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from random import randint, choice
import math
import random
import networkx as nx
import pickle
from os import path
import sys
from algorithms import dijkstra
def print_header(name, n, m):
head = "%32s | %12s | %21s | %21s" % ('algorithm', 'distance', 'settled', 'relaxed')
print ''
print "Graph: %s (vertices: %d, edges: %d)" % (name, n, m)
print '-' * len(head)
print head
print '-' * len(head)
def print_result(name, res, base_result, n, m):
d, _n, _m = res
_, n, m = base_result
print "%32s | %12.2f | %12d (%5.1f%%) | %12d (%5.1f%%)" %\
(name, d, _n, 100 * float(_n)/n, _m, 100 * float(_m)/m)
def build_graph(size=0, expected_neighbors=0, reverse_chance=0.9, min_weight=1, max_weight=3):
"""Build the graph
The number of nodes to be in the graph
The expected degree of each node in the graph
# Calculate the distance required to yield the expected # neighbors
if size == 1:
radius = 1
radius = math.sqrt( (expected_neighbors / (size - 1.0)) / math.pi)
# Create bins in which to put node so we only check a fraction of
# candidate neighbors
num_bins = int(math.ceil(1/radius))
neighbor_bins = []
for i in range(num_bins):
for j in range(num_bins):
G = nx.DiGraph() = "Nice Digraph"
# Create the collection of nodes and put them into bins with
# candidate neighbors
_rndm = random.random
_rndint = random.randint
_flr = math.floor
for n in G.nodes():
xcoord = _rndm()
ycoord = _rndm()
G.node[n]['coords'] = (xcoord, ycoord)
# Put node into bin with candidate neighbors
bin_x = int(_flr(xcoord/radius))
bin_y = int(_flr(ycoord/radius))
# Find actual neighbors and create edges between nodes
for x in range(num_bins):
for y in range(num_bins):
# Get all potential neighbors (those in adjacent bins)
potentials = []
for px in range(x-1, x+1+1):
if px < 0 or px >= num_bins:
for py in range(y-y, y+1+1):
if py < 0 or py >= num_bins:
potentials += neighbor_bins[px % num_bins][py % num_bins]
for node in neighbor_bins[x][y]:
node_coords = G.node[node]['coords']
for potential in potentials:
p_coords = G.node[potential]['coords']
dist = ((node_coords[0] - p_coords[0])**2 + (node_coords[1] - p_coords[1]) ** 2)**0.5
if dist < radius and node != potential:
weight = (min_weight + _rndm()* max_weight) * dist
G.add_edge(node, potential, weight=weight)
if _rndm() <= reverse_chance:
G.add_edge(potential, node, weight=weight)
if == 0:
neighbor_bins[x][y] = []
return G
def generate_g(file_name, nodes=10000):
#seed = nx.generators.random_graphs.random_regular_graph(4, 80000)
seed = build_graph(nodes, 8)
edges = [ (u, v, data['weight'] * nodes) for (u, v, data) in seed.edges(data=True) ]
g = nx.DiGraph()
for (u, v, w) in edges:
g.add_edge(u,v, weight=w)
s = g.nodes()[0]
cover, distances = dijkstra(g, s)
t = choice(cover.nodes())
reachable = dict((node, True) for node in cover.nodes())
edges= [ (u, v, w) for (u,v,w) in edges if u in reachable and v in
reachable ]
nodes = [ (node, seed.node[node]['coords'][0] * nodes,
seed.node[node]['coords'][1] * nodes) for node in cover.nodes() ]
with open(file_name, 'a') as file:
pickle.dump((nodes, edges, s), file)
def get_graph(name, size=10000):
file_name = "graphs/graph_%s_%d" % (name, size)
g = nx.DiGraph()
# random.seed(5)
if not path.exists(file_name):
sys.stdout.write("generating ... ")
generate_g(file_name, nodes=size)
with open(file_name) as file:
sys.stdout.write("loading ... ")
nodes, edges, s = pickle.load(file)
sys.stdout.write("constructing ... ")
g.add_nodes_from(((node, {'x': x, 'y': y}) for (node, x, y) in nodes))
for (u,v,w) in edges:
g.add_edge(u,v, weight=w)
n, m = len(g.nodes()), len(g.edges())
return g, n, m, s
def get_targets(g, s, ranks):
_, distances = dijkstra(g, s)
distances = distances.items()
distances.sort(key=lambda (x,y): y)
targets = [ (distances[r][0], r) for r in ranks]
return targets
def draw(title, graph, s, ts, search_spaces=list()):
from matplotlib import pylab
fig = pylab.figure()
import matplotlib.pyplot as plt
#pos = nx.graphviz_layout(graph, prog='dot')
layout = dict(((node, (graph.node[node]['x'], graph.node[node]['y'])) for
node in graph.nodes()))
graph = nx.Graph(graph)
nx.draw(graph, layout, node_color='black', with_labels=False, node_size=2)
colors = [ 'cyan', 'orange', 'y', 'g', 'purple', 'brown']
for i, search_space in enumerate(search_spaces):
nx.draw_networkx_nodes(graph, layout, nodelist=search_space, node_color=colors[i], node_size=100)
nx.draw_networkx_nodes(graph, layout, nodelist=[s], node_color='r', node_size=40)
nx.draw_networkx_nodes(graph, layout, nodelist=ts, node_color=range(len(ts),0,-1), vmin=len(ts)/2, vmax=len(ts),, node_size=50)
# edge_labels=dict([((u,v,),"%5.1f" % d['weight'])
# for u,v,d in graph.edges(data=True)])
# nx.draw_networkx_edge_labels(graph, layout, edge_labels=edge_labels)