test

.gitignore

@@ -1,5 +1,5 @@
 __pycache__/
 .idea/
 .ipynb_checkpoints/
-*.png
+
-*.gexf
+

models/ControlModelM2/ControlModelM2.py

@@ -24,12 +24,12 @@ class ControlModelM2(BaseBehaviour):
     """
 
     # Init infected
-    init_states[random.randint(0, settings.network_params["number_of_nodes"]-1)] = {'id': 1}
+    # init_states[random.randint(0, settings.network_params["number_of_nodes"]-1)] = {'id': 1}
-    init_states[random.randint(0, settings.network_params["number_of_nodes"]-1)] = {'id': 1}
+    # init_states[random.randint(0, settings.network_params["number_of_nodes"]-1)] = {'id': 1}
 
     # Init beacons
-    init_states[random.randint(0, settings.network_params["number_of_nodes"]-1)] = {'id': 4}
+    # init_states[random.randint(0, settings.network_params["number_of_nodes"]-1)] = {'id': 4}
-    init_states[random.randint(0, settings.network_params["number_of_nodes"]-1)] = {'id': 4}
+    # init_states[random.randint(0, settings.network_params["number_of_nodes"]-1)] = {'id': 4}
 
     def __init__(self, environment=None, agent_id=0, state=()):
         super().__init__(environment=environment, agent_id=agent_id, state=state)

models/ControlModelM2/__init__.py

@@ -1,2 +1 @@
 from .ControlModelM2 import ControlModelM2
-from .SpreadModelM2 import SpreadModelM2

models/SpreadModelM2/SpreadModelM2.py

@@ -1,8 +1,8 @@
 import settings
 import random
 import numpy as np
-from ..BaseBehaviour import *
+from models.BaseBehaviour import *
-from .. import init_states
+from models import init_states
 
 
 class SpreadModelM2(BaseBehaviour):
@@ -22,14 +22,13 @@ class SpreadModelM2(BaseBehaviour):
 
         prob_generate_anti_rumor
     """
-
+    for x in range(settings.environment_params['source']):
-    init_states[random.randint(0, settings.network_params["number_of_nodes"])] = {'id': 1}
         init_states[random.randint(0, settings.network_params["number_of_nodes"])] = {'id': 1}
 
     def __init__(self, environment=None, agent_id=0, state=()):
         super().__init__(environment=environment, agent_id=agent_id, state=state)
 
-        self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],
+        self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier']/settings.environment_params['source'],
                                                            environment.environment_params['standard_variance'])
 
         self.prob_infect = np.random.normal(environment.environment_params['prob_infect'],
@@ -47,16 +46,17 @@ class SpreadModelM2(BaseBehaviour):
         self.prob_generate_anti_rumor = np.random.normal(environment.environment_params['prob_generate_anti_rumor'],
                                                          environment.environment_params['standard_variance'])
 
+
     def step(self, now):
 
         if self.state['id'] == 0:  # Neutral
             self.neutral_behaviour()
         elif self.state['id'] == 1:  # Infected
             self.infected_behaviour()
-        elif self.state['id'] == 2:  # Cured
+        # elif self.state['id'] == 2:  # Cured
-            self.cured_behaviour()
+        #     self.cured_behaviour()
-        elif self.state['id'] == 3:  # Vaccinated
+        # elif self.state['id'] == 3:  # Vaccinated
-            self.vaccinated_behaviour()
+            # self.vaccinated_behaviour()
 
         self.attrs['status'] = self.state['id']
         super().step(now)
@@ -69,6 +69,12 @@ class SpreadModelM2(BaseBehaviour):
             if random.random() < self.prob_neutral_making_denier:
                 self.state['id'] = 3   # Vaccinated making denier
 
+        # Generate anti-rumor
+        infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
+        for neighbor in infected_neighbors_2:
+            if random.random() < self.prob_generate_anti_rumor:
+                neighbor.state['id'] = 2  # Cured
+
     def infected_behaviour(self):
 
         # Neutral
@@ -80,10 +86,10 @@ class SpreadModelM2(BaseBehaviour):
     def cured_behaviour(self):
 
         # Vaccinate
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
+        # neutral_neighbors = self.get_neighboring_agents(state_id=0)
-        for neighbor in neutral_neighbors:
+        # for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
+        #     if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated
+        #         neighbor.state['id'] = 3  # Vaccinated
 
         # Cure
         infected_neighbors = self.get_neighboring_agents(state_id=1)

models/SpreadModelM2/__init__.py

@@ -0,0 +1 @@
+from .SpreadModelM2 import SpreadModelM2

models/__init__.py

@@ -3,6 +3,7 @@ from .BaseBehaviour import *
 from .BassModel import *
 from .BigMarketModel import *
 from .IndependentCascadeModel import *
-from .ModelM2 import *
+from .ControlModelM2 import *
+from .SpreadModelM2 import *
 from .SentimentCorrelationModel import *
 from .SISaModel import *

settings.json

@@ -1,18 +1,18 @@
 [
   {
     "network_type": 1,
-    "number_of_nodes": 1000,
+    "number_of_nodes": 500,
-    "max_time": 50,
+    "max_time": 30,
-    "num_trials": 1,
+    "num_trials": 20,
-    "timeout": 2
+    "timeout": 1
   },
 
   {
-    "agent": ["SISaModel","ControlModelM2"],
+    "agent": ["SpreadModelM2"],
 
 
-    "bite_prob": 0.01,
+    "bite_prob": 0,
-    "heal_prob": 0.01,
+    "heal_prob": 0,
 
     "innovation_prob": 0.001,
     "imitation_prob": 0.005,
@@ -45,18 +45,18 @@
     "variance_c_d": 0.003,
     "content_neutral": 0.088,
 
-    "standard_variance": 0.055,
 
 
-    "prob_neutral_making_denier": 0.035,
+    "standard_variance": 1e-15,
+    "prob_neutral_making_denier": 0.07,
+    "prob_cured_healing_infected": 0,
+    "prob_cured_vaccinate_neutral": 0,
+    "prob_vaccinated_healing_infected": 0,
+    "prob_vaccinated_vaccinate_neutral": 0,
+    "prob_generate_anti_rumor": 0.01,
 
-    "prob_infect": 0.075,
+    "prob_infect": 0.0375,
+    "source": 2
 
-    "prob_cured_healing_infected": 0.035,
-    "prob_cured_vaccinate_neutral": 0.035,
-
-    "prob_vaccinated_healing_infected": 0.035,
-    "prob_vaccinated_vaccinate_neutral": 0.035,
-    "prob_generate_anti_rumor": 0.035
   }
 ]

soil.py

@@ -6,6 +6,7 @@ import networkx as nx
 import settings
 import models
 import math
+import random
 import json
 
 
@@ -49,7 +50,7 @@ def visualization(graph_name):
     with open('data.txt', 'w') as outfile:
         json.dump(models.networkStatus, outfile, sort_keys=True, indent=4, separators=(',', ': '))
 
-    nx.write_gexf(G, graph_name+".gexf", version="1.2draft")
+    nx.write_gexf(G, graph_name+"_"+str(random.randint(0,1000))+".gexf", version="1.2draft")
 
 
 ###########
@@ -77,15 +78,15 @@ def results(model_name):
                     if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 1:  ## Infected
                         value_infectados += 1
                         activity = True
-                    if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 0:  ## Neutral
+                    # if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 0:  ## Neutral
-                        value_neutral += 1
+                    #     value_neutral += 1
-                        activity = True
+                    #     activity = True
                     if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 2:  ## Cured
                         value_cured += 1
                         activity = True
-                    if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 3:  ## Vaccinated
+                    # if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 3:  ## Vaccinated
-                        value_vaccinated += 1
+                    #     value_vaccinated += 1
-                        activity = True
+                    #     activity = True
 
         if activity:
             x_values.append(real_time)
@@ -98,13 +99,13 @@ def results(model_name):
     fig1 = plt.figure()
     ax1 = fig1.add_subplot(111)
 
-    infected_line = ax1.plot(x_values, infected_values, label='Infected')
+    infected_line = ax1.plot(x_values, infected_values, label='Endorses')
-    neutral_line = ax1.plot(x_values, neutral_values, label='Neutral')
+    # neutral_line = ax1.plot(x_values, neutral_values, label='Neutral')
-    cured_line = ax1.plot(x_values, cured_values, label='Cured')
+    cured_line = ax1.plot(x_values, cured_values, label='Denies')
-    vaccinated_line = ax1.plot(x_values, vaccinated_values, label='Vaccinated')
+    # vaccinated_line = ax1.plot(x_values, vaccinated_values, label='Vaccinated')
     ax1.legend()
-    fig1.savefig(model_name + '.png')
+    fig1.savefig(model_name + '_' + str(random.randint(0, 1000)) + '.png')
-    # plt.show()
+    plt.show()
 
 
 ####################
@@ -133,12 +134,12 @@ if len(agents) > 1:
                                 num_trials=settings.network_params["num_trials"], logging_interval=1.0, **settings.environment_params)
         sim.run_simulation()
         print(str(agent))
-        results(str(agent))
         visualization(str(agent))
+        results(str(agent))
 else:
     agent = agents[0]
     sim = NetworkSimulation(topology=G, states=init_states, agent_type=locals()[agent], max_time=settings.network_params["max_time"],
                             num_trials=settings.network_params["num_trials"], logging_interval=1.0, **settings.environment_params)
     sim.run_simulation()
-    results(str(agent))
     visualization(str(agent))
+    results(str(agent))