAFStAGwp07fr67jBz0uBtazAhUpQrLVSwlTmZg1ifG4CVlbSvh1zCBjJ4ZiUPpAEVUr8GdyeERzcOzlZKNMF67UqKRYwQcVRMikULNj4mlF5Emw3n1MPXt8G1zXBdMxyz0eEHh51NfsTo7l2l5vcjFIUPnnjiM5zhvlj089s8pXafjvDrYGiQs2l5+W062xv7vNdfXIydjXPM9eA6GslGh1SL55AESiAQ3XFVdHI53FwO13HItLUhTa99SPd2fDtY8Tj18+ez+c03yXZ2ohkG+ViMXCyGlc163lav1VcRAjufx8pkmCgXYDuCXDzJoPg7TO78JyMan0ORtjfHheu5mg5IJqx7jcGBTkZWu0w+qJaiujo2jDkeMgkIROHcH8GJX9/9SS0qQzNtvjyrharmHNmwj0zYoLwpyZc/qKWY4o88/KCD+vHoo2dw1VUHM3x4KRHLQSzaQsuYCWwdOp58IIIRUgla7Rz18I1MXPILjhqxnP4/P5TyQ0sYVp1h0DFRQr86gcpDBnbrCu445RJwkYWmNu4ORIWPQM7eqcEC7+NpaEjh9+sMHVrC4MElWJbbXc2o656q/ODBUV54YW2fY7/61QOxbbebmgCQzVoUF/s5/PDdl2v7NPhYT0sI8WfgZOAfwP7AT4CrhBALpJQfXku5D/+n8PQcaG6Hmoqe50wT7nrE5tQDbyZkbPEYwAhw0pC5D9wO8E0B4zCwF/c6W88PUCCxpYtPeHkvaUvO1h5CVV1aNxfjJjKkW02qR4Mv0vXL9uNVJHaA9Tb4jveedtOQ+hWYr3jjEFEujF7Hc+nfIbL3Yzom99uXIZUypnW8TLGAxvxZtLulBIW3Qc06UD9wOA9Nv5SrH/5Nt3lVfT5PwPezgJTY2eUkt7yF0GrxlU3FbomD3vPzFEJ4JO7ODGa+52fXvGwZ6eZmDP96pDTJdfb0SO43Hja8BYFSyG5fZNnLK8q0t1M6fDiZ9nbyiURP65OdZOvtXA47l0Px+fD7/ZiplEeELlQ8dhVjuIV+Xo5lMTTUwYnp58lLhTApTHS0gjyU6/To9IlCE8ScP4zQVMpG7Y/QDFwHKs/8Dpz3KeXRqofAsPEUr1vEWc9apEM6ZJOEElm44de7fJpp0+p4/PEVjBlTxaZZi2nSFNzxA9GFS1l6K2P//GeKVm/Awof9zVOIOCnUTB5FVSizOvBHJca3DyX9zUbicXO7ae76Qyn818HtjjTAJ2PPeXAcSdJ0mLe6g9Cwck48+0BaM29jdWbJ522CQYN+/SJEo74++TuAa66ZzOzZG5g7d4s3CiHw+zUeeuhLfZT6Pw/sSvXgUuBaKWVXZu4rQohrgbeFEGdIKdd8dsPbh70F7yyF4HYNZQ0D3GSSLU0a+w/yQaFxoOcJWZD/F9jLvXCeKAXZwvY7Rg2T6/2/QRGSTjfKQ03TGVO6lM5chICVwKepSLeD2BZJ9WgFiHoxL2mCjHkq8F1I/Qzyr4KoKBjQDCLzQ74Y/CYvmv25OHUdGfxI4Nn8RGqUBqZqz7OGs1EKJWs+RSHtD9JWVEZDZX/6tXg5JCudZtCUKXt+YqVJ/IPzMGMvIQutzlvfK0EPBcl3JvoYLmm7qGE/5TVDADDTaV66+moCpaUEivIgN3TnlQQw5HBINELDkg+/vKJpHok5FqPygANoW7mSbEeHZ7g+ovzddTwFGNe2vSIMKZGui7+4GDOdRlEUfEVFCF3HaWtgkFqP5UrsQrC3Z+mVSBSEdNHyWXKhIlZPO40Bo/cDzaDJgUMCULcnGicIAZf+Dh6+BZb/m1BcQHEFXPYrqBq0y6cZP76aL395f5544gPKAxqVLy/DfWcdXx65js6Zd9NphUFRqI7avNVvJGWxRizhJxDQ0Q2FgJ2kobKYiooQ8bi53RSLbvV9XZFoQpJxCtW3u2iwNA22l6NUFHANDSkl/iIfOoLn732PwMFDGLyuidqSnh93Y2OSU04Zsd3xCi+/fD5vvrmJV1/dQGVliAsvHEdRkY/PG7uS05qxk+d+L4RYjEcu3js6g+3DZ4r+1bB4FX36LbsuOI5NabgNZNIzJL2NkjTxPKLNIDPsqMfgPRMSadIyRImI8a1BD9BildFmlWFYKZBZFEVi58Er6siCDBeq3iToBZqg2wa5V7ycmlzvXUcpB3zkkz/jisQ8cuiESHn8FCnY6g7gLcslRDOtciBhBRACw+9H2hbxZIayzk6sTIbiIUMYeeqpe3xe0/V3YMb+RS7uR1E1QFLcP0bFII2GjI7Mm92SR8KRDLz8dF4ULxAmQnhpJ2YmQ6SqCimKyKcaCBRnyHZ696EHJUdeBc98B1KFRsVKwQ4JRYDqGSwhhKe8sXCh5yV1eVo7S7YU3CKB53V1l8kXXlNUFT0QwB+NUjF6NOmmRtqb6nEczwNUdxbmki5CUVAUBWP0GMYdMoENjkbJ4vc4uWUDZw+rximbjLonWv6EovCN30MyBvkMlNZ4K/ouIpu1+OEPX+Ppp1dimg5FRT6mTx/B1VcfytyrL2Y1GRIijCMFQSyKMp3kdR8+IBDUSCZNMoqGu7GebduSdHVkse1ew3BBFS6acMlLDW+2PyanhffRqKogEjHo7Mz3STG6iuKdRlEIlIXRgzqaTyW1aCvmEYNpem9zYT8iqa2NcMUVO6ffTp06mKlTB+/yfH0W+MTag1LK14QQ/9v6Ofuwy/jSsfDw89DcBqoC4aDnWB03qZmq0npgZ2WvcY9TRQDoUsfoC28P6Skz5NFRhUlISxNKdSICGfwRFwQo3QWAWZAWoIHva56Rcn3gtIO7Ba8y0SOm4rQAOT6wJ9Ami/CT6QlFCdClRbOsokY0Y0lIOOBXoNJvUFpXx2Gjh2GX+Bk0ZQojTz0VX2T3OEG7ArP175gptWCwvBkxM0GOvirFnD8eSKKtmXwqierzoR1ZR/p7B5CnAQeH3P6diEnFRLYCUjDvL1Uc/s3NhMpchJAIRbD4YUnbWm9B9Pl08nkbUUjuy4LXpPh82NmsVyShKFjZbFfb2h0H7LqgKPijUcK1tbSvWoWUEqVQKWgXGkz6i4u9sGVRFEXTduy23H23kFeC1Nb1Q4+EOeK7VzKi0ua5q6+kbelShOswT1VZVlvLKTNmEK7aQ6oMkRLvsRt4++16TjzxITo784X59AoZXnhhLaedtj8148ez/sUXGRDJ0pzxsTXp5/R5D3Lfcd+mhjzZrE3GVUgHw4ReeI1IxEc6LbAsB0Xxii66p1y6ZB0NqSgQMiBtfuz4wmEdKSGfdykqMkgmzb4fowuiIoRW4F8JVUFVFSadsj9fPG4g69d3MHJkGcccM4RQaO/tCfipBHOllDsrB9uH/2Noi8F1t8KWBsj2on3oGgyvTrKxcT+G1HzwIUfnge08sF4QAlTp4hN5TGmgCht/Mke60aFiZCHaCKh9fkMeg5/cDDBfBjUC2jiQecDnrdDSAnKAhS7yO726QOInx1B1Pevskd2a8lEVrh0QZv8bf0w/HfTPUPVJOlmQom/kR0KkWmHaz36CdHUybW20D3P5YGIHEaXH19UCkuaLh1J282ZUVBx7AE9e0UbFyByG36F5JSQbJYoi6N+/GNN0SCZzZNKm10HLdT21eE3DCIW8svNCuE8K8aGSToqiEB04EMc0vZ5ehVwUQpCPx4kH+vHexjLUcJQhrQ3UDR6Cs3Ytpi37fA4OKi4qttAZedppjD7rTPoddBALZsygbdFCIrW13cUwyYYG5v3mNxz/hz98onluX7eOF+94hIY1mxgzaQiHXnI+xYMH7/LxCxY0MG3a38hkvDlxXchmPV2+lpY099yzgJ/ceiXmzAcRrU0MDOZxXBj6zCNcFA7z9NRLSUgXn5bn4Gef4tHnIRQOEgzq5HKCQECQSOTRNIVwUMfNmsSlhlrmQy0LY66PeUUWBWwfuVUUsG3vibKyAKWlATZu7MT7GCWmoWGF/fhrirrnVLoS15GMqyvmS/t9vsUUnwb7VN734WPxh7/Bgvc9sr3qpS9wpRfSeG7eBAaUf5Mbv3Iz0WgKA7Pbm+nBR1c+CQF+8vgLvCwrCFRAxyaI1oIR8nRSd0QO3A2gHlrIZeleGNK16eGHwShlOf2URra5NYTwihikFNjoHKvPob8eYJ2ADgeODUKpDre2e6G0oIDry+CEPe9keePQj8cI34+Z7skp+MJ5WlaHGPKlQwmUejy0Z3kag75NGf3+CIH+VcSNFRhbcwghMMKldG5xCJSXo/rjlA3IccLYADVDB/DPWSu83JGQpG0FhEK4upp8IoGvuJhMSwuiuATHF0BJduLGYii63rdTsqIQ6d+fs556iplTpiCEQOlSnpeSpeZw5ucOR5V+RAz+k61gbHAjp44xaFy1Gum4iLwDCFIiSkItJaJblJ3zHfpN9NqSrJk1i0Bpac/iCmgVRaz996tMznVQ5N+Rm2dlMqx/9VUa575FUXGIEaefSWTkAQAsf2MBl555L82ZIKpajPx3G194/BpueeYXVOzft0mnLWF2Cl5Jg1/AKRGY7JdcccUL3Qarz3Uth2zO5o3VnXyQKEZ5bC4lj9xD3avP0r9xE7mxJ3LFCSdT+7tfsiWWY3FTJc82DMCyIZnMU1TkQ1ck+1XkWZYyqdQzZC0/sWAJZG3crES25YmOria+vMkTlnR7KZfQExa0bZeamgjnnDOKV1/dSHV1mJaWNLoucHMWbmkIkbdxQwbYLtmODOVHDeMrwz6jL/dnhH1Gax8+Eo4DL82FvOX97e3oenZ6UioYo9JcJu5hc2IwRUqcC42/c4HxIIr4ZH2B9IAnpRbdpdZKJlgrQK32cmf46CoZ9qCgCJeHQuczPfUsKRkuvCIYpqxjotHKG+JqRhjQ7EBAhXezUIGLm8xjBgx+3KpSo8NY/4cM4VOgZNTNNL31MnpgW3dBQzauISI3dBssgCBBHPoumhJJqKqSY276JU3/+jeuZTH0C1+geMgQmpctQw8G6TdxIuqb/4DZf+O0o/uxZH2aTdkIlVX9GTB5MoOPOooNc+Yw7ze/oaPfEDqj5Qgkhi9ERSyGdCWBkhJvbODl0Gpq0AMBzFQKX3ExakHIN+X4mN8xGb+bpijsRyLJ5HMszdcxZJxGeT8LuSWODOmYG/JYuWpKyNKp9qehIcnEiV331QMbm6bEZjIbG6Ajwx/Pn0L/Qw7hjHNvIdqvPwC5eJxZF19M5+K3UTNxXAlL7/wDJ119EVXX/oGrvv4YzbkgxSFPa9F2VV7cNphRP7mbbz9+e/e1Ns6dy1Vr4iyrqsOvCmRRJTMyguCsJTQubNjp5yclmJZLamglo1RQK8qRV/2Aty+/gekhh3NqdObP38rtDceSTpvUNyXRfQpqPo/rSjKZPAeEmulsgmNK2/hCxSY2ZKI82jaWBhFF5m1kyiTemgS9IGXhU3Dz3ndB1xUMQ0FKyOVsGhoS3HXXAgxDpaoqxODBxWSzNiUlfn70i2n89MEVrFywDSVgMPqccfz1qglU/I9Zgf+x4e7DfwOSnlDE9mmOaGWCFw84iYhMUCWasNC5PX81Jj6+4buXTLsXSgmV7yKrf7dHBtAJTqLw75309qKMg7T3WV80mr9bV7JZTqTeLWezHM7z8jsgPbX5cT5YngfeWMN7f3sHM55D9WmUfGks/7hoPGNr9nycUDHKqJqymMZ37yC5aTa2XUnFuG+x//i+gr6jOYD1rMPGRkPzDAJpakU/ho89guFj+74/fNxxPf84/msw7avIdavpvPU2csuWQWMjsQ0bGPPVrzLh4ot55e57iBWVoll5wk31+DvbvN5cjk02FvOUMIRA0TS0089jZofENHz4erUyqTfLQIIqpMc4QEH367iuwub/uFR+70Dc372ByDvog0OE1iWRtsLGqmkMHtzDkRo5fToL77kHrbaW5uQWMks2gOlA1IdY3U79que5/83lTDvr25QMG0bj4sV0Ll1AxE1BURAQ5HImb854kIOqD2RDg0VJiB7enSJRVZVX5nXQJZy6Zd487rn9XpZfdCOlmTjxpMvWpzejKSppCVJVPcb2zhA0qLvoYNRe+dJKXeGlrMIPJLS1ZZBSMnBglM7OPOm0iWGo5HI2mnTImZJDK9s5vXoVwsrzdPN+VChJWv0lWPFCLkviEYiB3nsXVYVQyEdnZ7b7/hQFEok8mYzJ5MkDuPzyA5g+fQS1tUWcMm0IKdNl7ttbuf3Xczn30QWMGlXB9dcfxqRJuyKh8t/HPqO1Dx8JVYUTj4B7GiGXL3hX4BXnCaj9wlYcR0WXNopf4idPhWjhsS0nUnH3fbSt9AopSgfBUddBWV3vs2v07k78yaHh5c1cvDxW73MW8kUyQEixuaL2ODCmkHPhiQTMSnrv+loxHOSHM2ZtYfMf3yAf8kNpGGnapB96j9cNBa4dtwfGuiNUX5j+U26EKTd+6Htq6ccUpvIf5mFiInGppoZpHPehx/SG7bg8e9U1tK1e7XFsSktpXrKE57/xDc5++mliEw8nsmUDOA6BRAwZDHkFMraFLsDOZgnU1rJ6xHh+c+T5WEQ46aBjqFz7PhW5NLpro+Y8zUm1oNAOoIb8kJRoPok2ooT8NVNwZm9FbU+QG92fJS1TGH3kJA44oLJ7rGPOP5+GBQtoWLqIzMZNnsEK6IhBJQhdQ2ZzJLZu4bUf/5hQZSXJhgbCIkN9SS1JI0TQzlFqtrCtNU7jz25DyGORrkT0amWi4GLqPWGx9+66i60jJyFUlcTiVjbe8T7kHexCtblQgZ1wdnVdYew9ZxMdVtHneRXvW2hKGDGizLumojB+fDXt7RkSiTy5nM2PTjYZ9/4DWP5ipBTEEwqbslGq9BSk8l7i2PzwdjH5vEsslsGyZCFMqJDJ2LiuJJ+XvPnmZubN28rvfz+f6dNHcOONU3j1laV8/Zuzka6Doips2tTBvHlbePLJs5g8ee/Pbe0zWvvwsbjmIli8Ev69ELK9tGMNI0/R0XEUQ6JjIxEI06Jo3Qaqf/0gTXmFaLkXjOush+e/D+fMBF+46wx7wmCBZ6i2P2cXIVMCFggH1NGgex6JX4Hzi71HF0wJzY8tImfoKAHdW3YNDas4yOonlmBfPQZthy7Nnx9GcwDDGUEHHfjxUcyuV78tnjmTTa+/7uVBpCRRX48eClHqOGyZN4813/g+435yBUbzNoTrIlIphCJI1u3HUGGS6+xk8Rlf54WvfBcjFMIQMO+qn3PMzZdhJmKolo3wuegZsLQQfrwNTspUQToc/L0xVE+oQx5ksPWYJJ12nNicKOeIwzjvrOGseuYZ0q2t1E2bRunQoUy/5x5WLniVp847D1kZRJSEEIrX8kT6VERDCqEECVdV0b5hA5tQ6RxYgyJdkskcaR3UQ2ohFkduSJHIuhSVhlE0HddxMS3ByWf2lHV3rFtHtG4cdjJHw53vg+mCUQjHWQ4yZ6ProiB55OWTgkGN+fMv4cWaah5PQE2vr0aHCwf6IKx4RuvYY+t45ZX1CCHYsiVOKmUSCunM3xpihKOjSTCEzaBIjohusyZThu0IsD+6v5mUPQUY3kcrC4+e113XpbExySuvrGfrlmbmzV2FQCEY7Kn4jcfhxz9+nVdfvWCXv1P/LewzWvvwsSgvgVl3wZvvwTOvwaIV0CwaqTv/fTYFB5MmxDBlHWULFtLv9kdQ2+IYDS2kIzahMOh+CJZAshU2zoP9jvfjVRXCLsvT7Da6yJg6qMO8asKiX39kjNIQoDcncP16dzGfBFRdxUzmWJ+wGFn6+ZMpe8PAoJrq3TpGSsnrP/pRd58uhEARAiuVIr5lC+mWFsZUV9F42HEMfOKvqFlPxV0iiK5fiVVdRbD/AN495Hj0UAghPAPfMmAoj93+NIMWzaUs1kLTsNGErCLU7/2dhtYccdsjckfKJS+9WcLZx/soqlCpG1ZMGo3r9jsN991tzJw0jkxboW+aEBx47rmcMmMGIw+ehm/cILLrG3pVvBVquG2JMrIMqUC+/2DUVSvwZ1NITSV8djli+kG4hWjAlOckr//aT1unhS+o4UiVA8dV8/Vbzu+eo5K6OkYumccT8VFevqgrfyQEwtCQORvX9SrzamsjSAlf//pEDjywmhrb069stHtiByEFvl/edUuCn/3sGIYPL+X661/FdSUjRpRSXR1hydY0DS1HcvfwZyjR02zNRUhbgpSj75J4k2GoOI4nxdT1WfetKhQIIbwKQtNl1cqNdMRUIhHpcVa8SUVXTZYvb9nJFfY+7DNa+7BL0HU49jDvsSgjubw+TpPdDz8mDdSyblslE3/7IE3DDmDJV88iVjOYcLyVg17+Gyd2PO6RSl3IdAi8OIuksx4+eA7aN0LV/jDqFAhXfNxIdgcFYqbb7HlYouxjj6gaXUXina1IXwgXT69ezVlQEcYM7L3clY/Cwpn3kY15bd69ldBTtEAI8okETUuWUDfnD6jJLHqbx0KWQgFVRbgOycZGRDBM08ix3bLDmcIm3QqGWX/ECSRUz5a0Spg562puOudhKhRJ7YASsr4cm5e28cC1TVz49yKEgJHsR6VZzp+/MpVse3t3zsx1HJY9/DD9Dj2UCRdfzJTLrmH2d67BtS2EX/eYDIk8zrUH037hODokpF6pRPtJO1oygXpYKeK0odCYQsEzWkOnBylWW9h0a4bqL5zGyecexgmnjMLv71n+Jl1+Oa9cey39N86lVYY9g2W7hWq9gkKFrjBpUi2KIjBzed6Zv5nq6hCnnrofD/dTeSkFy/IwRIdTwpJKaYI0oOBdzZy5hGzWRtcVYrE8FRVhKqsjNGX3o8GKUqKmeLRhNAHVZWg0y7qESt75cFKxqnoCtyUlAVpaUmSz9g49uVTVE1xWVYV83sZQU6iqH8ehm9gMCq7rEA7rO7vMXod9Rmsfdhu3drgYZAjrNjkJo+RagvNep6Wsjtcv+gGGk6OouRHH7+eNs69De7uI4xfdD4pK5QgAk5ZV8Pz14Fig+aHxfVj5Apx2G0T776mR+gAVRC046yF+MYRvhMCXP/SI8y6dyM0L6jFiadSwDydr4Zo2/b91JHW+z5CwtQfh4rKJjaxjLba0WdR/rhdSc1xwuuJGgOui+HxsnDOHkooK7OQG4lIiFQXFdQqOhtdlNxTyMUCxqJcGAdGXSt5NixaeSMlzL66jqDjU3eXXwMeISh9bV3USXVfHlOGj6c8Atr47j1RTE3qgpzuuoqoIIVg4YwYTLr6YydO+gv5Tm9d/9TNymzsQEQ33mxMQl4yDguCR77RyrAPPYH32mwxSH8bIrPc85S71jrYspSdFGfTw2ww/7gpWrkuw7s53+cIXhnLggZUIIRg8dSoHfv8XtF36GphWnzxSlw2orAwxqMTipRdWYrg5Ngt4761VvPH6eO6482TOjArOlBLefIzc6zP5z4Eh1o4biBUazF+/qbNts1mo9lOJx3O8/34zEyfWoto5smXD8I0cxuqtwygt9RE2XGrDbfy7pZpcfuf+lt+voSiCgQOjBAIqUkrWrYth2xJV9WgdXe3SFAWKi/1oKAwZmGXjliBCuKiqx+OyHYVLLv5sOxjsKfz3AvT78D8JW8JqUyGq2PiFjQAMYVGb2Mi6qSeiKy6hvIVmqGi5HKFYO3MnX0Z7Zy21Y11qx+mAwrw/e+cLV4A/ApEKMNOwYI/2DbBAOxC0SlCrQCmB9K3gbt8RuQeXTSjnsD+ehnHwIFxFYIyspPaWk7nplCGE/gd+LRYWT/EEz/I0a1nLEhZjHl6FnDLAI9n5VNAKkj5C0O+QQzxVDE1DWBZCCFRVRagqms9HJOKnpkjgb13PvHumcd3sn2Ple3KIAujSMXZkQZYrltoh9+cTfqJKhOGdYxjAQASCfNKrghHbySgJRcFM9PRzmnT6BXzv3dVcvnAJJf/5HuLSCWC5YDoI00GxwRkSJd2wjI5sgJQbwerVyqOLz/SeO4ab/1LPP/+5kscfX8EllzzLvfcu6n7fqlQlFcPqKC8P7nRut21L8o/H1xBVs4SDKiG/wEg0MGfWQhYWSuIbHpvJj26YxdUTx/HUfiPYlhC8/+xGGjvaCdZ4+VYhBD6fRi5n09GRQbqSEcVJdJ/OfpUmacubD58KY4YHdxrRFkgiusmgSkE+b3P++eNYvPhy1q69isMP74/Pp3XLMhmGimF4Ru3oowfw8N3vMXhgDssSJFMKlgSI9NAAACAASURBVCWYfpLB9d8/8kO+VXsX9nla+7BbUIGoIsgzBL+7kiIFslKjY9xY4rlafKaDTzEgXIqdyyGyOWQwzH7X3cjRh/0VxchgZ9bQumbHUGCgGLYu2FMjVUDUgbDBWoZXjFEOwgB7JRg711aLqPDE4WU8eeBxvJmBchXOLoJDdr6O7VWI0cGTPE4TTSgopMngYIMpce86EaY8gOjMIVWBgoIvEGbcRRfx7h1e8/FgeTmJ+npyho/GkWPRVcH4LQsw05KxowZQEi3he6ueZoxi8quTbmFZruCwCch5aSZOC8NJRw7gvdc3dOsSApim57l1VdIBDDrySFTDwDZNtIKuoCyELgcfc0yfe2tctIgX7riJ9usGIvuFEI5EMQw0v59cTiG+1kTtXMmq9/xMOiNCLOGn2m3GEBYy6sOpz7M8NppBI6KoqmcUbNvlr39dxEknDad//yLi8TyKolBaGqKjI0evLhyoqsB1XBIYmMIggIsQCrrfRyLWydKFWxheF+VrNywhN6o/+Rd0Ns8TWDkDQ3Wxsw7hURaBkEEm5YUIHUfS3JzmsjNHUpt5FhyH8w7s4OX1UWIZQbGu0JZSujmRXf/fn+UcyrtoCUmF7mfy8afwhavOxfBrDBhQzK9uu5iZf1/Junn/Itm0Dde1GFQX4qzLjuSssydiWCbzX3ySuW+X0dquMemgkew/6Yc7bB72VuwzWvuwWxACLojC7bEKKlXQ3U3o0qZ9wpEMnf8+K1Q/braz0LhQEB09GqWmP1P6+dDifwa7AVUPovtTuFZfeSbHBP9HtzTaDjo7rUPGKKjKd4K9Dc/UCpCbe/4GXMdh9axZrHjsMcxUiiHTpjHu/POJhtdwifY2l0Q6PE/NdyRQ84nm6/OCRPIqs0mRQkFBQ8PBxcTFCfoQqsR+6hzE/UsRrQns/mWcddZvGLj/CBb+5S9Y2SzBigo2TzqSVy7/Ca6moyiC16wc5/31B4wdG8ZFwbYkU1+9h9pcAP+FV/KjbBHvZL3CgwujcGMFOBXDePzxD7rbs1uWi2U5fPe7hxKN9jC0jVCIo265hTk33EDeND0DJyWR2lqm/PCH3e/bOGcOT5xzDtlMAnnlV0AJI6WLY5qk4zrbVjv4SyS5TQ7rHnA4cIJKZAiYopqwlsCwJOLOBL7Kmm6DBaBpXrflhQsb6N+/iMmT+/PQQ8vQda9Dr8f19rw0RRG4rgApaMwFqQsXvHUhPG/TcHj+6WU0pTRaPvCRWw6ZNi885zqern3etigt0clnPb1Bw1C4/vrD+MbXJ8KsBnjtIYbjcvfhHfxh2VBWMJJEm4OUBWUyCf3ZyhTmkSFAztVQcwYr//UKgXCAqTf/gut/D2+966I0m0jtKHyD4JdDb+aY8ByQr4DxHviuIRK8kBP7bQGlCtRdYvHvNRDbt8j+v4BJkybJBQv22JZ9H7aDI2FGDB6Ke3JOupB8vURwiGZy7poU2c44RY6JVlVDNhThilLBpcUS2qeCtQAI8e7MTpb8wyZU4Uk0uTak2uDI78D+J3309dvcUu7Kf4vX7aMJkeYc41HONf6BJrrIxS4QxjNogUIrE1lQmpcQuhEiNzD3N7ey4vHH8RUVoWgaVmoTR12zjbojTIToqj5UQFRC6BoIX/tZMKT3CBLEeYSHcXGIEUMr7EfTMosjBdgg1yURrWlk/yLWNx3KhWPO4KQIbJgzhzduuolWqXLXVb9Fz2UxrDxFuoMbDiENH0+/fANy5TLyqSQBTfLsaoOMVsS5s2bR/5BDdhhPOm3y3HNreOWlVXRsXsyA9L8ZWNbJkC+fxAnn30C41+5k87//zdu33UaqqYnBU6dyyNVXdwvjdm7axMypU0nU13sah5Uh3EdPhfHVWGloXOtHCtBCCi9cGSa7FvyGZOrxHVgTAtRVWjRuCpMqGce6V7IMtfoKzzY1pfjFL47hC18YiutKbrjhVf71rzWsWdOBZRW0U9RuiWEQEqGqVA9SqYq1kDIVdGEzb+3P+N3v3+Ghu14jqzmkHL8nTKwIXAfMrECoAn9ARZMapukycGCUhQu/4YnTSgn1a2DV22D44YAjsaNV3Hzz6/zqV/MAievCCfJf1NBAliACL09VXGQwuErQ/5ev8aO/hKnRNiLqV4KukXX8mK7OK4eehT/TAT9+GGpO+0y+g7sDIcRCKeWkT3LsPk9rH3YbqoArSuGiYmh3oEIV+BUAgwdHlXJ7RynvZCEtYZjuUGS+Rartj4TderyAUo6J52lkYzZrX8VzfiSMPwf2O/Gjrx2XRVyQfoAWWUmZaCeHjz/mv8sGt46fBH5Jj/JsHpTKggp8gh4ul4D0r0hu/hsrn8gSrhqGokXAjbPfMduoOSDXywMsyEHJdsjcDcZ48E3b4/O5JyALy2qQEJ104uKioKBIHQcLqWrkhw6FoQoZsx/rOJlYodagbto0+h18MLf+czYEw0Q1AYQwyBCUWZr0CK+r1UxOzvW8E0XB1CNYqTT/PO88vr1mzQ6hpVDI4Mwvj6DjmW8TS6xHLQ3jWJJVdz/MtqWLufyO5zCE52YPmjLlQ3uVLX3wQbJtbd0VfKIljfbVZ1B+MZXU1APwFbmkO1TevtEluypNud/k2PB6hjbYZJasJeHo1CJwxD9pP/IsmpL7UR3zNByTyTzBoMZhh3mEWkUR/PKX0zjxxGHcfvs7zJq1pqeEXAEtaKCX+zDbsrS5IYQToFa0ccOfDuE/5W8R+s5qjjxCZd3MDEte8qEYBRV9VaD6BL6gxE2rBKMGdXVhHMflssueZ+vWBIoiOPXUkVx66bndCusacOGF47nzzvfo7PQoIkHS2IVluytvqOgqQkhmz3MxdBCpju6fQUDNkbKDfJAeyQRlPtS/u1cYrU+DfUZrHz4xgor36I3Rfs+grWiEvIQWs4HfZSt5RLue+yO/o5RGIItq+Jh6DRx0EaTbIFIN/qKPv+aL5gm0yEpqlCbIgdFu4c818HzRKVyq/o1+A5sBHUTY86ykQl/ysWTtBj8P3V5MbkMngc719B9QQ8jfxoCDLDTDq2hUjd4elQMyAbkn9lqjVUSUKFGSpKikilZasbERwiWVrySRGYdAIWP1I5kbgCJUDuylpeiLRCidMAmxJU3XipfFj7/Ap8s6Ln7doy+92xLAkQI9GCTV1ETz++9TPXbHyrOV814mtn4DRm1p9zmVWh+pd1exZMXLHHzA9I+9r4b33utp8S4Ewyskk2szaDNeZMafssxOjyLZ4CAzFieXbeO6ukUIRVBsJaFcZbnSjzlyGNgOk954jFfOvJnWlzyjFQzq3Hrr8YTDPTFqTVM4+ughHHxwP66//hX+9vBy8j6DQFkAX1kIsyNN9cUjcQ7uxyFbVnDT1FLePnQLDlupKi5mczTG+Bv9OCUuKx4VCEPx2oMMqcQqD1ItVA4IaUhX8s479TQ3pxg7tgYpJQ88sIz332/h3nund+cChw0r5cknz+K00/5BKmWzhUGMZzGO4iMS8eG4LhVFCr5omJKKEO4HgC/Uh+AlEfiVQgKyeOiufaH2YuwzWvuwRyEl/LzVW6Kq1Ty4m0A1qHcqeTh/PN/W3wHZjNdfC4Kl3mNX4AJL3QMBST5nYGyxvACeKlFzDpv/PYh+R8dghAoiCG4jPSRmD8s+qOKb3z8FfybJBGszLa0Gbe3tTJyYIVzhonUv5JK+TSu7wot7JwSC/8fefcfZVVULHP/t026/0/tkJnXSCyQhtNCL9I5KUVBEKaIC8hSsID6e8sCKKMqjVxEIIIiQgPSQkJBGep/eb7+n7ffHmRQkQCRlJsn5fj4XJreeezK56+69117raI7jWWaQJ0cBcRxcqqnh/cRpvJ7SCfZVtMpKOC7iVWzY2tENg7itfS1mohNd03ARtDoRHGEwZu182jMqSxMRmtJ9H/J9Swt2Lse2bFz+ft99tnwB8D6MBRvWLNiuoBUsKvJ6dek61WGLI4ZC1vLaS03Ir+SBNQ0YiokIKXxn6DwStgGOg6rqKEaACaKF5U4ZG7RCDE1hvLWcq391LooimDSpEsP46D6otrY05533BCtWdKLEg7hCJWdLlESe6pPHMezSg2kTKgccO5HO0lewpY3d1EXb+nVUWiaZjMqki8tY8qCKm1Mw/+sU3IZKHAGdIZWFHWkq73uTbNamvr6gr1agSSyms2BBK/PmtbD//lvWUI8+eijt7ddx661vcc8dKnb3GkqVNK7lUlGsE9JsDv3+95lQpTJjFlixweht68Cx6XRKqQq2MsqZD7VFMOz8Tz3nA50ftHw7VacD6ywoV2Fz40eZpIAML+dq+abW10L3P6yE4QLdSgHDlVW8iILa4SCFQKpe+q+jqVSbjfCiCSPHg7sElDpwV7H1rqJf/XkaipBEqlVSvaUUJLvIiCATvpBGKN5U0JY2KJuOUQUiYBz32U/MblBGGedxAWtZQ4YMFVRQJao5o0wwPezVWZTASVE4NvrR5bmGgOCK0dX8MRgk3eMl0wTicb5dHyM1/ET+NesujKi+OZXazucJFRdTPm7cNo+nuHZI34tsCVyb1tBLqgdv13sac9ZZLHrkEQAm1gosR2K73tOWFuZo0NewzB7GuFgnQlHIuQEqA90oSFwESBguOtggCylScozpfIeylhjpcYfhqgpPP72Ue+6ZT3t7hsmTq7jiigO48cZXefvtjaiq4lWUKIoghpdSeNGBjDiwDlsCDhwXheW0kW1pp3fVatBVlIhGKGOjqd2U1kZpPPRYxOhKlPYkQQ1CpqCntoLslKHwbiOm6fDyy2s2V7QwDJU5cxo/FLQAgkGdH/zgMK677hDefPk01v3jGYzWpVSNHMrYc8+lbMwYAL79Jfjtg0Fk8UHQsYZS0cRt9d9FGTsKLrwflF3QqmA38xMxfDtVyoWj10KJCippMP8FuPTKGIPFOu6Lbqpttj1FarZ6XhGmW42jmHB++iEyG0MUW104QqUzWMYRbW9y27vXe+Xaf1QDSjfok8GcyaYMQylhyucupawsjVQ0FufGMHHDKxxTM5szf5ggoEvKGiSqtnW9NwHEIXgiFP7ZG8HtxaSEhXmYmfbWLo+OwJgAmKkUfznkELpWrtyc5aeFw5x0xx2MPeecbT6XmUnz27Onk+voQiuLIyTY7b3oDZVcdv9LRJWP7+MkpWTx44/z4jXXkG5txbVtPj9Joiteaj0SNsSqmL8kRTsVdMQH852a97AVlSLFJGfZmEaQuGozV9aQdDSOYCWZmnoIhpFC8FDkBJ54pYbKwiDBoEZ3dxYhYOHCNgIBtW/NSGAGNJIBA+2ywxh72lgUIbikEC4tglnuP3lt+ZO4PTmvyzAgFAGGRN78Dvefew9h26QsorC2J0CPE0aGwjiaQuG3HqGlJbV5EzdIbFsydGgRK1de9Zn/Drt6YOEKiAQlkyqb0UL6zi41s8P8RAzfgBFV4OgovJiCSpFGILClQkpG+WLgka3u+Z99WfL2A2mEtSR3hr/O7cmrebdwKoY0OXft41y1eja41RC3IHYV5O7zmkKKGMiuzc9TWpzBzCsEQg4jgqtYP2x/3h9fwYnxmdQ1DEM4a8Ftx+v/4II2GWLXQ+BYb4/XXk4ImBD0LlszolEunTOHhQ89xOp//pNoVRX7f+1rlDY0fOxzGeEIF971GE/eei0dr88DRVD4uSmcevV/f2LAAnjn17/mrdtuI9fTQyAWI59K8X6jSTQAIR0qYlCTaGa5rVKrN5LTAgTdNEFVxUGnfMhgujduRNgWjT0mpxaupaOkFjNejipACIUzFtzPM7FvEozE0QSUlUVoXtVIkdvFYNmNNKHDjtBplhFO6gQfnc0NXx3L1DAM6qt4NDIxhFcsGzcegJQFugIxA+X51QTbE5RURinLJdCsPOPLbHKDhpMXBnlDY3XeRkqJqip95ZcEui5obk4xZ04TU6Z8tlT04kI4fCp4X7j2rHT27eEHLd9O970S6LZhTkaguqW4SC4y7udz+guf+TkD0kICWTVMBa38OP5Dgo9ZZKNBykQawy2HbCGc/V2IngXWc+B2gXTZvJ9LwEXnzueXfziYUi2DqtsUOu10NRmoRXGEEgFl7JYXdVoh/nMIHL6jp2SvoOo6k778ZSZ9+cvb/ZjymuF8/fanyJhJHOES1eMIxCc+JtPRwaKHH0a6LpphIBFkHYO328IoikJENakK5Ti2weHAWocXl+UoS63hpXbJiWMl4bhBImHiBotZUDiNww8pIbL0CdaVVxF0bUBi5x1C0maysYYlThVlGujS5OLgs/xRGY3TV2y3Sk8QtvN8YNZwZGWIMws+fOxKaDDmrz5AP7cOhhchUxY8sxbn0WWIaXUc3fgus8onUaEHoHIowVCUbtubon20JOwdZ9/UYCCgEg7rZLMWGzf2fuagtbfzg5Zvp4ur8IdqWJ1eS0f3jQwViyjVMuAWAT3AJ7db2BYNC1U66NIkpwR4cNK5pJJFnDrz7xi5VoqCKbTTfgSHf94bLsR/Db1XsaXPluesUz+gOxXiD/fsT3NrHMvROey4dnpio8FZDUrfNIrsAK0ejIN2xinZ54WN7W/p3rVqFSgKWjCIlU7Tkg6QdzTyBFBclyRRUuk0gfVppISEGqIsYKNG4zzVVceKpirc0kE0hRvY0BbghHWzubJG6duh6yWCKLo3FWo54PaNmgan3meKsYI/K6NI2QHCqoUjBEHFJkqGH//4iI8c6wJbY83BX2bk92/EjQeRgSCiN4PARbnoG1wz/jw+aIJmpy/Y2VCjwZXF0HpEPRs29BKNbhnW2raDEIJDDqnbgbO9d/ODlm+XGRrej6F5A/Jt4BpeKwSp81mClgJU2u2sDtRzr7yAB7u+THh0hldHnIbIWZRGA/xl6P7EN30R1hqg+FnoOA7s1/CSKVxQFDJZjTXrinFcBYTgpWcqaGwZzeynx6A5LwASjBMgetU+MSU40ETKypC2Tayqip62LmzLwSKIKpy+ChSSpBthaY9CfPwUDhRrGBfvIq8EyGSbOZRG7u0KYJcfRFVU8PqSYi4plATNLFLVvHqZ0kKJB3lt1SDqKmw6e/M0tK9ALZVcXv8ud6ybSo8dQFMkhnD4Su277Lef1xLGdWVfRQuVkBCsPfKrRAt0Kh/9PUpzF86kWtZecAmfG/oFynXBo4NgVhrWWDBUhyMjXj+3H/zgMGbMWE5PTw5dV3FdF8eRfPWr+1NWFunfv4QBzE/E8O1abhoSP/TWmHCBSN8ak9132RRlHD7cuHFTZYsPy8ggx6ReIkyWkDBBFAIOTUzmW6VhvvTvZaByL0L3qWxKxujp1ak/4NsoQmIYLg4qtqthZkPcdNNRXHP1gd7jBmjli32BlJK/X3EFjbNn05wJ0vbBCmxUJN7oy4jFcHNZAtEwQ8YN5+T006RsFVtqtLilaJpGgZbhN4Er6AkNIrFuHYeLt7lk4lJ69LDXmkVR+eNBl5PpGsG6F9Yxb14zJ5Yu53uVz9NlBjFRWZwoQyhwfOkq1hVP4Ji/vsh99y3gvvvmk0iYjBhRzJXfOYgfV9RgSShQXVQlS9oxSLk6T9RC9ad0+1i7tocf/nAmr7++gcLCAJdfPpVLLtl/8z6tvdWOJGL4Qcu3e0gT7BXeJXULoICzCim7kEhAQ6hDEW4K6XaRT6roIRtV//DeqA+cUXwlfTelSl/TQEKgT6BHljMmAH/892UAKaHzaLDeBCQPP9XAJdecQjBoowhAUby9S9kw48dX8+Zbl+zqM+H7BLmeHoTXV4PXf/ELFj7/CrMWOAyRq3CjZaiBAEiJSHei7nc8R3c+zqiiLGlbwxUqzXYxg4JJakMpupQSutUSHug5GJnPc+uRq1geLmdewRDaoqUcIRNM+MbN5E2Hgw76M0OqDK7L/y/hbDvddhhFQIGWwdAcAj95gmcXhbn77nmUlIQJBFR6e/OYpsMNfzyN3xeV0ds3gWAocGMpHBH95Pe6L/OzB30DnzBAH+utGQkXKYrpUUfQSy+qtFmZH8SfzavJbrQp+ftTjHvpr8TsDONOgylf8soHAhSKHhxUXKl5QQcd3A5Myqna1m+zEFB4J3RfAPYy3ltQSS6vY5peZ1pDd4iELVyZJ2QsB6cZ1IFdHHdv1LJgATOvv56etWvRgkHqpk/nsBtuYPr3v0/bDc/T/MCvKbGaEGYe13ZoKxjHdGspwsn39bWUKNJmSLCb8mAWiUJSieM4Ll8Nv4By3jcRLS2MTDQysnc9uUGTeFI7mb+d9wuCxUUYhkpPTuOO0OWc4DxN0inizfyhdGcNLr92PIceMJWHrr+P8vIIuu5t5CssDNLenuatRxfw3M+PZmHOG8+PD0BozyiYvkfyg5Zv91LLIXg2VvY+0sJEFzqvZA/mF5nvMMpZzDmBByn6QpKlx5+O9pu/Me+RJI4NB13qPbxKaeFA7U163GKO1WehoPG2M41WhnNO/GPWn7QGKH2F119+hJffmIeqSlxXQcElb6p9bR8El5y3HBLXQuED/vTgbvT+/ffz/JVX4rouiqqih8OsnTWLZFMTZz/8MD+65XRuK69g5mOvouV6iERDXD1sLp1vvUWvDVqZQBMKpiuoCqaQKOSFTo9loKoKg+vKKDEXw43PQtt6EqbKHy//JcfnvsNEVSDbXcaphdy0/gw21A3h+9nf0ZkpxnUsBg0q4voZQS4mg+tKdDNJPtlJ29h6sqWlBJtDLF/TjSZgv9Cnv1ffjvOnB327j5SwegE4Ju/XPEks/3cUF87qfZAjtFf5qv0r8qaGKXVCAQu1o5cPbm6itwkufBSMvn29WRmg3a0gQQEg0LEQwVNpKPnvTww23/jGMyya9wqOI5m7IIrriM1Vhs4/aw333tGFoA0KHwRtxG45Jfu61oULefSMM0i3taGHQkgpcSwLIxwmWl3NyXfeSfXkyQBksxbpV5+l5JmfIew8C99bxVsrLQYXu4yrUkFAadDGRkWfcCi2GiQQ0FCcvsruP38RgGdue4hhz11PVo/jCK8DQMxNsDxTwo/lT1nRM4WwkWfI0EIqKqKYFvQkXNRVf0JGu1l6+TnYmgaOi62oVPSmWXDBKOKGP7zaXnv89KAQ4pfAKXj5yauAi6WUPX23fR/4Kt5K/VVSyn/024H6PrvFb8AvL4TuVkDQEAsx61tfYMmEo8gm45yn3UtPOoblqAgBmXyEqniWwQfD/Mcg1wtG2KsaEBJ56tRG8rIDW4QJIVHkS+BcBNqojz2E1tYMgXAdYWMZx0zvobE5jGUraKrkFz/pQCgquCrI5O46K/u8ZU8/jWNZm6vECyFQdR0zncbJ50m3tW2+byikE3rr/yAah3yOkdUGK9ola7tsGhMQjwc4qNKleGg9ejTG5hyIRC/u2OmsmzWLtkWLMF54GCnwApb3oiSVOMODbUwcNgJzbZjBlSHUsPctydBBoHB0VZLfnHwyjiXRexPYUkFHIgcFuPKp1dx37vDdeOb2XQPlq8E/gXFSygnAcuD7AEKIMcAXgLHA54A7hBAfrXDpG9jSvfDT06GnHYIRCIYx0lmO+p+/UNLbQjkt6Jg4ira5UIZUFMw8FA0BLQiREthSRcPrcxUQJhG6ULQqwAVz7icexqGHDqI3GQJtIqpeRF1tkooym5oahbIyFWQeUEEductOhe/Dct3dBKIfzljYlDknHYeSrStuSAkdG70q5rEijIDBqfuFOHxchLoShdq6MmIjRhOMF0CiE/JZ6G7FReGfs1bwz+uuY8F991HYtYRAuhXF2VJM2XYk2azDwlmzWb+mg3feXEvHe+95z4P3m3dI7XqKagwC2Qwugrhu0RBPUGF18XxSksvZu/x8+QZI0JJSviil3PQ3/jZQ2/fzacAjUsq8lHINsBI4oD+O0bcDXrzH+wAJhukrtIYSiKBZLuOef4bCYAbXdtAMBwQ4KEgERZlWejd67Ute5ATOTD3FtMRsLk7fwzx7f7wOxWGQfenyyiena1144URKSkI0twoS2TG0dVWTzhp876pVqLSC2w2R74Li75HZXeqPPBI1EECPRLBzOaRj45h5XNtmxEknUTxsq1YaQkBNA2QSYFtQWIYmJCMLTY4eE2ba0VOI3fwkfPsuGL4/BMIw+TgW1Z3IuuUbiVZUEK2spDteT0BxMZLNgBcLzWQSV0Jp/n004SKFxtLOIJmVH9DRlqWqDCbVpYhqJqPiPUwo7GZoNElENckrAUTOpqdn29XuP46U0ONA7qM7O3yfYEBMD/6brwCP9v1cgxfENtnYd91HCCEuBS4FqKvzd5MPKF3Nm9tYbCIAVSqM7oxxSa6HpU11TKxbSWesCPJQ27mYiJJFjIZ3x57Gz3I/JCZSlCg2K5wGvpH5PXdFr2WCOg9kDpRiMA77xMMoL4/w4INn8dhji5k9u5FBg+r44hndjBmmgnIgBE8HfdsVy327xrBjj2XZU0/R9M7bqG6CSr2boA7V40Yx+lqvaOySJe088cQSWlvTnDbiJI5aeRNqdzMgwDa9ABYIQ3cLLHoNjr0Irpi8+TWWnH46wcLCzSO43nANvbKNIquTXrML15UEdIdHm8cQyDczyn6EFeHTycswq5OCyV2zuezSKC1vmkxpfJe5VfsRTyfQkCCgVS+jdvEHlH59O0fobpoFyTn8vLuaVXYFihLl5JjC1SUQGRDDiIFttyViCCFeAiq3cdMNUsqn++5zAzAFOFNKKYUQvwPellI+0Hf7X4DnpZR//aTX8hMxBph3/g4/P8eb1lH6EiWkhFwarvwDKy2TkPpt4mMkmuIQEPktW46lwgnpF5BohEQGiILQ6XB0pqrv8ZvI1aCNgYI7wJjaX+/QtwOcVIL1XzmUWMcyAppCMKihKy6ysJJ/nPE3fnTzbAACAQ0nm+GB8lsZHM+h5NNg5TZn6KSL61nflEI5+DQGXXYD4ZISAB4/91wy7e0YW01DCscm3r2SaWeeQGM2yo/v6cROughNygAAIABJREFUp/u+XEkcdNa5wzi5bBk3jphBR7SUrlg5OQf++4hrWVM8BAudTgrR3mjk9weWcOopHxO0pCRnzuNvq1azNqFTKuZyb9FZCFwKRQpHxGgX45keUbl9W5+Qe6E9IhFDSnnMJ90uhLgIOBk4Wm6JpI3AoK3uVtt3nW9PcsAJMGwSrJwHquZN89gW1IyAI79IyZIvYnVLRI8kULwlYCEh4cZps8qoUlqQqoIQaZARYkKwVI6D0MUQ/19QAp90BL4BTF3+DkNkExSFQTdwXZeungxu11ravnkUE8Q0uhpOxgpGGVbQRDoraIo0UKuuBFMHVSPdm2Lp3GXMbTHgvT+jPvcWR918M0OOOorRZ53FG/9zC8Uhr4xXigjJjk6KDj2Fku/ehlzfRtPvfkaRm0FXvLk6RVqErI2cGn8N6dqEzTSNBeUAXPvWXTRZBndqn2eCXs7l54zm0EM/ZnZH2qxovZlL2w6jQw7HDSl0MZlEIsr02EqEEkAjQQXreT0zhA3Wlgryvm0bENODQojPAdcBh0v5ofawM4CHhBC34dXYHwHM7odD9O0IIeCWmfDwz+Bfj4Ir4eDT4IKfgKpRULGGjRsC2HmJEbbQAjZCAdPWmLt4f0K1OTrzxQSwiASzGIZCSgxmv6ABBb/191Tt6TYuBzMLeoB0dw+vLjVZ3w2aAoWxNEW9HxB/v4lVk6/CUVUUBTo7s9RqORBgmjbpdA5FCRCNBpH5LHlFMPMHP+D855/HCRRzeFEb4eQHaIrAdBWWFoxm+re96cfSkgBXNizmN4uGI6SLKiSWq3JkyRoOKNyILYIoW81IaZEAY7ubueKwLGd84fiPvh8poWWN93tZsJgfd4yi1SqmhB6QFr1GHDegs7irlEnlbSB1FNmGJobQZvtB69MMiKAF/A4IAP/sm3d+W0r5DSnlYiHEY8ASvEJ1V0gp//Nqq77+ZwTgyzd5l3+jqDHKx46kY8lKOleHiFflCBVaNHXWUFXYwvHd/+DB+HnEnCR2RkNRDGw9wiUlcT6ly4VvT1A5BKSLnexlxnxJZwoCfYmk6zstNFKAIN6+iPWV+5NwwwyVzZBLgW2RzUpwoKUji8xksVBpW7wENRDg3hv+yOTFd+G6Gj12KcKxKQpYHBtZjvqrC+i64HY2anWcMEFhgvYPnmseTNpSOaZkNdOL1qMIcB2b3ljphw5ZCjAi8Y+8FWfNYsQ9P0DpaQIJ+YIueg77X4qKejf/rkacFN16CR2iAPBS+m1UHGCIX5/5Uw2IoCWl/NgNDlLKm4Gbd+Ph+Ha34NkE5d3UTJ1KPpUCXGzRxZzlExles5RpPW8jLcnz8RPp0EoYba7ghvKN7Bc6pb+P3LczTD4eAmHWNObozULI6EvUUcCWCqqZwlGDBDKtOGaenKtRZjaxqbhyQIP5TdCd8R6nCZeYIelOplny5DMcMbyXXjvkpUqrGi1mkOJckrtfL+OJ++9DCYVx3f05Leby3cGvoost34sloLs2Ri6Fals4mo6eS2FrBmOnHbX5fi0tKW77n1don/EgpxStpL7AoqGhiGB3hh8+9XNuOP9GrEAQUCh22mnVq8loAbKujiUVkmIwFxVAsb+h51MNiKDl28dFLgF3HSL/KsGYAtKhJ38K9798Kj86/9sgBdOSc5iWnIsroaa0hYnDb+nvo/btLKoK44+ga+kzCGH1dRYG01UQCFRVIC2TNruI061nOKhgPYoSg3wG6VioCvRmwXFBUb3hjOFkcNQ4IU1BFZs6BwgQoCB5aP0IHmmqojLQi6o6OIrgry2jKNOSfK1+ATiWV5jXCCKFQkG2h+C6OWwoHYYZjOJccjuDUy3wjzuwu9q490WdiV1zOXPoOyC8GUJlFailBZSF2hm6YTXLho8BoRJQTYrzbdS3dmAUCMr0IOeXFHDS9rcc26f5QcvX/0QQ4r8Eey24G0EZREzU8+ZilyXrRjN28BI6ekuRUlBe0MH61kHYK6ZzwMT+PnDfTjN+OgVvz0dpayVnWaSzDi4KEpeALqgaN5wLb/0GYx/7CmozYJpI1xsRaQocOBieWgiOKb0lTsUlPmQYC1orcORSRF8vgU2ebm2gWM+hKgIUFVXVKAlleKh5PF+b3AVdTd5GeEVBsUyCNQ2o+QyFZ3yTiqPOxZj7PPzqZyAUkkmHs/NN1MdbSDk6ruwbLrkSrauH2jJJldnKO9qB2GgIVxJZluSFw7uoKh/hVXHx12W3m78rwDdwaIPBOBS0elQV6qsVrvrd73jiX2cTCyUpinXzj3dP5Krf/4n1Lf7k/17lsM8ztKGGcEhFCQQIhgO4roMtVYpHjOCCGX9jwqgC1I4N3kZ1Ow/S3TyAKo3A2RPgwHo4cIjCsIZ6ViQrsKTKfRvHoeISVfOEFZMSI0PKCWAoXlNJVO+7uxHQSNgGUjNA0/s6HXujNBGOY+gBBkWCGK4Nj/8SIoVQWE7CDaPgIJB9ozqPi4IL6HmF706ZwuX5dZzU0sK3klHePXM/qmpOBX20H7D+Q/5Iyzdgnfs5+Onv4/zmqe/xm6f+C1UD6QoKYlBe0t9H59upSqowvncfp9b+grcemcG65jQllbU0fOFCpn3rWwRiMZg/E/IZMILInIOU7oc+7yNBGFMJMztq+cGyY5FAxIAZbUW82lXPZXVzMRSHhmgPB5e28kZnNRXFWz4Cu8wg00rWIuy8F6wc27uUVHuJRIoC5XXQvApcB4yg97oRnV6E1xVOSKzNcUuiIEmUDqFo1Ilc5genncIPWr4B69Jz4P4Z0NgKm9Yj4nEYORgOnNDPB+fb+SoHE7v2Do679g5c20YoyuZCugAsfh2Kq7z6g6qGY1tsyluwhIqrqjRnNG5dfxjhgCAe01GEoDCQpjFpsCBVzkUjVzPsqBO4Kjqa+TetpiUTIKgLcpYgrOT49iUjYdIJ8OCNXoCsGgYFpdDVAvVjYPhkaN/gBS2vpw3FxSHWGGXUO21IwBEquC6G6mJrOh9c8lsO9gPWTuMHLd+AVVQAD98K37sNmtq8L7rD6+CWq8HwZwf3aoq2jY8m2/JKggHCyqN5Rf+RAtrMEG81R2hKaiRkhEotB6k0IHElFKoWixJlvNHSzltPfcAJv7mMh/8ynCdue5wlLToji1KcffZYqi+9DgIhOPRMePYP8O7fveA1/Rw4+TIvaaSiHgaPh7ULobAcRRGMGVVO64o2CvI9hIWJCCigqTx65Lc4Y+K03Xru9nZ+Py3fgOe6sLbRC1r11f4SwN7MdRysTAYjEvnwKAvgprPg3Re8UY6d35xYIaV3aU5Bpx3h68u/SJGeQxNu3+2SLjvEmGgHXx+/msXpYrRgkPOee86rKrb6fcimoH6sN6raHr0dcPf3YM37XlttzWDuKddxU2Qcx81+ENWxeWXqF/jm+AYO9usvf8QeUcbJ5/usFAWGDvr0+/n2XFJK3v7Vr5hz553Y2SzxmhoOveB0Rk4e41V2jxVD2waorIf1HwDenizJ5sYBVMehRjU5LbmKvzaOoCKQRhEueVcl52icXr2COtnKyvhg0m1tdC5ZRNmcR2H+y6B4U3ocehacdY03ovokBaXwnT9D23qv6nz1cCYbQe51YO7o61GAC0J+AdxdwQ9aPp+v37149dXM+dOfUFSvCWhbaxMvL5lDbHo51VUFMOloLzhkej/0OPHvPzsW3xn0OnnL5fmOBgQuhnD49tC32a+oA006SOmlv4feegwW/MNbJ1MUcB2Wz3+df9UOo3fiIA4ISSYooyig4OMPvPzDNQcLVDjKH1ntUv70oM/n61ep1lZ+19CAUFVUXYdMgqiSx3Ulx47WGDEoAmbOm4ZzbC/dfTt0O1E6KKLSbcbQQcEhT4AFuSoa42M4e3g3IhQF1cBNdPKz0Wfyt5HH4KoqiepyIlqeC6pe5yLjCKqo3sVnYd+yI9OD/uDV5/P1q8bZs3Fs2wtYUhIUJiHdS6BY1+WCULy+WdL1kiS2U5GaYri6gahuY0gbrS+NfaTRxkkHlyLMPKg6tK7hZb2cJxqOozjXSWWimYreRrTmNP96vYLlb/wCaWZ34Rnw/Sf8oOXz+fqVFgqhBQK4tg1IQppEyr5yTHpfwEJ4o6zakV7g+vckjc3ER/4kAFQVqShENIei8hLCjYuhrBZWL4CNy3lq5AloZgY7ZZF3BIWN7ZR1NtKklBKe8Ra9v/k6WOauOwm+7eYHLZ/P168GHXggBYMGYZsmju0gENiOV45pdH2UzcX8NA3CUdCDEC4AIwTx8u16DeE6KIBAoCTavOnG5tWQaMexbdb1Qm+nSXePTVdbnvZuB0sIXEWhq6CAluWLWDZ75i49D77t4wctn8/Xr4xolFP/8hdKRoxA0TR6bQ1XCo4cHaAs5HjTgorwpvK6Wr1+bKlurwdXqhPE1pl+n7BGL13vYuagcTkku6B+PIvbVepmPocdjqAoAlUBy5S05kOUZ9uoSPXiqAHmzX0Dd+9LAdjj+NmDPp9vt2qzYZ0FVRrU9jU8rJ4yha+88QZNc+bgppNUvX4nwQ9eg1zaS0VHejUH1y/xHqCooBl95ZYsUI2+acT/QLoHpGRJC0ze8Dxrph7BsonTcRQVIV30bIbLlt6JaZcSdtvYGCmj2YYav0ljv/KDls/n2y0cCb/shL8lQAWy6RQjlszh8zMfoWH6oYw6/XTqp0/37rzxVWj6AAJhb1RlBL0RUl9xW6w8IL21LVt6QWzzzq1PoahQUOYFxFwK23bRXZcL77iGNSMns3boBMLJbmrfe4Pq0ydjYOIKldfHn8JX/LmpfucHLZ/Pt1v8NQGPJaBShXRTI6nVq5hdVImoGc8xt9/O8mef5dS//AXDNWHBLKgZ4VWrCISwXEFPr0XOdNAVSVkEFCyEkN6Un5X3dhhvzxYeISBaCNkkmHmGFbssbIKYCkOXz6N29RKyWZOyuEJxvpu8Eeb2029hSN0Qv0njAOAHLZ/Pt1s83AsFCgjHpmftGjTDoDCXZv6Uozh17ot0rVzJqhdeYPQhk73A0jd6sm2XlvY0ruOiSonlQM4G4biE9b4g1Ve8drs4NmxYCqE42Hn2q4ENPdCT9SpzrJCVrJSDidcM4e7Dvk735EmMjRr8uGyXnRrff8Af7Pp8vt0i4YIhwEynQYIQCorrYGsajqKiBQKsf/NNKK3xGjDmM1BUTi6TIaDYFAYkIR1COrQkYWW73Coxom/EtT2UvuFSuhuAoA5njofDhgle1Y7heedY1ogRzF4cY/n1M/niG/P5UxUU+qOsAcEPWj6fb7c4PAxdDqi6tnkaLxWOUde0BsO2cCyLaEWF14DxnOsgnQBXImybooAkrEviQYgasLId3t0A6e0rjvFhruNdAK/ljYKmQptWxzJ7CBE3icynMZwMeucGfvWth1n5+uyddh58O8YPWj6fb7e4tAhKVOg0wuTLK+kKRtAcm5P/9SRmOo0QglFnnMGGt97iubtn8OjyOM3rW7GloD0jSOa9Kby0CYMKAQnhHW5Rs2mEJpibqMJyHHIOaOEIWjhCMGQAggeuuxXXcT7tyXy7gb+m5fP5dosqHR6phWeSgrlKLfazTzL46fspTHYjolGOvuUW2hYt4rWbb0YLBBgXbqXCasZSoNeGlA2q8PIDSyMwthLUnfi1e2JBO7k1CuFAyMta7CNUFZnspnv1akpGjNh5L+j7TPyg5fP5dptCFS4shAsLQ3DleSTPOhIzlaJw8GCQkvuOPZZQURFFhsV+zlqvhb3qBalEDizXS2wPGzC17lNebHsIpW+qUrJfUQdCD2CpkkDfzSlTJabb1IR6vdqIvn7nTw/6fL5+E6uqomTECFRdJ9nUhJPPowWDDHE3AuCgIPCSLypiUFsANQUQDeykUZbAq2WoahQdfw7XXncIOUclkVdJmBoRw+HcmvcpHT6Mgvr6nfCCvh3lj7R8Pt+AECwqQkqJ6zho2H3Xbtl3tUs6Vruul6VoBCEY5pIDAjS4RTz/6JvoikttJEWsrISjb7kF4bfMHhD8oOXz+QaEYEEBDSedxNKnn6aprJh6NmJs2iy8q+OFlYcZv0U8dyeHF5Qx7cpz2Tj4KIIlpVRPmYKi+R+VA4U/Pejz+QaMQ667jjFnncXaXp3WfNTLSBe7PmZ5m5NVLxU+kyA491mGa+3UHnigH7AGGD9o+Xy+AUMLBpl+/fWc/9JMiu96C1E7CqHq7IawBa69pRSUlYdXHt71r+n7j/lBy+fzDThGJEJs+GjEhT8F3egbbu34x5WUkHU0Xu8eRI8Z2PYdRN/wLpvc4dfz7Xz+uNfn8w1c4w6F0lqv91UmuaW6+2fg9AWsX6+bxjPtoxG4PD7pcWqCyS3juE0BSw/AuOk76134diJ/pOXz+QauYATCcWiYCvsfA2W1fHiqcKufN4/EPjoqk0C7GWFNtoj3k1VUGknqgr18b/kxpO2t9l85jldQt7gSTr1yF70p347wR1o+n2/gihTAhCNg/kwoLIdMAkIxyKe9wreODYi+7sYKSMVbm/o3roSYmqdbBDmqeDWHFG3AUBxStsGLHUM5s3KZ93yKDvFSGDIRynfG7mXfzuaPtHw+38D2xRtg5FToaQXbBkVATQNUDvGaQgoFEKBo3tSeZsCg0RCKehmBwTA5Lc78ZCVxLc+5lUuwpEqvHcSWClMLmrw2JfESCAShYjAsfRsSnf39zn3b4I+0fD7fwBYpgG/+AVrWwl3XQOtabzS08j3vduniALl0DguNTqOMcksllst4a1OmSUhYCOKEFQtdcYlpJiHFImkbhFXLqw8VCPa9oPQCYbrXC2S+AcUPWj6fb89QORguvQ1+fanX0TjVDa5L0lZZ1i4wTZuWhEO72YXuNnP+VB09EEIGInS2Jyh0O9AVB11xqDCSIAXFepaQYoOretUxFMUbvSmqlwDiG3D86UGfz7fnqKiHL1wPuZQXXCJx3lqvMm+9w7pcnB43jGVD3obFTTZWbw/tbb1kTZcCPU9QsRFS4rpgKDYhxfbKQ7mul5lYUO5lKZ51jZdq7xtw/JGWz+fbs7x0H4QLvJqBusGGrjThgILMZ2jvdolFDUKawjvrYHCZhrDzhHUVBYHlgookJCzAazuConrTiJOO8tbKjvgiDN+vn9+k7+P4Qcvn8+1Zmld5I671S8B1CegKjiNRXAdUHVsN4jomiipoMSO8u9ykrChA3lU5aJiGKhwCdg5DE1Be46W3A/zoyV1Ulde3M/nTgz7fzmQthvxr4Jr9fSR7r9oGL7jUNACSCbUqWdPBVTSkHiAvNVKWyqhKjaKQS94WtPY65IrqWK/WogLdToB85UioGw1mDg492w9Yewg/aPl8O4O9EtonQMcB0HUCtFVD6o7+Pqq904lfB9v01pxGTGXcgeMYO6qcXOEgXNvByedRKupw68aQUOLU18bIxatJKxGWpgtYnwkRDSpEI7qXRj9uOpz0jf5+V77tJKT8bCVRBrIpU6bIOXPm9Pdh+PYVUkLbSHAbgYi3j8jNg7Ch6O8QOKy/j3Dvs+xdmPFb2LgMSgfBiZeSqd+fhQ8/zPz/+z+E0teRWFGYcMEFDD7iCBY/9hiplhbqDjmEMdOnEMj1QEkNVA/r73ezzxFCzJVSTvlMj/WDls+3g/IzoetUIOwFrE3cFASOheKn+u3Q9kWJjRtZM2sWjmky6OCDKR01ym/gOMDsSNDyEzF8vh3lbNxSaPVDFHCa+uWQ9mXx2lomXnhhfx+Gbxfx17R8vh1lHO7933W2XCcl4IJ+RH8ckc+31/KDls+3o7R6CJ4LZMDNeOtZMgVKCcS+299H5/PtVfzpQd9eS0pY3+RV5qmt3HUZzU0W3GPeRZd5Lkcr9zJRW0ZVcH9E/Keglu2aF/X59lF+0PLtdVo74HcPwf0zvKLgxQUwehjc/C3IVcAaC6o02D/44byJj7PKhCcSsNGCaWE4JQpxte+1bPhSIyRchbhyPO/Yx5Ox4OoAjFoKja2QSEMsDFPGQU3Frn3vPt/ebkAFLSHENcCtQJmUskN4KT+/Bk4EMsBFUsr3+vMYfQPbxha44DqY+wE4EbAMaLGhsQ5eWgRVJgQVUAUMN+C3VVCsfvzzvZGGa9q8fkyGgDez8Fgv3FMDRSo82gs9rhcEAUKAkoOr3oPgLdDW6o34YlEYWgNXng8Xn7lbToXPt1caMGtaQohBwHHA+q2uPgEY0Xe5FPhDPxyabw9y1+PQ1A5mOeQrwa4AJwhd70DXWlidhl4XSlVYZsJtn9AyyZXwsw4ICqjQvCBVpUGjDQ92wOoN8EYCogqAA7IXZJI1ayXJHLT07SZRFEikYE0T/P4hWLJyN5wIn28vNWCCFnA7cB1eZ+xNTgPuk563gUIhRFW/HJ1vj/DmfCAAZszLh6ATUAETeA7yj0Gb7U3rlavwzxRYH7NVsdmGLgdi//avJD8HfnIVTP8S/O0heGOxw9KVTeTSy3DN+RTFFuFIF5HyApYQ3jRkOgPJNLz01i49BT7fXm1ATA8KIU4DGqWU7//bJsAaYMNWf97Yd13zNp7jUrzRGHV1fpvsfVVpIaztBaJAN1CC9zVIBYLgLgTaoLXKGz25eCMqtrG2FVG8h7pyy9pX7ypYfT/kUyDbwH1FwliFlela1rVWMKy2iXRAJTavg1RnHqkGIVAKCGwb1jXBA89AJgdjhsGJh4E2IP4V+nx7ht32z0UI8RJQuY2bbgCux5sa/MyklH8C/gReRYwdeS7fnutLp8G82/HmELYORKLvOgXsVpCV0O7AwSEIfMx8Q6EKR0RgZhoqVW/EtGEWmA64m6YVG4F7JZwusIo01iarGPHkDNr/rxU3H8ABhF6ILP8c6DEsG95bAnMWQ9CA4kJ4+FaYPnnXnROfb2+y24KWlPKYbV0vhBgPDAE2jbJqgfeEEAfgfSQM2urutX3X+Xzb9LnpsLEVrvhX37SfizfKUvCGTRLsIi+pokSF60o/+fl+WAo5F97OgpDQ1QKyC6Tt3S6EhOUg/0ciYpKoXEN74yqioQCWW4jjKEizF9pehJozAYEQXiIIQE8SLvwvePMhqC7fVWfF59t79PualpRyoZSyXEo5WEo5GG8KcH8pZQswA/iS8BwI9EopPzI16PNtIgR87Rx44lrQC0DJ4I2yHBA9oA+GA4bBbZXwxCCo0T/5+WIq/KYKHiiDsschthZIfegVN/9XSUO+bRVCgZKiLEIoCEWAGgari4DmdclVFBAKOC6EAtDZA8+92vd0+SwsfgMWvQ659M49OT7fXmCgz6b/HS/dfSVeyvvF/Xs4vj3FKfXwl5vgv+6C3uXgKFB1MNz+VTilfMtIZ3vNew2a58CYOkj1Qm/Su96rNy0AieMK0ikFIUKkrUKKCgRSQndG4CgCU5NIG6TjBVe971+fENDUBnLJ22y841YUK0d1oB2h6XDxz2G8XyXe59tkwAWtvtHWpp8lcEX/HY1vT3bhSPj8L2F5EuIa1IX/gwdLCW4LoIFaxsx3IByCgAHTJsD7S6G1EzYFLBAIJETqSLStRzF0AgGwI+AWW2CryGkqrPSKv9PrrWmZFsSjUBZKcuqlOTbkbgJFZXRhE/896k8Mvvt78JMZUPAp85g+3z6i36cHfb5dyRAwLv4fBix7OfScB92nQfdJ0PNV6isasfvWsQwdpo6HYw6CSBhUIQgFBYUFCsU1I1FC1bhminQmQ8JNQToPJx0Gk1SIA1EQGuTyXsirKoFb/mDSmC4kLJOE7B4WdVRw6ftXY5ouLHptF5wZn2/PNOBGWj5fv3IT0PsNkHkQfZkR1mIuO/Eynn71CUxLx9C9gVhvEiJBUBUIGhLLcrEkBOs/R6ZzLVRuQJ8awpnYgDOmBHQwRoNcDoGXwfwAomFYvRFkVmGwkcBVggigkB7as2He7izksO7/gawGwZP9lvC+fZ4ftHy+reVngZsEdavdGWoZRdEWbvz6O/zupykq1z+MZiaoH30UdaPP5/m5hXSn82xqqOoiIDoMe+JwrJME0gIc0ALgREFOgVALZBZ6SRg5EwwRwXEVEk4YXXMoC7eSMgN0OkUwuBtSPwHZCuFL+uW0+HwDhR+0fPs0KWHhcpi7GGIROH7/dmK427xj0fv3M6mnkzdjZ9JBPdVty5iWu4F/5G7CIY4qHFwpcNEAl+JWh1YtADlgMcgCEEPADUGbCrjgWt7Tm9JgjjkFAxNhSzZY1ZRpbYw5vNfLhZcmZO6G4BdBiezGM+TzDSx+0PLtEywLOnqgKA7BgHed68KNf4BnX9lS9eLF4eO47TKFaERumYqTLplumxn39/CI+BkoEBQ5VsiDWdo9mTHZB5kX/hqWDLIpBV4ji9uVw3glgLkOZCc4AggCRwBr+556q23wJkFsNHRpkbODaGGXwWcZePn6Rl9ySBMoI3b9CfP5Big/EcO313vyn3DcJXDq5XD0xfDbB7yWJW+8B8/MgopiqCmHqjJY1TKVmXOn4DrN3vqW24t0mlm6cD+ezVyE6zjElASGYhJXEwigUZ2Ki4aCg4I3dLIJ0dZVQP4lbw1LWKDkIaKBeApYuO1jddHIE8RCpzdXwGOzDvVukA4gQfF3IPv2bf5Iy7dXe/ltuOq/vUK1SAgH4Y5HvA2+L7wOK9bByvVQGM0ztGoV0VAXtz72LcaPfZUhZa9huzo//r8vce/fJtGOStBMkM9ZhDUTXYMQSTYYk3GlQFdsHKltVfJZgSwQAtnjZQyqNSAzQNYb2bkfW3BMkDMNrrvjYoJGlrMPexIRPgeUgt1w1ny+gcsPWr692rW/gO5eCAW92b6cCZk2uOUusPs2+Qb1HIlUlvdXVrL/iFbMfIJk+yMw5A5u/tNofvWol+YulBRZp4CcEqfYXEPQTGGJMAQDKK6F64Lz75MXDt6algpuMaQSeNvkHba1ctbH2/slhCSXV/nJ3eeTdQ/ki6cdxmsvNpE2DaZOK6XSb4rs2wcJKfcXS5THAAAgAElEQVS+2rJTpkyRc+bM6e/D8O0Gb86D+5726g3aDpgmjBsBXzkT6qpgyHFe5Ql1q0aPuTxkczBpNCxfIzG0TgQueStAUaybkYNW8+zNp6MGD6DunJfoTXmPT6dspG0hpE3Q7SHsdNIdHIFJhFDARUpJztw6aH04PV0oID8+Un2EQBIwvICpCpsaexEp06s7pYXCXP7VYr799UI/C963xxFCzJVSTvksj/VHWr491lMvwc/uBCS8v9wLWiB5dQ488Izge5dANATprLeXCuEFDcvychpKCyFTabOxWQMEjquQN4P872U/QVN10smlpLOSTE4gXXClBooG0sVSImSDtYSCAjsD2byyre4mH7IpYP1/e2ceJ0dV7fHvqeplevY9+zJJSEIIewii7BIJskQUXJ4oPvJ8ioi4CwgqIoogbs8VAR+oiBGJRhElgOIDJOwJhCQkIWRPZpLZt+6uqvv+uNXpnslMVpKZnpzv5zPpqVvbvV2Z/vU599xzJPxnV98XBXBdobgQjPFpbBFSTGBy4QoA0l0t/M/tASecUMLJx++i9LKiDDFUtJS8JJWCH/wKysvg6ZfA88MU7oAxhpY24dt3QMpz6OyCzm5DeXEr8Wg73U4pvhSyaInLkYc5DC95kdbOMlo6SrjsnN9yRN0Klr8xgbse+jDtneD7vW4u1poqLbZzUhnt2VOfRSRi3ZKpdLatIGbn2ZJpG9UYce31HRe62j3AxcclGRQQd7qJFkSR7k5+e189Jx+vdVGVQwcVLSUv2bodupJQKFaQ7BwQO5TDYGhsFcYMBwjAdNLZJcyY/BJTx6zkjGP/zce+/3OWrChn5tQSRDyGVzZwyaw/8NLKw/n4d2/BpwrZhe+ttd1masdYS87fC9dfrhCKWB1Mpqwr0wv3d3TZwBHPF6KkcJyec2ZCQFtrVvnWpODRDkgaOLkQjoprAg1l6KGipeQFK5OwuBsmxuCYAigvte1dyZ5uNuOExao8wDXUjkwyrLyBbU0+13/oa8yY8jyTRq0kHk3yws+e5PTPLGJD49Gcdcx9XH7Bd4g4jVx3502s3jyBaLQCR+wHf1+uPC9HeHYnWGOGw8at1jJLez33GWPn2Byxc2fjRsHr662IxSJw2MhuVq0LMCZKgXTbcwJD2sQ5d5ZdaDy/FW7eZuM+AP63GS4uhS9UqXApQwsVLWVQkzbwgQ3wcLuNthPgyAJ4cAzMORN++1dwHR8/cDGZD2fXQFqgCNa4DjMq1vORs+/j5COfpL65mqgbEAQuNWWbeeCGOWyVZzn9hPcx99q38eiiEtZttRnVw7zt4e977v7ri/rGXYW3WwJjras3Ntps8jFjLbB4aTEVBVvwkimaU0UIhiAwzJjazYUXVNHow7e3QbwRNv0Ztr8CsXK48wKYfTYcVbAfHVeUQYaKljKouaEeHmwDJw2BZ+d4XjTwoY3wwEesdXLTz7tp7yrEGLFVij376pZ5tHRG2RCvpKSgmat+9H22NI7g6ImL+cScn3DspBcZN2wFo8sWM//RY5i3cEKPeaZcjdnfGNtkyr46obDuSsAyx8ZjNiHvO091OP+6Ktqee5j5D2ylQ8qZdV4d777sOKJReLEdurbA0hugs8FaVkEA9c/BxX+Er74bzj0dykr2cxCKMgjQkHdlUDNyGdR3gXhhFqMAjAPxQth+BBQ5cN33mlnnPcQDz56H3xwhWpciWRvF+3ccSbgUJ5sZ4WzCdTwS8S66kgkE4aefvpwTp71EtOp3zPrkuTz+7M6uu4Ei4kI0Ch+eAz/76q6P/VcHXPw12PYIuHFAIN2B9RWGC6rHjoT5P4SpEw583xVld+xPyLumcVIGNY3d4Kdt2iXfDy2ewEbZ3fGgPeaqS8tJd59OzReaOf6WF3HOhuhRQsn5Dk6lR/vWEpKpODVl24lHU5QXtwLwkwWfxHVLuf+xOp58YfeC5bpWTPaWzLzY3swteb51SZ54pN3eXA+PPAVrN+187AkJaF8KONad6HVjBUuy/V63Ga64cddh9oqSD6hoKYOWF18FbzUQzWk0dlva4O67bbaLmkq4+0sjODMyhnXeW0lFy0iUxag+eT1nfOqfxJwk7V3FpLwoUTdN1E1TXtzEK2um8X9LZ/PNOw/f7XxTxuW2NxGCGYIwGn9vBEOwx6dS8OFrYOq58K4r4YjzYdZcaGvPHlsgUFeEzRpvwHj0WNfsiA3oWPIaNDTuff8VZTChc1rKoOVnv4PSldB0FRAHUsBLwGJwVkF7BJaugpOPh1gMbj8G5jfBJ9bAiPI1XBT5HTO3vcKTnER3qoBla6dRnGijONFBc3sprZ1lXP79nzBhjLB+q1031Z+wRF1rie1Od5zcr4EmZ+5qbyM5wnVcN/zUFpssiENBxFqc//ccnP1RuPc2+PL34cHHbfRhkIYg1fM+jmPdjEFgrbd9sRQVZTChoqUMWl57AyZ6sOTrkDoPeB5YDbRCxIeNDvz+71a0wEa6X1AMz6+HknW/YUT1dkrHdjKtbhmLV02no9ulpaOEwER73KMzaYMf+hMsESuK0aiN7uvvmMycG/RO4MSOtVzGWCEpiNn7OmKtt973NgZ8A5sb7HmFCZt+qrPLCuHTi2HSO0LLahcRI0UJe7/OLpu2qrK8nzdbUfIEFS1l0DJhDKxYA5McWP6jrNXiOFBQaJPgPvUi/GMRrF4HzW3wwCOw/HVDe+fVuE7AxJGrufLCn3Pdndexva16JzHxA6jfZkWpP2MoHrOlSzbWZ+enersJHQEjWdHa6TpiM2GUl9jciMmUnWfa1mTPSYZWXl+LlP3ALmQOAjtn5RpI57gqe/fbDa29wNj7+L4tcHn7DXvwpivKIEejB5VBy6LFcMU3rLWwdhOs32LbC2JQUQaHT7BiFYnYD+U166GpzYqKIzZ0ziCcePhSjqp7kvn/mkNHqoiO7p7lPWyev56LhXvjOjZbBYRWWT/H9SV8ghWbgpi9xujhUDfKFqUMAusGbG6FdVtsfsL+ru26th9pr6d11fue8RhMm2Cvn/Zg7nvgqkugpqr/8SnKwUSjB5UhyYlHw/e+ZAMtBCgutB/Gbzkajp1qj9naCJVlMKwKWjpsmzEg4uC61u/2wmtTqCptobW7jM7unRcrGXYtWGCtmlTaWkr9ZcYVsW68kiI7d5SZPxKxllokYn82bLFpqDwPTjrGtk0YY0UtV3wcp+etfD8775Ybidhb5IyBqgoYWQsfugC+cZUKljJ0UNFSBhUr1tgEuKlwge0pM+D+78Mz8+CYqTZ9U6LAflC/vt5+qBcmoKk165oDm6/P86M4juD5ER5f+hFS6QJMH//lRXoFUPRBJjN7fVO/moUJ3XFBYMuJxKKhlSUwbSKMGmb3t3fBstUwvAZeXQUr37BZMHoTBBmrMdvmOpCI7zoSsbTYWlhBAJecv+txKUq+oaKlDApWr4OjL4TjL4Kz5sLoM+CO32f311TCj6+HqnKo3w6Ll0NDOB/0wqt2OxcBgkDwfZd4zKWpbTjHH+FSEN+5vlZvYegLE/4TcXadzcILE912JaGz255XnLDWlOvY+8Sj9v4Ln7J9HzXMuj6TKStIueTeKxbZXbXjbGdLCuE7X4SjpuzmWEXJM1S0lAEnCOCcj8GqtdaKKkpAyoPPfNsGWmSYfhjM/x/4yLusm27mkTYUXLCBDLm6k1suZNokKCyE4iIYPcxaKbmWShD0tNL6w2BFc3cCF430vH5bFyx/3QpTeXELc8/5ISMrX+W4iU9RWbwKY9KUFttgkHGj7HxXJJIdT2DsPb0wbN33QYxHzG8h7jcR9dtxTRIh4NipNuvFgp/Aqfs0Y6AogxsVLWXA+ccimwE9E56N2GCCIIDbftnzWBFY+G+orrQCN34UdIcuub4MkIhr54G2bLOBHWs3WREIgp4it1vrBStEW7bv/tjemTWCAFavh3Ta41tzP8Hxh71grycOwys3URh5mXgswA3Xgk2pg8Ici8t1svNbBju/dmrDjVSmVlGZXEVVeiXDki8zrHsxre0BY0doZndl6KKipQw46zZna0rl4jiwqSG7vX4z/OL38NJyO4e1cSu8voEealUQ6+liO/Zw27atMSd3YSZ03rWW2p7i7Kba8K7OA0jEWhlbuw7ExQsiBIFDMl1AUUEH1aXN+OHi35dfg/bO7LD8jIUVWoOlyTVM7vgrYzufpDtSASbAIcANknS2tPORC/e+j4qSL+g6LWXAOX2mffX97FxTRmBOC11c8xfCF2+DTfV27icTuFBaZD/cM3M93ansdUWs23D95myYeMYCcUKzpS+DZIeVYrLbOy3i3QtcFwjAcQzPLJ/BtPGrmDx6Ja+tn0xpUQtgCAKfscPtfeobd508ozq9EoMwsetRAifGG4Wn4YlDxHTwgSn/x1knnbtvHVWUPEAtLWXAqRsN755lAxc6uyGZtJZGZTl8/j/tAtyv/di6+MI0fjto7YB0un+X3aq10NaRTVjruqEgGmu5ZMqASBggMbI2m7nCAGSS3bJrIclco78oRBGoKI3xxtaJYOCyc+5m5uHP0ZksYXtrNXVj4P4fwIVnWjdpeYkNppCc+8djNlLSLash4gpiAia3P8iZDddx6vZv8uHuK5h7fqe6BpUhjVpayqDg7m/B8dPgjj9YwTpjpl1fVF0Jf33cugMxVqDccH4n4y7LFazMQt5MuHgqbV1uyT7WN+WeawykfRg30obbN9lE8MSi1r3Y3LYHgzBQWgIdnT3ntQIDNRUQjRaxafs0/vFSkqnjXufyC+4hEfsRKTOFmSffjuPCiForrIGBggIbkOL72fGMqILCoqMJmuuoaH6drmgNRYVJRhdvpnZ4gomzZu3bA1CUPEEzYiiDnoefhI9cazO6p30rWpkEsH3hOjlWUZjrz/NDS8vJKXHSC8EWSsykPhpWbQWopX332d0zYhgJBcf3s+2Tx9u8gU0tMHF0msCvxwStvOuUvzP3wjbGTPwk4lYANvR/9n9bqzJRYPvf2WXvf+w0uPdWGFEDr72yjVW/uIltzz+JMYaaqVM59frrqZo8ea/eW0UZCPYnI4aKlnJQ8X1YtMSGsns+nDDdJryNx/o+3vPgJ7+Fa75nM5kDuBHwd5Fx3REbZNH7GBEbjp5OZ9v7zSXoZFMqxSL2tb96Wzuu2yu9kutYSy0asQI0ZYKdgwPwPUNDE/z6FmFKXc/rLXwS/usrsL3ZXi9RYN+nu26ygpVLsq2NIJ0mUVnZz7uhKIOP/REtdQ8qB42uLvj4DfDUS9DYbIMmxIGJo+G2L8FZJ+18zg9+Bfc+aPMMvvyadZd5uynWGBgIwmMknIzKaEkqvfOxYrKBHJnMF7nrtlK7uZ8jNsN8yljXXmGBFaogsKmnEnErtBnBAnAjQhDYMPzeojXrbbDyIXj0aRs9OW2iFa1IH3+t8ZKd01IpylBGRUs54Gxrgs/fAvc9lA18cBwbcBCL2JD3a74L478Dk8Zlz2ttt6VHaivtB3ZVhS0lsmGLtYwyhRId11pFfQVjZDKn54a673QMOWHwTmh59eNC7As/gK5U9n7ptA2lj7jQ1mmtSOnDvShO6ALsg1gMzjl1DzugKIcQGj2oHFA8D87+L/j1X7KCBVZEurqtizAIbOqjP/+z57n1262AZCyMWNRmxZh5JFSU2sKMTpgaKRO1lxtosSOjRLD73IIZMsftrdPcCRdEO2HwR1u7nQvr6ITubpsPsL0ze3zG1bmpAa7+LtzzR2t9Koqya9TSUg4o8/4Gy9awowhi7tyR59sPdoDu9fDs0p7njqixIpRO2wKMac+GsG9usJnUg3A+ysuZu8pYTLlrrUR6zW1ld/XADQM29mWaV8RaViI26KI7lc1p6LjWWmzvsGuwCNeg+R78eoEd26NPw6/+DHd9A8aM2Pv7K8qhglpaygHlX89lQ813Ze14Hix4DN7YkG0rKrR5BhsarZWyeJl1JfqBjewbOzwMmOjjer3D23MrCvde6wXWTZk5b0/JXD4Srv3y/Wx2+gzlJXDEJGs1Xn+5LbVy2xdhxnSIxW2m96pyGF5tw+p/8ts9v7+iHIqoaCkHlNrKbIQe9MyuniETZWcC+N49Pfd99GL48sdtJN3W7dYSSnuwfI0Vs3ROwEU00jOFU2Y+K5N81u1H4KIRu76quKiPnbvAcbLJcWNRK6ZuaG0l4laM0r7N4mGA19bCW4+Ftx0Hz74C1eU9r1ddDo8/u3d9UJRDDRUt5YBy6buy0XSml4njODZgobjQzvkgsOKN7P6WNmuhnHQMbK63gQuRsHqvl4bWDmPz7olPPOrvCMTIVBiOujbQIZNMN9c12TtphGCFJ5MnsL+sEj2KMgbZgJBM5WMhLP4o1joMAti8zV5vZG323KKCnUPo0/7eC6eiHGqoaCkHlIljbTh7dYUNVIhEbNRgNAKVpVas4hnryMDM6TYg4TM3w1s/CNPOh0mzYXtLKHo7rDaz4ycWSZKItYLx6EqaHWJgCBcK9xG5l2txpT1oarPBE5n2TO2rXZ2XaUgkrLsy4thXY6yb0PPswuDmVpuS6e1vsaeIwMWzbVRlxm0ZBHbc7z9nL99gRTnE0EAM5YDz4Tkw+2R45mVrhZx0DLzzY/DiMhva7YgVl+JCKx5HzrHBDMk0gCGdDjDGfr/yPHBdwYQKZjD4xqWtszhsC5WNvQuq8Dwb2JE5PpOfcHcExuY2bO+0t95YbwUqlQ4tKbHzZR841waPZPjoxbBhKzz2dNZKO+90+NAFe9ZfRTlUUdFSDgq1VfZDOcPDd9gij396zEbRzZhuP/wff9YKQDIFybTBcYIeC31twEOACYUpFknhCHQHURwxuE6AHzg7jt0bckVqd2mbepOpRtzRBe1d1v0XjVqhGlULazb2Oj4GN3/WrjnbuNVGDOa6DxVF6RsVLWVAcF245HyYcwYcPRV+dC/84xk7x+U4tkovgAmyAeoSvhpjbSwRQ8T1ycQEivjUlG1nS9O+xYxHItnikHsrWikPJOOWDGwQxqhhNpfh1u0wrKrv80YPtz+KouwZKlrKQWfFGrjiRpvxIkN7F4wM12VlammBwSCUJNqoKatn6rhlrNp4GF3JBJFImoqSNto6y2jrKMQPHISAhpZ9N1cCP5uzcK/PDcIADGz/G5psotzOLjs/dsEZ+9wtRVFyUNFSDijGwN/a4ZfNsMWHY2Ow+HbwUjZoAex8zpqwAnFluS3j0daRKe7oMbxyM6NrNnDZ7F8SjSSpKm0k7cX55r3XU1rYjlfhsL2llJUbp+xwDfbRk16vQu8YQoN183Xsg2iJZLPOGyCdNry2vJVytvDBSQuRVdMxo05DtNiVouwXKlrKAeW3LXBbI5QIFDrwj2ZYcxqc8E8grFnlOnY+afkaG7QQcW34eTIFtRUNXHzaPI6auIR4NIWIoaWjgprybRQWpPjbMyeT9lyMyYhQX6JggIBYJGUzbHgRjHEwOD2ON8bOSe0LO3IXChTEDDO23Mr0xqc4ZkwjzgqPhz+f4vB3v5tTrr12326gKAowiELeReRKEVkuIktF5Jac9mtEZJWIrBCRsweyj8rekTLw82aocqDEhYKWRkauXYn43Wyckj3u1VW26KIjdm6oo9tG30WjwjknLOSiUx+gKN6FiMERQleg4ZU1k0im4wQmgsENRag3WcsqCFzi0S4qiptwHDsntrcI1qoqiMPkcfCZS+38XMS1bWUlMI6XObz19xRG0xRXlVFYVUVRbS3L5s+ncdWqfXkrFUUJGRSWloicAcwBjjbGJEWkNmyfBrwfOAIYCTwiIpONMf2U/1MGC6vXwVProKE4zWivkeG//D4l/3qMQBzG+BFemX0l8F5SaVi/NZzHIky/FF7D84UtrW8hHncoK0lhTAQRl65kwJI109nQMGYvemQwQHeqkERxC1HXw+Diug6dmTpdrp3X2pWUmfCfdBouezdceBb87iEb8ZjJvFHasgQXH8nJW+W4NtfT1pdfpnLSpL3ot6IouQwWS+ty4GZjTBLAGFMfts8B7jPGJI0xa4BVwMwB6qOyB3geXP9DeN9nA268bgOvv57GfPuHRP76V7ZFymmKV5OOJzhm3rdJLnuCNRuyVX535AsMa1thYNGrk3n5jdkMr9xOdVkLJYlGakq3cP/j7yGZju+qKzsQCXZkuEh7URrbKiiI2SSBufW19qQcSWbRcXkp3L/Qlh6pGwVjR1jXpuNAcXUZ0ZhLRVmvfjgO8dLSPeqzoih9M1hEazJwiogsEpHHReSEsH0UsD7nuA1h206IyH+LyHMi8lxDQ8MB7q7SH3/+Jzz4OMTb11Kw7XVGPL6G8Useoa3UxnUbCTDxOE4qwYnJXzPnzGyy2gy5C3yTKeGWeTfw3Qe+xUurjuOFlcdx9R0389Az76Tv+audydTd8oNomENDiMXSRKOOLVuyF7ERJiz0OG6E/f2J5+GqD9kAjgljbOmU9pGn4hYkqIq3hOcYupqaiJWUMOakPipdKoqyxxw096CIPAL0tSLly2E/KoG3ACcA80Rkwt5c3xhzO3A7wIwZM/ahuITyZvCHh6Gk0NCyahNuLEblPzcizQFmdBTjBjhJj5LuGI7ESTdt5etXwo/vzZ6fuyDYkKmFJfzr35O5+49foduUYIjRn2BlsrjntgTG6bHt+S5dyVK6kjZGPVGw5wEYgYGxtbZsytZGu33xbLsm6zd/sbkSzz+9jNmf+xEvfOtqOurrMUDxsGHMuvVWooWFe3YjRVH65KCJljHmrP72icjlwAPG5uF5RkQCoBrYCOROXIwO25RBSjJlE9sGvk8kEiHpVNHdUUF8eQvRqCCBT7SyEkm30DHinWzcupsLimHZym5SqWpK4+14aUgHPva/bjZlU4aCAugyQBE2OtGDnlJmj095UTC2NddF2BeRnGS44lhXoB+6L9/+Fiu09Vtb2bSuA88XRlYWMeVtRzHlz3+mceVKxHGonDSpxxyXoij7xmD5K/ojcAaAiEwGYsA2YAHwfhGJi0gdcBjwzID1Utkt554GbR1CvKwcP50m6vgsqv4MsaCTeGobMSdFpGMzyUgVMy+7hE9/q//sEwVxiLqGzlQU40RJmyhBIEAf9U2wKZPSDjhxcGqwoTvlQAFkw+Htj+dl59CKErseU6aYY2ZebGO9TXZ76bvgyMnwn5/dwAc/383DTzk8tki47Jpu3vPxrYjjUj11KlWTJ6tgKcqbxKCIHgTuAu4SkVeAFHBpaHUtFZF5wKvY78xXaOTg4Oa9s23+wJdSh9HathbT7SMlxfw78lUmtP6N8oJ2ni77EJsqTuPxeTFWrAlLlMRstd8MGTHxPYPBxQugNVW5y3u3dQEJe25JAtoNBFEgDWwitLosmZIiRQlrbbluNiCkNztqdmHXj517GnzqEpvB/tkX2/j9whiF0TROqKW+b3j4qRgL/9XOO04r3pe3UVGUfhgUomWMSQGX9LPvJuCmg9sjZV8pTMAvvg5PvZTg2WdH4q96iknJxymbcgRrq25mwVOlNK2GESXw2powi4RvhUtCL57BWkGd3eCIgw2G3wNLJQCiYCLQ2gJVVdDUAkE91tpq73m4G2ac37IN1m2ClvY+rhkSj9lyKoUJWLQElq6yOQXn/7kJYwp2CJa9rmBSwvy/tahoKcqbzKAQLWVoEYnAqTPg1BmlwOzwx+YavH0BTBwNi1dYocrMF2F2zsre3Z1Jk7sXrrUoNpznRWhrsFk42qNAAmi3wug69p6BsUJUVpy1piKurTrsurY8SnfKbsdiNr1USRG8shI+d6utB1a/oe9s8gIU78btqCjK3qOipRw0toQVfP0AupLW1RaPQZBkpwVSjmOzpVt2jgnMtveiHSgD5oJpgboj4dWvgbsdfDd7RiRir79+M7yxKVO7ywpZZ9K6DTOieuzhdn8yBavWha7LAltuRYIq1m9tJhl1iUetfzGddnCcgLn/UbF/b5iiKDuhoqUcNIZV5WZwtxQlrIil0oDJLtxFrGsvq1V7uJiqC1gIJCA2Gi6eBA+Pg+cboKQ4jDcU63o89nBYu9FaVcOqrCXoOla0fN+GtW9ugFdXh1WQk7afsZituAxQU1PE6NptbG/26fDtYmdXfG76VJqpkzS8XVHebFS0lINGWQm87xz41QJb3be1gx1C5YgNJ4/HrBW0uzD0XbIV+AVcexVcWwNzvwFnXGorBfth+a3xI+HeW+A9n4bh1VZMl6zw2d4MrhiMcaitcuhOQnObtcDSnn31/WwkIUDt6HHcel0XjfUN+AFcdH4ttTUF+/dmKYrSJypaykHlU5fYAol3/gEWL4eulE9N2VZKC5uZMnY5T7x8Cp5XhZubt8/paZ3tCY5AUaEVo5G1Njjko1+1OQITcWt1PfI0lBZZt19rSztt2xpxTIxUkEAkzbDCdjrKx9HWKXR3ZkPzPd9WIj56CqRStn9vf2uCyvKxb+I7pShKX4jZ25rkecCMGTPMc889N9DdUHbDp25sZMbYL5BKuxQVtlNd0sDLa6bz0wVXsKV5PN1JFz+sXCyE+QhzyJQ0yfwXFmx2C4CxwyGRsGupOrvgf/8Iw6ugtMTuT3uwvQkuOhvu/UvAG6s3EYuCF8RpT5cRpw0Jukk6tfgS3bFOyw+sgLoOHDPVCtYXLrNZMRRF2TNE5HljzIx9OVctLWVA8DwolL8wvW4xDS0jQnebyxF1r/Dzz17GR2+7h4bmarpTtiqxASIlEH0feJvBWwyOgaI2kMCGq0ci1mpyHKhvgq7N8LlbIOpad+P6LTDjCOumjEas2BUXwTtmbONnq1zSQYxOr5AibxNV6dUEAWyMlxC4Lm5EEBEiLvhYATtjJnzsfTBp3MC+l4pyKKGipQwIjgPjapeR8mI4QcCoZWsZ+foGnJTPgm3nI40ekWhAWXELXakyXNdh0g0QmQyVAby+ADY8AZFCqIvByyusFZQJ9GhqzWa8KCoH1EoAAAsESURBVCi0llUqBS8tg1Nm2PsjIAbOO6WTfy54BIomsXLLREq9TRgnYisnOwLGEHge4sYAm6kDbFi/CpaiHFw0t4wyIDgOSHQKETfNhGdeY/TSdRighVKmdK3gjor/oia6DccJKEp4JD1YPQ9qIxCJweSL4LRbYcyNkBplcw5mRCojXpCtRiyhe68raee1PM+6E884EY4/fiRNZhqvbjuKTlPB1uh0GtzJBLiURZpAwMGnMGGjBl3Xru2aqFNYinLQUdFSBoyzzzqPYHuMYRs3kSqKIlFIBXHq/eGUuS28I/K38EgDDnT3SpXsRm2uwVQRVJSGkX19BGwEQTbLhh/YTOzbmuGjF8PhE+HfS2JEqmYQowPHeIjxSEkRHYk6aiP1REgDgogNwkjE4eyT7bmKohxc1D2oDBh1Y6ppefbzdHmfIB7rJAiEZKoQEUPaRJnkrAyPjICB+Mie5xtjw+RjTTbHoCM7B2tANjzdjdiMFtdfbtM31Y227b97CKqrotSNHcmri9ZQn6pFHIcOKmkLujnR+SvVE8ey2swgHoeL3gGf/I+eYe+KohwcVLSUAeWYU2fiPzaGFqnGcTYzZdwbNHdU4HT6rPAmY0whXUmXggjMuAQ2paHCtWkGm304uwJWCixO9191OLM2KxKBu78J7zwtu++FFzaz8P6naayvJ1FUxMi6aRQ0tdLqldFFCbPl55w8fgUX3nEH8Qrr1tSE7YoycOifnzKwVI/CPX4Wlf4WyotqcCPTOXH0ehLlMf7uvYeO7gTjR8G878IfTodLyiAiUOzAVVXwreHww2uhunznS2cqEkcidm3YL2/qKVhLl9bziU88SNQ0IdESgiBgzavPEi83jBxTzPRRjVx53Ym8977fUFRTQySigqUoA42u01IGnnQK/n4n/GsedHfA9FNgzqcwteMwZs+EYvU6OPrCMBDDh5QHhO7DwgL44HnwP1+2QRRtbUmuvvoR7rlnCcmkR21tEWmnhqQXgcDD9z2mn/lhfvQVl+OPOOCjV5RDjv1Zp6WipQwuMiGA+3DarMtg6WqbV9DzrEBFXFv/6s4bbc5A3w+YMeMXrFixjXTax4SLkxOJCJOnjaO1w8Hrbmf+H/+DY48sOgADVBRFFxcrQ4d9jG4Qga9/Cj77bUinbWaM7pR1G972RStYAPPnL+e117ZTXByjoyNNKuXjukJXl4efbKVuZDGpVJQjpmjuQEUZjKhoKUOGtx4L99wM8x6CtZvhhOnw7llQnVMh5Ikn1iFiEBESiQiplE8QGMCwdWsHkYjDZz97ErGY2+99FEUZOFS0lCHF5PFw3eX976+rK8cYwRiD6zqUlsbp7EyTTAaUl8f5yldO44ILphy0/iqKsndoLJRySHHppUdTWhqjszMdCpfgukJVVYInnpjLnDlTEV2ApSiDFhUt5ZCivDzBggUfYOzYMjo703R0pBk1qpQ//en9VFdr0UZFGeyoe1A55DjhhFEsXfoJ1q5tQQTGjetjkZeiKIMSFS3lkEREGD9exUpR8g11DyqKoih5g4qWoiiKkjeoaCmKoih5g4qWoiiKkjeoaCmKoih5g4qWoiiKkjeoaCmKoih5g4qWoiiKkjeoaCmKoih5g4qWoiiKkjeoaCmKoih5g4qWoiiKkjeIMWag+/CmIyINwNqcpmpg2wB152Cg48tvhvL4hvLYQMe3r4wzxtTsy4lDUrR6IyLPGWNmDHQ/DhQ6vvxmKI9vKI8NdHwDgboHFUVRlLxBRUtRFEXJGw4V0bp9oDtwgNHx5TdDeXxDeWyg4zvoHBJzWoqiKMrQ4FCxtBRFUZQhgIqWoiiKkjcMedESkStFZLmILBWRW3LarxGRVSKyQkTOHsg+7i8i8jkRMSJSHW6LiPwwHN8SETluoPu4L4jIreGzWyIi80WkPGdf3j8/EZkd9n+ViFw90P3ZX0RkjIj8Q0ReDf/ergrbK0VkoYisDF8rBrqv+4qIuCLyooj8JdyuE5FF4TP8nYjEBrqP+4qIlIvI/eHf3DIROWkwPrshLVoicgYwBzjaGHME8J2wfRrwfuAIYDbwExFxB6yj+4GIjAHeAazLaT4HOCz8+W/gpwPQtTeDhcB0Y8xRwGvANTA0nl/Y3x9jn9U04APhuPIZD/icMWYa8BbginBMVwOPGmMOAx4Nt/OVq4BlOdvfBr5njJkENAFzB6RXbw4/AP5mjJkKHI0d56B7dkNatIDLgZuNMUkAY0x92D4HuM8YkzTGrAFWATMHqI/7y/eALwK5ETVzgHuM5WmgXERGDEjv9gNjzMPGGC/cfBoYHf4+FJ7fTGCVMeZ1Y0wKuA87rrzFGLPZGPNC+Hsb9kNvFHZcd4eH3Q28a2B6uH+IyGjgXOCOcFuAM4H7w0PyeWxlwKnAnQDGmJQxpplB+OyGumhNBk4JzffHReSEsH0UsD7nuA1hW14hInOAjcaYxb12DYnx9eIy4KHw96EwvqEwhn4RkfHAscAiYJgxZnO4awswbIC6tb98H/sFMQi3q4DmnC9W+fwM64AG4Jeh+/MOESliED67yEB3YH8RkUeA4X3s+jJ2fJVYV8UJwDwRmXAQu7ff7GZ812Jdg3nLrsZnjPlTeMyXsa6n3xzMvin7hogUA38APm2MabUGicUYY0Qk79bZiMh5QL0x5nkROX2g+3MAiADHAVcaYxaJyA/o5QocLM8u70XLGHNWf/tE5HLgAWMXoz0jIgE2AeRGYEzOoaPDtkFHf+MTkSOx344Whx8Ko4EXRGQmQ2B8GUTkI8B5wNtNdlFh3oxvFwyFMeyEiESxgvUbY8wDYfNWERlhjNkcuqnr+7/CoOVtwAUi8k6gACjFzgGVi0gktLby+RluADYYYxaF2/djRWvQPbuh7h78I3AGgIhMBmLYjMULgPeLSFxE6rABC88MWC/3AWPMy8aYWmPMeGPMeOx/uuOMMVuw4/twGEX4FqAlx8TPG0RkNtYdc4ExpjNnV94/P+BZ4LAw+iyGDSxZMMB92i/COZ47gWXGmO/m7FoAXBr+finwp4Pdt/3FGHONMWZ0+Lf2fuAxY8wHgX8AF4WH5eXYAMLPjfUiMiVsejvwKoPw2eW9pbUb7gLuEpFXgBRwafhtfamIzMM+FA+4whjjD2A/32z+CrwTG6DQCfznwHZnn/kREAcWhtbk08aYjxtj8v75GWM8Efkk8HfABe4yxiwd4G7tL28DPgS8LCIvhW3XAjdjXfNzsSWD3jtA/TsQfAm4T0S+AbxIGMiQp1wJ/Cb8EvU69nPDYZA9O03jpCiKouQNQ909qCiKogwhVLQURVGUvEFFS1EURckbVLQURVGUvEFFS1EURckbVLQURVGUvEFFS1EURckbVLQUZZAhIh8XkZ/mbH9DRH41kH1SlMGCLi5WlEGGiBQCK4AjgZOBG4G3GmO6BrRjijIIUNFSlEGI2CrbRdgikbOMMasHuEuKMihQ0VKUQYiITMUWUZxjjMnrRLqK8maic1qKMjj5CrYo346k1iIyQUTuFJH7+z9NUYY2KlqKMsgQkc9haza9F7gq026Med0YM3fAOqYog4ChXppEUfIKETkTWxLiJGNMm4iUisgxxpiXdneuohwKqKWlKIMEERkL3AFcbIxpC5t/AHx64HqlKIMLDcRQlDxBRKqAm4BZwB3GmG8NcJcU5aCjoqUoiqLkDeoeVBRFUfIGFS1FURQlb1DRUhRFUfIGFS1FURQlb1DRUhRFUfIGFS1FURQlb1DRUhRFUfIGFS1FURQlb/h/+/xvzfnl5R8AAAAASUVORK5CYII=\n", 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\n", 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" ] @@ -1019,7 +995,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/demos/node-classification/directed-graphsage/example2-directed-graph-undirected-graphsage.ipynb b/demos/node-classification/directed-graphsage/example2-directed-graph-undirected-graphsage.ipynb index 35d2ee1c0..d14033d60 100644 --- a/demos/node-classification/directed-graphsage/example2-directed-graph-undirected-graphsage.ipynb +++ b/demos/node-classification/directed-graphsage/example2-directed-graph-undirected-graphsage.ipynb @@ -25,31 +25,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Using TensorFlow backend.\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n" + "Using TensorFlow backend.\n" ] } ], @@ -233,9 +209,9 @@ { "data": { "text/plain": [ - "Counter({'Neural_Networks': 81,\n", - " 'Probabilistic_Methods': 42,\n", + "Counter({'Probabilistic_Methods': 42,\n", " 'Reinforcement_Learning': 22,\n", + " 'Neural_Networks': 81,\n", " 'Genetic_Algorithms': 42,\n", " 'Theory': 35,\n", " 'Case_Based': 30,\n", @@ -422,7 +398,7 @@ "output_type": "stream", "text": [ "WARNING: Logging before flag parsing goes to stderr.\n", - "W0820 17:28:17.259684 4354622912 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", + "W0823 09:59:16.134663 4451100096 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", "\n" ] } @@ -452,14 +428,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0820 17:28:17.275812 4354622912 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", + "W0823 09:59:16.151041 4451100096 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", "\n", - "W0820 17:28:17.286010 4354622912 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", + "W0823 09:59:16.161491 4451100096 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", "\n", - "W0820 17:28:17.293032 4354622912 deprecation.py:506] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", + "W0823 09:59:16.169850 4451100096 deprecation.py:506] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", - "W0820 17:28:17.324139 4354622912 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", + "W0823 09:59:16.202038 4451100096 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", "\n" ] } @@ -492,9 +468,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0820 17:28:17.485618 4354622912 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", + "W0823 09:59:16.375536 4451100096 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", "\n", - "W0820 17:28:17.491878 4354622912 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", + "W0823 09:59:16.381622 4451100096 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", "\n" ] } @@ -533,7 +509,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0820 17:28:17.585758 4354622912 deprecation.py:323] From /anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", + "W0823 09:59:16.468930 4451100096 deprecation.py:323] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" ] @@ -543,45 +519,45 @@ "output_type": "stream", "text": [ "Epoch 1/20\n", - " - 2s - loss: 1.8759 - acc: 0.2296 - val_loss: 1.7155 - val_acc: 0.3966\n", + " - 2s - loss: 1.8731 - acc: 0.2576 - val_loss: 1.7150 - val_acc: 0.3626\n", "Epoch 2/20\n", - " - 2s - loss: 1.6638 - acc: 0.4686 - val_loss: 1.5716 - val_acc: 0.5119\n", + " - 2s - loss: 1.6263 - acc: 0.5289 - val_loss: 1.5721 - val_acc: 0.5295\n", "Epoch 3/20\n", - " - 2s - loss: 1.5172 - acc: 0.6314 - val_loss: 1.4221 - val_acc: 0.6637\n", + " - 2s - loss: 1.4863 - acc: 0.6612 - val_loss: 1.4325 - val_acc: 0.6452\n", "Epoch 4/20\n", - " - 2s - loss: 1.3586 - acc: 0.7348 - val_loss: 1.2962 - val_acc: 0.7252\n", + " - 2s - loss: 1.3453 - acc: 0.7770 - val_loss: 1.3021 - val_acc: 0.7108\n", "Epoch 5/20\n", - " - 2s - loss: 1.2279 - acc: 0.7889 - val_loss: 1.1894 - val_acc: 0.7666\n", + " - 2s - loss: 1.2234 - acc: 0.7989 - val_loss: 1.1891 - val_acc: 0.7584\n", "Epoch 6/20\n", - " - 2s - loss: 1.0991 - acc: 0.8763 - val_loss: 1.0952 - val_acc: 0.8019\n", + " - 2s - loss: 1.0801 - acc: 0.8797 - val_loss: 1.0999 - val_acc: 0.7892\n", "Epoch 7/20\n", - " - 2s - loss: 0.9647 - acc: 0.9357 - val_loss: 1.0214 - val_acc: 0.8084\n", + " - 2s - loss: 0.9673 - acc: 0.9415 - val_loss: 1.0160 - val_acc: 0.8027\n", "Epoch 8/20\n", - " - 2s - loss: 0.8706 - acc: 0.9458 - val_loss: 0.9461 - val_acc: 0.8212\n", + " - 2s - loss: 0.8554 - acc: 0.9594 - val_loss: 0.9526 - val_acc: 0.8027\n", "Epoch 9/20\n", - " - 2s - loss: 0.7655 - acc: 0.9643 - val_loss: 0.8892 - val_acc: 0.8228\n", + " - 2s - loss: 0.7607 - acc: 0.9585 - val_loss: 0.8977 - val_acc: 0.8052\n", "Epoch 10/20\n", - " - 2s - loss: 0.7107 - acc: 0.9797 - val_loss: 0.8512 - val_acc: 0.8285\n", + " - 2s - loss: 0.6837 - acc: 0.9695 - val_loss: 0.8600 - val_acc: 0.8126\n", "Epoch 11/20\n", - " - 2s - loss: 0.6266 - acc: 0.9695 - val_loss: 0.8037 - val_acc: 0.8290\n", + " - 2s - loss: 0.6175 - acc: 0.9686 - val_loss: 0.8191 - val_acc: 0.8187\n", "Epoch 12/20\n", - " - 2s - loss: 0.5707 - acc: 0.9619 - val_loss: 0.7714 - val_acc: 0.8285\n", + " - 2s - loss: 0.5695 - acc: 0.9822 - val_loss: 0.7727 - val_acc: 0.8290\n", "Epoch 13/20\n", - " - 2s - loss: 0.5125 - acc: 0.9763 - val_loss: 0.7449 - val_acc: 0.8294\n", + " - 2s - loss: 0.4883 - acc: 0.9966 - val_loss: 0.7511 - val_acc: 0.8265\n", "Epoch 14/20\n", - " - 2s - loss: 0.4601 - acc: 0.9898 - val_loss: 0.7109 - val_acc: 0.8335\n", + " - 2s - loss: 0.4536 - acc: 0.9889 - val_loss: 0.7458 - val_acc: 0.8191\n", "Epoch 15/20\n", - " - 2s - loss: 0.4220 - acc: 0.9932 - val_loss: 0.6968 - val_acc: 0.8310\n", + " - 2s - loss: 0.4177 - acc: 0.9966 - val_loss: 0.7115 - val_acc: 0.8244\n", "Epoch 16/20\n", - " - 2s - loss: 0.3883 - acc: 0.9898 - val_loss: 0.6823 - val_acc: 0.8244\n", + " - 2s - loss: 0.3912 - acc: 0.9831 - val_loss: 0.6906 - val_acc: 0.8199\n", "Epoch 17/20\n", - " - 2s - loss: 0.3549 - acc: 0.9831 - val_loss: 0.6658 - val_acc: 0.8244\n", + " - 2s - loss: 0.3554 - acc: 0.9889 - val_loss: 0.6768 - val_acc: 0.8236\n", "Epoch 18/20\n", - " - 2s - loss: 0.3252 - acc: 0.9966 - val_loss: 0.6518 - val_acc: 0.8322\n", + " - 2s - loss: 0.3328 - acc: 0.9932 - val_loss: 0.6964 - val_acc: 0.8138\n", "Epoch 19/20\n", - " - 2s - loss: 0.2904 - acc: 0.9966 - val_loss: 0.6423 - val_acc: 0.8277\n", + " - 2s - loss: 0.2951 - acc: 0.9966 - val_loss: 0.6707 - val_acc: 0.8216\n", "Epoch 20/20\n", - " - 2s - loss: 0.2651 - acc: 0.9932 - val_loss: 0.6476 - val_acc: 0.8253\n" + " - 2s - loss: 0.2711 - acc: 0.9966 - val_loss: 0.6647 - val_acc: 0.8179\n" ] } ], @@ -625,7 +601,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -670,8 +646,8 @@ "text": [ "\n", "Test Set Metrics:\n", - "\tloss: 0.6440\n", - "\tacc: 0.8294\n" + "\tloss: 0.6586\n", + "\tacc: 0.8244\n" ] } ], @@ -982,7 +958,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1021,7 +997,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/demos/node-classification/directed-graphsage/example3-directed-graph-half-directed-graphsage.ipynb b/demos/node-classification/directed-graphsage/example3-directed-graph-half-directed-graphsage.ipynb index 8ef1e3d11..3223ee236 100644 --- a/demos/node-classification/directed-graphsage/example3-directed-graph-half-directed-graphsage.ipynb +++ b/demos/node-classification/directed-graphsage/example3-directed-graph-half-directed-graphsage.ipynb @@ -25,31 +25,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Using TensorFlow backend.\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n" + "Using TensorFlow backend.\n" ] } ], @@ -233,12 +209,12 @@ { "data": { "text/plain": [ - "Counter({'Rule_Learning': 18,\n", - " 'Probabilistic_Methods': 42,\n", - " 'Genetic_Algorithms': 42,\n", + "Counter({'Probabilistic_Methods': 42,\n", + " 'Theory': 35,\n", + " 'Rule_Learning': 18,\n", " 'Neural_Networks': 81,\n", + " 'Genetic_Algorithms': 42,\n", " 'Case_Based': 30,\n", - " 'Theory': 35,\n", " 'Reinforcement_Learning': 22})" ] }, @@ -422,7 +398,7 @@ "output_type": "stream", "text": [ "WARNING: Logging before flag parsing goes to stderr.\n", - "W0820 17:29:59.615134 4619847104 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", + "W0823 10:08:37.639082 4535567808 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", "\n" ] } @@ -452,14 +428,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0820 17:29:59.630453 4619847104 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", + "W0823 10:08:37.654679 4535567808 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", "\n", - "W0820 17:29:59.636193 4619847104 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", + "W0823 10:08:37.660939 4535567808 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", "\n", - "W0820 17:29:59.642680 4619847104 deprecation.py:506] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", + "W0823 10:08:37.668406 4535567808 deprecation.py:506] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", - "W0820 17:29:59.691473 4619847104 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", + "W0823 10:08:37.716286 4535567808 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", "\n" ] } @@ -492,9 +468,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0820 17:29:59.893568 4619847104 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", + "W0823 10:08:37.997756 4535567808 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", "\n", - "W0820 17:29:59.899368 4619847104 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", + "W0823 10:08:38.003920 4535567808 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", "\n" ] } @@ -533,7 +509,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0820 17:30:00.058759 4619847104 deprecation.py:323] From /anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", + "W0823 10:08:38.170191 4535567808 deprecation.py:323] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" ] @@ -543,45 +519,45 @@ "output_type": "stream", "text": [ "Epoch 1/20\n", - " - 3s - loss: 1.9150 - acc: 0.2314 - val_loss: 1.7971 - val_acc: 0.3175\n", + " - 3s - loss: nan - acc: 0.1230 - val_loss: nan - val_acc: 0.1099\n", "Epoch 2/20\n", - " - 2s - loss: 1.8004 - acc: 0.3296 - val_loss: 1.7488 - val_acc: 0.3035\n", + " - 3s - loss: nan - acc: 0.1059 - val_loss: nan - val_acc: 0.1099\n", "Epoch 3/20\n", - " - 2s - loss: 1.7122 - acc: 0.3465 - val_loss: 1.7000 - val_acc: 0.3097\n", + " - 2s - loss: nan - acc: 0.1059 - val_loss: nan - val_acc: 0.1099\n", "Epoch 4/20\n", - " - 3s - loss: 1.6186 - acc: 0.3790 - val_loss: 1.6162 - val_acc: 0.3532\n", + " - 2s - loss: nan - acc: 0.1102 - val_loss: nan - val_acc: 0.1099\n", "Epoch 5/20\n", - " - 2s - loss: 1.5003 - acc: 0.5246 - val_loss: 1.5056 - val_acc: 0.4877\n", + " - 2s - loss: nan - acc: 0.1144 - val_loss: nan - val_acc: 0.1099\n", "Epoch 6/20\n", - " - 3s - loss: 1.3483 - acc: 0.6637 - val_loss: 1.4063 - val_acc: 0.5697\n", + " - 2s - loss: nan - acc: 0.1187 - val_loss: nan - val_acc: 0.1099\n", "Epoch 7/20\n", - " - 2s - loss: 1.2484 - acc: 0.7366 - val_loss: 1.3357 - val_acc: 0.5919\n", + " - 2s - loss: nan - acc: 0.1187 - val_loss: nan - val_acc: 0.1099\n", "Epoch 8/20\n", - " - 3s - loss: 1.1346 - acc: 0.7551 - val_loss: 1.2837 - val_acc: 0.5956\n", + " - 2s - loss: nan - acc: 0.1144 - val_loss: nan - val_acc: 0.1099\n", "Epoch 9/20\n", - " - 3s - loss: 1.0483 - acc: 0.7526 - val_loss: 1.2417 - val_acc: 0.6087\n", + " - 2s - loss: nan - acc: 0.1059 - val_loss: nan - val_acc: 0.1099\n", "Epoch 10/20\n", - " - 3s - loss: 0.9278 - acc: 0.8068 - val_loss: 1.2093 - val_acc: 0.6181\n", + " - 2s - loss: nan - acc: 0.1102 - val_loss: nan - val_acc: 0.1099\n", "Epoch 11/20\n", - " - 2s - loss: 0.9240 - acc: 0.7871 - val_loss: 1.1892 - val_acc: 0.6263\n", + " - 2s - loss: nan - acc: 0.1187 - val_loss: nan - val_acc: 0.1099\n", "Epoch 12/20\n", - " - 2s - loss: 0.7247 - acc: 0.8661 - val_loss: 1.1747 - val_acc: 0.6300\n", + " - 2s - loss: nan - acc: 0.1102 - val_loss: nan - val_acc: 0.1099\n", "Epoch 13/20\n", - " - 3s - loss: 0.7574 - acc: 0.8526 - val_loss: 1.1725 - val_acc: 0.6296\n", + " - 2s - loss: nan - acc: 0.1102 - val_loss: nan - val_acc: 0.1099\n", "Epoch 14/20\n", - " - 3s - loss: 0.6766 - acc: 0.8578 - val_loss: 1.1701 - val_acc: 0.6280\n", + " - 3s - loss: nan - acc: 0.1102 - val_loss: nan - val_acc: 0.1099\n", "Epoch 15/20\n", - " - 3s - loss: 0.6190 - acc: 0.8745 - val_loss: 1.1707 - val_acc: 0.6259\n", + " - 2s - loss: nan - acc: 0.1230 - val_loss: nan - val_acc: 0.1099\n", "Epoch 16/20\n", - " - 3s - loss: 0.6101 - acc: 0.8524 - val_loss: 1.1637 - val_acc: 0.6333\n", + " - 3s - loss: nan - acc: 0.1144 - val_loss: nan - val_acc: 0.1099\n", "Epoch 17/20\n", - " - 3s - loss: 0.5425 - acc: 0.8711 - val_loss: 1.1559 - val_acc: 0.6366\n", + " - 2s - loss: nan - acc: 0.1059 - val_loss: nan - val_acc: 0.1099\n", "Epoch 18/20\n", - " - 3s - loss: 0.5056 - acc: 0.9077 - val_loss: 1.1601 - val_acc: 0.6354\n", + " - 2s - loss: nan - acc: 0.1102 - val_loss: nan - val_acc: 0.1099\n", "Epoch 19/20\n", - " - 3s - loss: 0.4844 - acc: 0.9111 - val_loss: 1.1712 - val_acc: 0.6337\n", + " - 2s - loss: nan - acc: 0.1059 - val_loss: nan - val_acc: 0.1099\n", "Epoch 20/20\n", - " - 3s - loss: 0.4269 - acc: 0.9348 - val_loss: 1.1881 - val_acc: 0.6362\n" + " - 2s - loss: nan - acc: 0.1102 - val_loss: nan - val_acc: 0.1099\n" ] } ], @@ -625,7 +601,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] @@ -670,8 +646,8 @@ "text": [ "\n", "Test Set Metrics:\n", - "\tloss: 1.1881\n", - "\tacc: 0.6362\n" + "\tloss: nan\n", + "\tacc: 0.1099\n" ] } ], @@ -718,7 +694,22 @@ "cell_type": "code", "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ValueError", + "evalue": "Input contains NaN, infinity or a value too large for dtype('float32').", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnode_predictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtarget_encoding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_predictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/sklearn/feature_extraction/dict_vectorizer.py\u001b[0m in \u001b[0;36minverse_transform\u001b[0;34m(self, X, dict_type)\u001b[0m\n\u001b[1;32m 253\u001b[0m \"\"\"\n\u001b[1;32m 254\u001b[0m \u001b[0;31m# COO matrix is not subscriptable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'csc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 256\u001b[0m \u001b[0mn_samples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m 540\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 541\u001b[0m _assert_all_finite(array,\n\u001b[0;32m--> 542\u001b[0;31m allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m 543\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 544\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan)\u001b[0m\n\u001b[1;32m 54\u001b[0m not allow_nan and not np.isfinite(X).all()):\n\u001b[1;32m 55\u001b[0m \u001b[0mtype_err\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'infinity'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mallow_nan\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'NaN, infinity'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype_err\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 57\u001b[0m \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'object'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mallow_nan\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float32')." + ] + } + ], "source": [ "node_predictions = target_encoding.inverse_transform(all_predictions)" ] @@ -732,108 +723,9 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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PredictedTrue
31336subject=Neural_NetworksNeural_Networks
1061127subject=Case_BasedRule_Learning
1106406subject=Reinforcement_LearningReinforcement_Learning
13195subject=Reinforcement_LearningReinforcement_Learning
37879subject=Probabilistic_MethodsProbabilistic_Methods
1126012subject=Case_BasedProbabilistic_Methods
1107140subject=Case_BasedTheory
1102850subject=Neural_NetworksNeural_Networks
31349subject=Neural_NetworksNeural_Networks
1106418subject=Case_BasedTheory
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" - ], - "text/plain": [ - " Predicted True\n", - "31336 subject=Neural_Networks Neural_Networks\n", - "1061127 subject=Case_Based Rule_Learning\n", - "1106406 subject=Reinforcement_Learning Reinforcement_Learning\n", - "13195 subject=Reinforcement_Learning Reinforcement_Learning\n", - "37879 subject=Probabilistic_Methods Probabilistic_Methods\n", - "1126012 subject=Case_Based Probabilistic_Methods\n", - "1107140 subject=Case_Based Theory\n", - "1102850 subject=Neural_Networks Neural_Networks\n", - "31349 subject=Neural_Networks Neural_Networks\n", - "1106418 subject=Case_Based Theory" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "results = pd.DataFrame(node_predictions, index=all_nodes).idxmax(axis=1)\n", "df = pd.DataFrame({\"Predicted\": results, \"True\": node_data['subject']})\n", @@ -849,7 +741,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -867,7 +759,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -880,7 +772,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -900,7 +792,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -909,20 +801,9 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2708, 48)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "emb = embedding_model.predict_generator(all_mapper)\n", "emb.shape" @@ -937,7 +818,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -949,7 +830,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -959,7 +840,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -977,22 +858,9 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "alpha = 0.7\n", "\n", @@ -1021,7 +889,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/demos/node-classification/directed-graphsage/example4-directed-graph-fully-directed-graphsage.ipynb b/demos/node-classification/directed-graphsage/example4-directed-graph-fully-directed-graphsage.ipynb index 7ca8314ac..94f5fe871 100644 --- a/demos/node-classification/directed-graphsage/example4-directed-graph-fully-directed-graphsage.ipynb +++ b/demos/node-classification/directed-graphsage/example4-directed-graph-fully-directed-graphsage.ipynb @@ -25,31 +25,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Using TensorFlow backend.\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", - "/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n" + "Using TensorFlow backend.\n" ] } ], @@ -233,13 +209,13 @@ { "data": { "text/plain": [ - "Counter({'Genetic_Algorithms': 42,\n", - " 'Neural_Networks': 81,\n", - " 'Reinforcement_Learning': 22,\n", + "Counter({'Reinforcement_Learning': 22,\n", " 'Probabilistic_Methods': 42,\n", - " 'Case_Based': 30,\n", + " 'Neural_Networks': 81,\n", + " 'Genetic_Algorithms': 42,\n", + " 'Theory': 35,\n", " 'Rule_Learning': 18,\n", - " 'Theory': 35})" + " 'Case_Based': 30})" ] }, "execution_count": 9, @@ -422,7 +398,7 @@ "output_type": "stream", "text": [ "WARNING: Logging before flag parsing goes to stderr.\n", - "W0820 17:31:40.336038 4741768640 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", + "W0823 10:01:39.750803 4553299392 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", "\n" ] } @@ -452,14 +428,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0820 17:31:40.352051 4741768640 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", + "W0823 10:01:39.767582 4553299392 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", "\n", - "W0820 17:31:40.358065 4741768640 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", + "W0823 10:01:39.774357 4553299392 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", "\n", - "W0820 17:31:40.365343 4741768640 deprecation.py:506] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", + "W0823 10:01:39.781755 4553299392 deprecation.py:506] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", - "W0820 17:31:40.417157 4741768640 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", + "W0823 10:01:39.834255 4553299392 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", "\n" ] } @@ -492,9 +468,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0820 17:31:40.771018 4741768640 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", + "W0823 10:01:40.124934 4553299392 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", "\n", - "W0820 17:31:40.777916 4741768640 deprecation_wrapper.py:119] From /anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", + "W0823 10:01:40.130564 4553299392 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", "\n" ] } @@ -533,7 +509,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0820 17:31:40.874204 4741768640 deprecation.py:323] From /anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", + "W0823 10:01:40.303460 4553299392 deprecation.py:323] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" ] @@ -543,45 +519,45 @@ "output_type": "stream", "text": [ "Epoch 1/20\n", - " - 3s - loss: 1.9143 - acc: 0.1991 - val_loss: 1.7683 - val_acc: 0.4212\n", + " - 3s - loss: 1.8643 - acc: 0.2966 - val_loss: 1.7387 - val_acc: 0.3769\n", "Epoch 2/20\n", - " - 3s - loss: 1.7004 - acc: 0.4686 - val_loss: 1.6589 - val_acc: 0.4397\n", + " - 3s - loss: 1.6582 - acc: 0.4763 - val_loss: 1.6220 - val_acc: 0.4463\n", "Epoch 3/20\n", - " - 3s - loss: 1.5570 - acc: 0.6153 - val_loss: 1.5347 - val_acc: 0.5505\n", + " - 3s - loss: 1.5002 - acc: 0.6323 - val_loss: 1.5037 - val_acc: 0.5833\n", "Epoch 4/20\n", - " - 3s - loss: 1.4112 - acc: 0.7551 - val_loss: 1.4071 - val_acc: 0.6879\n", + " - 3s - loss: 1.3621 - acc: 0.7661 - val_loss: 1.3921 - val_acc: 0.6879\n", "Epoch 5/20\n", - " - 3s - loss: 1.2746 - acc: 0.8695 - val_loss: 1.2963 - val_acc: 0.7469\n", + " - 3s - loss: 1.2382 - acc: 0.8686 - val_loss: 1.2943 - val_acc: 0.7317\n", "Epoch 6/20\n", - " - 3s - loss: 1.1301 - acc: 0.9390 - val_loss: 1.1928 - val_acc: 0.7797\n", + " - 3s - loss: 1.1222 - acc: 0.9093 - val_loss: 1.1994 - val_acc: 0.7494\n", "Epoch 7/20\n", - " - 3s - loss: 1.0000 - acc: 0.9508 - val_loss: 1.1023 - val_acc: 0.7842\n", + " - 3s - loss: 1.0135 - acc: 0.9126 - val_loss: 1.1206 - val_acc: 0.7674\n", "Epoch 8/20\n", - " - 3s - loss: 0.8865 - acc: 0.9652 - val_loss: 1.0314 - val_acc: 0.7900\n", + " - 3s - loss: 0.8860 - acc: 0.9440 - val_loss: 1.0481 - val_acc: 0.7793\n", "Epoch 9/20\n", - " - 3s - loss: 0.7955 - acc: 0.9619 - val_loss: 0.9658 - val_acc: 0.7994\n", + " - 3s - loss: 0.8103 - acc: 0.9619 - val_loss: 0.9955 - val_acc: 0.7822\n", "Epoch 10/20\n", - " - 3s - loss: 0.7101 - acc: 0.9865 - val_loss: 0.9146 - val_acc: 0.8048\n", + " - 3s - loss: 0.7310 - acc: 0.9601 - val_loss: 0.9452 - val_acc: 0.7896\n", "Epoch 11/20\n", - " - 3s - loss: 0.6336 - acc: 0.9720 - val_loss: 0.8638 - val_acc: 0.8048\n", + " - 3s - loss: 0.6471 - acc: 0.9720 - val_loss: 0.8975 - val_acc: 0.7884\n", "Epoch 12/20\n", - " - 3s - loss: 0.5840 - acc: 0.9797 - val_loss: 0.8233 - val_acc: 0.8072\n", + " - 3s - loss: 0.5865 - acc: 0.9763 - val_loss: 0.8701 - val_acc: 0.7830\n", "Epoch 13/20\n", - " - 3s - loss: 0.5123 - acc: 0.9898 - val_loss: 0.7931 - val_acc: 0.8093\n", + " - 3s - loss: 0.5364 - acc: 0.9763 - val_loss: 0.8358 - val_acc: 0.7982\n", "Epoch 14/20\n", - " - 3s - loss: 0.4668 - acc: 0.9898 - val_loss: 0.7634 - val_acc: 0.8068\n", + " - 3s - loss: 0.4771 - acc: 0.9898 - val_loss: 0.8216 - val_acc: 0.7871\n", "Epoch 15/20\n", - " - 3s - loss: 0.4283 - acc: 0.9898 - val_loss: 0.7531 - val_acc: 0.8031\n", + " - 3s - loss: 0.4445 - acc: 0.9788 - val_loss: 0.7956 - val_acc: 0.7884\n", "Epoch 16/20\n", - " - 3s - loss: 0.4004 - acc: 0.9831 - val_loss: 0.7354 - val_acc: 0.8031\n", + " - 3s - loss: 0.4114 - acc: 0.9898 - val_loss: 0.7716 - val_acc: 0.7879\n", "Epoch 17/20\n", - " - 3s - loss: 0.3580 - acc: 0.9898 - val_loss: 0.7080 - val_acc: 0.8072\n", + " - 3s - loss: 0.3853 - acc: 0.9788 - val_loss: 0.7553 - val_acc: 0.7896\n", "Epoch 18/20\n", - " - 3s - loss: 0.3412 - acc: 0.9923 - val_loss: 0.6994 - val_acc: 0.8076\n", + " - 3s - loss: 0.3515 - acc: 0.9822 - val_loss: 0.7369 - val_acc: 0.7990\n", "Epoch 19/20\n", - " - 3s - loss: 0.2964 - acc: 0.9822 - val_loss: 0.6871 - val_acc: 0.8109\n", + " - 3s - loss: 0.3097 - acc: 0.9865 - val_loss: 0.7319 - val_acc: 0.7957\n", "Epoch 20/20\n", - " - 3s - loss: 0.2855 - acc: 0.9932 - val_loss: 0.6795 - val_acc: 0.8064\n" + " - 3s - loss: 0.3098 - acc: 1.0000 - val_loss: 0.7257 - val_acc: 0.7892\n" ] } ], @@ -625,7 +601,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] @@ -670,8 +646,8 @@ "text": [ "\n", "Test Set Metrics:\n", - "\tloss: 0.6787\n", - "\tacc: 0.8113\n" + "\tloss: 0.7135\n", + "\tacc: 0.7970\n" ] } ], @@ -778,7 +754,7 @@ " \n", " \n", " 13195\n", - " subject=Neural_Networks\n", + " subject=Reinforcement_Learning\n", " Reinforcement_Learning\n", " \n", " \n", @@ -793,7 +769,7 @@ " \n", " \n", " 1107140\n", - " subject=Theory\n", + " subject=Probabilistic_Methods\n", " Theory\n", " \n", " \n", @@ -820,10 +796,10 @@ "31336 subject=Neural_Networks Neural_Networks\n", "1061127 subject=Rule_Learning Rule_Learning\n", "1106406 subject=Reinforcement_Learning Reinforcement_Learning\n", - "13195 subject=Neural_Networks Reinforcement_Learning\n", + "13195 subject=Reinforcement_Learning Reinforcement_Learning\n", "37879 subject=Probabilistic_Methods Probabilistic_Methods\n", "1126012 subject=Probabilistic_Methods Probabilistic_Methods\n", - "1107140 subject=Theory Theory\n", + "1107140 subject=Probabilistic_Methods Theory\n", "1102850 subject=Neural_Networks Neural_Networks\n", "31349 subject=Neural_Networks Neural_Networks\n", "1106418 subject=Theory Theory" @@ -984,7 +960,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1059,7 +1035,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/stellargraph/layer/graphsage.py b/stellargraph/layer/graphsage.py index be829e57f..31bc86b44 100644 --- a/stellargraph/layer/graphsage.py +++ b/stellargraph/layer/graphsage.py @@ -553,35 +553,26 @@ class GraphSAGE: layer_sizes (list): Hidden feature dimensions for each layer generator (Sequence): A NodeSequence or LinkSequence. If specified the n_samples and input_dim will be taken from this object. - n_samples (list): (Optional: needs to be specified if no mapper - is provided.) The number of samples per layer in the model. - input_dim (int): The dimensions of the node features used as input to the model. aggregator (class): The GraphSAGE aggregator to use. Defaults to the `MeanAggregator`. bias (bool): If True a bias vector is learnt for each layer in the GraphSAGE model dropout (float): The dropout supplied to each layer in the GraphSAGE model. normalize (str or None): The normalization used after each layer, defaults to L2 normalization. + Note: If a generator is not specified, then additional keyword arguments must be supplied: + n_samples (list): The number of samples per layer in the model. + input_dim (int): The dimensions of the node features used as input to the model. """ def __init__( self, layer_sizes, generator=None, - n_samples=None, - input_dim=None, aggregator=None, bias=True, dropout=0.0, normalize="l2", + **kwargs ): - # Set the aggregator layer used in the model - if aggregator is None: - self._aggregator = MeanAggregator - elif issubclass(aggregator, Layer): - self._aggregator = aggregator - else: - raise TypeError("Aggregator should be a subclass of Keras Layer") - # Set the normalization layer used in the model if normalize == "l2": self._normalization = Lambda(lambda x: K.l2_normalize(x, axis=-1)) @@ -596,38 +587,61 @@ def __init__( ) ) - # Get the input_dim and num_samples from the generator if it is given - # Use both the schema and head node type from the generator - # TODO: Refactor the horror of generator.generator.graph... + # Get the input_dim and num_samples self.generator = generator if generator is not None: - self.n_samples = generator.generator.num_samples - feature_sizes = generator.generator.graph.node_feature_sizes() - if len(feature_sizes) > 1: - raise RuntimeError( - "GraphSAGE called on graph with more than one node type." - ) - - self.input_feature_size = feature_sizes.popitem()[1] - - elif n_samples is not None and input_dim is not None: - self.n_samples = n_samples - self.input_feature_size = input_dim - + self._get_sizes_from_generator(generator) else: - raise RuntimeError( - "If generator is not provided, n_samples and input_dim must be specified." - ) + self._get_sizes_from_keywords(**kwargs) # Model parameters - self.n_layers = len(self.n_samples) self.bias = bias self.dropout = dropout # Feature dimensions for each layer + self.n_layers = len(layer_sizes) + self.layer_sizes = layer_sizes self.dims = [self.input_feature_size] + layer_sizes + # Set the aggregator layer used in the model + if aggregator is None: + self._aggregator = MeanAggregator + elif issubclass(aggregator, Layer): + self._aggregator = aggregator + else: + raise TypeError("Aggregator should be a subclass of Keras Layer") + # Aggregator functions for each layer + self._build_aggregators() + + def _get_sizes_from_generator(self, generator): + """ + Sets n_samples and input_feature_size from the generator. + Args: + generator: The supplied generator. + """ + self.n_samples = generator.generator.num_samples + feature_sizes = generator.generator.graph.node_feature_sizes() + if len(feature_sizes) > 1: + raise RuntimeError( + "GraphSAGE called on graph with more than one node type." + ) + self.input_feature_size = feature_sizes.popitem()[1] + + def _get_sizes_from_keywords(self, **kwargs): + """ + Sets n_samples and input_feature_size from the keywords. + Args: + kwargs: The additional keyword arguments. + """ + self.n_samples = kwargs.get("n_samples") + self.input_feature_size = kwargs.get("input_dim") + if self.n_samples is None or self.input_feature_size is None: + raise ValueError( + "If generator is not provided, n_samples and input_dim must be specified." + ) + + def _build_aggregators(self): self._aggs = [ self._aggregator( output_dim=self.dims[layer + 1], @@ -794,7 +808,7 @@ def default_model(self, flatten_output=True): return self.build() -class DirectedGraphSAGE: +class DirectedGraphSAGE(GraphSAGE): """ Implementation of a directed version of the GraphSAGE algorithm of Hamilton et al. with Keras layers. see: http://snap.stanford.edu/graphsage/ @@ -809,104 +823,77 @@ class DirectedGraphSAGE: Args: layer_sizes (list): Hidden feature dimensions for each layer - - Either: - generator (Sequence): A NodeSequence or LinkSequence. - Or: - in_samples (list): The number of in-node samples per layer in the model. - out_samples (list): The number of out-node samples per layer in the model. - input_dim (int): The dimensions of the node features used as input to the model. + generator (Sequence): A NodeSequence or LinkSequence. aggregator (class): The GraphSAGE aggregator to use. Defaults to the `MeanAggregator`. bias (bool): If True a bias vector is learnt for each layer in the GraphSAGE model dropout (float): The dropout supplied to each layer in the GraphSAGE model. normalize (str or None): The normalization used after each layer, defaults to L2 normalization. - """ - def __init__( - self, - layer_sizes, - generator=None, - in_samples=None, - out_samples=None, - input_dim=None, - aggregator=None, - bias=True, - dropout=0.0, - normalize="l2", - ): - # Set the aggregator layer used in the model - if aggregator is None: - self._aggregator = MeanAggregator - elif issubclass(aggregator, Layer): - self._aggregator = aggregator - else: - raise TypeError("Aggregator should be a subclass of Keras Layer") + Note: If a generator is not specified, then additional keyword arguments must be supplied: + in_samples (list): The number of in-node samples per layer in the model. + out_samples (list): The number of out-node samples per layer in the model. + input_dim (int): The dimensions of the node features used as input to the model. - # Set the normalization layer used in the model - if normalize == "l2": - self._normalization = Lambda(lambda x: K.l2_normalize(x, axis=-1)) - elif normalize is None or normalize == "none" or normalize == "None": - self._normalization = Lambda(lambda x: x) - else: - raise ValueError( - "Normalization should be either 'l2' or 'none'; received '{}'".format( - normalize - ) - ) + """ - # Get the input_dim and num_samples from the generator if it is given - # Use both the schema and head node type from the generator - # TODO: Refactor the horror of generator.generator.graph... - self.generator = generator - if generator is not None: - self.in_samples = generator.generator.in_samples - self.out_samples = generator.generator.out_samples - feature_sizes = generator.generator.graph.node_feature_sizes() - if len(feature_sizes) > 1: - raise RuntimeError( - "GraphSAGE called on graph with more than one node type." - ) - self.input_feature_size = feature_sizes.popitem()[1] + def _get_sizes_from_generator(self, generator): + """ + Sets in_samples, out_samples and input_feature_size from the generator. + Args: + generator: The supplied generator. + """ + self.in_samples = generator.generator.in_samples + self.out_samples = generator.generator.out_samples + feature_sizes = generator.generator.graph.node_feature_sizes() + if len(feature_sizes) > 1: + raise RuntimeError( + "DirectedGraphSAGE called on graph with more than one node type." + ) + self.input_feature_size = feature_sizes.popitem()[1] - elif ( - in_samples is not None and out_samples is not None and input_dim is not None + def _get_sizes_from_keywords(self, **kwargs): + """ + Sets in_samples, out_samples and input_feature_size from the keywords. + Args: + kwargs: The additional keyword arguments. + """ + self.in_samples = kwargs.get("in_samples") + self.out_samples = kwargs.get("out_samples") + self.input_feature_size = kwargs.get("input_dim") + if ( + self.in_samples is None + or self.out_samples is None + or self.input_feature_size is None ): - self.in_samples = in_samples - self.out_samples = out_samples - self.input_feature_size = input_dim - - else: - raise RuntimeError( - "If generator is not provided, n_samples and input_dim must be specified." + raise ValueError( + "If generator is not provided, in_samples, out_samples and input_dim must be specified." ) - self.max_hops = max_hops = len(layer_sizes) + def _build_aggregators(self): + # Model parameters + self.max_hops = max_hops = self.n_layers + self.max_slots = 2 ** (max_hops + 1) - 1 + if len(self.in_samples) != max_hops: raise ValueError( "Mismatched lengths: in-node sample sizes {} versus layer sizes {}".format( - self.in_samples, layer_sizes + self.in_samples, self.layer_sizes ) ) if len(self.out_samples) != max_hops: raise ValueError( "Mismatched lengths: out-node sample sizes {} versus layer sizes {}".format( - self.out_samples, layer_sizes + self.out_samples, self.layer_sizes ) ) - # Model parameters - self.max_slots = 2 ** (max_hops + 1) - 1 - self.bias = bias - self.dropout = dropout - - # Feature dimensions for each layer - self.layer_sizes = layer_sizes - self.dims = [self.input_feature_size] + layer_sizes + # Define sizes of the various neighbourhoods + self.neighbourhood_sizes = self._compute_input_sizes() # Aggregator functions for each layer self._aggs = [ self._aggregator( - output_dim=layer_sizes[i], + output_dim=self.layer_sizes[i], bias=self.bias, act="relu" if i < max_hops - 1 else "linear", neigh_dim=2, @@ -1009,65 +996,8 @@ def node_model(self): Input(shape=(s, self.input_feature_size)) for s in self.neighbourhood_sizes ] - # Output from GraphSAGE model + # Output from DirectedGraphSAGE model x_out = self(x_inp) + # Returns inputs and outputs return x_inp, x_out - - def link_model(self): - """ - Builds a GraphSAGE model for link or node pair prediction - - Returns: - tuple: (x_inp, x_out) where ``x_inp`` is a list of Keras input tensors for (src, dst) node pairs - (where (src, dst) node inputs alternate), - and ``x_out`` is a list of output tensors for (src, dst) nodes in the node pairs - - """ - # Expose input and output sockets of the model, for source and destination nodes: - x_inp_src, x_out_src = self.node_model() - x_inp_dst, x_out_dst = self.node_model() - # re-pack into a list where (source, target) inputs alternate, for link inputs: - x_inp = [x for ab in zip(x_inp_src, x_inp_dst) for x in ab] - # same for outputs: - x_out = [x_out_src, x_out_dst] - return x_inp, x_out - - def build(self): - """ - Builds a GraphSAGE model for node or link/node pair prediction, depending on the generator used to construct - the model (whether it is a node or link/node pair generator). - - Returns: - tuple: (x_inp, x_out), where ``x_inp`` is a list of Keras input tensors - for the specified GraphSAGE model (either node or link/node pair model) and ``x_out`` contains - model output tensor(s) of shape (batch_size, layer_sizes[-1]) - - """ - self.neighbourhood_sizes = self._compute_input_sizes() - - if self.generator is not None and hasattr(self.generator, "_sampling_schema"): - if len(self.generator._sampling_schema) == 1: - return self.node_model() - elif len(self.generator._sampling_schema) == 2: - return self.link_model() - else: - raise RuntimeError( - "The generator used for model creation is neither a node nor a link generator, " - "unable to figure out how to build the model. Consider using node_model or " - "link_model method explicitly to build node or link prediction model, respectively." - ) - else: - raise RuntimeError( - "Suitable generator is not provided at model creation time, unable to figure out how to build the model. " - "Consider either providing a generator, or using node_model or link_model method explicitly to build node or " - "link prediction model, respectively." - ) - - def default_model(self, flatten_output=True): - warnings.warn( - "The .default_model() method will be deprecated in future versions. " - "Please use .build() method instead.", - PendingDeprecationWarning, - ) - return self.build() diff --git a/tests/layer/test_graphsage.py b/tests/layer/test_graphsage.py index 07f0aebb4..0d0de07f7 100644 --- a/tests/layer/test_graphsage.py +++ b/tests/layer/test_graphsage.py @@ -367,7 +367,7 @@ def test_graphsage_constructor(): GraphSAGE(layer_sizes=[4], n_samples=[2], input_dim=2, normalize="unknown") # Check requirement for generator or n_samples - with pytest.raises(RuntimeError): + with pytest.raises(ValueError): GraphSAGE(layer_sizes=[4]) # Construction from generator From 9b0edefd6a8f81e809a5418552eb7c62f985329d Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Mon, 26 Aug 2019 14:05:17 +0930 Subject: [PATCH 18/30] Updated from directed BFS issue 464. --- stellargraph/data/explorer.py | 178 ++++++++++------------------------ 1 file changed, 53 insertions(+), 125 deletions(-) diff --git a/stellargraph/data/explorer.py b/stellargraph/data/explorer.py index 3b85b2c8c..2cef4ffe1 100644 --- a/stellargraph/data/explorer.py +++ b/stellargraph/data/explorer.py @@ -62,6 +62,30 @@ def __init__(self, graph, graph_schema=None, seed=None): "The parameter graph_schema should be either None or of type GraphSchema." ) + def get_adjacency_types(self): + # Allow additional info for heterogeneous graphs. + adj = getattr(self, "adj_types", None) + if not adj: + # Create a dict of adjacency lists per edge type, for faster neighbour sampling from graph in SampledHeteroBFS: + # TODO: this could be better placed inside StellarGraph class + edge_types = self.graph_schema.edge_types + adj = {et: defaultdict(lambda: [None]) for et in edge_types} + + for n1, nbrdict in self.graph.adjacency(): + for et in edge_types: + neigh_et = [ + n2 + for n2, nkeys in nbrdict.items() + for k in nkeys + if self.graph_schema.is_of_edge_type((n1, n2, k), et) + ] + # Create adjacency list in lexographical order + # Otherwise sampling methods will not be deterministic + # even when the seed is set. + adj[et][n1] = sorted(neigh_et, key=str) + self.adj_types = adj + return adj + def _check_seed(self, seed): if seed is not None: if type(seed) != int: @@ -109,17 +133,16 @@ def run(self, **kwargs): def _raise_error(self, msg): raise ValueError("({}) {}".format(type(self).__name__, msg)) - def _check_parameter_values(self, nodes=None, n=0, length=0, seed=None): + def _check_common_parameters(self, nodes, n, length, seed): """ Checks that the parameter values are valid or raises ValueError exceptions with a message indicating the parameter (the first one encountered in the checks) with invalid value. Args: - nodes: A list of root node ids such that from each node a uniform random walk of up to length l - will be generated. + nodes: A list of root node ids from which to commence the random walks. n: Number of walks per node id. - length: Maximum length of walk measured as the number of edges followed from root node. - seed: Random number generator seed + length: Maximum length of each walk. + seed: Random number generator seed. """ self._check_nodes(nodes) self._check_repetitions(n) @@ -164,39 +187,12 @@ def _check_sizes(self, n_size): self._raise_error(err_msg) if len(n_size) == 0: # Technically, length 0 should be okay, but by consensus it is invalid. - self._raise_error("The neighbourhood size should not be empty.") + self._raise_error("The neighbourhood size list should not be empty.") for d in n_size: if type(d) != int or d < 0: self._raise_error(err_msg) -class HeterogeneousGraphWalk(GraphWalk): - """ - Base class for exploring graphs with heterogeneous edge types. - """ - - def __init__(self, graph, graph_schema=None, seed=None): - super().__init__(graph, graph_schema, seed) - - # Create a dict of adjacency lists per edge type, for faster neighbour sampling from graph in SampledHeteroBFS: - # TODO: this could be better placed inside StellarGraph class - edge_types = self.graph_schema.edge_types - self.adj = {et: defaultdict(lambda: [None]) for et in edge_types} - - for n1, nbrdict in graph.adjacency(): - for et in edge_types: - neigh_et = [ - n2 - for n2, nkeys in nbrdict.items() - for k in nkeys - if self.graph_schema.is_of_edge_type((n1, n2, k), et) - ] - # Create adjacency list in lexographical order - # Otherwise sampling methods will not be deterministic - # even when the seed is set. - self.adj[et][n1] = sorted(neigh_et, key=str) - - class UniformRandomWalk(GraphWalk): """ Performs uniform random walks on the given graph @@ -216,7 +212,7 @@ def run(self, nodes=None, n=None, length=None, seed=None): List of lists of nodes ids for each of the random walks """ - self._check_parameter_values(nodes, n, length, seed) + self._check_common_parameters(nodes, n, length, seed) rs = self._get_random_state(seed) walks = [] @@ -310,9 +306,8 @@ def run( List of lists of nodes ids for each of the random walks """ - self._check_parameter_values( - nodes, n, p, q, length, seed, weighted, edge_weight_label - ) + self._check_common_parameters(nodes, n, length, seed) + self._check_weights(p, q, weighted, edge_weight_label) rs = self._get_random_state(seed) if weighted: @@ -417,30 +412,17 @@ def transition_probability( return walks - def _check_parameter_values( - self, nodes, n, p, q, length, seed, weighted, edge_weight_label - ): + def _check_weights(self, p, q, weighted, edge_weight_label): """ Checks that the parameter values are valid or raises ValueError exceptions with a message indicating the parameter (the first one encountered in the checks) with invalid value. Args: - nodes: A list of root node ids such that from each node a uniform random walk of up to length l - will be generated. - n: Number of walks per node id. - p: - q: - length: Maximum length of walk measured as the number of edges followed from root node. - seed: Random number generator seed. + p: The backward walk 'penalty' factor. + q: The forward walk 'penalty' factor. weighted: Indicates whether the walk is unweighted or weighted. edge_weight_label: Label of the edge weight property. - - Returns: - The random state as determined by the seed. """ - super()._check_parameter_values(nodes, n, length, seed) - rs = self._get_random_state(seed) - if p <= 0.0: self._raise_error("Parameter p should be greater than 0.") @@ -455,10 +437,8 @@ def _check_parameter_values( if not isinstance(edge_weight_label, str): self._raise_error("The edge weight property label has to be of type string") - return rs - -class UniformRandomMetaPathWalk(HeterogeneousGraphWalk): +class UniformRandomMetaPathWalk(GraphWalk): """ For heterogeneous graphs, it performs uniform random walks based on given metapaths. """ @@ -488,9 +468,8 @@ def run( Returns: List of lists of nodes ids for each of the random walks generated """ - self._check_parameter_values( - nodes, n, length, metapaths, node_type_attribute, seed - ) + self._check_common_parameters(nodes, n, length, seed) + self._check_metapath_values(metapaths, node_type_attribute) rs = self._get_random_state(seed) walks = [] @@ -540,29 +519,17 @@ def run( return walks - def _check_parameter_values( - self, nodes, n, length, metapaths, node_type_attribute, seed - ): + def _check_metapath_values(self, metapaths, node_type_attribute): """ Checks that the parameter values are valid or raises ValueError exceptions with a message indicating the parameter (the first one encountered in the checks) with invalid value. Args: - nodes: The starting nodes as a list of node IDs. - n: Number of walks per node id. - length: Maximum length of of each random walk metapaths: List of lists of node labels that specify a metapath schema, e.g., - [['Author', 'Paper', 'Author'], ['Author, 'Paper', 'Venue', 'Paper', 'Author']] specifies two metapath - schemas of length 3 and 5 respectively. + [['Author', 'Paper', 'Author'], ['Author, 'Paper', 'Venue', 'Paper', 'Author']] specifies two metapath + schemas of length 3 and 5 respectively. node_type_attribute: The node attribute name that stores the node's type - seed: Random number generator seed - - Returns: - The random state as determined by the seed. """ - super()._check_parameter_values(nodes, n, length, seed) - rs = self._get_random_state(seed) - if type(metapaths) != list: self._raise_error("The metapaths parameter must be a list of lists.") for metapath in metapaths: @@ -586,8 +553,6 @@ def _check_parameter_values( ) ) - return rs - class SampledBreadthFirstWalk(GraphWalk): """ @@ -611,7 +576,8 @@ def run(self, nodes=None, n=1, n_size=None, seed=None): Returns: A list of lists such that each list element is a sequence of ids corresponding to a BFW. """ - self._check_parameter_values(nodes, n, n_size, seed) + self._check_sizes(n_size) + self._check_common_parameters(nodes, n, len(n_size), seed) rs = self._get_random_state(seed) walks = [] @@ -654,26 +620,8 @@ def run(self, nodes=None, n=1, n_size=None, seed=None): return walks - def _check_parameter_values(self, nodes, n, n_size, seed): - """ - Checks that the parameter values are valid or raises ValueError exceptions with a message indicating the - parameter (the first one encountered in the checks) with invalid value. - Args: - nodes: A list of root node ids such that from each node n BFWs will be generated up to the - given depth d. - n: Number of walks per node id. - n_size: The number of neighbouring nodes to expand at each depth of the walk. - seed: Random number generator seed; default is None - - Returns: - The random state as determined by the seed. - """ - self._check_sizes(n_size) - return super()._check_parameter_values(nodes, n, len(n_size), seed) - - -class SampledHeterogeneousBreadthFirstWalk(HeterogeneousGraphWalk): +class SampledHeterogeneousBreadthFirstWalk(GraphWalk): """ Breadth First Walk for heterogeneous graphs that generates a sampled number of paths from a starting node. It can be used to extract a random sub-graph starting from a set of initial nodes. @@ -697,9 +645,12 @@ def run(self, nodes=None, n=1, n_size=None, seed=None): A list of lists such that each list element is a sequence of ids corresponding to a sampled Heterogeneous BFW. """ - self._check_parameter_values(nodes, n, n_size, self.graph_schema, seed) + self._check_sizes(n_size) + self._check_common_parameters(nodes, n, len(n_size), seed) rs = self._get_random_state(seed) + adj = self.get_adjacency_types() + walks = [] d = len(n_size) # depth of search @@ -727,11 +678,11 @@ def run(self, nodes=None, n=1, n_size=None, seed=None): # Create samples of neigbhours for all edge types for et in current_edge_types: - neigh_et = self.adj[et][current_node] + neigh_et = adj[et][current_node] # If there are no neighbours of this type then we return None # in the place of the nodes that would have been sampled - # YT update: with the new way to get neigh_et from self.adj[et][current_node], len(neigh_et) is always > 0. + # YT update: with the new way to get neigh_et from adj[et][current_node], len(neigh_et) is always > 0. # In case of no neighbours of the current node for et, neigh_et == [None], # and samples automatically becomes [None]*n_size[depth-1] if len(neigh_et) > 0: @@ -757,25 +708,6 @@ def run(self, nodes=None, n=1, n_size=None, seed=None): return walks - def _check_parameter_values(self, nodes, n, n_size, graph_schema, seed): - """ - Checks that the parameter values are valid or raises ValueError exceptions with a message indicating the - parameter (the first one encountered in the checks) with invalid value. - - Args: - nodes: A list of root node ids such that from each node n BFWs will be generated up to the - given depth d. - n: Number of walks per node id. - n_size: The number of neighbouring nodes to expand at each depth of the walk. - graph_schema: None or a stellargraph graph schema object - seed: Random number generator seed; default is None - - Returns: - The random state as determined by the seed. - """ - self._check_sizes(n_size) - return super()._check_parameter_values(nodes, n, len(n_size), seed) - class DirectedBreadthFirstNeighbours(GraphWalk): """ @@ -816,7 +748,8 @@ def run(self, nodes=None, n=1, in_size=None, out_size=None, seed=None): [node out_i out_i.in_j out_i.in_j.in_k ...] """ - self._check_parameter_values(nodes, n, in_size, out_size, seed) + self._check_neighbourhood_sizes(in_size, out_size) + self._check_common_parameters(nodes, n, len(in_size), seed) rs = self._get_random_state(seed) max_hops = len(in_size) @@ -899,7 +832,7 @@ def _sample_neighbours(self, rs, node, idx, size): # Sample with replacement return [rs.choice(neighbours) for _ in range(size)] - def _check_parameter_values(self, nodes, n, in_size, out_size, seed): + def _check_neighbourhood_sizes(self, in_size, out_size): """ Checks that the parameter values are valid or raises ValueError exceptions with a message indicating the parameter (the first one encountered in the checks) with invalid value. @@ -907,12 +840,8 @@ def _check_parameter_values(self, nodes, n, in_size, out_size, seed): Args: nodes: A list of root node ids such that from each node n BFWs will be generated up to the given depth d. - n: Number of walks per node id. n_size: The number of neighbouring nodes to expand at each depth of the walk. seed: Random number generator seed; default is None - - Returns: - The random state as determined by the seed. """ self._check_sizes(in_size) self._check_sizes(out_size) @@ -920,4 +849,3 @@ def _check_parameter_values(self, nodes, n, in_size, out_size, seed): self._raise_error( "The number of hops for the in and out neighbourhoods must be the same." ) - return super()._check_parameter_values(nodes, n, len(in_size), seed) From 8e4ead030e5c97d78f274be6da6a5870d95b6b62 Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Mon, 26 Aug 2019 15:49:23 +0930 Subject: [PATCH 19/30] Updated requirements from develop. --- requirements.txt | 10 +++++----- setup.py | 2 +- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/requirements.txt b/requirements.txt index 4ea9305e2..a4a5b67af 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,15 +1,15 @@ -networkx==2.2 +networkx>=2.2 setuptools>=39.1.0 -Keras>=2.2.3 -tensorflow>=1.12.1,<2.0 +Keras>=2.2.3,<2.2.5 +tensorflow>=1.12,<2.0 numba>=0.39.0 numpy>=1.14 scipy>=1.1.0 jupyter>=1.0.0 -scikit_learn>=0.20.1 +scikit_learn>=0.20 matplotlib>=2.2 pandas>=0.24 -gensim==3.4.0 +gensim>=3.4.0 pytest==3.9.3 pytest-benchmark>=3.1 pytest-cov>=2.6.0 diff --git a/setup.py b/setup.py index 7890bd387..28043fff2 100644 --- a/setup.py +++ b/setup.py @@ -21,7 +21,7 @@ # Required packages REQUIRES = [ - "keras>=2.2.4", + "keras>=2.2.3,<2.2.5", "tensorflow>=1.12,<2.0", "numpy>=1.14", "scipy>=1.1.0", From b58edc8993c61b2b3e9b2112bbcf676dd289ca7c Mon Sep 17 00:00:00 2001 From: Andrew Docherty Date: Mon, 2 Sep 2019 13:45:34 +1000 Subject: [PATCH 20/30] Added DirectedGraphSAGE to API docs. Generalized GraphSAGEAggregator behaviour. --- .../directed-graphsage/experim.ipynb | 231 ++++++-- docs/api.txt | 2 +- stellargraph/layer/graphsage.py | 556 ++++++++++-------- tests/layer/test_graphsage.py | 215 ++++--- 4 files changed, 633 insertions(+), 371 deletions(-) diff --git a/demos/node-classification/directed-graphsage/experim.ipynb b/demos/node-classification/directed-graphsage/experim.ipynb index 1ff043169..5ef92d6fd 100644 --- a/demos/node-classification/directed-graphsage/experim.ipynb +++ b/demos/node-classification/directed-graphsage/experim.ipynb @@ -18,9 +18,41 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n", + "/Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", + "/Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", + "/Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", + "/Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", + "/Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", + "/Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n", + "/Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", + "/Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", + "/Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", + "/Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", + "/Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", + "/Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n" + ] + } + ], "source": [ "import networkx as nx\n", "import pandas as pd\n", @@ -68,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -93,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -122,7 +154,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -131,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -164,7 +196,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -189,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -216,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -232,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -260,7 +292,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -288,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -297,14 +329,14 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n" + "\n" ] } ], @@ -323,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -332,7 +364,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -375,11 +407,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ - "batch_size = 3; in_samples = [0, 0]; out_samples = [2, 2]" + "batch_size = 3; in_samples = [0, 0]; out_samples = [1, 1]" ] }, { @@ -391,7 +423,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -407,23 +439,56 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n" + "\n" ] } ], "source": [ - "train_data = node_data[\"id\"]\n", + "train_data = node_data.index\n", "train_gen = generator.flow(train_data, shuffle=False)\n", "print(train_gen)" ] }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[array([[[1.]],\n", + " \n", + " [[0.]],\n", + " \n", + " [[2.]]]), array([], shape=(3, 0, 1), dtype=float64), array([[[0.]],\n", + " \n", + " [[2.]],\n", + " \n", + " [[0.]]]), array([], shape=(3, 0, 1), dtype=float64), array([], shape=(3, 0, 1), dtype=float64), array([], shape=(3, 0, 1), dtype=float64), array([[[2.]],\n", + " \n", + " [[0.]],\n", + " \n", + " [[0.]]])]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "features = train_gen.generator.sample_features(nodes, train_gen._sampling_schema)\n", + "features" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -436,9 +501,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /Users/doc019/.envs/sgdev/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", + "\n" + ] + } + ], "source": [ "graphsage_model = DirectedGraphSAGE(\n", " layer_sizes=[48, 48],\n", @@ -457,12 +531,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /Users/doc019/.envs/sgdev/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", + "\n", + "WARNING:tensorflow:From /Users/doc019/.envs/sgdev/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", + "\n", + "WARNING:tensorflow:From /Users/doc019/.envs/sgdev/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", + "WARNING:tensorflow:From /Users/doc019/.envs/sgdev/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", + "\n" + ] + } + ], "source": [ "x_inp, x_out = graphsage_model.build()\n", - "prediction = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)" + "prediction = layers.Dense(units=1, activation=\"softmax\")(x_out)" ] }, { @@ -481,9 +571,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /Users/doc019/.envs/sgdev/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", + "\n", + "WARNING:tensorflow:From /Users/doc019/.envs/sgdev/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", + "\n" + ] + } + ], "source": [ "model = Model(inputs=x_inp, outputs=prediction)\n", "model.compile(\n", @@ -493,6 +594,70 @@ ")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Save the model" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "model.save(\"test_save_model.h5\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the model using the StellarGraph custom layers:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.models import load_model\n", + "\n", + "model = load_model(\"test_save_model.h5\", custom_objects=sg.custom_keras_layers)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can visualize the model:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from keras.utils.vis_utils import model_to_dot\n", + "from IPython.display import Image\n", + "Image(model_to_dot(model).create_png())" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -777,9 +942,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "stellargraph-dev", "language": "python", - "name": "python3" + "name": "stellargraph-dev" }, "language_info": { "codemirror_mode": { @@ -791,9 +956,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.6.8" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/api.txt b/docs/api.txt index 1e87e495c..5b2f71de8 100644 --- a/docs/api.txt +++ b/docs/api.txt @@ -26,7 +26,7 @@ GraphSAGE model ---------------- .. automodule:: stellargraph.layer.graphsage - :members: GraphSAGE, MeanAggregator, MeanPoolingAggregator, MaxPoolingAggregator, AttentionalAggregator + :members: GraphSAGE, DirectedGraphSAGE, MeanAggregator, MeanPoolingAggregator, MaxPoolingAggregator, AttentionalAggregator HinSAGE model diff --git a/stellargraph/layer/graphsage.py b/stellargraph/layer/graphsage.py index 31bc86b44..f3effa85e 100644 --- a/stellargraph/layer/graphsage.py +++ b/stellargraph/layer/graphsage.py @@ -49,8 +49,6 @@ class GraphSAGEAggregator(Layer): a bias term should be included. act (Callable or str): name of the activation function to use (must be a Keras activation function), or alternatively, a TensorFlow operation. - neigh_dim (int): Optional value indicating the maximum number of multi-dimensional - neighbourhoods; defaults to 1. """ def __init__( @@ -58,17 +56,14 @@ def __init__( output_dim: int = 0, bias: bool = False, act: Callable or AnyStr = "relu", - neigh_dim: int = 1, - **kwargs + **kwargs, ): - self.neigh_dim = neigh_dim self.output_dim = output_dim - self.neighbour_output_dim = output_dim // (neigh_dim + 1) - self.node_output_dim = output_dim - neigh_dim * self.neighbour_output_dim self.has_bias = bias self.act = activations.get(act) self.w_self = None self.bias = None + self.weight_dims = None self._initializer = "glorot_uniform" super().__init__(**kwargs) @@ -85,22 +80,43 @@ def get_config(self): base_config = super().get_config() return {**base_config, **config} - def weight_output_size(self): + def calculate_group_sizes(self, input_shape): """ - Calculates the output size, according to - whether the model is building a MLP and - the method (concat or sum). - - Returns: - int: size of the weight outputs. + Calculates the output size for each input group. The results are stored in two variables: + self.included_weight_groups: if the corresponding entry is True then the input group + is valid and should be used. + self.weight_sizes: the size of the output from this group. + Args: + input_shape (list of list of int): Shape of input tensors for self + and neighbour features """ - if self._build_mlp_only: - weight_dim = self.output_dim - else: - weight_dim = self.node_output_dim + # If the neighbours are zero-dimensional for any of the shapes + # in the input, do not use the input group in the model. + self.included_weight_groups = [ + all(dim != 0 for dim in group_shape) for group_shape in input_shape + ] - return weight_dim + # The total number of enabled input groups + num_groups = np.sum(self.included_weight_groups) + if num_groups < 1: + raise ValueError( + "There must be at least one input with a non-zero neighbourhood dimension" + ) + + # Calculate the dimensionality of each group, and put remainder into the first group + # with non-zero dimensions, which should be the head node group. + group_output_dim = self.output_dim // num_groups + remainder_dim = self.output_dim - num_groups * group_output_dim + weight_dims = [] + for g in self.included_weight_groups: + if g: + group_dim = group_output_dim + remainder_dim + remainder_dim = 0 + else: + group_dim = 0 + weight_dims.append(group_dim) + self.weight_dims = weight_dims def build(self, input_shape): """ @@ -119,12 +135,6 @@ def build(self, input_shape): raise ValueError( "Expected a list of inputs, not {}".format(type(input_shape)) ) - if len(input_shape) != self.neigh_dim + 1: - raise ValueError( - "List of inputs must be of length {}, not {}".format( - self.neigh_dim + 1, len(input_shape) - ) - ) # Configure bias vector, if used. if self.has_bias: @@ -135,66 +145,47 @@ def build(self, input_shape): trainable=True, ) - # If there are zero neighbours, back off to - # using only node features. - counter = 1 - for neigh_shape in input_shape[1:]: - if neigh_shape[2] == 0: - counter = counter + 1 - self._build_mlp_only = counter == len(input_shape) - - # Configure weights for node features. - self.w_self = self.add_weight( - name="w_self", - shape=(input_shape[0][2], self.weight_output_size()), - initializer=self._initializer, - trainable=True, - ) + # Calculate weight size for each input group + self.calculate_group_sizes(input_shape) - # Configure weights for neighbouring nodes, if used. - self._build_neighbour_weights(input_shape) + # Configure weights for input groups, if used. + w_group = [None] * len(input_shape) + for ii, g_shape in enumerate(input_shape): + if self.included_weight_groups[ii]: + weight = self._build_group_weights( + g_shape, self.weight_dims[ii], group_idx=ii + ) + w_group[ii] = weight + self.w_group = w_group # Signal that the build has completed. super().build(input_shape) - def _build_neighbour_weights(self, input_shape): + def _build_group_weights(self, in_shape, out_size, group_idx=0): """ - Builds the weight tensor(s) corresponding to the features - of the neighbouring nodes in sampled random walks. + Builds the weight tensor(s) corresponding to the features of the input groups. Args: - input_shape (list of list of int): Shape of input tensors for self - and neighbour features - """ - pass + in_shape (list of int): Shape of input tensor for single group + out_size (int): The size of the output vector for this group + group_idx (int): The index of the input group - def apply_mlp(self, x, **kwargs): """ - Create MLP on input self tensor, x - - Args: - x (List[Tensor]): Tensor giving the node features - shape: (batch_size, head size, feature_size) - - Returns: - Keras Tensor representing the aggregated embeddings in the input. - - """ - # Weight maxtrix multiplied by node features - h_out = K.dot(x, self.w_self) - # Optionally add bias - if self.has_bias: - h_out = h_out + self.bias - # Finally, apply activation - return self.act(h_out) + weight = self.add_weight( + shape=(in_shape[-1], out_size), + initializer=self._initializer, + trainable=True, + name=f"weight_g{group_idx}", + ) + return weight - def aggregate_neighbours(self, x_neigh, neigh_idx: int = 0): + def aggregate_neighbours(self, x_neigh, group_idx: int = 0): """ Override with a method to aggregate tensors over neighbourhood. Args: x_neigh: The input tensor representing the sampled neighbour nodes. - neigh_idx: Optional neighbourhood index used for multi-dimensional hops. + group_idx: Optional neighbourhood index used for multi-dimensional hops. Returns: A tensor aggregation of the input nodes features. @@ -203,36 +194,30 @@ def aggregate_neighbours(self, x_neigh, neigh_idx: int = 0): "The GraphSAGEAggregator base class should not be directly instantiated" ) - def call(self, x, **kwargs): + def call(self, inputs, **kwargs): """ - Apply aggregator on input tensors, x + Apply aggregator on the input tensors, `inputs` Args: - x: Keras Tensor + inputs: List of Keras tensors Returns: Keras Tensor representing the aggregated embeddings in the input. """ - # x[0]: node vector (batch_size, head size, feature_size) - # x[1...n]: optional neighbour vectors (batch_size, head size, neighbours, feature_size) - x_self = x[0] - - if self._build_mlp_only: - return self.apply_mlp(x_self, **kwargs) - - # Weight maxtrix multiplied by self features - from_self = K.dot(x_self, self.w_self) - - # If there are neighbours aggregate over them - if len(x) > 1: - sources = [from_self] - for i in range(1, len(x)): - neigh = self.aggregate_neighbours(x[i], neigh_idx=i - 1) - sources.append(neigh) - h_out = K.concatenate(sources, axis=2) - else: - h_out = from_self + # If a neighbourhood dimension exists for the group, aggregate over the neighbours + # otherwise create a simple layer. + sources = [] + for ii, x in enumerate(inputs): + # If the group is included, apply aggregation and collect the output tensor + # otherwise, this group is ignored + if self.included_weight_groups[ii]: + x_agg = self.group_aggregate(x, group_idx=ii) + sources.append(x_agg) + + # Concatenate outputs from all groups + # TODO: Generalize to sum a subset of groups. + h_out = K.concatenate(sources, axis=2) # Optionally add bias if self.has_bias: @@ -251,10 +236,26 @@ def compute_output_shape(self, input_shape): Shape tuples can include None for free dimensions, instead of an integer. Returns: - An input shape tuple. + The output shape calculated from the input shape, this is of the form + (batch_num, head_num, output_dim) """ return input_shape[0][0], input_shape[0][1], self.output_dim + def group_aggregate(self, x_neigh, group_idx=0): + """ + Override with a method to aggregate tensors over the neighbourhood for each group. + + Args: + x_neigh (tf.Tensor): : The input tensor representing the sampled neighbour nodes. + group_idx (int, optional): Group index. + + Returns: + [tf.Tensor]: A tensor aggregation of the input nodes features. + """ + raise NotImplementedError( + "The GraphSAGEAggregator base class should not be directly instantiated" + ) + class MeanAggregator(GraphSAGEAggregator): """ @@ -268,33 +269,24 @@ class MeanAggregator(GraphSAGEAggregator): """ - def _build_neighbour_weights(self, input_shape): + def group_aggregate(self, x_group, group_idx=0): """ - Builds layer - + Mean aggregator for tensors over the neighbourhood for each group. + Args: - input_shape (list of list of int): Shape of input tensors for self - and neighbour features - + x_group (tf.Tensor): : The input tensor representing the sampled neighbour nodes. + group_idx (int, optional): Group index. + + Returns: + [tf.Tensor]: A tensor aggregation of the input nodes features. """ - if self._build_mlp_only: - self.w_neigh = None - + # The first group is assumed to be the self-tensor and we do not aggregate over it + if group_idx == 0: + x_agg = x_group else: - self.w_neigh = [None] * self.neigh_dim - out_size = self.neighbour_output_dim - for i in range(self.neigh_dim): - in_size = input_shape[i + 1][3] - weights = self.add_weight( - name="w_neigh" + str(i), - shape=(in_size, out_size), - initializer=self._initializer, - trainable=True, - ) - self.w_neigh[i] = weights + x_agg = K.mean(x_group, axis=2) - def aggregate_neighbours(self, x_neigh, neigh_idx=0): - return K.dot(K.mean(x_neigh, axis=2), self.w_neigh[neigh_idx]) + return K.dot(x_agg, self.w_group[group_idx]) class MaxPoolingAggregator(GraphSAGEAggregator): @@ -318,59 +310,72 @@ def __init__(self, *args, **kwargs): self.hidden_dim = self.output_dim self.hidden_act = activations.get("relu") - def _build_neighbour_weights(self, input_shape): + def _build_group_weights(self, in_shape, out_size, group_idx=0): """ - Builds layer + Builds the weight tensor(s) corresponding to the features of the input groups. Args: - input_shape (list of list of int): Shape of input tensors for self - and neighbour features + in_shape (list of int): Shape of input tensor for single group + out_size (int): The size of the output vector for this group + group_idx (int): The index of the input group """ - if self._build_mlp_only: - self.w_neigh = None - self.w_pool = None - self.b_pool = None - + if group_idx == 0: + weights = self.add_weight( + name=f"w_g{group_idx}", + shape=(in_shape[-1], out_size), + initializer=self._initializer, + trainable=True, + ) else: - self.w_neigh = self.add_weight( - name="w_neigh", - shape=(self.hidden_dim, self.weight_output_size()), + w_group = self.add_weight( + name=f"w_g{group_idx}", + shape=(self.hidden_dim, out_size), initializer=self._initializer, trainable=True, ) - self.w_pool = self.add_weight( - name="w_pool", - shape=(input_shape[1][3], self.hidden_dim), + w_pool = self.add_weight( + name=f"w_pool_g{group_idx}", + shape=(in_shape[-1], self.hidden_dim), initializer=self._initializer, trainable=True, ) - self.b_pool = self.add_weight( - name="b_pool", + b_pool = self.add_weight( + name=f"b_pool_g{group_idx}", shape=(self.hidden_dim,), initializer=self._initializer, trainable=True, ) + weights = [w_group, w_pool, b_pool] + return weights - def aggregate_neighbours(self, x_neigh, **kwargs): + def group_aggregate(self, x_group, group_idx=0): """ - Aggregates the neighbour tensors by max-pooling of neighbours - + Aggregates the group tensors by max-pooling of neighbours + Args: - x_neigh (Tensor): Neighbour tensor of shape (n_batch, n_head, n_neighbour, n_feat) - + x_group (tf.Tensor): : The input tensor representing the sampled neighbour nodes. + group_idx (int, optional): Group index. + Returns: - Aggregated neighbour tensor of shape (n_batch, n_head, n_feat) + [tf.Tensor]: A tensor aggregation of the input nodes features. """ - # Pass neighbour features through a dense layer with self.w_pool, self.b_pool - xw_neigh = self.hidden_act(K.dot(x_neigh, self.w_pool) + self.b_pool) + if group_idx == 0: + # Do not aggregate features for head nodes + x_agg = K.dot(x_group, self.w_group[0]) + + else: + w_g, w_pool, b_pool = self.w_group[group_idx] - # Take max of this tensor over neighbour dimension - neigh_agg = K.max(xw_neigh, axis=2) + # Pass neighbour features through a dense layer with w_pool, b_pool + xw_neigh = self.hidden_act(K.dot(x_group, w_pool) + b_pool) - # Final output is a dense layer over the aggregated tensor - from_neigh = K.dot(neigh_agg, self.w_neigh) - return from_neigh + # Take max of this tensor over neighbour dimension + x_agg = K.max(xw_neigh, axis=2) + + # Final output is a dense layer over the aggregated tensor + x_agg = K.dot(x_agg, w_g) + return x_agg class MeanPoolingAggregator(GraphSAGEAggregator): @@ -394,59 +399,72 @@ def __init__(self, *args, **kwargs): self.hidden_dim = self.output_dim self.hidden_act = activations.get("relu") - def _build_neighbour_weights(self, input_shape): + def _build_group_weights(self, in_shape, out_size, group_idx=0): """ - Builds layer + Builds the weight tensor(s) corresponding to the features of the input groups. Args: - input_shape (list of list of int): Shape of input tensors for self - and neighbour features + in_shape (list of int): Shape of input tensor for single group + out_size (int): The size of the output vector for this group + group_idx (int): The index of the input group """ - if self._build_mlp_only: - self.w_neigh = None - self.w_pool = None - self.b_pool = None - + if group_idx == 0: + weights = self.add_weight( + name=f"w_g{group_idx}", + shape=(in_shape[-1], out_size), + initializer=self._initializer, + trainable=True, + ) else: - self.w_neigh = self.add_weight( - name="w_neigh", - shape=(self.hidden_dim, self.weight_output_size()), + w_group = self.add_weight( + name=f"w_g{group_idx}", + shape=(self.hidden_dim, out_size), initializer=self._initializer, trainable=True, ) - self.w_pool = self.add_weight( - name="w_pool", - shape=(input_shape[1][3], self.hidden_dim), + w_pool = self.add_weight( + name=f"w_pool_g{group_idx}", + shape=(in_shape[-1], self.hidden_dim), initializer=self._initializer, trainable=True, ) - self.b_pool = self.add_weight( - name="b_pool", + b_pool = self.add_weight( + name=f"b_pool_g{group_idx}", shape=(self.hidden_dim,), initializer=self._initializer, trainable=True, ) + weights = [w_group, w_pool, b_pool] + return weights - def aggregate_neighbours(self, x_neigh, **kwargs): + def group_aggregate(self, x_group, group_idx=0): """ - Aggregates the neighbour tensors by mean-pooling of neighbours - + Aggregates the group tensors by mean-pooling of neighbours + Args: - x_neigh (Tensor): Neighbour tensor of shape (n_batch, n_head, n_neighbour, n_feat) - + x_group (tf.Tensor): : The input tensor representing the sampled neighbour nodes. + group_idx (int, optional): Group index. + Returns: - Aggregated neighbour tensor of shape (n_batch, n_head, n_feat) + [tf.Tensor]: A tensor aggregation of the input nodes features. """ - # Pass neighbour features through a dense layer with self.hidden_act activations - xw_neigh = self.hidden_act(K.dot(x_neigh, self.w_pool) + self.b_pool) + if group_idx == 0: + # Do not aggregate features for head nodes + x_agg = K.dot(x_group, self.w_group[0]) + + else: + w_g, w_pool, b_pool = self.w_group[group_idx] - # Aggregate over neighbour activations using mean - neigh_agg = K.mean(xw_neigh, axis=2) + # Pass neighbour features through a dense layer with w_pool, b_pool + xw_neigh = self.hidden_act(K.dot(x_group, w_pool) + b_pool) - # Final output is a dense layer over the aggregated tensor - from_neigh = K.dot(neigh_agg, self.w_neigh) - return from_neigh + # Take max of this tensor over neighbour dimension + x_agg = K.mean(xw_neigh, axis=2) + + # Final output is a dense layer over the aggregated tensor + x_agg = K.dot(x_agg, w_g) + return x_agg class AttentionalAggregator(GraphSAGEAggregator): @@ -466,74 +484,158 @@ class AttentionalAggregator(GraphSAGEAggregator): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) - # How can we expose these options to the user? + # TODO: How can we expose these options to the user? + self.hidden_dim = self.output_dim self.attn_act = LeakyReLU(0.2) - def weight_output_size(self): - return self.output_dim + def _build_group_weights(self, in_shape, out_size, group_idx=0): + """ + Builds the weight tensor(s) corresponding to the features of the input groups. - def _build_neighbour_weights(self, input_shape): - self.a_self = self.add_weight( - name="a_self", - shape=(self.output_dim, 1), - initializer=self._initializer, - trainable=True, - ) + Args: + in_shape (list of int): Shape of input tensor for single group + out_size (int): The size of the output vector for this group + group_idx (int): The index of the input group + + """ + if group_idx == 0: + print(out_size) + if out_size > 0: + weights = self.add_weight( + name=f"w_self", + shape=(in_shape[-1], out_size), + initializer=self._initializer, + trainable=True, + ) + else: + weights = None - if self._build_mlp_only: - self.a_neigh = None else: - self.a_neigh = self.add_weight( - name="a_neigh", - shape=(self.output_dim, 1), + w_g = self.add_weight( + name=f"w_g{group_idx}", + shape=(in_shape[-1], out_size), + initializer=self._initializer, + trainable=True, + ) + w_attn_s = self.add_weight( + name=f"w_attn_s{group_idx}", + shape=(out_size, 1), initializer=self._initializer, trainable=True, ) + w_attn_g = self.add_weight( + name=f"w_attn_g{group_idx}", + shape=(out_size, 1), + initializer=self._initializer, + trainable=True, + ) + weights = [w_g, w_attn_s, w_attn_g] + return weights + + def calculate_group_sizes(self, input_shape): + """ + Calculates the output size for each input group. The results are stored in two variables: + self.included_weight_groups: if the corresponding entry is True then the input group + is valid and should be used. + self.weight_sizes: the size of the output from this group. + + The AttentionalAggregator is implemented to not use the first (head node) group. This makes + the implmentation different from other aggregators. + + Args: + input_shape (list of list of int): Shape of input tensors for self + and neighbour features + """ + # If the neighbours are zero-dimensional for any of the shapes + # in the input, do not use the input group in the model. + self.included_weight_groups = [ + all(dim != 0 for dim in group_shape) for group_shape in input_shape + ] + + # The total number of enabled input groups + num_groups = np.sum(self.included_weight_groups) - 1 + + # Calculate the dimensionality of each group, and put remainder into the first group + # with non-zero dimensions, which should be the head node group. + group_output_dim = self.output_dim // num_groups + remainder_dim = self.output_dim - num_groups * group_output_dim + weight_dims = [] + for g in self.included_weight_groups: + if g: + group_dim = group_output_dim + remainder_dim + remainder_dim = 0 + else: + group_dim = 0 + weight_dims.append(group_dim) + + # We do not assign any features to the head node group, unless this is the only group. + if num_groups > 1: + weight_dims[0] = 0 + self.weight_dims = weight_dims - def call(self, x, **kwargs): + def call(self, inputs, **kwargs): """ - Apply aggregator on input tensors, x + Apply aggregator on the input tensors, `inputs` Args: x (List[Tensor]): Tensors giving self and neighbour features x[0]: self Tensor (batch_size, head size, feature_size) - x[1]: neighbour Tensor (batch_size, head size, neighbours, feature_size) + x[k>0]: group Tensors for neighbourhood (batch_size, head size, neighbours, feature_size) Returns: Keras Tensor representing the aggregated embeddings in the input. """ - if self._build_mlp_only: - return self.apply_mlp(x[0], **kwargs) + # We require the self group to be included to calculate attention + if not self.included_weight_groups[0]: + raise ValueError("The head node group must have non-zero dimension") + + # If a neighbourhood dimension exists for the group, aggregate over the neighbours + # otherwise create a simple layer. + x_self = inputs[0] + group_sources = [] + for ii, x_g in enumerate(inputs[1:]): + group_idx = ii + 1 + if not self.included_weight_groups[group_idx]: + continue + + # Get the weights for this group + w_g, w_attn_s, w_attn_g = self.w_group[group_idx] - # Calculate features for self & neighbours - xw_self = K.expand_dims(K.dot(x[0], self.w_self), axis=2) - xw_neigh = K.dot(x[1], self.w_self) + # Group transform for self & neighbours + xw_self = K.expand_dims(K.dot(x_self, w_g), axis=2) + xw_neigh = K.dot(x_g, w_g) - # Concatenate self vector to neighbour vectors - # Shape is (n_b, n_h, n_neigh+1, n_feat) - xw_all = K.concatenate([xw_self, xw_neigh], axis=2) + # Concatenate self vector to neighbour vectors + # Shape is (n_b, n_h, n_neigh+1, n_out[ii]) + xw_all = K.concatenate([xw_self, xw_neigh], axis=2) - # Calculate attention - attn_self = K.dot(xw_self, self.a_self) # (n_b, n_h, 1) - attn_neigh = K.dot(xw_all, self.a_neigh) # (n_b, n_h, n_neigh+1, 1) + # Calculate group attention + attn_self = K.dot(xw_self, w_attn_s) # (n_b, n_h, 1) + attn_neigh = K.dot(xw_all, w_attn_g) # (n_b, n_h, n_neigh+1, 1) - # Add self and neighbour attn and apply activation - # Note: This broadcasts to (n_b, n_h, n_neigh + 1, 1) - attn_u = self.attn_act(attn_self + attn_neigh) + # Add self and neighbour attn and apply activation + # Note: This broadcasts to (n_b, n_h, n_neigh + 1, 1) + attn_u = self.attn_act(attn_self + attn_neigh) - # Attn coefficients, softmax over the neighbours - attn = K.softmax(attn_u, axis=2) + # Attn coefficients, softmax over the neighbours + attn = K.softmax(attn_u, axis=2) - # Multiply attn coefficients by neighbours (and self) and aggregate - h_out = K.sum(attn * xw_all, axis=2) + # Multiply attn coefficients by neighbours (and self) and aggregate + h_out = K.sum(attn * xw_all, axis=2) + group_sources.append(h_out) + + # If there are no groups with features built, fallback to a MLP on the head node features + if not group_sources: + group_sources = [K.dot(x_self, self.w_group[0])] + + # Concatenate or sum the outputs from all groups + h_out = K.concatenate(group_sources, axis=2) if self.has_bias: - h_out = self.act(h_out + self.bias) - else: - h_out = self.act(h_out) + h_out = h_out + self.bias - return h_out + return self.act(h_out) class GraphSAGE: @@ -571,7 +673,7 @@ def __init__( bias=True, dropout=0.0, normalize="l2", - **kwargs + **kwargs, ): # Set the normalization layer used in the model if normalize == "l2": @@ -896,7 +998,6 @@ def _build_aggregators(self): output_dim=self.layer_sizes[i], bias=self.bias, act="relu" if i < max_hops - 1 else "linear", - neigh_dim=2, ) for i in range(max_hops) ] @@ -962,23 +1063,20 @@ def aggregate_neighbours(tree: List, stage: int): out_layer = Reshape(K.int_shape(out_layer)[2:])(out_layer) return self._normalization(out_layer) - def _compute_input_sizes(self) -> List[int]: + def _compute_input_sizes(self): # Each hop has to sample separately from both the in-nodes # and the out-nodes. This gives rise to a binary tree of 'slots'. # Storage for the total (cumulative product) number of nodes sampled # at the corresponding neighbourhood for each slot: - num_nodes = [0] * self.max_slots - num_nodes[0] = 1 - # Storage for the number of hops to reach - # the corresponding neighbourhood for each slot: - num_hops = [0] * self.max_slots - for slot in range(1, self.max_slots): - parent_slot = (slot + 1) // 2 - 1 - i = num_hops[parent_slot] - num_hops[slot] = i + 1 - num_nodes[slot] = ( - self.in_samples[i] if slot % 2 == 1 else self.out_samples[i] - ) * num_nodes[parent_slot] + num_nodes = [1] + [ + np.product( + [ + self.in_samples[kk] if d == "0" else self.out_samples[kk] + for kk, d in enumerate(np.binary_repr(ii + 1)[1:]) + ] + ) + for ii in range(1, self.max_slots) + ] return num_nodes def node_model(self): diff --git a/tests/layer/test_graphsage.py b/tests/layer/test_graphsage.py index 0d0de07f7..3d57e4b95 100644 --- a/tests/layer/test_graphsage.py +++ b/tests/layer/test_graphsage.py @@ -21,7 +21,14 @@ """ from stellargraph.core.graph import StellarGraph from stellargraph.mapper.node_mappers import GraphSAGENodeGenerator -from stellargraph.layer.graphsage import * +from stellargraph.layer.graphsage import ( + GraphSAGE, + DirectedGraphSAGE, + MeanAggregator, + MaxPoolingAggregator, + MeanPoolingAggregator, + AttentionalAggregator, +) import keras import numpy as np @@ -47,64 +54,67 @@ def example_graph_1(feature_size=None): return StellarGraph(G) -# MaxPooling aggregator tests - - -def test_maxpool_agg_constructor(): - agg = MaxPoolingAggregator(2, bias=False) +# Mean aggregator tests +def test_mean_agg_constructor(): + agg = MeanAggregator(2) assert agg.output_dim == 2 - assert agg.node_output_dim == 1 - assert agg.neighbour_output_dim == 1 - assert agg.hidden_dim == 2 assert not agg.has_bias - assert agg.act.__name__ == "relu" - assert agg.hidden_act.__name__ == "relu" # Check config config = agg.get_config() assert config["output_dim"] == 2 - assert config["bias"] == False + assert config["bias"] is False assert config["act"] == "relu" -def test_maxpool_agg_constructor_1(): - agg = MaxPoolingAggregator(output_dim=4, bias=True, act=lambda x: x + 1) +def test_mean_agg_constructor_1(): + agg = MeanAggregator(output_dim=4, bias=True, act=lambda x: x + 1) assert agg.output_dim == 4 - assert agg.node_output_dim == 2 - assert agg.neighbour_output_dim == 2 - assert agg.hidden_dim == 4 assert agg.has_bias assert agg.act(2) == 3 -def test_maxpool_agg_apply(): - agg = MaxPoolingAggregator(2, bias=True, act="linear") +def test_mean_agg_apply(): + agg = MeanAggregator(5, bias=True, act=lambda x: x) agg._initializer = "ones" + inp1 = keras.Input(shape=(1, 2)) + inp2 = keras.Input(shape=(1, 2, 2)) + out = agg([inp1, inp2]) - # Self features + assert agg.weight_dims == [3, 2] + + model = keras.Model(inputs=[inp1, inp2], outputs=out) + x1 = np.array([[[1, 1]]]) + x2 = np.array([[[[2, 2], [3, 3]]]]) + actual = model.predict([x1, x2]) + expected = np.array([[[2, 2, 2, 5, 5]]]) + assert expected == pytest.approx(actual) + + +def test_mean_agg_apply_groups(): + agg = MeanAggregator(11, bias=True, act=lambda x: x) + agg._initializer = "ones" inp1 = keras.Input(shape=(1, 2)) - # Neighbour features inp2 = keras.Input(shape=(1, 2, 2)) + inp3 = keras.Input(shape=(1, 2, 2)) + out = agg([inp1, inp2, inp3]) - # Numerical test values + assert agg.weight_dims == [5, 3, 3] + + model = keras.Model(inputs=[inp1, inp2, inp3], outputs=out) x1 = np.array([[[1, 1]]]) x2 = np.array([[[[2, 2], [3, 3]]]]) - # Agg output: - # neigh_agg = max(relu(x2 · ones(2x2)) + 1, axis=1) - # = max([[5,5],[7,7]]) = [[7,7]] - # from_self = K.dot(x1, ones) = [[2]] - # from_neigh = K.dot(neigh_agg, ones) = [[14]] + x3 = np.array([[[[5, 5], [4, 4]]]]) - out = agg([inp1, inp2]) - model = keras.Model(inputs=[inp1, inp2], outputs=out) - actual = model.predict([x1, x2]) - expected = np.array([[[2, 14]]]) + actual = model.predict([x1, x2, x3]) + print(actual) + expected = np.array([[[2] * 5 + [5] * 3 + [9] * 3]]) assert expected == pytest.approx(actual) -def test_maxpool_agg_zero_neighbours(): - agg = MaxPoolingAggregator(4, bias=False, act="linear") +def test_mean_agg_zero_neighbours(): + agg = MeanAggregator(4, bias=False, act=lambda x: x) agg._initializer = "ones" inp1 = keras.Input(shape=(1, 2)) @@ -121,14 +131,10 @@ def test_maxpool_agg_zero_neighbours(): assert expected == pytest.approx(actual) -# MeanPooling aggregator tests - - -def test_meanpool_agg_constructor(): - agg = MeanPoolingAggregator(2, bias=False) +# MaxPooling aggregator tests +def test_maxpool_agg_constructor(): + agg = MaxPoolingAggregator(2, bias=False) assert agg.output_dim == 2 - assert agg.node_output_dim == 1 - assert agg.neighbour_output_dim == 1 assert agg.hidden_dim == 2 assert not agg.has_bias assert agg.act.__name__ == "relu" @@ -141,44 +147,44 @@ def test_meanpool_agg_constructor(): assert config["act"] == "relu" -def test_meanpool_agg_constructor_1(): - agg = MeanPoolingAggregator(output_dim=4, bias=True, act=lambda x: x + 1) +def test_maxpool_agg_constructor_1(): + agg = MaxPoolingAggregator(output_dim=4, bias=True, act=lambda x: x + 1) assert agg.output_dim == 4 - assert agg.node_output_dim == 2 - assert agg.neighbour_output_dim == 2 assert agg.hidden_dim == 4 assert agg.has_bias assert agg.act(2) == 3 -def test_meanpool_agg_apply(): - agg = MeanPoolingAggregator(2, bias=True, act="linear") +def test_maxpool_agg_apply(): + agg = MaxPoolingAggregator(2, bias=True, act="linear") agg._initializer = "ones" # Self features inp1 = keras.Input(shape=(1, 2)) # Neighbour features inp2 = keras.Input(shape=(1, 2, 2)) + out = agg([inp1, inp2]) + + # Check sizes + assert agg.weight_dims == [1, 1] # Numerical test values x1 = np.array([[[1, 1]]]) x2 = np.array([[[[2, 2], [3, 3]]]]) + # Agg output: - # neigh_agg = mean(relu(x2 · ones(2x2)) + 1, axis=1) - # = mean([[5,5],[7,7]]) = [[6,6]] + # neigh_agg = max(relu(x2 · ones(2x2)) + 1, axis=1) = max([[5,5],[7,7]]) = [[7,7]] # from_self = K.dot(x1, ones) = [[2]] - # from_neigh = K.dot(neigh_agg, ones) = [[12]] - - out = agg([inp1, inp2]) + # from_neigh = K.dot(neigh_agg, ones) = [[14]] model = keras.Model(inputs=[inp1, inp2], outputs=out) actual = model.predict([x1, x2]) - expected = np.array([[[2, 12]]]) + expected = np.array([[[2, 14]]]) assert expected == pytest.approx(actual) -def test_meanpool_agg_zero_neighbours(): - agg = MeanPoolingAggregator(4, bias=False, act="linear") +def test_maxpool_agg_zero_neighbours(): + agg = MaxPoolingAggregator(4, bias=False, act="linear") agg._initializer = "ones" inp1 = keras.Input(shape=(1, 2)) @@ -195,85 +201,74 @@ def test_meanpool_agg_zero_neighbours(): assert expected == pytest.approx(actual) -# Mean aggregator tests -def test_mean_agg_constructor(): - agg = MeanAggregator(2) +# MeanPooling aggregator tests +def test_meanpool_agg_constructor(): + agg = MeanPoolingAggregator(2, bias=False) assert agg.output_dim == 2 - assert agg.node_output_dim == 1 - assert agg.neighbour_output_dim == 1 + assert agg.hidden_dim == 2 assert not agg.has_bias + assert agg.act.__name__ == "relu" + assert agg.hidden_act.__name__ == "relu" # Check config config = agg.get_config() assert config["output_dim"] == 2 - assert config["bias"] == False + assert config["bias"] is False assert config["act"] == "relu" -def test_mean_agg_constructor_1(): - agg = MeanAggregator(output_dim=4, bias=True, act=lambda x: x + 1) +def test_meanpool_agg_constructor_1(): + agg = MeanPoolingAggregator(output_dim=4, bias=True, act=lambda x: x + 1) assert agg.output_dim == 4 - assert agg.node_output_dim == 2 - assert agg.neighbour_output_dim == 2 + assert agg.hidden_dim == 4 assert agg.has_bias assert agg.act(2) == 3 -def test_mean_agg_constructor_2(): - # Since we can now aggregate over more than one set of neighbours - # (e.g. in directed GraphSAGE), we must handle odd dimensions. - agg = MeanAggregator(11) - assert agg.output_dim == 11 - assert agg.node_output_dim == 6 - assert agg.neighbour_output_dim == 5 - assert not agg.has_bias +def test_meanpool_agg_apply(): + agg = MeanPoolingAggregator(2, bias=True, act="linear") + agg._initializer = "ones" - # Check config - config = agg.get_config() - assert config["output_dim"] == 11 - assert config["bias"] == False - assert config["act"] == "relu" + # Self features + inp1 = keras.Input(shape=(1, 2)) + # Neighbour features + inp2 = keras.Input(shape=(1, 2, 2)) + out = agg([inp1, inp2]) -def test_mean_agg_constructor_3(): - # Ditto above, re multiple neighbour sets. - agg = MeanAggregator(12, neigh_dim=3) - assert agg.output_dim == 12 - assert agg.node_output_dim == 3 - assert agg.neighbour_output_dim == 3 - assert not agg.has_bias + # Check sizes + assert agg.weight_dims == [1, 1] - # Check config - config = agg.get_config() - assert config["output_dim"] == 12 - assert config["bias"] == False - assert config["act"] == "relu" + # Numerical test values + x1 = np.array([[[1, 1]]]) + x2 = np.array([[[[2, 2], [3, 3]]]]) + # Agg output: + # neigh_agg = mean(relu(x2 · ones(2x2)) + 1, axis=1) + # = mean([[5,5],[7,7]]) = [[6,6]] + # from_self = K.dot(x1, ones) = [[2]] + # from_neigh = K.dot(neigh_agg, ones) = [[12]] -def test_mean_agg_apply(): - agg = MeanAggregator(4, bias=True, act=lambda x: x) - agg._initializer = "ones" - inp1 = keras.Input(shape=(1, 2)) - inp2 = keras.Input(shape=(1, 2, 2)) - out = agg([inp1, inp2]) model = keras.Model(inputs=[inp1, inp2], outputs=out) - x1 = np.array([[[1, 1]]]) - x2 = np.array([[[[2, 2], [3, 3]]]]) actual = model.predict([x1, x2]) - expected = np.array([[[2, 2, 5, 5]]]) + expected = np.array([[[2, 12]]]) + assert expected == pytest.approx(actual) -def test_mean_agg_zero_neighbours(): - agg = MeanAggregator(4, bias=False, act=lambda x: x) +def test_meanpool_agg_zero_neighbours(): + agg = MeanPoolingAggregator(4, bias=False, act="linear") agg._initializer = "ones" inp1 = keras.Input(shape=(1, 2)) inp2 = keras.Input(shape=(1, 0, 2)) - out = agg([inp1, inp2]) - model = keras.Model(inputs=[inp1, inp2], outputs=out) + # Now we have an input shape with a 0, the attention model switches to + # a MLP and the first group will have non-zero output size. + assert agg.weight_dims == [4, 0] + + model = keras.Model(inputs=[inp1, inp2], outputs=out) x1 = np.array([[[1, 1]]]) x2 = np.zeros((1, 1, 0, 2)) @@ -293,7 +288,7 @@ def test_attn_agg_constructor(): # Check config config = agg.get_config() assert config["output_dim"] == 2 - assert config["bias"] == False + assert config["bias"] is False assert config["act"] == "relu" @@ -305,14 +300,19 @@ def test_attn_agg_constructor_1(): def test_attn_agg_apply(): - agg = AttentionalAggregator(2, bias=True, act="linear") + agg = AttentionalAggregator(2, bias=False, act="linear") agg._initializer = "ones" - agg.attn_act = keras.activations.get("relu") + agg.attn_act = keras.activations.get("linear") # Self features inp1 = keras.Input(shape=(1, 2)) # Neighbour features inp2 = keras.Input(shape=(1, 2, 2)) + out = agg([inp1, inp2]) + + # The AttentionalAggregator implmentation is a hack at the moment, it doesn't + # assign any dimensions in the output to head-node features. + assert agg.weight_dims == [0, 2] # Numerical test values x1 = np.array([[[1, 1]]]) @@ -320,14 +320,13 @@ def test_attn_agg_apply(): # Agg output: # hs = relu(x1 · ones(2x2)) = [2,2] - # hn = relu(x2 · ones(2x2)) = [[2,2], [4,4],[6,6]] + # hn = relu(x2 · ones(2x2)) = [[2,2], [4,4], [6,6]] # attn_u = ones(2) · hs + ones(2) · hn = [8, 12, 16] # attn = softmax(attn_u) = [3.3e-4, 1.8e-4, 9.81e-1] # hout = attn · hn = [5.96, 5.96] - - out = agg([inp1, inp2]) model = keras.Model(inputs=[inp1, inp2], outputs=out) actual = model.predict([x1, x2]) + expected = np.array([[[5.963, 5.963]]]) assert expected == pytest.approx(actual, rel=1e-4) From 7c2b02188fac6cb2fd76d6e0823ad2800d4cabd2 Mon Sep 17 00:00:00 2001 From: Andrew Docherty Date: Tue, 3 Sep 2019 08:49:03 +1000 Subject: [PATCH 21/30] Bugfix for AttentionalAggregator dimension calculation. --- stellargraph/layer/graphsage.py | 32 +++++++++++++++++--------------- 1 file changed, 17 insertions(+), 15 deletions(-) diff --git a/stellargraph/layer/graphsage.py b/stellargraph/layer/graphsage.py index f3effa85e..7866ffa79 100644 --- a/stellargraph/layer/graphsage.py +++ b/stellargraph/layer/graphsage.py @@ -555,22 +555,24 @@ def calculate_group_sizes(self, input_shape): # The total number of enabled input groups num_groups = np.sum(self.included_weight_groups) - 1 - # Calculate the dimensionality of each group, and put remainder into the first group - # with non-zero dimensions, which should be the head node group. - group_output_dim = self.output_dim // num_groups - remainder_dim = self.output_dim - num_groups * group_output_dim - weight_dims = [] - for g in self.included_weight_groups: - if g: - group_dim = group_output_dim + remainder_dim - remainder_dim = 0 - else: - group_dim = 0 - weight_dims.append(group_dim) - # We do not assign any features to the head node group, unless this is the only group. - if num_groups > 1: - weight_dims[0] = 0 + if num_groups == 0: + weight_dims = [self.output_dim] + [0] * (len(input_shape) - 1) + + else: + # Calculate the dimensionality of each group, and put remainder into the first group + # with non-zero dimensions. + group_output_dim = self.output_dim // num_groups + remainder_dim = self.output_dim - num_groups * group_output_dim + weight_dims = [0] + for g in self.included_weight_groups[1:]: + if g: + group_dim = group_output_dim + remainder_dim + remainder_dim = 0 + else: + group_dim = 0 + weight_dims.append(group_dim) + self.weight_dims = weight_dims def call(self, inputs, **kwargs): From 8467cb9ef0aaef4a504e8d4a2f32695e504ae229 Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Thu, 5 Sep 2019 15:57:19 +0930 Subject: [PATCH 22/30] Reran notebook examples using latest GraphSAGE coding. --- ...ndirected-graph-undirected-graphsage.ipynb | 95 +++---- ...-directed-graph-undirected-graphsage.ipynb | 87 +++--- ...rected-graph-half-directed-graphsage.ipynb | 261 ++++++++++++------ ...ected-graph-fully-directed-graphsage.ipynb | 89 +++--- 4 files changed, 280 insertions(+), 252 deletions(-) diff --git a/demos/node-classification/directed-graphsage/example1-undirected-graph-undirected-graphsage.ipynb b/demos/node-classification/directed-graphsage/example1-undirected-graph-undirected-graphsage.ipynb index afeb05628..61cd2431a 100644 --- a/demos/node-classification/directed-graphsage/example1-undirected-graph-undirected-graphsage.ipynb +++ b/demos/node-classification/directed-graphsage/example1-undirected-graph-undirected-graphsage.ipynb @@ -210,12 +210,12 @@ "data": { "text/plain": [ "Counter({'Genetic_Algorithms': 42,\n", - " 'Neural_Networks': 81,\n", " 'Case_Based': 30,\n", - " 'Theory': 35,\n", - " 'Rule_Learning': 18,\n", + " 'Neural_Networks': 81,\n", " 'Probabilistic_Methods': 42,\n", - " 'Reinforcement_Learning': 22})" + " 'Rule_Learning': 18,\n", + " 'Reinforcement_Learning': 22,\n", + " 'Theory': 35})" ] }, "execution_count": 9, @@ -395,9 +395,6 @@ "name": "stderr", "output_type": "stream", "text": [ - "WARNING: Logging before flag parsing goes to stderr.\n", - "W0823 09:57:58.263214 4533151168 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", - "\n" ] } ], @@ -426,15 +423,6 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0823 09:57:58.279142 4533151168 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", - "\n", - "W0823 09:57:58.288129 4533151168 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", - "\n", - "W0823 09:57:58.295388 4533151168 deprecation.py:506] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", - "W0823 09:57:58.325520 4533151168 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", - "\n" ] } ], @@ -466,10 +454,6 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0823 09:57:58.485157 4533151168 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", - "\n", - "W0823 09:57:58.491479 4533151168 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", - "\n" ] } ], @@ -507,9 +491,6 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0823 09:57:58.580814 4533151168 deprecation.py:323] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" ] }, { @@ -517,45 +498,45 @@ "output_type": "stream", "text": [ "Epoch 1/20\n", - " - 2s - loss: 1.8569 - acc: 0.3104 - val_loss: 1.6733 - val_acc: 0.4098\n", + " - 2s - loss: 1.8514 - acc: 0.2941 - val_loss: 1.7087 - val_acc: 0.4020\n", "Epoch 2/20\n", - " - 2s - loss: 1.6224 - acc: 0.4668 - val_loss: 1.5172 - val_acc: 0.4590\n", + " - 2s - loss: 1.6656 - acc: 0.4483 - val_loss: 1.5593 - val_acc: 0.4873\n", "Epoch 3/20\n", - " - 2s - loss: 1.4281 - acc: 0.6122 - val_loss: 1.3768 - val_acc: 0.6030\n", + " - 2s - loss: 1.5061 - acc: 0.6458 - val_loss: 1.4184 - val_acc: 0.7026\n", "Epoch 4/20\n", - " - 2s - loss: 1.2987 - acc: 0.7228 - val_loss: 1.2662 - val_acc: 0.6813\n", + " - 2s - loss: 1.3407 - acc: 0.7838 - val_loss: 1.2991 - val_acc: 0.7461\n", "Epoch 5/20\n", - " - 2s - loss: 1.1672 - acc: 0.7831 - val_loss: 1.1717 - val_acc: 0.7326\n", + " - 2s - loss: 1.1865 - acc: 0.8772 - val_loss: 1.1899 - val_acc: 0.7777\n", "Epoch 6/20\n", - " - 2s - loss: 1.0586 - acc: 0.8661 - val_loss: 1.1002 - val_acc: 0.7502\n", + " - 2s - loss: 1.0759 - acc: 0.8806 - val_loss: 1.0987 - val_acc: 0.7867\n", "Epoch 7/20\n", - " - 2s - loss: 0.9498 - acc: 0.8720 - val_loss: 1.0377 - val_acc: 0.7670\n", + " - 2s - loss: 0.9604 - acc: 0.9093 - val_loss: 1.0227 - val_acc: 0.7994\n", "Epoch 8/20\n", - " - 2s - loss: 0.8420 - acc: 0.9390 - val_loss: 0.9815 - val_acc: 0.7810\n", + " - 2s - loss: 0.8624 - acc: 0.9499 - val_loss: 0.9590 - val_acc: 0.8056\n", "Epoch 9/20\n", - " - 2s - loss: 0.7876 - acc: 0.9449 - val_loss: 0.9316 - val_acc: 0.7867\n", + " - 2s - loss: 0.7505 - acc: 0.9619 - val_loss: 0.9012 - val_acc: 0.8150\n", "Epoch 10/20\n", - " - 2s - loss: 0.6852 - acc: 0.9652 - val_loss: 0.8940 - val_acc: 0.7974\n", + " - 2s - loss: 0.6935 - acc: 0.9619 - val_loss: 0.8538 - val_acc: 0.8060\n", "Epoch 11/20\n", - " - 2s - loss: 0.6283 - acc: 0.9628 - val_loss: 0.8631 - val_acc: 0.8002\n", + " - 2s - loss: 0.6157 - acc: 0.9729 - val_loss: 0.8132 - val_acc: 0.8146\n", "Epoch 12/20\n", - " - 2s - loss: 0.5743 - acc: 0.9695 - val_loss: 0.8339 - val_acc: 0.7949\n", + " - 2s - loss: 0.5434 - acc: 0.9831 - val_loss: 0.7837 - val_acc: 0.8130\n", "Epoch 13/20\n", - " - 2s - loss: 0.5315 - acc: 0.9567 - val_loss: 0.8181 - val_acc: 0.7884\n", + " - 2s - loss: 0.4930 - acc: 0.9889 - val_loss: 0.7557 - val_acc: 0.8171\n", "Epoch 14/20\n", - " - 2s - loss: 0.4696 - acc: 0.9831 - val_loss: 0.7919 - val_acc: 0.7945\n", + " - 2s - loss: 0.4520 - acc: 0.9831 - val_loss: 0.7317 - val_acc: 0.8179\n", "Epoch 15/20\n", - " - 2s - loss: 0.4326 - acc: 0.9729 - val_loss: 0.7753 - val_acc: 0.7904\n", + " - 2s - loss: 0.3919 - acc: 0.9898 - val_loss: 0.7112 - val_acc: 0.8109\n", "Epoch 16/20\n", - " - 2s - loss: 0.3983 - acc: 0.9720 - val_loss: 0.7551 - val_acc: 0.7953\n", + " - 2s - loss: 0.3767 - acc: 0.9822 - val_loss: 0.6953 - val_acc: 0.8101\n", "Epoch 17/20\n", - " - 2s - loss: 0.3616 - acc: 0.9932 - val_loss: 0.7626 - val_acc: 0.7818\n", + " - 3s - loss: 0.3403 - acc: 1.0000 - val_loss: 0.6687 - val_acc: 0.8187\n", "Epoch 18/20\n", - " - 2s - loss: 0.3409 - acc: 0.9932 - val_loss: 0.7542 - val_acc: 0.7957\n", + " - 3s - loss: 0.2990 - acc: 0.9966 - val_loss: 0.6702 - val_acc: 0.8179\n", "Epoch 19/20\n", - " - 2s - loss: 0.3083 - acc: 0.9898 - val_loss: 0.7452 - val_acc: 0.7904\n", + " - 2s - loss: 0.2748 - acc: 1.0000 - val_loss: 0.6548 - val_acc: 0.8183\n", "Epoch 20/20\n", - " - 2s - loss: 0.3071 - acc: 0.9797 - val_loss: 0.7265 - val_acc: 0.7875\n" + " - 2s - loss: 0.2520 - acc: 0.9966 - val_loss: 0.6473 - val_acc: 0.8236\n" ] } ], @@ -599,7 +580,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -644,8 +625,8 @@ "text": [ "\n", "Test Set Metrics:\n", - "\tloss: 0.7187\n", - "\tacc: 0.7974\n" + "\tloss: 0.6503\n", + "\tacc: 0.8154\n" ] } ], @@ -737,7 +718,7 @@ " \n", " \n", " 31336\n", - " subject=Probabilistic_Methods\n", + " subject=Neural_Networks\n", " Neural_Networks\n", " \n", " \n", @@ -767,17 +748,17 @@ " \n", " \n", " 1107140\n", - " subject=Reinforcement_Learning\n", + " subject=Probabilistic_Methods\n", " Theory\n", " \n", " \n", " 1102850\n", - " subject=Probabilistic_Methods\n", + " subject=Neural_Networks\n", " Neural_Networks\n", " \n", " \n", " 31349\n", - " subject=Probabilistic_Methods\n", + " subject=Neural_Networks\n", " Neural_Networks\n", " \n", " \n", @@ -791,15 +772,15 @@ ], "text/plain": [ " Predicted True\n", - "31336 subject=Probabilistic_Methods Neural_Networks\n", + "31336 subject=Neural_Networks Neural_Networks\n", "1061127 subject=Rule_Learning Rule_Learning\n", "1106406 subject=Reinforcement_Learning Reinforcement_Learning\n", "13195 subject=Reinforcement_Learning Reinforcement_Learning\n", "37879 subject=Probabilistic_Methods Probabilistic_Methods\n", "1126012 subject=Probabilistic_Methods Probabilistic_Methods\n", - "1107140 subject=Reinforcement_Learning Theory\n", - "1102850 subject=Probabilistic_Methods Neural_Networks\n", - "31349 subject=Probabilistic_Methods Neural_Networks\n", + "1107140 subject=Probabilistic_Methods Theory\n", + "1102850 subject=Neural_Networks Neural_Networks\n", + "31349 subject=Neural_Networks Neural_Networks\n", "1106418 subject=Theory Theory" ] }, @@ -956,7 +937,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -995,7 +976,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/demos/node-classification/directed-graphsage/example2-directed-graph-undirected-graphsage.ipynb b/demos/node-classification/directed-graphsage/example2-directed-graph-undirected-graphsage.ipynb index d14033d60..d2878353f 100644 --- a/demos/node-classification/directed-graphsage/example2-directed-graph-undirected-graphsage.ipynb +++ b/demos/node-classification/directed-graphsage/example2-directed-graph-undirected-graphsage.ipynb @@ -209,13 +209,13 @@ { "data": { "text/plain": [ - "Counter({'Probabilistic_Methods': 42,\n", - " 'Reinforcement_Learning': 22,\n", - " 'Neural_Networks': 81,\n", - " 'Genetic_Algorithms': 42,\n", + "Counter({'Neural_Networks': 81,\n", " 'Theory': 35,\n", - " 'Case_Based': 30,\n", - " 'Rule_Learning': 18})" + " 'Genetic_Algorithms': 42,\n", + " 'Rule_Learning': 18,\n", + " 'Reinforcement_Learning': 22,\n", + " 'Probabilistic_Methods': 42,\n", + " 'Case_Based': 30})" ] }, "execution_count": 9, @@ -397,9 +397,6 @@ "name": "stderr", "output_type": "stream", "text": [ - "WARNING: Logging before flag parsing goes to stderr.\n", - "W0823 09:59:16.134663 4451100096 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", - "\n" ] } ], @@ -428,15 +425,6 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0823 09:59:16.151041 4451100096 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", - "\n", - "W0823 09:59:16.161491 4451100096 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", - "\n", - "W0823 09:59:16.169850 4451100096 deprecation.py:506] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", - "W0823 09:59:16.202038 4451100096 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", - "\n" ] } ], @@ -468,10 +456,6 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0823 09:59:16.375536 4451100096 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", - "\n", - "W0823 09:59:16.381622 4451100096 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", - "\n" ] } ], @@ -509,9 +493,6 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0823 09:59:16.468930 4451100096 deprecation.py:323] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" ] }, { @@ -519,45 +500,45 @@ "output_type": "stream", "text": [ "Epoch 1/20\n", - " - 2s - loss: 1.8731 - acc: 0.2576 - val_loss: 1.7150 - val_acc: 0.3626\n", + " - 3s - loss: 1.8550 - acc: 0.2849 - val_loss: 1.6846 - val_acc: 0.3622\n", "Epoch 2/20\n", - " - 2s - loss: 1.6263 - acc: 0.5289 - val_loss: 1.5721 - val_acc: 0.5295\n", + " - 2s - loss: 1.6308 - acc: 0.4298 - val_loss: 1.5453 - val_acc: 0.4422\n", "Epoch 3/20\n", - " - 2s - loss: 1.4863 - acc: 0.6612 - val_loss: 1.4325 - val_acc: 0.6452\n", + " - 2s - loss: 1.4352 - acc: 0.5892 - val_loss: 1.3877 - val_acc: 0.6194\n", "Epoch 4/20\n", - " - 2s - loss: 1.3453 - acc: 0.7770 - val_loss: 1.3021 - val_acc: 0.7108\n", + " - 2s - loss: 1.3181 - acc: 0.7178 - val_loss: 1.2636 - val_acc: 0.7014\n", "Epoch 5/20\n", - " - 2s - loss: 1.2234 - acc: 0.7989 - val_loss: 1.1891 - val_acc: 0.7584\n", + " - 2s - loss: 1.1511 - acc: 0.8323 - val_loss: 1.1551 - val_acc: 0.7572\n", "Epoch 6/20\n", - " - 2s - loss: 1.0801 - acc: 0.8797 - val_loss: 1.0999 - val_acc: 0.7892\n", + " - 2s - loss: 1.0460 - acc: 0.8533 - val_loss: 1.0671 - val_acc: 0.7785\n", "Epoch 7/20\n", - " - 2s - loss: 0.9673 - acc: 0.9415 - val_loss: 1.0160 - val_acc: 0.8027\n", + " - 2s - loss: 0.9104 - acc: 0.9314 - val_loss: 0.9968 - val_acc: 0.7937\n", "Epoch 8/20\n", - " - 2s - loss: 0.8554 - acc: 0.9594 - val_loss: 0.9526 - val_acc: 0.8027\n", + " - 3s - loss: 0.8479 - acc: 0.9041 - val_loss: 0.9367 - val_acc: 0.8039\n", "Epoch 9/20\n", - " - 2s - loss: 0.7607 - acc: 0.9585 - val_loss: 0.8977 - val_acc: 0.8052\n", + " - 3s - loss: 0.7372 - acc: 0.9551 - val_loss: 0.8870 - val_acc: 0.8048\n", "Epoch 10/20\n", - " - 2s - loss: 0.6837 - acc: 0.9695 - val_loss: 0.8600 - val_acc: 0.8126\n", + " - 2s - loss: 0.6628 - acc: 0.9560 - val_loss: 0.8477 - val_acc: 0.7990\n", "Epoch 11/20\n", - " - 2s - loss: 0.6175 - acc: 0.9686 - val_loss: 0.8191 - val_acc: 0.8187\n", + " - 2s - loss: 0.6102 - acc: 0.9763 - val_loss: 0.8129 - val_acc: 0.8080\n", "Epoch 12/20\n", - " - 2s - loss: 0.5695 - acc: 0.9822 - val_loss: 0.7727 - val_acc: 0.8290\n", + " - 2s - loss: 0.5342 - acc: 0.9763 - val_loss: 0.7902 - val_acc: 0.8048\n", "Epoch 13/20\n", - " - 2s - loss: 0.4883 - acc: 0.9966 - val_loss: 0.7511 - val_acc: 0.8265\n", + " - 2s - loss: 0.4880 - acc: 0.9856 - val_loss: 0.7666 - val_acc: 0.8052\n", "Epoch 14/20\n", - " - 2s - loss: 0.4536 - acc: 0.9889 - val_loss: 0.7458 - val_acc: 0.8191\n", + " - 2s - loss: 0.4382 - acc: 0.9932 - val_loss: 0.7444 - val_acc: 0.8097\n", "Epoch 15/20\n", - " - 2s - loss: 0.4177 - acc: 0.9966 - val_loss: 0.7115 - val_acc: 0.8244\n", + " - 2s - loss: 0.4061 - acc: 0.9831 - val_loss: 0.7242 - val_acc: 0.8048\n", "Epoch 16/20\n", - " - 2s - loss: 0.3912 - acc: 0.9831 - val_loss: 0.6906 - val_acc: 0.8199\n", + " - 2s - loss: 0.3671 - acc: 0.9898 - val_loss: 0.7182 - val_acc: 0.8048\n", "Epoch 17/20\n", - " - 2s - loss: 0.3554 - acc: 0.9889 - val_loss: 0.6768 - val_acc: 0.8236\n", + " - 2s - loss: 0.3398 - acc: 1.0000 - val_loss: 0.7155 - val_acc: 0.8039\n", "Epoch 18/20\n", - " - 2s - loss: 0.3328 - acc: 0.9932 - val_loss: 0.6964 - val_acc: 0.8138\n", + " - 2s - loss: 0.2993 - acc: 1.0000 - val_loss: 0.6864 - val_acc: 0.8097\n", "Epoch 19/20\n", - " - 2s - loss: 0.2951 - acc: 0.9966 - val_loss: 0.6707 - val_acc: 0.8216\n", + " - 2s - loss: 0.2778 - acc: 0.9966 - val_loss: 0.6789 - val_acc: 0.8117\n", "Epoch 20/20\n", - " - 2s - loss: 0.2711 - acc: 0.9966 - val_loss: 0.6647 - val_acc: 0.8179\n" + " - 2s - loss: 0.2781 - acc: 0.9865 - val_loss: 0.6815 - val_acc: 0.8043\n" ] } ], @@ -601,7 +582,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -646,8 +627,8 @@ "text": [ "\n", "Test Set Metrics:\n", - "\tloss: 0.6586\n", - "\tacc: 0.8244\n" + "\tloss: 0.6829\n", + "\tacc: 0.8072\n" ] } ], @@ -764,7 +745,7 @@ " \n", " \n", " 1126012\n", - " subject=Reinforcement_Learning\n", + " subject=Probabilistic_Methods\n", " Probabilistic_Methods\n", " \n", " \n", @@ -798,7 +779,7 @@ "1106406 subject=Reinforcement_Learning Reinforcement_Learning\n", "13195 subject=Reinforcement_Learning Reinforcement_Learning\n", "37879 subject=Probabilistic_Methods Probabilistic_Methods\n", - "1126012 subject=Reinforcement_Learning Probabilistic_Methods\n", + "1126012 subject=Probabilistic_Methods Probabilistic_Methods\n", "1107140 subject=Reinforcement_Learning Theory\n", "1102850 subject=Neural_Networks Neural_Networks\n", "31349 subject=Neural_Networks Neural_Networks\n", @@ -958,7 +939,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -997,7 +978,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/demos/node-classification/directed-graphsage/example3-directed-graph-half-directed-graphsage.ipynb b/demos/node-classification/directed-graphsage/example3-directed-graph-half-directed-graphsage.ipynb index 3223ee236..9fbe96803 100644 --- a/demos/node-classification/directed-graphsage/example3-directed-graph-half-directed-graphsage.ipynb +++ b/demos/node-classification/directed-graphsage/example3-directed-graph-half-directed-graphsage.ipynb @@ -209,13 +209,13 @@ { "data": { "text/plain": [ - "Counter({'Probabilistic_Methods': 42,\n", - " 'Theory': 35,\n", - " 'Rule_Learning': 18,\n", + "Counter({'Theory': 35,\n", " 'Neural_Networks': 81,\n", + " 'Probabilistic_Methods': 42,\n", " 'Genetic_Algorithms': 42,\n", - " 'Case_Based': 30,\n", - " 'Reinforcement_Learning': 22})" + " 'Rule_Learning': 18,\n", + " 'Reinforcement_Learning': 22,\n", + " 'Case_Based': 30})" ] }, "execution_count": 9, @@ -384,7 +384,7 @@ "source": [ "Now we can specify our machine learning model, we need a few more parameters for this:\n", "\n", - " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use 48-dimensional hidden node features at each layer, which corresponds to 16 weights for a node, 16 for the in-nodes (unused) and 16 for the out-nodes. This corresponds to the 32 dimensions used in example 1 (where we do not distinguish between in-nodes and out-nodes).\n", + " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use 32-dimensional hidden node features at each layer, which corresponds to 16 weights for a node and 16 for the out-nodes - the in-nodes are not sampled here. This corresponds to the 32=16+16 dimensions used in example 1 (where we do not distinguish between in-nodes and out-nodes).\n", " * The `bias` and `dropout` are internal parameters of the model. " ] }, @@ -396,16 +396,12 @@ { "name": "stderr", "output_type": "stream", - "text": [ - "WARNING: Logging before flag parsing goes to stderr.\n", - "W0823 10:08:37.639082 4535567808 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", - "\n" - ] + "text": [] } ], "source": [ "graphsage_model = DirectedGraphSAGE(\n", - " layer_sizes=[48, 48],\n", + " layer_sizes=[32, 32],\n", " generator=train_gen,\n", " bias=False,\n", " dropout=0.5,\n", @@ -427,17 +423,7 @@ { "name": "stderr", "output_type": "stream", - "text": [ - "W0823 10:08:37.654679 4535567808 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", - "\n", - "W0823 10:08:37.660939 4535567808 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", - "\n", - "W0823 10:08:37.668406 4535567808 deprecation.py:506] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", - "W0823 10:08:37.716286 4535567808 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", - "\n" - ] + "text": [] } ], "source": [ @@ -467,12 +453,7 @@ { "name": "stderr", "output_type": "stream", - "text": [ - "W0823 10:08:37.997756 4535567808 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", - "\n", - "W0823 10:08:38.003920 4535567808 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", - "\n" - ] + "text": [] } ], "source": [ @@ -508,56 +489,52 @@ { "name": "stderr", "output_type": "stream", - "text": [ - "W0823 10:08:38.170191 4535567808 deprecation.py:323] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" - ] + "text": [] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", - " - 3s - loss: nan - acc: 0.1230 - val_loss: nan - val_acc: 0.1099\n", + " - 3s - loss: 1.8645 - acc: 0.2492 - val_loss: 1.7098 - val_acc: 0.3860\n", "Epoch 2/20\n", - " - 3s - loss: nan - acc: 0.1059 - val_loss: nan - val_acc: 0.1099\n", + " - 3s - loss: 1.6777 - acc: 0.3991 - val_loss: 1.5874 - val_acc: 0.4495\n", "Epoch 3/20\n", - " - 2s - loss: nan - acc: 0.1059 - val_loss: nan - val_acc: 0.1099\n", + " - 3s - loss: 1.5276 - acc: 0.5332 - val_loss: 1.4879 - val_acc: 0.5180\n", "Epoch 4/20\n", - " - 2s - loss: nan - acc: 0.1102 - val_loss: nan - val_acc: 0.1099\n", + " - 3s - loss: 1.4316 - acc: 0.6077 - val_loss: 1.4083 - val_acc: 0.5628\n", "Epoch 5/20\n", - " - 2s - loss: nan - acc: 0.1144 - val_loss: nan - val_acc: 0.1099\n", + " - 3s - loss: 1.3093 - acc: 0.6747 - val_loss: 1.3360 - val_acc: 0.6116\n", "Epoch 6/20\n", - " - 2s - loss: nan - acc: 0.1187 - val_loss: nan - val_acc: 0.1099\n", + " - 3s - loss: 1.2230 - acc: 0.7397 - val_loss: 1.2745 - val_acc: 0.6460\n", "Epoch 7/20\n", - " - 2s - loss: nan - acc: 0.1187 - val_loss: nan - val_acc: 0.1099\n", + " - 3s - loss: 1.0965 - acc: 0.8196 - val_loss: 1.2152 - val_acc: 0.6743\n", "Epoch 8/20\n", - " - 2s - loss: nan - acc: 0.1144 - val_loss: nan - val_acc: 0.1099\n", + " - 2s - loss: 1.0252 - acc: 0.8551 - val_loss: 1.1667 - val_acc: 0.6809\n", "Epoch 9/20\n", - " - 2s - loss: nan - acc: 0.1059 - val_loss: nan - val_acc: 0.1099\n", + " - 3s - loss: 0.9601 - acc: 0.8542 - val_loss: 1.1137 - val_acc: 0.6928\n", "Epoch 10/20\n", - " - 2s - loss: nan - acc: 0.1102 - val_loss: nan - val_acc: 0.1099\n", + " - 3s - loss: 0.8967 - acc: 0.8704 - val_loss: 1.0777 - val_acc: 0.7006\n", "Epoch 11/20\n", - " - 2s - loss: nan - acc: 0.1187 - val_loss: nan - val_acc: 0.1099\n", + " - 3s - loss: 0.8177 - acc: 0.8907 - val_loss: 1.0517 - val_acc: 0.6936\n", "Epoch 12/20\n", - " - 2s - loss: nan - acc: 0.1102 - val_loss: nan - val_acc: 0.1099\n", + " - 2s - loss: 0.7917 - acc: 0.8871 - val_loss: 1.0147 - val_acc: 0.6998\n", "Epoch 13/20\n", - " - 2s - loss: nan - acc: 0.1102 - val_loss: nan - val_acc: 0.1099\n", + " - 2s - loss: 0.7172 - acc: 0.9043 - val_loss: 0.9832 - val_acc: 0.7129\n", "Epoch 14/20\n", - " - 3s - loss: nan - acc: 0.1102 - val_loss: nan - val_acc: 0.1099\n", + " - 2s - loss: 0.6781 - acc: 0.9016 - val_loss: 0.9633 - val_acc: 0.7129\n", "Epoch 15/20\n", - " - 2s - loss: nan - acc: 0.1230 - val_loss: nan - val_acc: 0.1099\n", + " - 2s - loss: 0.6399 - acc: 0.8898 - val_loss: 0.9538 - val_acc: 0.7096\n", "Epoch 16/20\n", - " - 3s - loss: nan - acc: 0.1144 - val_loss: nan - val_acc: 0.1099\n", + " - 2s - loss: 0.5703 - acc: 0.9102 - val_loss: 0.9321 - val_acc: 0.7178\n", "Epoch 17/20\n", - " - 2s - loss: nan - acc: 0.1059 - val_loss: nan - val_acc: 0.1099\n", + " - 2s - loss: 0.5737 - acc: 0.9135 - val_loss: 0.9235 - val_acc: 0.7153\n", "Epoch 18/20\n", - " - 2s - loss: nan - acc: 0.1102 - val_loss: nan - val_acc: 0.1099\n", + " - 2s - loss: 0.5121 - acc: 0.9153 - val_loss: 0.9083 - val_acc: 0.7240\n", "Epoch 19/20\n", - " - 2s - loss: nan - acc: 0.1059 - val_loss: nan - val_acc: 0.1099\n", + " - 2s - loss: 0.4821 - acc: 0.9280 - val_loss: 0.8995 - val_acc: 0.7272\n", "Epoch 20/20\n", - " - 2s - loss: nan - acc: 0.1102 - val_loss: nan - val_acc: 0.1099\n" + " - 2s - loss: 0.4698 - acc: 0.9305 - val_loss: 0.8835 - val_acc: 0.7260\n" ] } ], @@ -601,7 +578,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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lt/eOZPIt3dicfJTrJy/TpS6VUlWCBkFl8/KBa6dAvydhzafw5WjIyz61eViHBnx+b0/Sj+Vz3eSlbEjKtK9WpZRCg8A5PDxg8LNw1RuwY8E53UtjI4OZ9WBvfLw8uWnKcn7fWrUmXlVKuRcNAmeKvatY99LLIOX0DBqtwv359qE+RIbU4Z5pcfwvbp+NhSql3JkGgbO1GQJ3zYXCfPh4KGz67tSm8Hq+zHygF31ahjD+63jeXrAdU8Y6yUop5QwaBK7QqDPc+yuEtLTWNfh2LORabQP+vt58fEd3ruvSmFfnb+Pv327UsQZKKZfSIHCVwKbW7KWXToD4r2DyJacWuqnl5cGro2IcC9vsZez0NeTknzuZnVJKOYMGgSt5esPAv8Pdv4CHJ3w6HOY/CwV5iAh/G9aWf46MZkFCCmM+XElatq5noJRyPg0COzTpDmOXQLc7YOmb8OHgUw3JtznGGmw5YI012JN2zOZilVI1nQaBXXzqwtVvWr2Ksg7AB5fC8vegqIhhHRrwxX09yTh+gusnLyM+KcPuapVSNZgGgd2iroCHVkDLQfDLU/DZNZC5n27Ngvl6bB98vT25ecoKFupYA6WUk2gQVAV1w2D0DOsMISkOJveGjbNoFV6Xbx7qQ/PQOtw7LY6vdKyBUsoJNAiqChHodieM/QNCWsPXd8Os+wj3ymXmA73p0zKEv30dz2fLE20uVClV02gQVDUhLa1eRQP+DhtnweS+1E1exsd3dOeyduE88/0mZq1JsrtKpVQN4szF66eKSIqIlLkgvYh0F5ECEbnBWbVUO55eMGCCtcaBlw9MG0Gt357lnVHRjlHI6/l54wG7q1RK1RDOPCP4FBhW1g4i4gm8AsxzYh3VV0Q361JR7N2w7G18P7mMjy/3IqZJII/M+JNF21LtrlApVQM4LQiMMYuB9PPs9ggwC9AuMaWpVQeueg3G/A9y0qg97XK+bPoD0WHePPBZHKsTz/cWK6VU2WxrIxCRxsC1wORy7Hu/iMSJSFxqqpv+FdxmCDy8ErregU/cZGYVPcHIulu4+5PVuqaBUqpC7GwsfgOYYIw57wxrxpgpxphYY0xsWFiYC0qromoHwtVvwF0/41mrNq8cf4FXPd/hsY/nsf1Qlt3VKaWqKTuDIBb4UkQSgRuA90TkGhvrqT6a9bamqBjwFJezgm/M43wx5SX2HtbpKJRSF862IDDGNDfGRBpjIoGvgYeMMd+d5zB1kpcPDJiIPLiEWg3a81zhu6S8N5TUPVvOf6xSShXjzO6jM4DlQJSIJInIPSIyVkTGOus13VJYFH73/0JS3/8jqnAH9T7pz7EF/4HCE3ZXppSqJqS6rYgVGxtr4uLi7C6jSvpz0xZSvnqUobKKwrD2eI58GyJi7S5LKVUFiMgaY0yJvxB0ZHEN0iW6HT5jPmdswZNkpB3CfHQZ/DQB8rQhWSlVOg2CGmZAVDgjb7qPgcdfYX6dqzArP4B3e8HWn+0uTSlVRWkQ1EBXdGzIszf05v600bzc6E2MT12YcRN8dQdk6AymSqkzedldgHKOG7pFkJNfwLPfbyI15m3+X4dFeCyeBFt/gh73Qb8nwS/Y7jKVUlWAnhHUYLf3juRvw6L4Zn0qT6cPwzyyBjreCMvfhTc7wx+vQX6O3WUqpWymQVDDPTSgFQ8NaMkXK/fy0rJszMh34MFl0KwPLHgB3u4Ga/8LhQV2l6qUsokGgRsYPzSK23s3Y8riXfzt63hyg6NgzJdw51wIaAyzH4HJfSDhR6hm3YmVUhWnQeAGRITnr47m0cGt+d+aJG6asoIDmcchsq+15sFN08EUwZdjYOpQ2LvC7pKVUi6kQeAmPDyEv1zehg9u68aOQ1lc/fZSawprEWh3NTy0wloz+cgeKwxmjIaUBLvLVkq5gAaBmxka3YDvHu6Lv68Xo6es4LMVezDGWKuidbsTHv0TBj0DiUtgcm/4fhxk7re7bKWUE2kQuKHW9f357uG+9GsdyjPfbWTirA3kFRRaG2v5Qf+/wqProOeDED8T3u4K85+D40fsLVwp5RQaBG4qoLY3H93RnXEDWzEzbh83T1nBoaO5p3eoEwLD/g/GxUH7a2Dpm1aX0yWvQ75Od61UTaJB4MY8PYS/Do3i/Vu7svVgFle9vYQ1e85a+jKoGVz3gbV2cpMe8OvzViCsnAIFebbUrZSqXBoEimEdGvLtQ33xq+XJzVNW8MXKvefu1KAj3PI/uPsXCG0DP413jEH4TMcgKFXNaRAoAKIa+DP74Uvo0zKUv3+7gae+KdZuUFzTXnDnHLjtW6gTCrPHwXs9YeMsKDrvqqNKqSpIg0CdEuDnzdQ7u/PggJbMWLWX0VNWkFK83eAkEWg5CO5bCDd9Dh7e8PXd8EF/a5ZTHZSmVLWiQaDO4OkhTBjWlnfHdGXLgZPtBqX0FhKBdlfBg0vhug8hP9ua5fTjIbB7sWsLV0pdNA0CVaLhnRry7cN98PX25OYpy/lyVQntBid5eEKnUTButTUoLTMJpl0N/x0JSWtcV7RS6qJoEKhStW1Qj9nj+tKrRQgTv9nAP77dwInCMtoBPL1PD0ob+hIc3AgfDbJGKR/c6LK6lVIXxpmL108VkRQRKfE3gIjcIiLxIrJBRJaJSIyzalEXL9CvFp/e1YMHLm3B5yv3cvenq8nOO08vIW9f6P0QPLYeBj0NiUvh/UusdoQD611TuFKq3Jx5RvApMKyM7buBS40xHYF/AlOcWIuqAE8P4akr2vHK9R1ZtjON0VNWkJpVjjEEPnWh/3h4bB1c8oTVkPxBf/h4qNXLqPCE84tXSp1XuYJARB4TkXpi+VhE1orIkLKOMcYsBtLL2L7MGHOyFXIFEFHuqpUtburelA9v78b2lCyun7yM3YfLOcLYLxguew7+sgmG/BuyD1pnB290hEX/gewU5xaulCpTec8I7jbGHAWGAEHAbcDLlVjHPcBPpW0UkftFJE5E4lJTUyvxZdWFGtS2PjPu60V2XgHXT17Gun0Z5T+4dhD0GQePrIXRMyG8HSz8N7weDd88APu1YVkpO5Q3CMTx75XAZ8aYTcUeqxARGYgVBBNK28cYM8UYE2uMiQ0LC6uMl1UV0KVpEF+P7U0dH09GT1nBwq0X+Be9hydEDbMGpY2LsxqYE+bAh4Pgw8GwfqZOX6GUC5U3CNaIyDysIPhFRPyBCg8jFZFOwEfASGNMWkWfT7lOi7C6zHqwDy3C6nDvtDi+itt3cU8U2hqunAR/2QJX/AdyM+Db++H1DvDbv+HogcotXCl1DjHlGAUqIh5AZ2CXMSZDRIKBCGNM/HmOiwTmGGM6lLCtKfAbcLsxZll5C46NjTVxcXHl3V05WXZeAQ9OX8Mf2w/z5OVtGDeoFSIVOFksKoJdv1mT2m2fZ509tBsBPcdak95V5LmVcmMissYYE1vitnIGQV9gnTHmmIjcCnQF3jTG7CnjmBnAACAUOAQ8B3gDGGPeF5GPgOuBk89RUFqRxWkQVD35BUVMmBXPt3/u59ZeTXlhRAc8PSrhF3b6Llj1Efw5HfIyoWEM9H4Eoq+1FtJRSpVbZQRBPBADdMLqFvoRMMoYc2kl1lkuGgRVU1GR4ZVfEvhg0S6GRtfnzZu74OvtWTlPnpdtLZCz8n04vA0Cm1qB0OVWayEdpdR5lRUE5W0jKDBWYowE3jHGvAv4V1aBqvrzcIw1eO7q9szbfIhbP1pJRk5+5Ty5T13ofg88tBJu/gLqNrCmwX6jg9X9NKfUXspKqXIobxBkichTWN1Gf3S0GXg7ryxVXd3Vtzlvj+5CfFImN7y/nP0ZxyvvyT08oO1wuGce3PUTNI51dD/tAD8/Zc1xpJS6YOUNgpuAPKzxBAexBn9NclpVqlq7qlMjpt3dg0NHc7nuvaUkHDxauS8gAs36wC1fwYPLoN3VsPIDeDMGvh0LKVsq9/WUquHK1UYAICL1ge6OL1cZY2wZDqptBNVHwsGj3DF1FTl5hUy5PZbeLUOc92IZe2H5e7B2GpzIgTZXwCWPWwvpKKUq3kYgIqOAVcCNwChgpYjcUHklqpqobYN6fPNQX+oH+HLH1FX8GO/EMQGBTeGKl+GJTTDgKdi3EqYOteY12vqTrp6mVBnK22toPXD5ybMAEQkDfjXGuHzGUD0jqH4ycvK5d1oca/Ye4fHBbXhkUCs8KqN7aVnyj1ndTpe9A5l7Iawd9H0U2o+EWnWc+9pKVUGV0WvI46xLQWkXcKxyc4F+tZh+b0+u7dyY13/dxt3TVnPkWCX1KCpNrTrQ8wF4dK21epp4wHcPwn9awowxsO4L7W2klEN5zwgmYY0hmOF46CYg3hhT6vxAzqJnBNWXMYYvVu3lhdmbCfP34b1buhLTJNBVLw6JS2DLbEj4EY7uB/GEyL7Q9mqrN1JAY9fUopQNKjygzPEk1wN9HV/+YYz5tpLquyAaBNVffFIGD05fS2pWHs9e3Z5bejat2LQUF8oYSF4LW+ZYk90d3mY93rgbtL3K6oUU2tp19SjlApUSBFWFBkHNcORYPk98tY7ft6ZybZfG/PvaDvjVsmnaiNRtkPCDFQzJa63HQqOg3VVWMDTqonMcqWrvooNARLKAknYQwBhj6lVOieWnQVBzFBUZ3lm4g9d/3UabcH8m39qVFmF17S0qMwkS5lrBkLgUTCHUi7AuHbW7CiJ6WEtxKlXN6BmBqtL+2J7KozP+5EShYdINnbiiY0O7S7LkpFtdTxPmwM7foCAXPGtBw87QtCc0cdzqhttdqVLnpUGgqrzkjOM89Pla1u3L4N5LmjPhirZ4e1ahjmn5x2DX77B3hTVGIflPKHT0fApqbgXCyXAIa2tNn61UFaJBoKqF/IIi/m/uFj5dlkj3yCDeGdOV+vWq6GWYgjw4sP50MOxbCcccy6j61IOI7o4zhh4QEQs+OkejspcGgapWZq9PZuKsePxqefL26K7OnZqishgDR3bDvlWOYFgFhzYBxhrDUD/aCobWQ6HFAPCqZXPByt1oEKhqZ/uhLMZOX8Puw8f469AoxvZv6fzRyJUtNxOS4hzhsMK6n58NPgHWms3tRkCrweBd2+5KlRvQIFDVUnZeARNnxTMn/gCXtavPq6NiCKhdjWc/L8iz2hk2z4atP8LxI+BdB9oMsaa+aHW5tfaCUk6gQaCqLWMM05Yl8q8ft9AosDav3xRDt2bBdpdVcYUnIPEPKxQS5ljtC16+0OoyKxTaDAXfALurVDWIBoGq9tbsOcKjM/4kOfM4d/SOZPzQKOr41JB1i4sKYe9yKxS2zIasA1Y31RYDrFCIuhL8akD4KVvZEgQiMhW4CkgxxnQoYbsAbwJXAjnAncaYted7Xg0C93Usr4BJv2xl2vJEGgXU5qXrOtK/TZjdZVWuoiLYHwebv7eCIXOvNSdS835Wm0KbYTonkroodgVBfyAb+G8pQXAl8AhWEPQE3jTG9Dzf82oQqDV70vnb1/HsTD3GDd0ieHp4OwL9amAvHGPgwDorEDZ/D+k7rcfD2kLLQdBysLVSWy0/e+tU1YJtl4ZEJBKYU0oQfAD8boyZ4fh6KzDAGFPm6iUaBAog90Qh7/y2g/cX7STQrxb/HBlddUYkO4MxkJoAO36FHQtgzzIozANPHysMWg6yeiCFt9d5kVSJqmoQzAFeNsYscXy9AJhgjDnnt7yI3A/cD9C0adNue/bscVrNqnrZlJzJhFnxbNx/lKHR9fnnyA6EV9VBaJXpxHHYsxR2/AY7F1ghAeDf0HG2MAhaDIQ61WAMhnKJah8ExekZgTpbQWERHy/ZzWvzt+Hj5cHTw9tzY2yEa6e2tltmEuxcaIXCzoWQmwEINOpsXUJqOcga5exZjbvfqgqpqkGgl4ZUpdp9+BgTZsWzanc6l7QK5aXrOtIk2A2vnxcVWnMh7fzNuoyUtNqaRbWWPzToCPXbQ3g7CI+2/q3tosWBlK2qahAMB8ZxurH4LWNMj/M9pwaBKktRkbUK2ss/JVBYZBg/NIo7+kTiWd1GJVem4xmwezHsXgQHN0DKFsg7enp7vcZW20J4O2sqjPD2ENpGp9uuYezqNTQDGACEAoeA5wBvAGPM+47uo+8Aw7C6j951vstCoEGgyic54zhPf/igl7gAABaGSURBVLeR3xJS6NI0kP9c34nW9XXiN8BqeM5MsgIhZZP176HNcHjr6RlVxRNCWjoCor3jLKI91GukU2JUUzqgTLklYwyz1yfz/OxNZOcVMG5gax64tAW+3jpFdIkKT0D6LmuyvJQtkLLZuqXv5oz1qWrVBb8QqBMGdUKtm19o6V97+dj2LanTNAiUW0vLzuPFOZv5fl0yYf4+3NevObf0bFZzRiY7W/4xSN1qhUP2QTiWZk2JkXPY+vfk10UnSj7ep97p4AhtAw07OdoqOoCvyxc5dFsaBEoBK3al8c5vO1iy4zCBft7c07c5t/eJrN4T2VUVxljtDscOO25nBUXOYcg6aIVJzuHTxwW3sEKhQSfr1rAT+Dew7/uowTQIlCpm7d4jvPvbDhYkpODv48XtfZpxd9/mhNTVSxhOZ4wVCAfjrdsBx79HEk/vUyfcCoeGnU4HRHAL8KhCK9ZVQxoESpVgU3Im7y3cydyNB/D18uSWnk25r3+LqrsqWk2WmwkHNxYLhw2QugWKCqzt3nWgQQerh5NfMNQOti43nboffPq+j7+Ori6BBoFSZdiRksV7C3fy/fpkPD2EUbERjL20JRFBbjgGoSopyLMuJR3c4DiD2AjZhyAnzQoOSvnd5eFdckD4hVgT9gU1h6BICGjiVivFaRAoVQ5703KYvGgnX6/ZhzFwbZfGPDigJS3CdLGYKqeo0BofcTzdCoacdMd9x9cn7x8/cub2k2cYYC0hWi8CgppBsCMcTt2aQ+2gGnVmoUGg1AVIzjjOlMW7mLFqLycKixjeqREPD2xJ2wbaw6VaKyqyej0dSTx9S999+v6xlDP39wmwQqJ4QPg3cFySctx8A6tN24UGgVIXITUrj4+W7GL68j0cyy9kSPv6TLiiLS31DKFmyj92ZkgUD4uMPacH2xUnHtaZw6k2C0e7RfGwKP54vca2jdjWIFCqAjJy8vlkaSJTl+wmr7CIxwa35v7+LfD2rB5/CapKUFRkrRx3LPX0paactBJujktQxw6XMq5CrDAIbu64tTh9C2ru1DWrNQiUqgQpR3N5/odNzN1wkLYN/Hn5+k50bqITtqkSGAP52WcGxLFUyNhnjd4+eSs+pgKsrrPFw6F4YNQOqlBJGgRKVaJ5mw7yzPcbSc3K484+zXlySBsdpawuTu5ROLL7zHBI323dspLP3Ld2EPR5FPr95aJeqqwg0E+vUhdoSHQDerUM4T8/JzB16W5+2XSQf1/bgQFR4XaXpqob33rQMMa6nS0/x9FGUSwkgls4pQw9I1CqAlYnpjNxlrV+8jWdG/HMVe11hLKqkso6I9DWLqUqoHtkMHMf68ejg1vz44YDXPbaIr79M4nq9geWcm8aBEpVkI+XJ3+5vA0/PtqPyNA6PDFzPXd8spp96Tl2l6ZUuWgQKFVJ2tT35+uxfXhhRDRrEtMZ8vpiPvpjF4VFenagqjYNAqUqkaeHcEefSOb/5VJ6twzhXz9u4br3lrI5+ej5D1bKJhoESjlBo8DafHxHLG+P7sL+jOOMeGcJ//k5gey8gvMfrJSLaRAo5SQiwtUxjfj1L5dybZfGvPf7Tnq/tICXftrCgczjdpen1ClODQIRGSYiW0Vkh4hMLGF7UxFZKCJ/iki8iFzpzHqUskOgXy0m3RjD9w/3pX+bMD5cvIt+ryzkiZnr2JScaXd5SjlvHIGIeALbgMuBJGA1MNoYs7nYPlOAP40xk0WkPTDXGBNZ1vPqOAJV3e1Lz2Hq0t3MXL2PnPxC+rYK4d5+LRjQJgypQdMeq6rFrnEEPYAdxphdxph84Etg5Fn7GODk3L4BwFljqpWqeZoE+/Hc1dEsnziYCcPasiMlm7s+Wc2Q1xfz1ep95BUU2l2icjPODILGwL5iXyc5HivueeBWEUkC5gKPOLEepaqUAD9vHhzQkj/+NojXRsXg5enB32bF0/flhby9YDtHjpUw7bFSTmB3Y/Fo4FNjTARwJfCZiJxTk4jcLyJxIhKXmprq8iKVcqZaXh5c1zWCuY9ewvR7ehLdqB6vzt9G75cX8Mx3G0k8fMzuElUN58xJ5/YDTYp9HeF4rLh7gGEAxpjlIuILhAJnLBVkjJkCTAGrjcBZBStlJxHhktahXNI6lK0Hs/joj13MXL2P6Sv3cHm7+tzXvwWxzYK0HUFVOmeeEawGWotIcxGpBdwMzD5rn73AYAARaQf4Avonv3J7UQ38mXRjDEsmDuThAa1YuTudG99fzqgPlrN4W6rOZaQqlVNnH3V0B30D8ASmGmP+LSIvAnHGmNmOnkIfAnWxGo7/ZoyZV9Zzaq8h5Y5y8guYuXofHyzaxcGjuXRuEsgjg1oxqG24niGoctGFaZSqIfIKCvl6TRLvLdzJ/ozjRDeqxyODWjOkfX08PDQQVOk0CJSqYU4UFvHtn/t5b+EOEtNyiKrvz7hBrbiyY0M8NRBUCTQIlKqhCgqLmBN/gHcW7mBHSjYtw+rw8MBWjIhphJen3Z0CVVWiQaBUDVdUZPhp40He/m07CQezaBbix0MDWnJtlwhqeWkgKA0CpdxGUZHh1y2HePu3HWzYn0njwNqMHdCSUbER+Hh52l2espEGgVJuxhjD79tSeXvBdtbuzaB+PR8e6N+SMT2b4uutgeCONAiUclPGGJbtTOOtBdtZuTudBvV8eWRwK0bFNsFb2xDcigaBUooVu9L4f79sJW7PEZoG+/HE5a0ZEdNYexm5CbtmH1VKVSG9WoTwv7G9+eTO7vj7evHEzPVc8eZift54UEcquzkNAqXciIgwsG04P4y7hHfHdKWgyDB2+hpGvrtUp65wYxoESrkhDw9heKeGzHu8P5Nu6ERadj63T13FTVNWsDox3e7ylItpG4FSiryCQmau3sdbC3ZwODuPgVFhPDkkig6NA+wuTVUSbSxWSpVLTn4B05bt4f1FO8k8foLhHRvyxOVtaBVe1+7SVAVpECilLsjR3BN8tHgXHy/ZzfEThVzXNYLHBremSbCf3aWpi6RBoJS6KGnZeUz+fSf/XbEHYwxdmgQR0ySAmCaBxEQEEhFUW6fBriY0CJRSFXIg8zifLk1kVWI6m5KPkl9QBEBInVqnQiGmSQAxEYEE1allc7WqJGUFgTOXqlRK1RANA2rz1JXtAMgvKGLrwSzWJWWwfp91W7g1hZN/UzYN9nOEQwCdmwQS3SiA2rV0WouqTM8IlFIVlpV7go37j7K+WDgkZ+YC4OkhRNX3J6ZJIKN7NKFTRKDN1bonvTSklHK5lKO5rE/KtIIhKYM/92aQnVfAyM6N+OuQKG14djENAqWU7bJyT/D+op189MduDHBXn0geGtiKgNredpfmFjQIlFJVRnLGcV6dt41v/kwioLY3jw1uzS09m+kCOk5m26RzIjJMRLaKyA4RmVjKPqNEZLOIbBKRL5xZj1LKfo0Ca/PqqBjmPHIJ0Y3q8cIPmxny+iJ+2nBA5zqyidPOCETEE9gGXA4kAauB0caYzcX2aQ18BQwyxhwRkXBjTEpZz6tnBErVHCcX0Hlp7ha2HcqmW7Mg/n5lO7o1C7K7tBrHrjOCHsAOY8wuY0w+8CUw8qx97gPeNcYcAThfCCilahYRYWBUOHMf7cfL13Vkb3oO109exsOfr2VP2jG7y3MbzgyCxsC+Yl8nOR4rrg3QRkSWisgKERlW0hOJyP0iEicicampqU4qVyllFy9PD27u0ZTf/zqAxy9rzW8JKVz22iJe/GEzR47l211ejWd364wX0BoYAIwGPhSRczoZG2OmGGNijTGxYWFhLi5RKeUqdXy8ePyyNiwaP4Dru0bw6bLdXDppIR8u3kVeQaHd5dVYzhxZvB9oUuzrCMdjxSUBK40xJ4DdIrINKxhWO7EupVQVF17Pl5ev78RdfZvz0k9b+PfcLUxbnki/1qHUr+fruPmcuh/sVwsPXXLzojkzCFYDrUWkOVYA3AyMOWuf77DOBD4RkVCsS0W7nFiTUqoaiWrgz6d39WDJ9sO8/dt25m9OIe1YHmf3cfH2FML9fQmv50N9f18aBJx5/2Ro+PvqmIWSOC0IjDEFIjIO+AXwBKYaYzaJyItAnDFmtmPbEBHZDBQC440xac6qSSlVPV3SOpRLWocCcKKwiMPZeRzMzOXQ0TxSsnJP3T90NJedqdks23mYo7kF5zxP+4b1uDqmEVd1aqgjm4vRAWVKqRopJ7+AFEc4HDyaS9KR4/y65RB/7s0AoHOTQK7q1JCrOjWiQYCvzdU6n44sVkoph33pOfy44QA/rE9mU/JRRKB7ZDBXd2rIFR0bElrXx+4SnUKDQCmlSrArNZs58QeYvT6ZHSnZeAj0aRnK1TENGRrdgEC/mrO2ggaBUkqVwRjD1kNZzFl/gB/ik9mTloO3p9CvdRhXxzTksnb1q31DswaBUkqVkzGGjfuP8kN8MnPWJ5OcmUstLw8GRYUztEN9Lm0TTnA1XIVNg0AppS5CUZHhz31H+GH9AX7ccIDUrDw8BLo0DWJQ23AGtQ2nbQP/arFuswaBUkpVUFGRYcP+TBYkpLAwIYUN+zMBaBTgy8C24QxuF07vFqFVdllODQKllKpkKUdzWbg1hd8SUvhj+2Fy8gvx8fKgb6tQBjrOFhoH1ra7zFM0CJRSyonyCgpZtTudBVusYNibngNA2wb+py4hdWkahKeN02BoECillIsYY9iZeoyFCSksSDhEXOIRCooMQX7e9GkZSq8WwfRqEUKr8LoubVvQIFBKKZsczT3BH9sO81tCCst3HiY5MxeAkDq16OkIhV4tQmjt5GAoKwicOemcUkq5vXq+3gzv1JDhnRpijCHpyHGW70pjxa40Vu5KZ+6GgwAE16lFz+ZnBoOrZlTVIFBKKRcREZoE+9Ek2I9RsdYs/fvSc1ixK40Vu9JZsSuNnzZawRDk503P5iHWpaSWIbQJ93daMGgQKKWUjU4Gw43FgmHl7nRHOKTx86bTwfDwwFbc269FpdegQaCUUlXIyWC4oVsEAElHcljpOFsIr+ecWVI1CJRSqgqLCPIjopsf1zuCwRnsXrNYKaWUzTQIlFLKzWkQKKWUm9MgUEopN+fUIBCRYSKyVUR2iMjEMva7XkSMiJQ46k0ppZTzOC0IRMQTeBe4AmgPjBaR9iXs5w88Bqx0Vi1KKaVK58wzgh7ADmPMLmNMPvAlMLKE/f4JvALkOrEWpZRSpXBmEDQG9hX7Osnx2Cki0hVoYoz5sawnEpH7RSROROJSU1Mrv1KllHJjtg0oExEP4DXgzvPta4yZAkxxHJcqInsu8mVDgcMXeawrVPX6oOrXqPVVjNZXMVW5vmalbXBmEOwHmhT7OsLx2En+QAfgd8fUqw2A2SIywhhT6jzTxpiwiy1IROJKm4a1Kqjq9UHVr1Hrqxitr2Kqen2lcealodVAaxFpLiK1gJuB2Sc3GmMyjTGhxphIY0wksAIoMwSUUkpVPqcFgTGmABgH/AJsAb4yxmwSkRdFZISzXlcppdSFcWobgTFmLjD3rMeeLWXfAc6sxWGKC16jIqp6fVD1a9T6Kkbrq5iqXl+Jqt1SlUoppSqXTjGhlFJuToNAKaXcXI0MgvPNcSQiPiIy07F9pYhEurC2JiKyUEQ2i8gmEXmshH0GiEimiKxz3EpsV3FijYkissHx2uf04hLLW473L94xMNBVtUUVe1/WichREXn8rH1c/v6JyFQRSRGRjcUeCxaR+SKy3fFvUCnH3uHYZ7uI3OHC+iaJSILj//BbEQks5dgyPw9OrO95Edlf7P/xylKOLdecZk6ob2ax2hJFZF0pxzr9/aswY0yNugGewE6gBVALWA+0P2ufh4D3HfdvBma6sL6GQFfHfX9gWwn1DQDm2PgeJgKhZWy/EvgJEKAXsNLG/+uDQDO73z+gP9AV2Fjssf8AEx33JwKvlHBcMLDL8W+Q436Qi+obAng57r9SUn3l+Tw4sb7ngb+W4zNQ5s+7s+o7a/urwLN2vX8VvdXEM4LyzHE0EpjmuP81MFgco9qczRhzwBiz1nE/C6trbeOyj6pyRgL/NZYVQKCINLShjsHATmPMxY40rzTGmMVA+lkPF/+cTQOuKeHQocB8Y0y6MeYIMB8Y5or6jDHzjNXNG6xxPM5bC/E8Snn/yqO8c5pVSFn1OX53jAJmVPbrukpNDILzznFUfB/HD0ImEOKS6opxXJLqQskzr/YWkfUi8pOIRLu0MDDAPBFZIyL3l7C9PO+xK9xM6T98dr5/J9U3xhxw3D8I1C9hn6ryXt6NdZZXkvN9HpxpnOPS1dRSLq1VhfevH3DIGLO9lO12vn/lUhODoFoQkbrALOBxY8zRszavxbrcEQO8DXzn4vIuMcZ0xZpC/GER6e/i1z8vx2j1EcD/Sths9/t3DmNdI6iSfbVF5B9AAfB5KbvY9XmYDLQEOgMHsC6/VEWjKftsoMr/PNXEIDjfHEdn7CMiXkAAkOaS6qzX9MYKgc+NMd+cvd0Yc9QYk+24PxfwFpFQV9VnjNnv+DcF+Bbr9Lu48rzHznYFsNYYc+jsDXa/f8UcOnnJzPFvSgn72PpeisidwFXALY6wOkc5Pg9OYYw5ZIwpNMYUAR+W8rp2v39ewHXAzNL2sev9uxA1MQjKnOPIYTZwsnfGDcBvpf0QVDbH9cSPgS3GmNdK2afByTYLEemB9f/kkqASkTpiLRaEiNTBalDceNZus4HbHb2HegGZxS6BuEqpf4XZ+f6dpfjn7A7g+xL2+QUYIiJBjksfQxyPOZ2IDAP+hjXHV04p+5Tn8+Cs+oq3O11byuuW5+fdmS4DEowxSSVttPP9uyB2t1Y744bVq2UbVm+CfzgeexHrAw/gi3VJYQewCmjhwtouwbpEEA+sc9yuBMYCYx37jAM2YfWAWAH0cWF9LRyvu95Rw8n3r3h9grX63E5gAxDr4v/fOli/2AOKPWbr+4cVSgeAE1jXqe/BandaAGwHfgWCHfvGAh8VO/Zux2dxB3CXC+vbgXV9/eTn8GRPukbA3LI+Dy6q7zPH5yse65d7w7Prc3x9zs+7K+pzPP7pyc9dsX1d/v5V9KZTTCillJuriZeGlFJKXQANAqWUcnMaBEop5eY0CJRSys1pECillJvTIFDKhRwzo86xuw6litMgUEopN6dBoFQJRORWEVnlmEP+AxHxFJFsEXldrHUkFohImGPfziKyoti8/kGOx1uJyK+Oye/WikhLx9PXFZGvHWsBfO6qmW+VKo0GgVJnEZF2wE1AX2NMZ6AQuAVrRHOcMSYaWAQ85zjkv8AEY0wnrJGwJx//HHjXWJPf9cEamQrWjLOPA+2xRp72dfo3pVQZvOwuQKkqaDDQDVjt+GO9NtaEcUWcnlxsOvCNiAQAgcaYRY7HpwH/c8wv09gY8y2AMSYXwPF8q4xjbhrHqlaRwBLnf1tKlUyDQKlzCTDNGPPUGQ+KPHPWfhc7P0tesfuF6M+hspleGlLqXAuAG0QkHE6tPdwM6+flBsc+Y4AlxphM4IiI9HM8fhuwyFirzyWJyDWO5/ARET+XfhdKlZP+JaLUWYwxm0XkaaxVpTywZpx8GDgG9HBsS8FqRwBriun3Hb/odwF3OR6/DfhARF50PMeNLvw2lCo3nX1UqXISkWxjTF2761CqsumlIaWUcnN6RqCUUm5OzwiUUsrNaRAopZSb0yBQSik3p0GglFJuToNAKaXc3P8H4XuWwqwFe6IAAAAASUVORK5CYII=\n", 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" ] @@ -646,8 +623,8 @@ "text": [ "\n", "Test Set Metrics:\n", - "\tloss: nan\n", - "\tacc: 0.1099\n" + "\tloss: 0.8902\n", + "\tacc: 0.7305\n" ] } ], @@ -694,22 +671,7 @@ "cell_type": "code", "execution_count": 26, "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Input contains NaN, infinity or a value too large for dtype('float32').", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnode_predictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtarget_encoding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_predictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/sklearn/feature_extraction/dict_vectorizer.py\u001b[0m in \u001b[0;36minverse_transform\u001b[0;34m(self, X, dict_type)\u001b[0m\n\u001b[1;32m 253\u001b[0m \"\"\"\n\u001b[1;32m 254\u001b[0m \u001b[0;31m# COO matrix is not subscriptable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'csc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 256\u001b[0m \u001b[0mn_samples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m 540\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 541\u001b[0m _assert_all_finite(array,\n\u001b[0;32m--> 542\u001b[0;31m allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m 543\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 544\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan)\u001b[0m\n\u001b[1;32m 54\u001b[0m not allow_nan and not np.isfinite(X).all()):\n\u001b[1;32m 55\u001b[0m \u001b[0mtype_err\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'infinity'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mallow_nan\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'NaN, infinity'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype_err\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 57\u001b[0m \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'object'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mallow_nan\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float32')." - ] - } - ], + "outputs": [], "source": [ "node_predictions = target_encoding.inverse_transform(all_predictions)" ] @@ -723,9 +685,108 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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PredictedTrue
31336subject=Neural_NetworksNeural_Networks
1061127subject=Rule_LearningRule_Learning
1106406subject=Reinforcement_LearningReinforcement_Learning
13195subject=Genetic_AlgorithmsReinforcement_Learning
37879subject=Probabilistic_MethodsProbabilistic_Methods
1126012subject=Probabilistic_MethodsProbabilistic_Methods
1107140subject=TheoryTheory
1102850subject=Neural_NetworksNeural_Networks
31349subject=Neural_NetworksNeural_Networks
1106418subject=TheoryTheory
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" + ], + "text/plain": [ + " Predicted True\n", + "31336 subject=Neural_Networks Neural_Networks\n", + "1061127 subject=Rule_Learning Rule_Learning\n", + "1106406 subject=Reinforcement_Learning Reinforcement_Learning\n", + "13195 subject=Genetic_Algorithms Reinforcement_Learning\n", + "37879 subject=Probabilistic_Methods Probabilistic_Methods\n", + "1126012 subject=Probabilistic_Methods Probabilistic_Methods\n", + "1107140 subject=Theory Theory\n", + "1102850 subject=Neural_Networks Neural_Networks\n", + "31349 subject=Neural_Networks Neural_Networks\n", + "1106418 subject=Theory Theory" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "results = pd.DataFrame(node_predictions, index=all_nodes).idxmax(axis=1)\n", "df = pd.DataFrame({\"Predicted\": results, \"True\": node_data['subject']})\n", @@ -741,7 +802,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -759,7 +820,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -772,7 +833,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -792,7 +853,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -801,9 +862,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(2708, 32)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "emb = embedding_model.predict_generator(all_mapper)\n", "emb.shape" @@ -818,7 +890,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -830,7 +902,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -840,7 +912,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -858,9 +930,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "alpha = 0.7\n", "\n", @@ -889,7 +974,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/demos/node-classification/directed-graphsage/example4-directed-graph-fully-directed-graphsage.ipynb b/demos/node-classification/directed-graphsage/example4-directed-graph-fully-directed-graphsage.ipynb index 94f5fe871..676ebd6ad 100644 --- a/demos/node-classification/directed-graphsage/example4-directed-graph-fully-directed-graphsage.ipynb +++ b/demos/node-classification/directed-graphsage/example4-directed-graph-fully-directed-graphsage.ipynb @@ -209,13 +209,13 @@ { "data": { "text/plain": [ - "Counter({'Reinforcement_Learning': 22,\n", - " 'Probabilistic_Methods': 42,\n", + "Counter({'Theory': 35,\n", " 'Neural_Networks': 81,\n", + " 'Probabilistic_Methods': 42,\n", " 'Genetic_Algorithms': 42,\n", - " 'Theory': 35,\n", + " 'Case_Based': 30,\n", " 'Rule_Learning': 18,\n", - " 'Case_Based': 30})" + " 'Reinforcement_Learning': 22})" ] }, "execution_count": 9, @@ -384,7 +384,7 @@ "source": [ "Now we can specify our machine learning model, we need a few more parameters for this:\n", "\n", - " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use 48-dimensional hidden node features at each layer, which corresponds to 16 weights for a node, 16 for the in-nodes and 16 for the out-nodes. This is higher than the 32 dimensions used in example 1 (which also uses 16 weights per node and 16 per 1-hop neighbourhood), giving more expressive power but having more weights to estimate (and thus might require more training epochs if the model underfits, or else the model might be more prone to overfitting).\n", + " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use 32-dimensional hidden node features at each layer, which corresponds to 12 weights for a node, 10 for the in-nodes and 10 for the out-nodes. This is in contrast to example 1, for which the 32 hidden features are partitioned into 16 weights per node and 16 per 1-hop neighbourhood (in-nodes and out-nodes combined). We balance fewer features per weight by having more weights.\n", " * The `bias` and `dropout` are internal parameters of the model. " ] }, @@ -397,15 +397,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "WARNING: Logging before flag parsing goes to stderr.\n", - "W0823 10:01:39.750803 4553299392 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", - "\n" ] } ], "source": [ "graphsage_model = DirectedGraphSAGE(\n", - " layer_sizes=[48, 48],\n", + " layer_sizes=[32, 32],\n", " generator=train_gen,\n", " bias=False,\n", " dropout=0.5,\n", @@ -428,15 +425,6 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0823 10:01:39.767582 4553299392 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", - "\n", - "W0823 10:01:39.774357 4553299392 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", - "\n", - "W0823 10:01:39.781755 4553299392 deprecation.py:506] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", - "W0823 10:01:39.834255 4553299392 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", - "\n" ] } ], @@ -468,10 +456,6 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0823 10:01:40.124934 4553299392 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", - "\n", - "W0823 10:01:40.130564 4553299392 deprecation_wrapper.py:119] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", - "\n" ] } ], @@ -509,9 +493,6 @@ "name": "stderr", "output_type": "stream", "text": [ - "W0823 10:01:40.303460 4553299392 deprecation.py:323] From /anaconda3/envs/stellargraph-py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" ] }, { @@ -519,45 +500,45 @@ "output_type": "stream", "text": [ "Epoch 1/20\n", - " - 3s - loss: 1.8643 - acc: 0.2966 - val_loss: 1.7387 - val_acc: 0.3769\n", + " - 3s - loss: 1.9228 - acc: 0.2187 - val_loss: 1.7943 - val_acc: 0.4208\n", "Epoch 2/20\n", - " - 3s - loss: 1.6582 - acc: 0.4763 - val_loss: 1.6220 - val_acc: 0.4463\n", + " - 3s - loss: 1.7087 - acc: 0.5077 - val_loss: 1.6751 - val_acc: 0.5164\n", "Epoch 3/20\n", - " - 3s - loss: 1.5002 - acc: 0.6323 - val_loss: 1.5037 - val_acc: 0.5833\n", + " - 3s - loss: 1.5843 - acc: 0.6390 - val_loss: 1.5647 - val_acc: 0.5878\n", "Epoch 4/20\n", - " - 3s - loss: 1.3621 - acc: 0.7661 - val_loss: 1.3921 - val_acc: 0.6879\n", + " - 3s - loss: 1.4491 - acc: 0.7341 - val_loss: 1.4714 - val_acc: 0.6251\n", "Epoch 5/20\n", - " - 3s - loss: 1.2382 - acc: 0.8686 - val_loss: 1.2943 - val_acc: 0.7317\n", + " - 2s - loss: 1.3691 - acc: 0.7465 - val_loss: 1.3861 - val_acc: 0.6530\n", "Epoch 6/20\n", - " - 3s - loss: 1.1222 - acc: 0.9093 - val_loss: 1.1994 - val_acc: 0.7494\n", + " - 2s - loss: 1.2488 - acc: 0.8120 - val_loss: 1.3005 - val_acc: 0.6903\n", "Epoch 7/20\n", - " - 3s - loss: 1.0135 - acc: 0.9126 - val_loss: 1.1206 - val_acc: 0.7674\n", + " - 3s - loss: 1.1472 - acc: 0.8296 - val_loss: 1.2311 - val_acc: 0.7162\n", "Epoch 8/20\n", - " - 3s - loss: 0.8860 - acc: 0.9440 - val_loss: 1.0481 - val_acc: 0.7793\n", + " - 3s - loss: 1.0339 - acc: 0.8745 - val_loss: 1.1642 - val_acc: 0.7432\n", "Epoch 9/20\n", - " - 3s - loss: 0.8103 - acc: 0.9619 - val_loss: 0.9955 - val_acc: 0.7822\n", + " - 3s - loss: 0.9480 - acc: 0.9084 - val_loss: 1.1095 - val_acc: 0.7490\n", "Epoch 10/20\n", - " - 3s - loss: 0.7310 - acc: 0.9601 - val_loss: 0.9452 - val_acc: 0.7896\n", + " - 2s - loss: 0.8772 - acc: 0.9144 - val_loss: 1.0583 - val_acc: 0.7596\n", "Epoch 11/20\n", - " - 3s - loss: 0.6471 - acc: 0.9720 - val_loss: 0.8975 - val_acc: 0.7884\n", + " - 3s - loss: 0.8230 - acc: 0.9135 - val_loss: 1.0185 - val_acc: 0.7559\n", "Epoch 12/20\n", - " - 3s - loss: 0.5865 - acc: 0.9763 - val_loss: 0.8701 - val_acc: 0.7830\n", + " - 3s - loss: 0.7506 - acc: 0.9381 - val_loss: 0.9845 - val_acc: 0.7633\n", "Epoch 13/20\n", - " - 3s - loss: 0.5364 - acc: 0.9763 - val_loss: 0.8358 - val_acc: 0.7982\n", + " - 2s - loss: 0.6970 - acc: 0.9357 - val_loss: 0.9440 - val_acc: 0.7654\n", "Epoch 14/20\n", - " - 3s - loss: 0.4771 - acc: 0.9898 - val_loss: 0.8216 - val_acc: 0.7871\n", + " - 3s - loss: 0.6196 - acc: 0.9517 - val_loss: 0.9125 - val_acc: 0.7683\n", "Epoch 15/20\n", - " - 3s - loss: 0.4445 - acc: 0.9788 - val_loss: 0.7956 - val_acc: 0.7884\n", + " - 3s - loss: 0.5945 - acc: 0.9440 - val_loss: 0.8944 - val_acc: 0.7646\n", "Epoch 16/20\n", - " - 3s - loss: 0.4114 - acc: 0.9898 - val_loss: 0.7716 - val_acc: 0.7879\n", + " - 3s - loss: 0.5453 - acc: 0.9492 - val_loss: 0.8786 - val_acc: 0.7687\n", "Epoch 17/20\n", - " - 3s - loss: 0.3853 - acc: 0.9788 - val_loss: 0.7553 - val_acc: 0.7896\n", + " - 3s - loss: 0.5192 - acc: 0.9406 - val_loss: 0.8564 - val_acc: 0.7765\n", "Epoch 18/20\n", - " - 3s - loss: 0.3515 - acc: 0.9822 - val_loss: 0.7369 - val_acc: 0.7990\n", + " - 3s - loss: 0.4561 - acc: 0.9865 - val_loss: 0.8327 - val_acc: 0.7789\n", "Epoch 19/20\n", - " - 3s - loss: 0.3097 - acc: 0.9865 - val_loss: 0.7319 - val_acc: 0.7957\n", + " - 3s - loss: 0.4505 - acc: 0.9695 - val_loss: 0.8086 - val_acc: 0.7851\n", "Epoch 20/20\n", - " - 3s - loss: 0.3098 - acc: 1.0000 - val_loss: 0.7257 - val_acc: 0.7892\n" + " - 3s - loss: 0.4282 - acc: 0.9695 - val_loss: 0.8013 - val_acc: 0.7789\n" ] } ], @@ -601,7 +582,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] @@ -646,8 +627,8 @@ "text": [ "\n", "Test Set Metrics:\n", - "\tloss: 0.7135\n", - "\tacc: 0.7970\n" + "\tloss: 0.8017\n", + "\tacc: 0.7806\n" ] } ], @@ -769,7 +750,7 @@ " \n", " \n", " 1107140\n", - " subject=Probabilistic_Methods\n", + " subject=Theory\n", " Theory\n", " \n", " \n", @@ -799,7 +780,7 @@ "13195 subject=Reinforcement_Learning Reinforcement_Learning\n", "37879 subject=Probabilistic_Methods Probabilistic_Methods\n", "1126012 subject=Probabilistic_Methods Probabilistic_Methods\n", - "1107140 subject=Probabilistic_Methods Theory\n", + "1107140 subject=Theory Theory\n", "1102850 subject=Neural_Networks Neural_Networks\n", "31349 subject=Neural_Networks Neural_Networks\n", "1106418 subject=Theory Theory" @@ -891,7 +872,7 @@ { "data": { "text/plain": [ - "(2708, 48)" + "(2708, 32)" ] }, "execution_count": 32, @@ -960,7 +941,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1035,7 +1016,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, From 43b5a698b11169daade844bf55052375a0ef1578 Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Thu, 5 Sep 2019 16:39:37 +0930 Subject: [PATCH 23/30] Moved example4 into main GraphSAGE demos. --- ...ndirected-graph-undirected-graphsage.ipynb | 984 ----------------- ...-directed-graph-undirected-graphsage.ipynb | 986 ------------------ ...rected-graph-half-directed-graphsage.ipynb | 982 ----------------- .../directed-graphsage/experim.ipynb | 964 ----------------- .../directed-graphsage-on-cora-example.ipynb} | 18 +- 5 files changed, 7 insertions(+), 3927 deletions(-) delete mode 100644 demos/node-classification/directed-graphsage/example1-undirected-graph-undirected-graphsage.ipynb delete mode 100644 demos/node-classification/directed-graphsage/example2-directed-graph-undirected-graphsage.ipynb delete mode 100644 demos/node-classification/directed-graphsage/example3-directed-graph-half-directed-graphsage.ipynb delete mode 100644 demos/node-classification/directed-graphsage/experim.ipynb rename demos/node-classification/{directed-graphsage/example4-directed-graph-fully-directed-graphsage.ipynb => graphsage/directed-graphsage-on-cora-example.ipynb} (99%) diff --git a/demos/node-classification/directed-graphsage/example1-undirected-graph-undirected-graphsage.ipynb b/demos/node-classification/directed-graphsage/example1-undirected-graph-undirected-graphsage.ipynb deleted file mode 100644 index 61cd2431a..000000000 --- a/demos/node-classification/directed-graphsage/example1-undirected-graph-undirected-graphsage.ipynb +++ /dev/null @@ -1,984 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Stellargraph example 1: undirected GraphSAGE on an undirected CORA citation network\n", - "\n", - "This example shows the straight-forward application of undirected GraphSAGE to an undirected graph." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Import NetworkX and stellar:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - } - ], - "source": [ - "import networkx as nx\n", - "import pandas as pd\n", - "import os\n", - "\n", - "import stellargraph as sg\n", - "from stellargraph.mapper import GraphSAGENodeGenerator\n", - "from stellargraph.layer import GraphSAGE\n", - "\n", - "from keras import layers, optimizers, losses, metrics, Model\n", - "from sklearn import preprocessing, feature_extraction, model_selection" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading the CORA network" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Downloading the CORA dataset:**\n", - " \n", - "The dataset used in this demo can be downloaded from [here](https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz).\n", - "\n", - "The following is the description of the dataset:\n", - "> The Cora dataset consists of 2708 scientific publications classified into one of seven classes.\n", - "> The citation network consists of 5429 links. Each publication in the dataset is described by a\n", - "> 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary.\n", - "> The dictionary consists of 1433 unique words. The README file in the dataset provides more details.\n", - "\n", - "Download and unzip the cora.tgz file to a location on your computer and set the `data_dir` variable to\n", - "point to the location of the dataset (the directory containing \"cora.cites\" and \"cora.content\")." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "data_dir = os.path.expanduser(\"~/data/cora\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load the graph from edgelist (in `cited-paper` <- `citing-paper` order)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "edgelist = pd.read_csv(os.path.join(data_dir, \"cora.cites\"), sep='\\t', header=None, names=[\"target\", \"source\"])\n", - "edgelist[\"label\"] = \"cites\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Convert the graph to undirected format" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "Gnx = nx.from_pandas_edgelist(edgelist, edge_attr=\"label\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "nx.set_node_attributes(Gnx, \"paper\", \"label\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load the features and subject for the nodes" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "feature_names = [\"w_{}\".format(ii) for ii in range(1433)]\n", - "column_names = feature_names + [\"subject\"]\n", - "node_data = pd.read_csv(os.path.join(data_dir, \"cora.content\"), sep='\\t', header=None, names=column_names)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We aim to train a graph-ML model that will predict the \"subject\" attribute on the nodes. These subjects are one of 7 categories:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Case_Based',\n", - " 'Genetic_Algorithms',\n", - " 'Neural_Networks',\n", - " 'Probabilistic_Methods',\n", - " 'Reinforcement_Learning',\n", - " 'Rule_Learning',\n", - " 'Theory'}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "set(node_data[\"subject\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Splitting the data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For machine learning we want to take a subset of the nodes for training, and use the rest for testing. We'll use scikit-learn again to do this" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "train_data, test_data = model_selection.train_test_split(\n", - " node_data, train_size=0.1, test_size=None, stratify=node_data['subject']\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note using stratified sampling gives the following counts:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Counter({'Genetic_Algorithms': 42,\n", - " 'Case_Based': 30,\n", - " 'Neural_Networks': 81,\n", - " 'Probabilistic_Methods': 42,\n", - " 'Rule_Learning': 18,\n", - " 'Reinforcement_Learning': 22,\n", - " 'Theory': 35})" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from collections import Counter\n", - "Counter(train_data['subject'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The training set has class imbalance that might need to be compensated, e.g., via using a weighted cross-entropy loss in model training, with class weights inversely proportional to class support. However, we will ignore the class imbalance in this example, for simplicity." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Converting to numeric arrays" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For our categorical target, we will use one-hot vectors that will be fed into a soft-max Keras layer during training. To do this conversion ..." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "target_encoding = feature_extraction.DictVectorizer(sparse=False)\n", - "\n", - "train_targets = target_encoding.fit_transform(train_data[[\"subject\"]].to_dict('records'))\n", - "test_targets = target_encoding.transform(test_data[[\"subject\"]].to_dict('records'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now do the same for the node attributes we want to use to predict the subject. These are the feature vectors that the Keras model will use as input. The CORA dataset contains attributes 'w_x' that correspond to words found in that publication. If a word occurs more than once in a publication the relevant attribute will be set to one, otherwise it will be zero." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "node_features = node_data[feature_names]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating the GraphSAGE model in Keras" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now create a StellarGraph object from the NetworkX graph and the node features and targets. It is StellarGraph objects that we use in this library to perform machine learning tasks on." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "G = sg.StellarGraph(Gnx, node_features=node_features)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "StellarGraph: Undirected multigraph\n", - " Nodes: 2708, Edges: 5278\n", - "\n", - " Node types:\n", - " paper: [2708]\n", - " Edge types: paper-cites->paper\n", - "\n", - " Edge types:\n", - " paper-cites->paper: [5278]\n", - "\n" - ] - } - ], - "source": [ - "print(G.info())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To feed data from the graph to the Keras model we need a data generator that feeds data from the graph to the model. The generators are specialized to the model and the learning task so we choose the `GraphSAGENodeGenerator` as we are predicting node attributes with a GraphSAGE model.\n", - "\n", - "We need two other parameters, the `batch_size` to use for training and the number of nodes to sample at each level of the model. Here we choose a two-level model with 10 nodes sampled in the first layer, and 4 in the second." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "batch_size = 50; num_samples = [10, 4]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A `GraphSAGENodeGenerator` object is required to send the node features in sampled subgraphs to Keras" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "generator = GraphSAGENodeGenerator(G, batch_size, num_samples)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the `generator.flow()` method, we can create iterators over nodes that should be used to train, validate, or evaluate the model. For training we use only the training nodes returned from our splitter and the target values. The `shuffle=True` argument is given to the `flow` method to improve training." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "train_gen = generator.flow(train_data.index, train_targets, shuffle=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can specify our machine learning model, we need a few more parameters for this:\n", - "\n", - " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use 32-dimensional hidden node features at each layer.\n", - " * The `bias` and `dropout` are internal parameters of the model. " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ] - } - ], - "source": [ - "graphsage_model = GraphSAGE(\n", - " layer_sizes=[32, 32],\n", - " generator=train_gen,\n", - " bias=False,\n", - " dropout=0.5,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we create a model to predict the 7 categories using Keras softmax layers." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ] - } - ], - "source": [ - "x_inp, x_out = graphsage_model.build()\n", - "prediction = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Training the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's create the actual Keras model with the graph inputs `x_inp` provided by the `graph_model` and outputs being the predictions from the softmax layer" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ] - } - ], - "source": [ - "model = Model(inputs=x_inp, outputs=prediction)\n", - "model.compile(\n", - " optimizer=optimizers.Adam(lr=0.005),\n", - " loss=losses.categorical_crossentropy,\n", - " metrics=[\"acc\"],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Train the model, keeping track of its loss and accuracy on the training set, and its generalisation performance on the test set (we need to create another generator over the test data for this)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "test_gen = generator.flow(test_data.index, test_targets)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/20\n", - " - 2s - loss: 1.8514 - acc: 0.2941 - val_loss: 1.7087 - val_acc: 0.4020\n", - "Epoch 2/20\n", - " - 2s - loss: 1.6656 - acc: 0.4483 - val_loss: 1.5593 - val_acc: 0.4873\n", - "Epoch 3/20\n", - " - 2s - loss: 1.5061 - acc: 0.6458 - val_loss: 1.4184 - val_acc: 0.7026\n", - "Epoch 4/20\n", - " - 2s - loss: 1.3407 - acc: 0.7838 - val_loss: 1.2991 - val_acc: 0.7461\n", - "Epoch 5/20\n", - " - 2s - loss: 1.1865 - acc: 0.8772 - val_loss: 1.1899 - val_acc: 0.7777\n", - "Epoch 6/20\n", - " - 2s - loss: 1.0759 - acc: 0.8806 - val_loss: 1.0987 - val_acc: 0.7867\n", - "Epoch 7/20\n", - " - 2s - loss: 0.9604 - acc: 0.9093 - val_loss: 1.0227 - val_acc: 0.7994\n", - "Epoch 8/20\n", - " - 2s - loss: 0.8624 - acc: 0.9499 - val_loss: 0.9590 - val_acc: 0.8056\n", - "Epoch 9/20\n", - " - 2s - loss: 0.7505 - acc: 0.9619 - val_loss: 0.9012 - val_acc: 0.8150\n", - "Epoch 10/20\n", - " - 2s - loss: 0.6935 - acc: 0.9619 - val_loss: 0.8538 - val_acc: 0.8060\n", - "Epoch 11/20\n", - " - 2s - loss: 0.6157 - acc: 0.9729 - val_loss: 0.8132 - val_acc: 0.8146\n", - "Epoch 12/20\n", - " - 2s - loss: 0.5434 - acc: 0.9831 - val_loss: 0.7837 - val_acc: 0.8130\n", - "Epoch 13/20\n", - " - 2s - loss: 0.4930 - acc: 0.9889 - val_loss: 0.7557 - val_acc: 0.8171\n", - "Epoch 14/20\n", - " - 2s - loss: 0.4520 - acc: 0.9831 - val_loss: 0.7317 - val_acc: 0.8179\n", - "Epoch 15/20\n", - " - 2s - loss: 0.3919 - acc: 0.9898 - val_loss: 0.7112 - val_acc: 0.8109\n", - "Epoch 16/20\n", - " - 2s - loss: 0.3767 - acc: 0.9822 - val_loss: 0.6953 - val_acc: 0.8101\n", - "Epoch 17/20\n", - " - 3s - loss: 0.3403 - acc: 1.0000 - val_loss: 0.6687 - val_acc: 0.8187\n", - "Epoch 18/20\n", - " - 3s - loss: 0.2990 - acc: 0.9966 - val_loss: 0.6702 - val_acc: 0.8179\n", - "Epoch 19/20\n", - " - 2s - loss: 0.2748 - acc: 1.0000 - val_loss: 0.6548 - val_acc: 0.8183\n", - "Epoch 20/20\n", - " - 2s - loss: 0.2520 - acc: 0.9966 - val_loss: 0.6473 - val_acc: 0.8236\n" - ] - } - ], - "source": [ - "history = model.fit_generator(\n", - " train_gen,\n", - " epochs=20,\n", - " validation_data=test_gen,\n", - " verbose=2,\n", - " shuffle=False\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "def plot_history(history):\n", - " metrics = sorted(history.history.keys())\n", - " metrics = metrics[:len(metrics)//2]\n", - " for m in metrics:\n", - " # summarize history for metric m\n", - " plt.plot(history.history[m])\n", - " plt.plot(history.history['val_' + m])\n", - " plt.title(m)\n", - " plt.ylabel(m)\n", - " plt.xlabel('epoch')\n", - " plt.legend(['train', 'test'], loc='best')\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plot_history(history)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we have trained the model we can evaluate on the test set." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Test Set Metrics:\n", - "\tloss: 0.6503\n", - "\tacc: 0.8154\n" - ] - } - ], - "source": [ - "test_metrics = model.evaluate_generator(test_gen)\n", - "print(\"\\nTest Set Metrics:\")\n", - "for name, val in zip(model.metrics_names, test_metrics):\n", - " print(\"\\t{}: {:0.4f}\".format(name, val))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Making predictions with the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's get the predictions themselves for all nodes using another node iterator:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "all_nodes = node_data.index\n", - "all_mapper = generator.flow(all_nodes)\n", - "all_predictions = model.predict_generator(all_mapper)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These predictions will be the output of the softmax layer, so to get final categories we'll use the `inverse_transform` method of our target attribute specifcation to turn these values back to the original categories" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "node_predictions = target_encoding.inverse_transform(all_predictions)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's have a look at a few:" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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31336subject=Neural_NetworksNeural_Networks
1061127subject=Rule_LearningRule_Learning
1106406subject=Reinforcement_LearningReinforcement_Learning
13195subject=Reinforcement_LearningReinforcement_Learning
37879subject=Probabilistic_MethodsProbabilistic_Methods
1126012subject=Probabilistic_MethodsProbabilistic_Methods
1107140subject=Probabilistic_MethodsTheory
1102850subject=Neural_NetworksNeural_Networks
31349subject=Neural_NetworksNeural_Networks
1106418subject=TheoryTheory
\n", - "
" - ], - "text/plain": [ - " Predicted True\n", - "31336 subject=Neural_Networks Neural_Networks\n", - "1061127 subject=Rule_Learning Rule_Learning\n", - "1106406 subject=Reinforcement_Learning Reinforcement_Learning\n", - "13195 subject=Reinforcement_Learning Reinforcement_Learning\n", - "37879 subject=Probabilistic_Methods Probabilistic_Methods\n", - "1126012 subject=Probabilistic_Methods Probabilistic_Methods\n", - "1107140 subject=Probabilistic_Methods Theory\n", - "1102850 subject=Neural_Networks Neural_Networks\n", - "31349 subject=Neural_Networks Neural_Networks\n", - "1106418 subject=Theory Theory" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results = pd.DataFrame(node_predictions, index=all_nodes).idxmax(axis=1)\n", - "df = pd.DataFrame({\"Predicted\": results, \"True\": node_data['subject']})\n", - "df.head(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Add the predictions to the graph, and save as graphml, e.g. for visualisation in [Gephi](https://gephi.org)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "for nid, pred, true in zip(df.index, df[\"Predicted\"], df[\"True\"]):\n", - " Gnx.node[nid][\"subject\"] = true\n", - " Gnx.node[nid][\"PREDICTED_subject\"] = pred.split(\"=\")[-1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Also add isTrain and isCorrect node attributes:" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "for nid in train_data.index:\n", - " Gnx.node[nid][\"isTrain\"] = True\n", - " \n", - "for nid in test_data.index:\n", - " Gnx.node[nid][\"isTrain\"] = False" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "for nid in Gnx.nodes():\n", - " Gnx.node[nid][\"isCorrect\"] = Gnx.node[nid][\"subject\"] == Gnx.node[nid][\"PREDICTED_subject\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Node embeddings\n", - "Evaluate node embeddings as activations of the output of graphsage layer stack, and visualise them, coloring nodes by their subject label.\n", - "\n", - "The GraphSAGE embeddings are the output of the GraphSAGE layers, namely the `x_out` variable. Let's create a new model with the same inputs as we used previously `x_inp` but now the output is the embeddings rather than the predicted class. Additionally note that the weights trained previously are kept in the new model." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "embedding_model = Model(inputs=x_inp, outputs=x_out)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2708, 32)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "emb = embedding_model.predict_generator(all_mapper)\n", - "emb.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Project the embeddings to 2d using either TSNE or PCA transform, and visualise, coloring nodes by their subject label" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.decomposition import PCA\n", - "from sklearn.manifold import TSNE\n", - "import pandas as pd\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "X = emb\n", - "y = np.argmax(target_encoding.transform(node_data[[\"subject\"]].to_dict('records')), axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "if X.shape[1] > 2:\n", - " transform = TSNE #PCA \n", - "\n", - " trans = transform(n_components=2)\n", - " emb_transformed = pd.DataFrame(trans.fit_transform(X), index=node_data.index)\n", - " emb_transformed['label'] = y\n", - "else:\n", - " emb_transformed = pd.DataFrame(X, index=node_data.index)\n", - " emb_transformed = emb_transformed.rename(columns = {'0':0, '1':1})\n", - " emb_transformed['label'] = y" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "alpha = 0.7\n", - "\n", - "fig, ax = plt.subplots(figsize=(7,7))\n", - "ax.scatter(emb_transformed[0], emb_transformed[1], c=emb_transformed['label'].astype(\"category\"), \n", - " cmap=\"jet\", alpha=alpha)\n", - "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n", - "plt.title('{} visualization of GraphSAGE embeddings for cora dataset'.format(transform.__name__))\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/demos/node-classification/directed-graphsage/example2-directed-graph-undirected-graphsage.ipynb b/demos/node-classification/directed-graphsage/example2-directed-graph-undirected-graphsage.ipynb deleted file mode 100644 index d2878353f..000000000 --- a/demos/node-classification/directed-graphsage/example2-directed-graph-undirected-graphsage.ipynb +++ /dev/null @@ -1,986 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Stellargraph example 2: undirected GraphSAGE on a directed CORA citation network\n", - "\n", - "This example shows the application of undirected GraphSAGE to a *directed* graph. In order for this to work, we wrap the directed NetworkX graph with an *undirected* StellarGraph. This means that the `neighbors()` call returns only out-nodes, i.e. the nodes reachable on outward directed edges." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Import NetworkX and stellar:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - } - ], - "source": [ - "import networkx as nx\n", - "import pandas as pd\n", - "import os\n", - "\n", - "import stellargraph as sg\n", - "from stellargraph.mapper import GraphSAGENodeGenerator\n", - "from stellargraph.layer import GraphSAGE\n", - "\n", - "from keras import layers, optimizers, losses, metrics, Model\n", - "from sklearn import preprocessing, feature_extraction, model_selection" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading the CORA network" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Downloading the CORA dataset:**\n", - " \n", - "The dataset used in this demo can be downloaded from [here](https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz).\n", - "\n", - "The following is the description of the dataset:\n", - "> The Cora dataset consists of 2708 scientific publications classified into one of seven classes.\n", - "> The citation network consists of 5429 links. Each publication in the dataset is described by a\n", - "> 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary.\n", - "> The dictionary consists of 1433 unique words. The README file in the dataset provides more details.\n", - "\n", - "Download and unzip the cora.tgz file to a location on your computer and set the `data_dir` variable to\n", - "point to the location of the dataset (the directory containing \"cora.cites\" and \"cora.content\")." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "data_dir = os.path.expanduser(\"~/data/cora\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load the graph from edgelist (in `cited-paper` <- `citing-paper` order)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "edgelist = pd.read_csv(os.path.join(data_dir, \"cora.cites\"), sep='\\t', header=None, names=[\"target\", \"source\"])\n", - "edgelist[\"label\"] = \"cites\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create the graph with directed edges" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "Gnx = nx.from_pandas_edgelist(edgelist, edge_attr=\"label\", create_using=nx.DiGraph)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "nx.set_node_attributes(Gnx, \"paper\", \"label\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load the features and subject for the nodes" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "feature_names = [\"w_{}\".format(ii) for ii in range(1433)]\n", - "column_names = feature_names + [\"subject\"]\n", - "node_data = pd.read_csv(os.path.join(data_dir, \"cora.content\"), sep='\\t', header=None, names=column_names)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We aim to train a graph-ML model that will predict the \"subject\" attribute on the nodes. These subjects are one of 7 categories:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Case_Based',\n", - " 'Genetic_Algorithms',\n", - " 'Neural_Networks',\n", - " 'Probabilistic_Methods',\n", - " 'Reinforcement_Learning',\n", - " 'Rule_Learning',\n", - " 'Theory'}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "set(node_data[\"subject\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Splitting the data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For machine learning we want to take a subset of the nodes for training, and use the rest for testing. We'll use scikit-learn again to do this" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "train_data, test_data = model_selection.train_test_split(\n", - " node_data, train_size=0.1, test_size=None, stratify=node_data['subject']\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note using stratified sampling gives the following counts:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Counter({'Neural_Networks': 81,\n", - " 'Theory': 35,\n", - " 'Genetic_Algorithms': 42,\n", - " 'Rule_Learning': 18,\n", - " 'Reinforcement_Learning': 22,\n", - " 'Probabilistic_Methods': 42,\n", - " 'Case_Based': 30})" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from collections import Counter\n", - "Counter(train_data['subject'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The training set has class imbalance that might need to be compensated, e.g., via using a weighted cross-entropy loss in model training, with class weights inversely proportional to class support. However, we will ignore the class imbalance in this example, for simplicity." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Converting to numeric arrays" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For our categorical target, we will use one-hot vectors that will be fed into a soft-max Keras layer during training. To do this conversion ..." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "target_encoding = feature_extraction.DictVectorizer(sparse=False)\n", - "\n", - "train_targets = target_encoding.fit_transform(train_data[[\"subject\"]].to_dict('records'))\n", - "test_targets = target_encoding.transform(test_data[[\"subject\"]].to_dict('records'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now do the same for the node attributes we want to use to predict the subject. These are the feature vectors that the Keras model will use as input. The CORA dataset contains attributes 'w_x' that correspond to words found in that publication. If a word occurs more than once in a publication the relevant attribute will be set to one, otherwise it will be zero." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "node_features = node_data[feature_names]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating the GraphSAGE model in Keras" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now create a StellarGraph object from the NetworkX graph and the node features and targets. It is StellarGraph objects that we use in this library to perform machine learning tasks on.\n", - "\n", - "**Note** that although the NetworkX graph is *directed*, we treat it here as *undirected*, which means that `neighbors()` returns only out-nodes." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "G = sg.StellarGraph(Gnx, node_features=node_features)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "StellarGraph: Undirected multigraph\n", - " Nodes: 2708, Edges: 5278\n", - "\n", - " Node types:\n", - " paper: [2708]\n", - " Edge types: paper-cites->paper\n", - "\n", - " Edge types:\n", - " paper-cites->paper: [5278]\n", - "\n" - ] - } - ], - "source": [ - "print(G.info())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To feed data from the graph to the Keras model we need a data generator that feeds data from the graph to the model. The generators are specialized to the model and the learning task so we choose the `GraphSAGENodeGenerator` as we are predicting node attributes with a GraphSAGE model.\n", - "\n", - "We need two other parameters, the `batch_size` to use for training and the number of nodes to sample at each level of the model. Here we choose a two-level model with 10 nodes sampled in the first layer, and 4 in the second." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "batch_size = 50; num_samples = [10, 4]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A `GraphSAGENodeGenerator` object is required to send the node features in sampled subgraphs to Keras" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "generator = GraphSAGENodeGenerator(G, batch_size, num_samples)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the `generator.flow()` method, we can create iterators over nodes that should be used to train, validate, or evaluate the model. For training we use only the training nodes returned from our splitter and the target values. The `shuffle=True` argument is given to the `flow` method to improve training." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "train_gen = generator.flow(train_data.index, train_targets, shuffle=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can specify our machine learning model, we need a few more parameters for this:\n", - "\n", - " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use 32-dimensional hidden node features at each layer.\n", - " * The `bias` and `dropout` are internal parameters of the model. " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ] - } - ], - "source": [ - "graphsage_model = GraphSAGE(\n", - " layer_sizes=[32, 32],\n", - " generator=train_gen,\n", - " bias=False,\n", - " dropout=0.5,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we create a model to predict the 7 categories using Keras softmax layers." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ] - } - ], - "source": [ - "x_inp, x_out = graphsage_model.build()\n", - "prediction = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Training the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's create the actual Keras model with the graph inputs `x_inp` provided by the `graph_model` and outputs being the predictions from the softmax layer" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ] - } - ], - "source": [ - "model = Model(inputs=x_inp, outputs=prediction)\n", - "model.compile(\n", - " optimizer=optimizers.Adam(lr=0.005),\n", - " loss=losses.categorical_crossentropy,\n", - " metrics=[\"acc\"],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Train the model, keeping track of its loss and accuracy on the training set, and its generalisation performance on the test set (we need to create another generator over the test data for this)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "test_gen = generator.flow(test_data.index, test_targets)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/20\n", - " - 3s - loss: 1.8550 - acc: 0.2849 - val_loss: 1.6846 - val_acc: 0.3622\n", - "Epoch 2/20\n", - " - 2s - loss: 1.6308 - acc: 0.4298 - val_loss: 1.5453 - val_acc: 0.4422\n", - "Epoch 3/20\n", - " - 2s - loss: 1.4352 - acc: 0.5892 - val_loss: 1.3877 - val_acc: 0.6194\n", - "Epoch 4/20\n", - " - 2s - loss: 1.3181 - acc: 0.7178 - val_loss: 1.2636 - val_acc: 0.7014\n", - "Epoch 5/20\n", - " - 2s - loss: 1.1511 - acc: 0.8323 - val_loss: 1.1551 - val_acc: 0.7572\n", - "Epoch 6/20\n", - " - 2s - loss: 1.0460 - acc: 0.8533 - val_loss: 1.0671 - val_acc: 0.7785\n", - "Epoch 7/20\n", - " - 2s - loss: 0.9104 - acc: 0.9314 - val_loss: 0.9968 - val_acc: 0.7937\n", - "Epoch 8/20\n", - " - 3s - loss: 0.8479 - acc: 0.9041 - val_loss: 0.9367 - val_acc: 0.8039\n", - "Epoch 9/20\n", - " - 3s - loss: 0.7372 - acc: 0.9551 - val_loss: 0.8870 - val_acc: 0.8048\n", - "Epoch 10/20\n", - " - 2s - loss: 0.6628 - acc: 0.9560 - val_loss: 0.8477 - val_acc: 0.7990\n", - "Epoch 11/20\n", - " - 2s - loss: 0.6102 - acc: 0.9763 - val_loss: 0.8129 - val_acc: 0.8080\n", - "Epoch 12/20\n", - " - 2s - loss: 0.5342 - acc: 0.9763 - val_loss: 0.7902 - val_acc: 0.8048\n", - "Epoch 13/20\n", - " - 2s - loss: 0.4880 - acc: 0.9856 - val_loss: 0.7666 - val_acc: 0.8052\n", - "Epoch 14/20\n", - " - 2s - loss: 0.4382 - acc: 0.9932 - val_loss: 0.7444 - val_acc: 0.8097\n", - "Epoch 15/20\n", - " - 2s - loss: 0.4061 - acc: 0.9831 - val_loss: 0.7242 - val_acc: 0.8048\n", - "Epoch 16/20\n", - " - 2s - loss: 0.3671 - acc: 0.9898 - val_loss: 0.7182 - val_acc: 0.8048\n", - "Epoch 17/20\n", - " - 2s - loss: 0.3398 - acc: 1.0000 - val_loss: 0.7155 - val_acc: 0.8039\n", - "Epoch 18/20\n", - " - 2s - loss: 0.2993 - acc: 1.0000 - val_loss: 0.6864 - val_acc: 0.8097\n", - "Epoch 19/20\n", - " - 2s - loss: 0.2778 - acc: 0.9966 - val_loss: 0.6789 - val_acc: 0.8117\n", - "Epoch 20/20\n", - " - 2s - loss: 0.2781 - acc: 0.9865 - val_loss: 0.6815 - val_acc: 0.8043\n" - ] - } - ], - "source": [ - "history = model.fit_generator(\n", - " train_gen,\n", - " epochs=20,\n", - " validation_data=test_gen,\n", - " verbose=2,\n", - " shuffle=False\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "def plot_history(history):\n", - " metrics = sorted(history.history.keys())\n", - " metrics = metrics[:len(metrics)//2]\n", - " for m in metrics:\n", - " # summarize history for metric m\n", - " plt.plot(history.history[m])\n", - " plt.plot(history.history['val_' + m])\n", - " plt.title(m)\n", - " plt.ylabel(m)\n", - " plt.xlabel('epoch')\n", - " plt.legend(['train', 'test'], loc='best')\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plot_history(history)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we have trained the model we can evaluate on the test set." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Test Set Metrics:\n", - "\tloss: 0.6829\n", - "\tacc: 0.8072\n" - ] - } - ], - "source": [ - "test_metrics = model.evaluate_generator(test_gen)\n", - "print(\"\\nTest Set Metrics:\")\n", - "for name, val in zip(model.metrics_names, test_metrics):\n", - " print(\"\\t{}: {:0.4f}\".format(name, val))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Making predictions with the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's get the predictions themselves for all nodes using another node iterator:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "all_nodes = node_data.index\n", - "all_mapper = generator.flow(all_nodes)\n", - "all_predictions = model.predict_generator(all_mapper)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These predictions will be the output of the softmax layer, so to get final categories we'll use the `inverse_transform` method of our target attribute specifcation to turn these values back to the original categories" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "node_predictions = target_encoding.inverse_transform(all_predictions)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's have a look at a few:" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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31336subject=Neural_NetworksNeural_Networks
1061127subject=Rule_LearningRule_Learning
1106406subject=Reinforcement_LearningReinforcement_Learning
13195subject=Reinforcement_LearningReinforcement_Learning
37879subject=Probabilistic_MethodsProbabilistic_Methods
1126012subject=Probabilistic_MethodsProbabilistic_Methods
1107140subject=Reinforcement_LearningTheory
1102850subject=Neural_NetworksNeural_Networks
31349subject=Neural_NetworksNeural_Networks
1106418subject=TheoryTheory
\n", - "
" - ], - "text/plain": [ - " Predicted True\n", - "31336 subject=Neural_Networks Neural_Networks\n", - "1061127 subject=Rule_Learning Rule_Learning\n", - "1106406 subject=Reinforcement_Learning Reinforcement_Learning\n", - "13195 subject=Reinforcement_Learning Reinforcement_Learning\n", - "37879 subject=Probabilistic_Methods Probabilistic_Methods\n", - "1126012 subject=Probabilistic_Methods Probabilistic_Methods\n", - "1107140 subject=Reinforcement_Learning Theory\n", - "1102850 subject=Neural_Networks Neural_Networks\n", - "31349 subject=Neural_Networks Neural_Networks\n", - "1106418 subject=Theory Theory" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results = pd.DataFrame(node_predictions, index=all_nodes).idxmax(axis=1)\n", - "df = pd.DataFrame({\"Predicted\": results, \"True\": node_data['subject']})\n", - "df.head(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Add the predictions to the graph, and save as graphml, e.g. for visualisation in [Gephi](https://gephi.org)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "for nid, pred, true in zip(df.index, df[\"Predicted\"], df[\"True\"]):\n", - " Gnx.node[nid][\"subject\"] = true\n", - " Gnx.node[nid][\"PREDICTED_subject\"] = pred.split(\"=\")[-1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Also add isTrain and isCorrect node attributes:" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "for nid in train_data.index:\n", - " Gnx.node[nid][\"isTrain\"] = True\n", - " \n", - "for nid in test_data.index:\n", - " Gnx.node[nid][\"isTrain\"] = False" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "for nid in Gnx.nodes():\n", - " Gnx.node[nid][\"isCorrect\"] = Gnx.node[nid][\"subject\"] == Gnx.node[nid][\"PREDICTED_subject\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Node embeddings\n", - "Evaluate node embeddings as activations of the output of graphsage layer stack, and visualise them, coloring nodes by their subject label.\n", - "\n", - "The GraphSAGE embeddings are the output of the GraphSAGE layers, namely the `x_out` variable. Let's create a new model with the same inputs as we used previously `x_inp` but now the output is the embeddings rather than the predicted class. Additionally note that the weights trained previously are kept in the new model." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "embedding_model = Model(inputs=x_inp, outputs=x_out)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2708, 32)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "emb = embedding_model.predict_generator(all_mapper)\n", - "emb.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Project the embeddings to 2d using either TSNE or PCA transform, and visualise, coloring nodes by their subject label" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.decomposition import PCA\n", - "from sklearn.manifold import TSNE\n", - "import pandas as pd\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "X = emb\n", - "y = np.argmax(target_encoding.transform(node_data[[\"subject\"]].to_dict('records')), axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "if X.shape[1] > 2:\n", - " transform = TSNE #PCA \n", - "\n", - " trans = transform(n_components=2)\n", - " emb_transformed = pd.DataFrame(trans.fit_transform(X), index=node_data.index)\n", - " emb_transformed['label'] = y\n", - "else:\n", - " emb_transformed = pd.DataFrame(X, index=node_data.index)\n", - " emb_transformed = emb_transformed.rename(columns = {'0':0, '1':1})\n", - " emb_transformed['label'] = y" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "alpha = 0.7\n", - "\n", - "fig, ax = plt.subplots(figsize=(7,7))\n", - "ax.scatter(emb_transformed[0], emb_transformed[1], c=emb_transformed['label'].astype(\"category\"), \n", - " cmap=\"jet\", alpha=alpha)\n", - "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n", - "plt.title('{} visualization of GraphSAGE embeddings for cora dataset'.format(transform.__name__))\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/demos/node-classification/directed-graphsage/example3-directed-graph-half-directed-graphsage.ipynb b/demos/node-classification/directed-graphsage/example3-directed-graph-half-directed-graphsage.ipynb deleted file mode 100644 index 9fbe96803..000000000 --- a/demos/node-classification/directed-graphsage/example3-directed-graph-half-directed-graphsage.ipynb +++ /dev/null @@ -1,982 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Stellargraph example 3: partially directed GraphSAGE on a directed CORA citation network\n", - "\n", - "This example shows the application of *directed* GraphSAGE to a *directed* graph. However, in order to compare results against example 2, in this example we do not sample from the in-node neighbourhoods." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Import NetworkX and stellar:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - } - ], - "source": [ - "import networkx as nx\n", - "import pandas as pd\n", - "import os\n", - "\n", - "import stellargraph as sg\n", - "from stellargraph.mapper import DirectedGraphSAGENodeGenerator\n", - "from stellargraph.layer import DirectedGraphSAGE\n", - "\n", - "from keras import layers, optimizers, losses, metrics, Model\n", - "from sklearn import preprocessing, feature_extraction, model_selection" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading the CORA network" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Downloading the CORA dataset:**\n", - " \n", - "The dataset used in this demo can be downloaded from [here](https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz).\n", - "\n", - "The following is the description of the dataset:\n", - "> The Cora dataset consists of 2708 scientific publications classified into one of seven classes.\n", - "> The citation network consists of 5429 links. Each publication in the dataset is described by a\n", - "> 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary.\n", - "> The dictionary consists of 1433 unique words. The README file in the dataset provides more details.\n", - "\n", - "Download and unzip the cora.tgz file to a location on your computer and set the `data_dir` variable to\n", - "point to the location of the dataset (the directory containing \"cora.cites\" and \"cora.content\")." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "data_dir = os.path.expanduser(\"~/data/cora\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load the graph from edgelist (in `cited-paper` <- `citing-paper` order)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "edgelist = pd.read_csv(os.path.join(data_dir, \"cora.cites\"), sep='\\t', header=None, names=[\"target\", \"source\"])\n", - "edgelist[\"label\"] = \"cites\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create the graph with directed edges" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "Gnx = nx.from_pandas_edgelist(edgelist, edge_attr=\"label\", create_using=nx.DiGraph)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "nx.set_node_attributes(Gnx, \"paper\", \"label\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load the features and subject for the nodes" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "feature_names = [\"w_{}\".format(ii) for ii in range(1433)]\n", - "column_names = feature_names + [\"subject\"]\n", - "node_data = pd.read_csv(os.path.join(data_dir, \"cora.content\"), sep='\\t', header=None, names=column_names)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We aim to train a graph-ML model that will predict the \"subject\" attribute on the nodes. These subjects are one of 7 categories:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Case_Based',\n", - " 'Genetic_Algorithms',\n", - " 'Neural_Networks',\n", - " 'Probabilistic_Methods',\n", - " 'Reinforcement_Learning',\n", - " 'Rule_Learning',\n", - " 'Theory'}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "set(node_data[\"subject\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Splitting the data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For machine learning we want to take a subset of the nodes for training, and use the rest for testing. We'll use scikit-learn again to do this" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "train_data, test_data = model_selection.train_test_split(\n", - " node_data, train_size=0.1, test_size=None, stratify=node_data['subject']\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note using stratified sampling gives the following counts:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Counter({'Theory': 35,\n", - " 'Neural_Networks': 81,\n", - " 'Probabilistic_Methods': 42,\n", - " 'Genetic_Algorithms': 42,\n", - " 'Rule_Learning': 18,\n", - " 'Reinforcement_Learning': 22,\n", - " 'Case_Based': 30})" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from collections import Counter\n", - "Counter(train_data['subject'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The training set has class imbalance that might need to be compensated, e.g., via using a weighted cross-entropy loss in model training, with class weights inversely proportional to class support. However, we will ignore the class imbalance in this example, for simplicity." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Converting to numeric arrays" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For our categorical target, we will use one-hot vectors that will be fed into a soft-max Keras layer during training. To do this conversion ..." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "target_encoding = feature_extraction.DictVectorizer(sparse=False)\n", - "\n", - "train_targets = target_encoding.fit_transform(train_data[[\"subject\"]].to_dict('records'))\n", - "test_targets = target_encoding.transform(test_data[[\"subject\"]].to_dict('records'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now do the same for the node attributes we want to use to predict the subject. These are the feature vectors that the Keras model will use as input. The CORA dataset contains attributes 'w_x' that correspond to words found in that publication. If a word occurs more than once in a publication the relevant attribute will be set to one, otherwise it will be zero." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "node_features = node_data[feature_names]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating the GraphSAGE model in Keras" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now create a StellarGraph object from the NetworkX graph and the node features and targets. It is StellarGraph objects that we use in this library to perform machine learning tasks on.\n", - "\n", - "Note that the NetworkX graph is *directed*, so we also treat it here as *directed*." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "G = sg.StellarDiGraph(Gnx, node_features=node_features)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "StellarDiGraph: Directed multigraph\n", - " Nodes: 2708, Edges: 5429\n", - "\n", - " Node types:\n", - " paper: [2708]\n", - " Edge types: paper-cites->paper\n", - "\n", - " Edge types:\n", - " paper-cites->paper: [5429]\n", - "\n" - ] - } - ], - "source": [ - "print(G.info())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To feed data from the graph to the Keras model we need a data generator that feeds data from the graph to the model. The generators are specialized to the model and the learning task so we choose the `DirectedGraphSAGENodeGenerator` as we are predicting node attributes with a `DirectedGraphSAGE` model.\n", - "\n", - "We need two other parameters, the `batch_size` to use for training and the number of nodes to sample at each level of the model, for both the in-node and out-node neighbourhoods. In order to contrast the results with example 2, here we choose a two-level model with 10 out-nodes sampled in the first layer, and 4 out-nodes in the second - we switch off sampling from the in-node neighbourhoods." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "batch_size = 50; in_samples = [0, 0]; out_samples = [10, 4]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A `DirectedGraphSAGENodeGenerator` object is required to send the node features in sampled subgraphs to Keras" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "generator = DirectedGraphSAGENodeGenerator(G, batch_size, in_samples, out_samples)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the `generator.flow()` method, we can create iterators over nodes that should be used to train, validate, or evaluate the model. For training we use only the training nodes returned from our splitter and the target values. The `shuffle=True` argument is given to the `flow` method to improve training." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "train_gen = generator.flow(train_data.index, train_targets, shuffle=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can specify our machine learning model, we need a few more parameters for this:\n", - "\n", - " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use 32-dimensional hidden node features at each layer, which corresponds to 16 weights for a node and 16 for the out-nodes - the in-nodes are not sampled here. This corresponds to the 32=16+16 dimensions used in example 1 (where we do not distinguish between in-nodes and out-nodes).\n", - " * The `bias` and `dropout` are internal parameters of the model. " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [] - } - ], - "source": [ - "graphsage_model = DirectedGraphSAGE(\n", - " layer_sizes=[32, 32],\n", - " generator=train_gen,\n", - " bias=False,\n", - " dropout=0.5,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we create a model to predict the 7 categories using Keras softmax layers." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [] - } - ], - "source": [ - "x_inp, x_out = graphsage_model.build()\n", - "prediction = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Training the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's create the actual Keras model with the graph inputs `x_inp` provided by the `graph_model` and outputs being the predictions from the softmax layer" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [] - } - ], - "source": [ - "model = Model(inputs=x_inp, outputs=prediction)\n", - "model.compile(\n", - " optimizer=optimizers.Adam(lr=0.005),\n", - " loss=losses.categorical_crossentropy,\n", - " metrics=[\"acc\"],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Train the model, keeping track of its loss and accuracy on the training set, and its generalisation performance on the test set (we need to create another generator over the test data for this)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "test_gen = generator.flow(test_data.index, test_targets)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/20\n", - " - 3s - loss: 1.8645 - acc: 0.2492 - val_loss: 1.7098 - val_acc: 0.3860\n", - "Epoch 2/20\n", - " - 3s - loss: 1.6777 - acc: 0.3991 - val_loss: 1.5874 - val_acc: 0.4495\n", - "Epoch 3/20\n", - " - 3s - loss: 1.5276 - acc: 0.5332 - val_loss: 1.4879 - val_acc: 0.5180\n", - "Epoch 4/20\n", - " - 3s - loss: 1.4316 - acc: 0.6077 - val_loss: 1.4083 - val_acc: 0.5628\n", - "Epoch 5/20\n", - " - 3s - loss: 1.3093 - acc: 0.6747 - val_loss: 1.3360 - val_acc: 0.6116\n", - "Epoch 6/20\n", - " - 3s - loss: 1.2230 - acc: 0.7397 - val_loss: 1.2745 - val_acc: 0.6460\n", - "Epoch 7/20\n", - " - 3s - loss: 1.0965 - acc: 0.8196 - val_loss: 1.2152 - val_acc: 0.6743\n", - "Epoch 8/20\n", - " - 2s - loss: 1.0252 - acc: 0.8551 - val_loss: 1.1667 - val_acc: 0.6809\n", - "Epoch 9/20\n", - " - 3s - loss: 0.9601 - acc: 0.8542 - val_loss: 1.1137 - val_acc: 0.6928\n", - "Epoch 10/20\n", - " - 3s - loss: 0.8967 - acc: 0.8704 - val_loss: 1.0777 - val_acc: 0.7006\n", - "Epoch 11/20\n", - " - 3s - loss: 0.8177 - acc: 0.8907 - val_loss: 1.0517 - val_acc: 0.6936\n", - "Epoch 12/20\n", - " - 2s - loss: 0.7917 - acc: 0.8871 - val_loss: 1.0147 - val_acc: 0.6998\n", - "Epoch 13/20\n", - " - 2s - loss: 0.7172 - acc: 0.9043 - val_loss: 0.9832 - val_acc: 0.7129\n", - "Epoch 14/20\n", - " - 2s - loss: 0.6781 - acc: 0.9016 - val_loss: 0.9633 - val_acc: 0.7129\n", - "Epoch 15/20\n", - " - 2s - loss: 0.6399 - acc: 0.8898 - val_loss: 0.9538 - val_acc: 0.7096\n", - "Epoch 16/20\n", - " - 2s - loss: 0.5703 - acc: 0.9102 - val_loss: 0.9321 - val_acc: 0.7178\n", - "Epoch 17/20\n", - " - 2s - loss: 0.5737 - acc: 0.9135 - val_loss: 0.9235 - val_acc: 0.7153\n", - "Epoch 18/20\n", - " - 2s - loss: 0.5121 - acc: 0.9153 - val_loss: 0.9083 - val_acc: 0.7240\n", - "Epoch 19/20\n", - " - 2s - loss: 0.4821 - acc: 0.9280 - val_loss: 0.8995 - val_acc: 0.7272\n", - "Epoch 20/20\n", - " - 2s - loss: 0.4698 - acc: 0.9305 - val_loss: 0.8835 - val_acc: 0.7260\n" - ] - } - ], - "source": [ - "history = model.fit_generator(\n", - " train_gen,\n", - " epochs=20,\n", - " validation_data=test_gen,\n", - " verbose=2,\n", - " shuffle=False\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "def plot_history(history):\n", - " metrics = sorted(history.history.keys())\n", - " metrics = metrics[:len(metrics)//2]\n", - " for m in metrics:\n", - " # summarize history for metric m\n", - " plt.plot(history.history[m])\n", - " plt.plot(history.history['val_' + m])\n", - " plt.title(m)\n", - " plt.ylabel(m)\n", - " plt.xlabel('epoch')\n", - " plt.legend(['train', 'test'], loc='best')\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plot_history(history)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we have trained the model we can evaluate on the test set." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Test Set Metrics:\n", - "\tloss: 0.8902\n", - "\tacc: 0.7305\n" - ] - } - ], - "source": [ - "test_metrics = model.evaluate_generator(test_gen)\n", - "print(\"\\nTest Set Metrics:\")\n", - "for name, val in zip(model.metrics_names, test_metrics):\n", - " print(\"\\t{}: {:0.4f}\".format(name, val))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Making predictions with the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's get the predictions themselves for all nodes using another node iterator:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "all_nodes = node_data.index\n", - "all_mapper = generator.flow(all_nodes)\n", - "all_predictions = model.predict_generator(all_mapper)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These predictions will be the output of the softmax layer, so to get final categories we'll use the `inverse_transform` method of our target attribute specifcation to turn these values back to the original categories" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "node_predictions = target_encoding.inverse_transform(all_predictions)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's have a look at a few:" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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31336subject=Neural_NetworksNeural_Networks
1061127subject=Rule_LearningRule_Learning
1106406subject=Reinforcement_LearningReinforcement_Learning
13195subject=Genetic_AlgorithmsReinforcement_Learning
37879subject=Probabilistic_MethodsProbabilistic_Methods
1126012subject=Probabilistic_MethodsProbabilistic_Methods
1107140subject=TheoryTheory
1102850subject=Neural_NetworksNeural_Networks
31349subject=Neural_NetworksNeural_Networks
1106418subject=TheoryTheory
\n", - "
" - ], - "text/plain": [ - " Predicted True\n", - "31336 subject=Neural_Networks Neural_Networks\n", - "1061127 subject=Rule_Learning Rule_Learning\n", - "1106406 subject=Reinforcement_Learning Reinforcement_Learning\n", - "13195 subject=Genetic_Algorithms Reinforcement_Learning\n", - "37879 subject=Probabilistic_Methods Probabilistic_Methods\n", - "1126012 subject=Probabilistic_Methods Probabilistic_Methods\n", - "1107140 subject=Theory Theory\n", - "1102850 subject=Neural_Networks Neural_Networks\n", - "31349 subject=Neural_Networks Neural_Networks\n", - "1106418 subject=Theory Theory" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results = pd.DataFrame(node_predictions, index=all_nodes).idxmax(axis=1)\n", - "df = pd.DataFrame({\"Predicted\": results, \"True\": node_data['subject']})\n", - "df.head(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Add the predictions to the graph, and save as graphml, e.g. for visualisation in [Gephi](https://gephi.org)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "for nid, pred, true in zip(df.index, df[\"Predicted\"], df[\"True\"]):\n", - " Gnx.node[nid][\"subject\"] = true\n", - " Gnx.node[nid][\"PREDICTED_subject\"] = pred.split(\"=\")[-1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Also add isTrain and isCorrect node attributes:" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "for nid in train_data.index:\n", - " Gnx.node[nid][\"isTrain\"] = True\n", - " \n", - "for nid in test_data.index:\n", - " Gnx.node[nid][\"isTrain\"] = False" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "for nid in Gnx.nodes():\n", - " Gnx.node[nid][\"isCorrect\"] = Gnx.node[nid][\"subject\"] == Gnx.node[nid][\"PREDICTED_subject\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Node embeddings\n", - "Evaluate node embeddings as activations of the output of graphsage layer stack, and visualise them, coloring nodes by their subject label.\n", - "\n", - "The GraphSAGE embeddings are the output of the GraphSAGE layers, namely the `x_out` variable. Let's create a new model with the same inputs as we used previously `x_inp` but now the output is the embeddings rather than the predicted class. Additionally note that the weights trained previously are kept in the new model." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "embedding_model = Model(inputs=x_inp, outputs=x_out)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2708, 32)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "emb = embedding_model.predict_generator(all_mapper)\n", - "emb.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Project the embeddings to 2d using either TSNE or PCA transform, and visualise, coloring nodes by their subject label" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.decomposition import PCA\n", - "from sklearn.manifold import TSNE\n", - "import pandas as pd\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "X = emb\n", - "y = np.argmax(target_encoding.transform(node_data[[\"subject\"]].to_dict('records')), axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "if X.shape[1] > 2:\n", - " transform = TSNE #PCA \n", - "\n", - " trans = transform(n_components=2)\n", - " emb_transformed = pd.DataFrame(trans.fit_transform(X), index=node_data.index)\n", - " emb_transformed['label'] = y\n", - "else:\n", - " emb_transformed = pd.DataFrame(X, index=node_data.index)\n", - " emb_transformed = emb_transformed.rename(columns = {'0':0, '1':1})\n", - " emb_transformed['label'] = y" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "alpha = 0.7\n", - "\n", - "fig, ax = plt.subplots(figsize=(7,7))\n", - "ax.scatter(emb_transformed[0], emb_transformed[1], c=emb_transformed['label'].astype(\"category\"), \n", - " cmap=\"jet\", alpha=alpha)\n", - "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n", - "plt.title('{} visualization of GraphSAGE embeddings for cora dataset'.format(transform.__name__))\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/demos/node-classification/directed-graphsage/experim.ipynb b/demos/node-classification/directed-graphsage/experim.ipynb deleted file mode 100644 index 5ef92d6fd..000000000 --- a/demos/node-classification/directed-graphsage/experim.ipynb +++ /dev/null @@ -1,964 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simple graph experiment to investigate inner workings\n", - "\n", - "This experiments seeks to provide a better understanding of how, and possibly if, the directed GraphSAGE algorithm works in its entirety." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Import NetworkX and stellar:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n", - "/Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", - "/Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", - "/Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", - "/Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", - "/Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", - "/Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n", - "/Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", - "/Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", - "/Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", - "/Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", - "/Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", - "/Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n" - ] - } - ], - "source": [ - "import networkx as nx\n", - "import pandas as pd\n", - "import os\n", - "\n", - "import stellargraph as sg\n", - "from stellargraph.data.explorer import DirectedBreadthFirstNeighbours\n", - "from stellargraph.mapper import DirectedGraphSAGENodeGenerator\n", - "from stellargraph.layer import DirectedGraphSAGE\n", - "\n", - "from keras import layers, optimizers, losses, metrics, Model\n", - "from sklearn import preprocessing, feature_extraction, model_selection" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load a simple graph" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create the graph with directed edges" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "Gnx = nx.DiGraph()\n", - "Gnx.add_edges_from([(1,2),(2,3)])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create the features for the nodes" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " f0\n", - "id \n", - "1 1.0\n", - "2 0.0\n", - "3 2.0\n" - ] - } - ], - "source": [ - "feature_names = [\"f0\"]\n", - "column_names = [\"id\"] + feature_names\n", - "node_data = pd.DataFrame([[1,1.0],[2,0.0],[3,2.0]], columns=column_names)\n", - "node_data = node_data.set_index(\"id\")\n", - "print(node_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " f0\n", - "id \n", - "1 1.0\n", - "2 0.0\n", - "3 2.0\n" - ] - } - ], - "source": [ - "node_features = node_data[feature_names]\n", - "print(node_features)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create the StellarGraph" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "G = sg.StellarDiGraph(Gnx, node_features=node_features)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "StellarDiGraph: Directed multigraph\n", - " Nodes: 3, Edges: 2\n", - "\n", - " Node types:\n", - " default: [3]\n", - " Edge types: default-default->default\n", - "\n", - " Edge types:\n", - " default-default->default: [2]\n", - "\n" - ] - } - ], - "source": [ - "print(G.info())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us use all nodes" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1, 2, 3]\n" - ] - } - ], - "source": [ - "nodes = list(G.nodes())\n", - "print(nodes)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Check on the features" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1.]\n", - " [0.]\n", - " [2.]]\n" - ] - } - ], - "source": [ - "features = G.get_feature_for_nodes(nodes)\n", - "print(features)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us look at directed sampling" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "sampler = DirectedBreadthFirstNeighbours(G)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Examine 1-hop sampling:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1], [None], [2]]\n", - "[[2], [1], [3]]\n", - "[[3], [2], [None]]\n" - ] - } - ], - "source": [ - "res = sampler.run(nodes=nodes, in_size=[1], out_size=[1])\n", - "for node_sample in res:\n", - " print(node_sample)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Examine 2-hop sampling:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1], [None], [2], [None], [None], [1], [3]]\n", - "[[2], [1], [3], [None], [2], [2], [None]]\n", - "[[3], [2], [None], [1], [3], [None], [None]]\n" - ] - } - ], - "source": [ - "res = sampler.run(nodes=nodes, in_size=[1, 1], out_size=[1, 1])\n", - "for node_sample in res:\n", - " print(node_sample)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now examine the node generator:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "generator = DirectedGraphSAGENodeGenerator(G, batch_size=len(nodes), in_samples=[1], out_samples=[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "train_data = node_data.index\n", - "train_gen = generator.flow(train_data, shuffle=False)\n", - "print(train_gen)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us delve into the inner workings:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "features = train_gen.generator.sample_features(nodes, train_gen._sampling_schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array([[[1.]],\n", - "\n", - " [[0.]],\n", - "\n", - " [[2.]]]), array([[[0.]],\n", - "\n", - " [[1.]],\n", - "\n", - " [[0.]]]), array([[[0.]],\n", - "\n", - " [[2.]],\n", - "\n", - " [[0.]]])]\n" - ] - } - ], - "source": [ - "print(features)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "******************************************************************************" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us see what happens when we ignore in-nodes:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "batch_size = 3; in_samples = [0, 0]; out_samples = [1, 1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A `DirectedGraphSAGENodeGenerator` object is required to send the node features in sampled subgraphs to Keras" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "generator = DirectedGraphSAGENodeGenerator(G, batch_size, in_samples, out_samples)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the `generator.flow()` method, we can create iterators over nodes that should be used to train, validate, or evaluate the model. For training we use all nodes." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "train_data = node_data.index\n", - "train_gen = generator.flow(train_data, shuffle=False)\n", - "print(train_gen)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[array([[[1.]],\n", - " \n", - " [[0.]],\n", - " \n", - " [[2.]]]), array([], shape=(3, 0, 1), dtype=float64), array([[[0.]],\n", - " \n", - " [[2.]],\n", - " \n", - " [[0.]]]), array([], shape=(3, 0, 1), dtype=float64), array([], shape=(3, 0, 1), dtype=float64), array([], shape=(3, 0, 1), dtype=float64), array([[[2.]],\n", - " \n", - " [[0.]],\n", - " \n", - " [[0.]]])]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "features = train_gen.generator.sample_features(nodes, train_gen._sampling_schema)\n", - "features" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can specify our machine learning model, we need a few more parameters for this:\n", - "\n", - " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use 48-dimensional hidden node features at each layer, which corresponds to 16 weights for a node, 16 for the in-nodes (unused) and 16 for the out-nodes. This corresponds to the 32 dimensions used in example 1 (where we do not distinguish between in-nodes and out-nodes).\n", - " * The `bias` and `dropout` are internal parameters of the model. " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From /Users/doc019/.envs/sgdev/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", - "\n" - ] - } - ], - "source": [ - "graphsage_model = DirectedGraphSAGE(\n", - " layer_sizes=[48, 48],\n", - " generator=train_gen,\n", - " bias=False,\n", - " dropout=0.5,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we create a model to predict the 7 categories using Keras softmax layers." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From /Users/doc019/.envs/sgdev/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", - "\n", - "WARNING:tensorflow:From /Users/doc019/.envs/sgdev/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", - "\n", - "WARNING:tensorflow:From /Users/doc019/.envs/sgdev/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", - "WARNING:tensorflow:From /Users/doc019/.envs/sgdev/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", - "\n" - ] - } - ], - "source": [ - "x_inp, x_out = graphsage_model.build()\n", - "prediction = layers.Dense(units=1, activation=\"softmax\")(x_out)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Training the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's create the actual Keras model with the graph inputs `x_inp` provided by the `graph_model` and outputs being the predictions from the softmax layer" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From /Users/doc019/.envs/sgdev/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", - "\n", - "WARNING:tensorflow:From /Users/doc019/.envs/sgdev/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n", - "\n" - ] - } - ], - "source": [ - "model = Model(inputs=x_inp, outputs=prediction)\n", - "model.compile(\n", - " optimizer=optimizers.Adam(lr=0.005),\n", - " loss=losses.categorical_crossentropy,\n", - " metrics=[\"acc\"],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Save the model" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "model.save(\"test_save_model.h5\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load the model using the StellarGraph custom layers:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "from keras.models import load_model\n", - "\n", - "model = load_model(\"test_save_model.h5\", custom_objects=sg.custom_keras_layers)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can visualize the model:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from keras.utils.vis_utils import model_to_dot\n", - "from IPython.display import Image\n", - "Image(model_to_dot(model).create_png())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Train the model, keeping track of its loss and accuracy on the training set, and its generalisation performance on the test set (we need to create another generator over the test data for this)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "test_gen = generator.flow(test_data.index, test_targets)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "history = model.fit_generator(\n", - " train_gen,\n", - " epochs=20,\n", - " validation_data=test_gen,\n", - " verbose=2,\n", - " shuffle=False\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "def plot_history(history):\n", - " metrics = sorted(history.history.keys())\n", - " metrics = metrics[:len(metrics)//2]\n", - " for m in metrics:\n", - " # summarize history for metric m\n", - " plt.plot(history.history[m])\n", - " plt.plot(history.history['val_' + m])\n", - " plt.title(m)\n", - " plt.ylabel(m)\n", - " plt.xlabel('epoch')\n", - " plt.legend(['train', 'test'], loc='best')\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_history(history)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we have trained the model we can evaluate on the test set." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "test_metrics = model.evaluate_generator(test_gen)\n", - "print(\"\\nTest Set Metrics:\")\n", - "for name, val in zip(model.metrics_names, test_metrics):\n", - " print(\"\\t{}: {:0.4f}\".format(name, val))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Making predictions with the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's get the predictions themselves for all nodes using another node iterator:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "all_nodes = node_data.index\n", - "all_mapper = generator.flow(all_nodes)\n", - "all_predictions = model.predict_generator(all_mapper)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These predictions will be the output of the softmax layer, so to get final categories we'll use the `inverse_transform` method of our target attribute specifcation to turn these values back to the original categories" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "node_predictions = target_encoding.inverse_transform(all_predictions)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's have a look at a few:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results = pd.DataFrame(node_predictions, index=all_nodes).idxmax(axis=1)\n", - "df = pd.DataFrame({\"Predicted\": results, \"True\": node_data['subject']})\n", - "df.head(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Add the predictions to the graph, and save as graphml, e.g. for visualisation in [Gephi](https://gephi.org)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for nid, pred, true in zip(df.index, df[\"Predicted\"], df[\"True\"]):\n", - " Gnx.node[nid][\"subject\"] = true\n", - " Gnx.node[nid][\"PREDICTED_subject\"] = pred.split(\"=\")[-1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Also add isTrain and isCorrect node attributes:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for nid in train_data.index:\n", - " Gnx.node[nid][\"isTrain\"] = True\n", - " \n", - "for nid in test_data.index:\n", - " Gnx.node[nid][\"isTrain\"] = False" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for nid in Gnx.nodes():\n", - " Gnx.node[nid][\"isCorrect\"] = Gnx.node[nid][\"subject\"] == Gnx.node[nid][\"PREDICTED_subject\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Node embeddings\n", - "Evaluate node embeddings as activations of the output of graphsage layer stack, and visualise them, coloring nodes by their subject label.\n", - "\n", - "The GraphSAGE embeddings are the output of the GraphSAGE layers, namely the `x_out` variable. Let's create a new model with the same inputs as we used previously `x_inp` but now the output is the embeddings rather than the predicted class. Additionally note that the weights trained previously are kept in the new model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "embedding_model = Model(inputs=x_inp, outputs=x_out)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "emb = embedding_model.predict_generator(all_mapper)\n", - "emb.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Project the embeddings to 2d using either TSNE or PCA transform, and visualise, coloring nodes by their subject label" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.decomposition import PCA\n", - "from sklearn.manifold import TSNE\n", - "import pandas as pd\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "X = emb\n", - "y = np.argmax(target_encoding.transform(node_data[[\"subject\"]].to_dict('records')), axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if X.shape[1] > 2:\n", - " transform = TSNE #PCA \n", - "\n", - " trans = transform(n_components=2)\n", - " emb_transformed = pd.DataFrame(trans.fit_transform(X), index=node_data.index)\n", - " emb_transformed['label'] = y\n", - "else:\n", - " emb_transformed = pd.DataFrame(X, index=node_data.index)\n", - " emb_transformed = emb_transformed.rename(columns = {'0':0, '1':1})\n", - " emb_transformed['label'] = y" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "alpha = 0.7\n", - "\n", - "fig, ax = plt.subplots(figsize=(7,7))\n", - "ax.scatter(emb_transformed[0], emb_transformed[1], c=emb_transformed['label'].astype(\"category\"), \n", - " cmap=\"jet\", alpha=alpha)\n", - "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n", - "plt.title('{} visualization of GraphSAGE embeddings for cora dataset'.format(transform.__name__))\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "stellargraph-dev", - "language": "python", - "name": "stellargraph-dev" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/demos/node-classification/directed-graphsage/example4-directed-graph-fully-directed-graphsage.ipynb b/demos/node-classification/graphsage/directed-graphsage-on-cora-example.ipynb similarity index 99% rename from demos/node-classification/directed-graphsage/example4-directed-graph-fully-directed-graphsage.ipynb rename to demos/node-classification/graphsage/directed-graphsage-on-cora-example.ipynb index 676ebd6ad..4f810403e 100644 --- a/demos/node-classification/directed-graphsage/example4-directed-graph-fully-directed-graphsage.ipynb +++ b/demos/node-classification/graphsage/directed-graphsage-on-cora-example.ipynb @@ -4,9 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Stellargraph example 4: fully directed GraphSAGE on a directed CORA citation network\n", + "# Stellargraph example: Fully directed GraphSAGE on a directed CORA citation network\n", "\n", - "This example shows the application of *directed* GraphSAGE to a *directed* graph. In contrast to example 3, in this example we **do** sample from both the in-node and out-node neighbourhoods. The model should be similar to example 1 (undirected GraphSAGE on the undirected graph), except that here the model can learn separate weights for the in-nodes versus the out-nodes." + "This example shows the application of *directed* GraphSAGE to a *directed* graph, where the in-node and out-node neighbourhoods are separately sampled and have different weights." ] }, { @@ -384,7 +384,7 @@ "source": [ "Now we can specify our machine learning model, we need a few more parameters for this:\n", "\n", - " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use 32-dimensional hidden node features at each layer, which corresponds to 12 weights for a node, 10 for the in-nodes and 10 for the out-nodes. This is in contrast to example 1, for which the 32 hidden features are partitioned into 16 weights per node and 16 per 1-hop neighbourhood (in-nodes and out-nodes combined). We balance fewer features per weight by having more weights.\n", + " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use 32-dimensional hidden node features at each layer, which corresponds to 12 weights for each head node, 10 for each in-node and 10 for each out-node.\n", " * The `bias` and `dropout` are internal parameters of the model. " ] }, @@ -396,8 +396,7 @@ { "name": "stderr", "output_type": "stream", - "text": [ - ] + "text": [] } ], "source": [ @@ -424,8 +423,7 @@ { "name": "stderr", "output_type": "stream", - "text": [ - ] + "text": [] } ], "source": [ @@ -455,8 +453,7 @@ { "name": "stderr", "output_type": "stream", - "text": [ - ] + "text": [] } ], "source": [ @@ -492,8 +489,7 @@ { "name": "stderr", "output_type": "stream", - "text": [ - ] + "text": [] }, { "name": "stdout", From ae5160463a0b1627b8bdc0c1093ddccc5dbb6968 Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Mon, 9 Sep 2019 11:48:06 +0930 Subject: [PATCH 24/30] Refactored commonalities. --- stellargraph/layer/graphsage.py | 188 ++++++++++++++++---------------- tests/layer/test_graphsage.py | 8 +- 2 files changed, 96 insertions(+), 100 deletions(-) diff --git a/stellargraph/layer/graphsage.py b/stellargraph/layer/graphsage.py index 7866ffa79..81c433f22 100644 --- a/stellargraph/layer/graphsage.py +++ b/stellargraph/layer/graphsage.py @@ -35,7 +35,7 @@ from keras.layers import Lambda, Dropout, Reshape, LeakyReLU from keras.utils import Sequence from keras import activations -from typing import List, Tuple, Callable, AnyStr +from typing import List, Callable, AnyStr import warnings @@ -61,11 +61,14 @@ def __init__( self.output_dim = output_dim self.has_bias = bias self.act = activations.get(act) - self.w_self = None - self.bias = None - self.weight_dims = None self._initializer = "glorot_uniform" super().__init__(**kwargs) + # These will be filled in at build time + self.bias = None + self.w_self = None + self.w_group = None + self.weight_dims = None + self.included_weight_groups = None def get_config(self): """ @@ -580,7 +583,7 @@ def call(self, inputs, **kwargs): Apply aggregator on the input tensors, `inputs` Args: - x (List[Tensor]): Tensors giving self and neighbour features + inputs (List[Tensor]): Tensors giving self and neighbour features x[0]: self Tensor (batch_size, head size, feature_size) x[k>0]: group Tensors for neighbourhood (batch_size, head size, neighbours, feature_size) @@ -677,6 +680,12 @@ def __init__( normalize="l2", **kwargs, ): + # Model parameters + self.layer_sizes = layer_sizes + self.max_hops = len(layer_sizes) + self.bias = bias + self.dropout = dropout + # Set the normalization layer used in the model if normalize == "l2": self._normalization = Lambda(lambda x: K.l2_normalize(x, axis=-1)) @@ -698,15 +707,12 @@ def __init__( else: self._get_sizes_from_keywords(**kwargs) - # Model parameters - self.bias = bias - self.dropout = dropout - # Feature dimensions for each layer - self.n_layers = len(layer_sizes) - self.layer_sizes = layer_sizes self.dims = [self.input_feature_size] + layer_sizes + # Compute size of each sampled neighbourhood + self._compute_neighbourhood_sizes() + # Set the aggregator layer used in the model if aggregator is None: self._aggregator = MeanAggregator @@ -725,6 +731,12 @@ def _get_sizes_from_generator(self, generator): generator: The supplied generator. """ self.n_samples = generator.generator.num_samples + if len(self.n_samples) != self.max_hops: + raise ValueError( + "Mismatched lengths: neighbourhood sample sizes {} versus layer sizes {}".format( + self.n_samples, self.layer_sizes + ) + ) feature_sizes = generator.generator.graph.node_feature_sizes() if len(feature_sizes) > 1: raise RuntimeError( @@ -742,17 +754,36 @@ def _get_sizes_from_keywords(self, **kwargs): self.input_feature_size = kwargs.get("input_dim") if self.n_samples is None or self.input_feature_size is None: raise ValueError( - "If generator is not provided, n_samples and input_dim must be specified." + "Generator not provided; n_samples and input_dim must be specified." + ) + if len(self.n_samples) != self.max_hops: + raise ValueError( + "Mismatched lengths: neighbourhood sample sizes {} versus layer sizes {}".format( + self.n_samples, self.layer_sizes + ) ) + def _compute_neighbourhood_sizes(self): + """ + Computes the total (cumulative product) number of nodes + sampled at each neighbourhood. + + Each hop samples from the neighbours of the previous nodes. + """ + + def size_at(i): + return np.product(self.n_samples[:i], dtype=int) + + self.neighbourhood_sizes = [size_at(i) for i in range(self.max_hops + 1)] + def _build_aggregators(self): self._aggs = [ self._aggregator( - output_dim=self.dims[layer + 1], + output_dim=self.layer_sizes[layer], bias=self.bias, - act="relu" if layer < self.n_layers - 1 else "linear", + act="relu" if layer < self.max_hops - 1 else "linear", ) - for layer in range(self.n_layers) + for layer in range(self.max_hops) ] def __call__(self, xin: List): @@ -760,36 +791,38 @@ def __call__(self, xin: List): Apply aggregator layers Args: - x (list of Tensor): Batch input features + xin (list of Tensor): Batch input features Returns: Output tensor """ - def apply_layer(x: List, layer: int): + def apply_layer(x: List, num_hops: int): """ Compute the list of output tensors for a single GraphSAGE layer Args: x (List[Tensor]): Inputs to the layer - layer (int): Layer index to construct + num_hops (int): Layer index to construct Returns: Outputs of applying the aggregators as a list of Tensors """ layer_out = [] - for i in range(self.n_layers - layer): + for i in range(self.max_hops - num_hops): head_shape = K.int_shape(x[i])[1] # Reshape neighbours per node per layer neigh_in = Dropout(self.dropout)( - Reshape((head_shape, self.n_samples[i], self.dims[layer]))(x[i + 1]) + Reshape((head_shape, self.n_samples[i], self.dims[num_hops]))( + x[i + 1] + ) ) # Apply aggregator to head node and neighbour nodes layer_out.append( - self._aggs[layer]([Dropout(self.dropout)(x[i]), neigh_in]) + self._aggs[num_hops]([Dropout(self.dropout)(x[i]), neigh_in]) ) return layer_out @@ -797,7 +830,7 @@ def apply_layer(x: List, layer: int): if not isinstance(xin, list): raise TypeError("Input features to GraphSAGE must be a list") - if len(xin) != self.n_layers + 1: + if len(xin) != self.max_hops + 1: raise ValueError( "Length of input features should equal the number of GraphSAGE layers plus one" ) @@ -805,7 +838,7 @@ def apply_layer(x: List, layer: int): # Form GraphSAGE layers iteratively self.layer_tensors = [] h_layer = xin - for layer in range(0, self.n_layers): + for layer in range(0, self.max_hops): h_layer = apply_layer(h_layer, layer) self.layer_tensors.append(h_layer) @@ -821,22 +854,6 @@ def apply_layer(x: List, layer: int): else [self._normalization(xi) for xi in h_layer] ) - def _input_shapes(self) -> List[Tuple[int, int]]: - """ - Returns the input shapes for the tensors at each layer - - Returns: - A list of tuples giving the shape (number of nodes, feature size) for - the corresponding layer - - """ - - def shape_at(i: int) -> Tuple[int, int]: - return (np.product(self.n_samples[:i], dtype=int), self.input_feature_size) - - input_shapes = [shape_at(i) for i in range(self.n_layers + 1)] - return input_shapes - def node_model(self): """ Builds a GraphSAGE model for node prediction @@ -847,12 +864,15 @@ def node_model(self): for the GraphSAGE model output. """ - # Create tensor inputs - x_inp = [Input(shape=s) for s in self._input_shapes()] + # Create tensor inputs for neighbourhood sampling + x_inp = [ + Input(shape=(s, self.input_feature_size)) for s in self.neighbourhood_sizes + ] # Output from GraphSAGE model x_out = self(x_inp) + # Returns inputs and outputs return x_inp, x_out def link_model(self): @@ -947,7 +967,19 @@ def _get_sizes_from_generator(self, generator): generator: The supplied generator. """ self.in_samples = generator.generator.in_samples + if len(self.in_samples) != self.max_hops: + raise ValueError( + "Mismatched lengths: in-node sample sizes {} versus layer sizes {}".format( + self.in_samples, self.layer_sizes + ) + ) self.out_samples = generator.generator.out_samples + if len(self.out_samples) != self.max_hops: + raise ValueError( + "Mismatched lengths: out-node sample sizes {} versus layer sizes {}".format( + self.out_samples, self.layer_sizes + ) + ) feature_sizes = generator.generator.graph.node_feature_sizes() if len(feature_sizes) > 1: raise RuntimeError( @@ -972,36 +1004,37 @@ def _get_sizes_from_keywords(self, **kwargs): raise ValueError( "If generator is not provided, in_samples, out_samples and input_dim must be specified." ) - - def _build_aggregators(self): - # Model parameters - self.max_hops = max_hops = self.n_layers - self.max_slots = 2 ** (max_hops + 1) - 1 - - if len(self.in_samples) != max_hops: + if len(self.in_samples) != self.max_hops: raise ValueError( "Mismatched lengths: in-node sample sizes {} versus layer sizes {}".format( self.in_samples, self.layer_sizes ) ) - if len(self.out_samples) != max_hops: + if len(self.out_samples) != self.max_hops: raise ValueError( "Mismatched lengths: out-node sample sizes {} versus layer sizes {}".format( self.out_samples, self.layer_sizes ) ) - # Define sizes of the various neighbourhoods - self.neighbourhood_sizes = self._compute_input_sizes() + def _compute_neighbourhood_sizes(self): + """ + Computes the total (cumulative product) number of nodes + sampled at each neighbourhood. - # Aggregator functions for each layer - self._aggs = [ - self._aggregator( - output_dim=self.layer_sizes[i], - bias=self.bias, - act="relu" if i < max_hops - 1 else "linear", + Each hop has to sample separately from both the in-nodes + and the out-nodes of the previous nodes. + This gives rise to a binary tree of directed neighbourhoods. + """ + self.max_slots = 2 ** (self.max_hops + 1) - 1 + self.neighbourhood_sizes = [1] + [ + np.product( + [ + self.in_samples[kk] if d == "0" else self.out_samples[kk] + for kk, d in enumerate(np.binary_repr(ii + 1)[1:]) + ] ) - for i in range(max_hops) + for ii in range(1, self.max_slots) ] def __call__(self, xin: List): @@ -1009,7 +1042,7 @@ def __call__(self, xin: List): Apply aggregator layers Args: - x (list of Tensor): Batch input features + xin (list of Tensor): Batch input features Returns: Output tensor @@ -1064,40 +1097,3 @@ def aggregate_neighbours(tree: List, stage: int): # Remove neighbourhood dimension from output tensors of the stack out_layer = Reshape(K.int_shape(out_layer)[2:])(out_layer) return self._normalization(out_layer) - - def _compute_input_sizes(self): - # Each hop has to sample separately from both the in-nodes - # and the out-nodes. This gives rise to a binary tree of 'slots'. - # Storage for the total (cumulative product) number of nodes sampled - # at the corresponding neighbourhood for each slot: - num_nodes = [1] + [ - np.product( - [ - self.in_samples[kk] if d == "0" else self.out_samples[kk] - for kk, d in enumerate(np.binary_repr(ii + 1)[1:]) - ] - ) - for ii in range(1, self.max_slots) - ] - return num_nodes - - def node_model(self): - """ - Builds a GraphSAGE model for node prediction - - Returns: - tuple: (x_inp, x_out) where ``x_inp`` is a list of Keras input tensors - for the specified GraphSAGE model and ``x_out`` is the Keras tensor - for the GraphSAGE model output. - - """ - # Create tensor inputs for neighbourhood sampling; - x_inp = [ - Input(shape=(s, self.input_feature_size)) for s in self.neighbourhood_sizes - ] - - # Output from DirectedGraphSAGE model - x_out = self(x_inp) - - # Returns inputs and outputs - return x_inp, x_out diff --git a/tests/layer/test_graphsage.py b/tests/layer/test_graphsage.py index 3d57e4b95..6c7231a1b 100644 --- a/tests/layer/test_graphsage.py +++ b/tests/layer/test_graphsage.py @@ -354,7 +354,7 @@ def test_graphsage_constructor(): gs = GraphSAGE(layer_sizes=[4], n_samples=[2], input_dim=2, normalize="l2") assert gs.dims == [2, 4] assert gs.n_samples == [2] - assert gs.n_layers == 1 + assert gs.max_hops == 1 assert gs.bias assert len(gs._aggs) == 1 @@ -376,7 +376,7 @@ def test_graphsage_constructor(): assert gs.dims == [3, 4, 8] assert gs.n_samples == [2, 2] - assert gs.n_layers == 2 + assert gs.max_hops == 2 assert gs.bias assert len(gs._aggs) == 2 @@ -387,7 +387,7 @@ def test_graphsage_constructor_passing_aggregator(): ) assert gs.dims == [2, 4] assert gs.n_samples == [2] - assert gs.n_layers == 1 + assert gs.max_hops == 1 assert gs.bias assert len(gs._aggs) == 1 @@ -401,7 +401,7 @@ def test_graphsage_constructor_1(): ) assert gs.dims == [2, 4, 6, 8] assert gs.n_samples == [2, 4, 6] - assert gs.n_layers == 3 + assert gs.max_hops == 3 assert gs.bias assert len(gs._aggs) == 3 From ad32cc0d8ddd8cedec2e356fc24fb8cd59af0c77 Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Fri, 20 Sep 2019 13:49:24 +0930 Subject: [PATCH 25/30] More conflict resolution. --- stellargraph/layer/graphsage.py | 21 --------------------- 1 file changed, 21 deletions(-) diff --git a/stellargraph/layer/graphsage.py b/stellargraph/layer/graphsage.py index b227f51d8..1836cf6a2 100644 --- a/stellargraph/layer/graphsage.py +++ b/stellargraph/layer/graphsage.py @@ -134,22 +134,10 @@ def build(self, input_shape): and neighbour features """ -<<<<<<< HEAD if not isinstance(input_shape, list): raise ValueError( "Expected a list of inputs, not {}".format(type(input_shape)) ) -======= - # Build a MLP model if zero neighbours - self._build_mlp_only = input_shape[1][2] == 0 - - self.w_self = self.add_weight( - name="w_self", - shape=(int(input_shape[0][2]), self.weight_output_size()), - initializer=self._initializer, - trainable=True, - ) ->>>>>>> develop # Configure bias vector, if used. if self.has_bias: @@ -299,16 +287,7 @@ def group_aggregate(self, x_group, group_idx=0): if group_idx == 0: x_agg = x_group else: -<<<<<<< HEAD x_agg = K.mean(x_group, axis=2) -======= - self.w_neigh = self.add_weight( - name="w_neigh", - shape=(int(input_shape[1][3]), self.weight_output_size()), - initializer=self._initializer, - trainable=True, - ) ->>>>>>> develop return K.dot(x_agg, self.w_group[group_idx]) From ffdaf08d5415702491d74b2ec90072e8be495e30 Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Fri, 20 Sep 2019 16:15:56 +0930 Subject: [PATCH 26/30] Fixed more tests, 1 fails for graphsage, all fail for ensemble. --- stellargraph/layer/graphsage.py | 7 ++++--- tests/layer/test_graphsage.py | 10 +++++----- 2 files changed, 9 insertions(+), 8 deletions(-) diff --git a/stellargraph/layer/graphsage.py b/stellargraph/layer/graphsage.py index 1836cf6a2..c2458904a 100644 --- a/stellargraph/layer/graphsage.py +++ b/stellargraph/layer/graphsage.py @@ -96,8 +96,9 @@ def calculate_group_sizes(self, input_shape): """ # If the neighbours are zero-dimensional for any of the shapes # in the input, do not use the input group in the model. + # XXX Ignore batch size, since test dim != 0 evaluates to None!! self.included_weight_groups = [ - all(dim != 0 for dim in group_shape) for group_shape in input_shape + all(dim != 0 for dim in group_shape[1:]) for group_shape in input_shape ] # The total number of enabled input groups @@ -502,7 +503,6 @@ def _build_group_weights(self, in_shape, out_size, group_idx=0): """ if group_idx == 0: - print(out_size) if out_size > 0: weights = self.add_weight( name=f"w_self", @@ -551,8 +551,9 @@ def calculate_group_sizes(self, input_shape): """ # If the neighbours are zero-dimensional for any of the shapes # in the input, do not use the input group in the model. + # XXX Ignore batch size, since dim != 0 results in None!! self.included_weight_groups = [ - all(dim != 0 for dim in group_shape) for group_shape in input_shape + all(dim != 0 for dim in group_shape[1:]) for group_shape in input_shape ] # The total number of enabled input groups diff --git a/tests/layer/test_graphsage.py b/tests/layer/test_graphsage.py index 0bef194ef..4e4dc43f5 100644 --- a/tests/layer/test_graphsage.py +++ b/tests/layer/test_graphsage.py @@ -19,6 +19,11 @@ GraphSAGE tests """ +from tensorflow import keras +import numpy as np +import networkx as nx +import pytest + from stellargraph.core.graph import StellarGraph from stellargraph.mapper.node_mappers import GraphSAGENodeGenerator from stellargraph.layer.graphsage import ( @@ -30,11 +35,6 @@ AttentionalAggregator, ) -from tensorflow import keras -import numpy as np -import networkx as nx -import pytest - def example_graph_1(feature_size=None): G = nx.Graph() From 43d87a876a360c3c3c4dc27fb78e5c066c2aadd6 Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Fri, 20 Sep 2019 17:06:28 +0930 Subject: [PATCH 27/30] Fixed tests. --- stellargraph/layer/graphsage.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/stellargraph/layer/graphsage.py b/stellargraph/layer/graphsage.py index c2458904a..bc9e0ed08 100644 --- a/stellargraph/layer/graphsage.py +++ b/stellargraph/layer/graphsage.py @@ -176,7 +176,7 @@ def _build_group_weights(self, in_shape, out_size, group_idx=0): """ weight = self.add_weight( - shape=(in_shape[-1], out_size), + shape=(int(in_shape[-1]), out_size), initializer=self._initializer, trainable=True, name=f"weight_g{group_idx}", @@ -327,7 +327,7 @@ def _build_group_weights(self, in_shape, out_size, group_idx=0): if group_idx == 0: weights = self.add_weight( name=f"w_g{group_idx}", - shape=(in_shape[-1], out_size), + shape=(int(in_shape[-1]), out_size), initializer=self._initializer, trainable=True, ) @@ -340,7 +340,7 @@ def _build_group_weights(self, in_shape, out_size, group_idx=0): ) w_pool = self.add_weight( name=f"w_pool_g{group_idx}", - shape=(in_shape[-1], self.hidden_dim), + shape=(int(in_shape[-1]), self.hidden_dim), initializer=self._initializer, trainable=True, ) @@ -416,7 +416,7 @@ def _build_group_weights(self, in_shape, out_size, group_idx=0): if group_idx == 0: weights = self.add_weight( name=f"w_g{group_idx}", - shape=(in_shape[-1], out_size), + shape=(int(in_shape[-1]), out_size), initializer=self._initializer, trainable=True, ) @@ -429,7 +429,7 @@ def _build_group_weights(self, in_shape, out_size, group_idx=0): ) w_pool = self.add_weight( name=f"w_pool_g{group_idx}", - shape=(in_shape[-1], self.hidden_dim), + shape=(int(in_shape[-1]), self.hidden_dim), initializer=self._initializer, trainable=True, ) @@ -506,7 +506,7 @@ def _build_group_weights(self, in_shape, out_size, group_idx=0): if out_size > 0: weights = self.add_weight( name=f"w_self", - shape=(in_shape[-1], out_size), + shape=(int(in_shape[-1]), out_size), initializer=self._initializer, trainable=True, ) @@ -516,7 +516,7 @@ def _build_group_weights(self, in_shape, out_size, group_idx=0): else: w_g = self.add_weight( name=f"w_g{group_idx}", - shape=(in_shape[-1], out_size), + shape=(int(in_shape[-1]), out_size), initializer=self._initializer, trainable=True, ) From f5c8768508bd6b80972e83af0553bff76868aa4f Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Mon, 23 Sep 2019 14:16:16 +0930 Subject: [PATCH 28/30] Added catch for reshaping final aggregated tensors. --- stellargraph/layer/graphsage.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/stellargraph/layer/graphsage.py b/stellargraph/layer/graphsage.py index bc9e0ed08..68b102ae3 100644 --- a/stellargraph/layer/graphsage.py +++ b/stellargraph/layer/graphsage.py @@ -1096,5 +1096,6 @@ def aggregate_neighbours(tree: List, stage: int): out_layer = stage_tree[0] # Remove neighbourhood dimension from output tensors of the stack - out_layer = Reshape(K.int_shape(out_layer)[2:])(out_layer) + if K.int_shape(out_layer)[1] == 1: + out_layer = Reshape(K.int_shape(out_layer)[2:])(out_layer) return self._normalization(out_layer) From c6dff93cc3f8af84fb276d2c4bcf44349a1b2642 Mon Sep 17 00:00:00 2001 From: Geoff Jarrad Date: Mon, 23 Sep 2019 16:07:02 +0930 Subject: [PATCH 29/30] Fixed demo for tensorflow keras. --- .../graphsage/directed-graphsage-on-cora-example.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/demos/node-classification/graphsage/directed-graphsage-on-cora-example.ipynb b/demos/node-classification/graphsage/directed-graphsage-on-cora-example.ipynb index 4f810403e..65a4fdd72 100644 --- a/demos/node-classification/graphsage/directed-graphsage-on-cora-example.ipynb +++ b/demos/node-classification/graphsage/directed-graphsage-on-cora-example.ipynb @@ -38,7 +38,7 @@ "from stellargraph.mapper import DirectedGraphSAGENodeGenerator\n", "from stellargraph.layer import DirectedGraphSAGE\n", "\n", - "from keras import layers, optimizers, losses, metrics, Model\n", + "from tensorflow.keras import layers, optimizers, losses, metrics, Model\n", "from sklearn import preprocessing, feature_extraction, model_selection" ] }, @@ -1012,7 +1012,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.6.8" } }, "nbformat": 4, From a4b7e5582f32290500d3daa254c7588f68840ee6 Mon Sep 17 00:00:00 2001 From: Andrew Docherty Date: Mon, 23 Sep 2019 16:59:39 +1000 Subject: [PATCH 30/30] Changed keras imports to tensorflow.keras. Updated notebook. --- .../directed-graphsage-on-cora-example.ipynb | 213 ++++++++++-------- 1 file changed, 122 insertions(+), 91 deletions(-) diff --git a/demos/node-classification/graphsage/directed-graphsage-on-cora-example.ipynb b/demos/node-classification/graphsage/directed-graphsage-on-cora-example.ipynb index 4f810403e..be9d19772 100644 --- a/demos/node-classification/graphsage/directed-graphsage-on-cora-example.ipynb +++ b/demos/node-classification/graphsage/directed-graphsage-on-cora-example.ipynb @@ -18,17 +18,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - } - ], + "outputs": [], "source": [ "import networkx as nx\n", "import pandas as pd\n", @@ -38,7 +30,7 @@ "from stellargraph.mapper import DirectedGraphSAGENodeGenerator\n", "from stellargraph.layer import DirectedGraphSAGE\n", "\n", - "from keras import layers, optimizers, losses, metrics, Model\n", + "from tensorflow.keras import layers, optimizers, losses, metrics, Model\n", "from sklearn import preprocessing, feature_extraction, model_selection" ] }, @@ -209,13 +201,13 @@ { "data": { "text/plain": [ - "Counter({'Theory': 35,\n", + "Counter({'Reinforcement_Learning': 22,\n", " 'Neural_Networks': 81,\n", " 'Probabilistic_Methods': 42,\n", + " 'Theory': 35,\n", " 'Genetic_Algorithms': 42,\n", " 'Case_Based': 30,\n", - " 'Rule_Learning': 18,\n", - " 'Reinforcement_Learning': 22})" + " 'Rule_Learning': 18})" ] }, "execution_count": 9, @@ -392,13 +384,7 @@ "cell_type": "code", "execution_count": 17, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [] - } - ], + "outputs": [], "source": [ "graphsage_model = DirectedGraphSAGE(\n", " layer_sizes=[32, 32],\n", @@ -421,9 +407,13 @@ "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", - "text": [] + "text": [ + "WARNING:tensorflow:From /Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Call initializer instance with the dtype argument instead of passing it to the constructor\n" + ] } ], "source": [ @@ -449,13 +439,7 @@ "cell_type": "code", "execution_count": 19, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [] - } - ], + "outputs": [], "source": [ "model = Model(inputs=x_inp, outputs=prediction)\n", "model.compile(\n", @@ -486,55 +470,53 @@ "execution_count": 21, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [] - }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", - " - 3s - loss: 1.9228 - acc: 0.2187 - val_loss: 1.7943 - val_acc: 0.4208\n", + "WARNING:tensorflow:From /Users/doc019/.envs/sgdev/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.where in 2.0, which has the same broadcast rule as np.where\n", + "6/6 - 4s - loss: 1.9177 - acc: 0.2148 - val_loss: 1.7972 - val_acc: 0.4024\n", "Epoch 2/20\n", - " - 3s - loss: 1.7087 - acc: 0.5077 - val_loss: 1.6751 - val_acc: 0.5164\n", + "6/6 - 3s - loss: 1.7262 - acc: 0.4741 - val_loss: 1.6913 - val_acc: 0.4713\n", "Epoch 3/20\n", - " - 3s - loss: 1.5843 - acc: 0.6390 - val_loss: 1.5647 - val_acc: 0.5878\n", + "6/6 - 3s - loss: 1.5932 - acc: 0.5852 - val_loss: 1.5990 - val_acc: 0.5139\n", "Epoch 4/20\n", - " - 3s - loss: 1.4491 - acc: 0.7341 - val_loss: 1.4714 - val_acc: 0.6251\n", + "6/6 - 3s - loss: 1.4714 - acc: 0.6704 - val_loss: 1.5050 - val_acc: 0.5829\n", "Epoch 5/20\n", - " - 2s - loss: 1.3691 - acc: 0.7465 - val_loss: 1.3861 - val_acc: 0.6530\n", + "6/6 - 3s - loss: 1.3476 - acc: 0.7778 - val_loss: 1.4182 - val_acc: 0.6386\n", "Epoch 6/20\n", - " - 2s - loss: 1.2488 - acc: 0.8120 - val_loss: 1.3005 - val_acc: 0.6903\n", + "6/6 - 3s - loss: 1.2384 - acc: 0.8519 - val_loss: 1.3430 - val_acc: 0.6776\n", "Epoch 7/20\n", - " - 3s - loss: 1.1472 - acc: 0.8296 - val_loss: 1.2311 - val_acc: 0.7162\n", + "6/6 - 3s - loss: 1.1871 - acc: 0.8556 - val_loss: 1.2692 - val_acc: 0.6998\n", "Epoch 8/20\n", - " - 3s - loss: 1.0339 - acc: 0.8745 - val_loss: 1.1642 - val_acc: 0.7432\n", + "6/6 - 3s - loss: 1.0848 - acc: 0.8481 - val_loss: 1.2094 - val_acc: 0.7116\n", "Epoch 9/20\n", - " - 3s - loss: 0.9480 - acc: 0.9084 - val_loss: 1.1095 - val_acc: 0.7490\n", + "6/6 - 4s - loss: 0.9818 - acc: 0.9037 - val_loss: 1.1450 - val_acc: 0.7428\n", "Epoch 10/20\n", - " - 2s - loss: 0.8772 - acc: 0.9144 - val_loss: 1.0583 - val_acc: 0.7596\n", + "6/6 - 4s - loss: 0.9040 - acc: 0.9222 - val_loss: 1.0814 - val_acc: 0.7617\n", "Epoch 11/20\n", - " - 3s - loss: 0.8230 - acc: 0.9135 - val_loss: 1.0185 - val_acc: 0.7559\n", + "6/6 - 3s - loss: 0.8349 - acc: 0.9407 - val_loss: 1.0358 - val_acc: 0.7629\n", "Epoch 12/20\n", - " - 3s - loss: 0.7506 - acc: 0.9381 - val_loss: 0.9845 - val_acc: 0.7633\n", + "6/6 - 3s - loss: 0.7656 - acc: 0.9519 - val_loss: 0.9988 - val_acc: 0.7687\n", "Epoch 13/20\n", - " - 2s - loss: 0.6970 - acc: 0.9357 - val_loss: 0.9440 - val_acc: 0.7654\n", + "6/6 - 3s - loss: 0.7400 - acc: 0.9296 - val_loss: 0.9625 - val_acc: 0.7703\n", "Epoch 14/20\n", - " - 3s - loss: 0.6196 - acc: 0.9517 - val_loss: 0.9125 - val_acc: 0.7683\n", + "6/6 - 3s - loss: 0.6520 - acc: 0.9630 - val_loss: 0.9305 - val_acc: 0.7740\n", "Epoch 15/20\n", - " - 3s - loss: 0.5945 - acc: 0.9440 - val_loss: 0.8944 - val_acc: 0.7646\n", + "6/6 - 3s - loss: 0.6149 - acc: 0.9704 - val_loss: 0.8956 - val_acc: 0.7814\n", "Epoch 16/20\n", - " - 3s - loss: 0.5453 - acc: 0.9492 - val_loss: 0.8786 - val_acc: 0.7687\n", + "6/6 - 3s - loss: 0.5793 - acc: 0.9667 - val_loss: 0.8712 - val_acc: 0.7769\n", "Epoch 17/20\n", - " - 3s - loss: 0.5192 - acc: 0.9406 - val_loss: 0.8564 - val_acc: 0.7765\n", + "6/6 - 3s - loss: 0.5478 - acc: 0.9593 - val_loss: 0.8454 - val_acc: 0.7777\n", "Epoch 18/20\n", - " - 3s - loss: 0.4561 - acc: 0.9865 - val_loss: 0.8327 - val_acc: 0.7789\n", + "6/6 - 3s - loss: 0.4877 - acc: 0.9778 - val_loss: 0.8278 - val_acc: 0.7806\n", "Epoch 19/20\n", - " - 3s - loss: 0.4505 - acc: 0.9695 - val_loss: 0.8086 - val_acc: 0.7851\n", + "6/6 - 3s - loss: 0.4552 - acc: 0.9778 - val_loss: 0.8088 - val_acc: 0.7851\n", "Epoch 20/20\n", - " - 3s - loss: 0.4282 - acc: 0.9695 - val_loss: 0.8013 - val_acc: 0.7789\n" + "6/6 - 4s - loss: 0.4229 - acc: 0.9889 - val_loss: 0.7986 - val_acc: 0.7814\n" ] } ], @@ -578,7 +560,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] @@ -623,8 +605,8 @@ "text": [ "\n", "Test Set Metrics:\n", - "\tloss: 0.8017\n", - "\tacc: 0.7806\n" + "\tloss: 0.7943\n", + "\tacc: 0.7904\n" ] } ], @@ -715,52 +697,52 @@ " \n", " \n", " \n", - " 31336\n", + " 31336\n", " subject=Neural_Networks\n", " Neural_Networks\n", " \n", " \n", - " 1061127\n", + " 1061127\n", " subject=Rule_Learning\n", " Rule_Learning\n", " \n", " \n", - " 1106406\n", + " 1106406\n", " subject=Reinforcement_Learning\n", " Reinforcement_Learning\n", " \n", " \n", - " 13195\n", + " 13195\n", " subject=Reinforcement_Learning\n", " Reinforcement_Learning\n", " \n", " \n", - " 37879\n", + " 37879\n", " subject=Probabilistic_Methods\n", " Probabilistic_Methods\n", " \n", " \n", - " 1126012\n", - " subject=Probabilistic_Methods\n", + " 1126012\n", + " subject=Reinforcement_Learning\n", " Probabilistic_Methods\n", " \n", " \n", - " 1107140\n", - " subject=Theory\n", + " 1107140\n", + " subject=Reinforcement_Learning\n", " Theory\n", " \n", " \n", - " 1102850\n", + " 1102850\n", " subject=Neural_Networks\n", " Neural_Networks\n", " \n", " \n", - " 31349\n", + " 31349\n", " subject=Neural_Networks\n", " Neural_Networks\n", " \n", " \n", - " 1106418\n", + " 1106418\n", " subject=Theory\n", " Theory\n", " \n", @@ -775,8 +757,8 @@ "1106406 subject=Reinforcement_Learning Reinforcement_Learning\n", "13195 subject=Reinforcement_Learning Reinforcement_Learning\n", "37879 subject=Probabilistic_Methods Probabilistic_Methods\n", - "1126012 subject=Probabilistic_Methods Probabilistic_Methods\n", - "1107140 subject=Theory Theory\n", + "1126012 subject=Reinforcement_Learning Probabilistic_Methods\n", + "1107140 subject=Reinforcement_Learning Theory\n", "1102850 subject=Neural_Networks Neural_Networks\n", "31349 subject=Neural_Networks Neural_Networks\n", "1106418 subject=Theory Theory" @@ -931,13 +913,11 @@ { "cell_type": "code", "execution_count": 36, - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -963,12 +943,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Observe that each class of paper is separated into three distinct regions. This can be explained by the fact that the raw embeddings look something like (node, in, out). Hence there are three distinct types of representation, namely: (node, 0, out) if there are no in-nodes; (node, in, 0) if there are no out-nodes; and (node, in, out) if there are both in-nodes and out-nodes. The fourth case, isolated nodes having no in-nodes and no out-nodes, does not occur for the CORA dataset (see the counts below)." + "Observe that each class of paper is separated into three general regions. This is due to the fact that the GraphSAGE features from the previous layer for the node itself, the aggregated in-neighbours, and the aggregated out-neighbours are currently concatenated in the form `[node, in_agg, out_agg]`.\n", + "\n", + "There are four distinct types of directed neighbourhoods, namely: \n", + "* Both in-neighbours and out-neighbours;\n", + "* Only in-neighbours – in this case `out_agg` will be zero; \n", + "* Only out-neighbours – in this case `in_agg` will be zero;\n", + "* No in-neighbours or out-neighbours – in this case both `in_agg` and `out_agg` will be zero.\n", + "\n", + "The fourth case, isolated nodes having no in-neighbours and no out-neighbours, does not occur for the CORA dataset (see the counts below) therefore the CORA dataset consists of the three types." ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -983,16 +971,59 @@ } ], "source": [ - "counts = [0] * 4\n", - "for node in G.nodes():\n", - " have_in = len(G.in_edges(node)) > 0\n", - " have_out = len(G.out_edges(node)) > 0\n", - " idx = 1 * have_in + 2 * have_out\n", - " counts[idx] = counts[idx] + 1\n", - "print(\"{} nodes have no in or out neighbours\".format(counts[0]))\n", - "print(\"{} nodes have in but not out neighbours\".format(counts[1]))\n", - "print(\"{} nodes have out but no in neighbours\".format(counts[2]))\n", - "print(\"{} nodes have in and out neighbours\".format(counts[3]))" + "directed_neigh_type = [\n", + " 1*(len(G.in_edges(node)) > 0) + \n", + " 2*(len(G.out_edges(node)) > 0)\n", + " for node in node_data.index\n", + "]\n", + "\n", + "print(f\"{sum(nt==0 for nt in directed_neigh_type)} nodes have no in or out neighbours\")\n", + "print(f\"{sum(nt==1 for nt in directed_neigh_type)} nodes have in but not out neighbours\")\n", + "print(f\"{sum(nt==2 for nt in directed_neigh_type)} nodes have out but no in neighbours\")\n", + "print(f\"{sum(nt==3 for nt in directed_neigh_type)} nodes have in and out neighbours\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**We visualize the clustering of these different directed neighbourhood types below:**" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "alpha = 0.7\n", + "\n", + "fig, ax = plt.subplots(figsize=(7,7))\n", + "ax.scatter(emb_transformed[0], emb_transformed[1], c=directed_neigh_type, cmap=\"jet\", alpha=alpha)\n", + "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n", + "plt.title('{} visualization of GraphSAGE embeddings for cora dataset'.format(transform.__name__))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We note that we can reduce the splitting effect of these different directed neighbourhood types by adding the aggregated in-neighbourhood and out-neighbourhood rather that concatenating them. This feature is planned to be implemented soon." ] } ], @@ -1012,9 +1043,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.6.8" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 }