🆕 Ising Fluctuation Model

ndlib/models/epidemics/IsingFluctuationModel.py

@@ -0,0 +1,124 @@
+from ..DiffusionModel import DiffusionModel
+import numpy as np
+import networkx as nx
+import future.utils
+import random
+
+__author__ = "Giulio Rossetti"
+__license__ = "BSD-2-Clause"
+__email__ = "giulio.rossetti@gmail.com"
+
+
+class IFModel(DiffusionModel):
+    """
+    Model Parameters to be specified via ModelConfig
+
+    :param beta: The infection rate (float value in [0,1])
+    """
+
+    def __init__(self, graph):
+        """
+             Model Constructor
+
+             :param graph: A networkx graph object
+         """
+        super(self.__class__, self).__init__(graph)
+        self.available_statuses = {
+            "1": 1,
+            "-1": -1,
+            "Infected": 0
+        }
+
+        self.parameters = {
+            "model": {
+                "N1": {
+                    "descr": "Node 1 id",
+                    "optional": False},
+                "N2": {
+                    "descr": "Node 2 id",
+                    "optional": False},
+                "T1": {
+                    "descr": "Temperature node 1",
+                    "range": "[-1,1]",
+                    "optional": False},
+                "T2": {
+                    "descr": "Temperature node 2",
+                    "range": "[-1,1]",
+                    "optional": False}
+            },
+            "nodes": {},
+            "edges": {},
+        }
+
+        self.name = "IF"
+
+    def __change_status(self, u, actual_status):
+
+        neighbors = self.graph.neighbors(u)
+        if isinstance(self.graph, nx.DiGraph):
+            neighbors = self.graph.predecessors(u)
+
+        s_u = 0
+        s_u_inv = 0
+        for n in neighbors:
+            s_u += actual_status[u] * actual_status[n]
+            s_u_inv += (-1 * actual_status[u]) * actual_status[n]
+
+        if u != self.params['model']['N1'] and u != self.params['model']['N2']:
+            if s_u == s_u_inv:
+                flip = random.randint(0, 1)
+                if flip == 1:
+                    return True
+                return False
+
+        dif = s_u_inv - s_u
+        if u == self.params['model']['N1']:
+            dif /= self.params['model']['T1']
+        else:
+            dif /=  self.params['model']['T2']
+
+        if s_u_inv < s_u:
+            return True
+        else:
+            r = np.random.uniform(0, 1)
+            if r < np.e**dif:
+                return True
+        return False
+
+    def iteration(self, node_status=True):
+        """
+        Execute a single model iteration
+
+        :return: Iteration_id, Incremental node status (dictionary node->status)
+        """
+        self.clean_initial_status(self.available_statuses.values())
+
+        actual_status = {node: nstatus for node, nstatus in future.utils.iteritems(self.status)}
+
+        if self.actual_iteration == 0:
+            self.actual_iteration += 1
+            delta, node_count, status_delta = self.status_delta(actual_status)
+            if node_status:
+                return {"iteration": 0, "status": actual_status.copy(),
+                        "node_count": node_count.copy(), "status_delta": status_delta.copy()}
+            else:
+                return {"iteration": 0, "status": {},
+                        "node_count": node_count.copy(), "status_delta": status_delta.copy()}
+
+        for u in self.graph.nodes():
+            change = self.__change_status(u, actual_status)
+            if change:
+                actual_status[u] = -1*actual_status[u]
+
+
+        delta, node_count, status_delta = self.status_delta(actual_status)
+        self.status = actual_status
+        self.actual_iteration += 1
+
+        if node_status:
+            return {"iteration": self.actual_iteration - 1, "status": delta.copy(),
+                    "node_count": node_count.copy(), "status_delta": status_delta.copy()}
+        else:
+            return {"iteration": self.actual_iteration - 1, "status": {},
+                    "node_count": node_count.copy(), "status_delta": status_delta.copy()}
+

ndlib/models/epidemics/__init__.py

@@ -29,4 +29,5 @@
     'SISModel',
     'SWIRModel',
     'ThresholdModel',
+    'IFModel',
 ]