Paper model 1

models.py

@@ -57,6 +57,7 @@ class ComportamientoBase(BaseNetworkAgent):
         return final
 
 class SpreadModelM2(ComportamientoBase):
+    init_states[random.randint(0,settings.number_of_nodes)] = {'id':1}
     init_states[random.randint(0,settings.number_of_nodes)] = {'id':1}
     init_states[random.randint(0,settings.number_of_nodes)] = {'id': 1}
     def __init__(self, environment=None, agent_id=0, state=()):
@@ -125,6 +126,7 @@ class SpreadModelM2(ComportamientoBase):
         for neighbor in infected_neighbors:
             if random.random() < self.prob_cured_healing_infected:
                 neighbor.state['id'] = 2  # Cured
+                return
 
         # Vaccinate
         neutral_neighbors = self.get_neighboring_agents(state_id=0)
@@ -133,9 +135,11 @@ class SpreadModelM2(ComportamientoBase):
                 neighbor.state['id'] = 3  # Vaccinated
 
         # Generate anti-rumor
+        infected_neighbors = self.get_neighboring_agents(state_id=1)
         for neighbor in infected_neighbors:
             if random.random() < self.prob_generate_anti_rumor:
                 neighbor.state['id'] = 2  # Cured
+                return
 
 
 class SISaModel(ComportamientoBase):

settings.py

@@ -40,7 +40,7 @@ def init():
     global prob_generate_anti_rumor
 
     network_type=1
-    number_of_nodes=1000
+    number_of_nodes=100
     max_time=500
     num_trials=1
     timeout=20
@@ -88,16 +88,16 @@ def init():
     standard_variance = 0.055
 
     #Spread Model M2
-    prob_neutral_making_denier = 0.055
+    prob_neutral_making_denier = 0.035
 
-    prob_infect = 0.1
+    prob_infect = 0.075
 
-    prob_cured_healing_infected = 0.055
+    prob_cured_healing_infected = 0.035
-    prob_cured_vaccinate_neutral = 0.055
+    prob_cured_vaccinate_neutral = 0.035
 
-    prob_vaccinated_healing_infected = 0.055
+    prob_vaccinated_healing_infected = 0.035
-    prob_vaccinated_vaccinate_neutral = 0.055
+    prob_vaccinated_vaccinate_neutral = 0.035
-    prob_generate_anti_rumor = 0.055
+    prob_generate_anti_rumor = 0.035
 
 
 

soil.py

@@ -37,26 +37,48 @@ sim.run_simulation()
 # Results #
 ###########
 x_values = []
-y_values = []
+infected_values = []
+neutral_values = []
+cured_values = []
+vaccinated_values = []
 
 attribute_plot = 'status'
 for time in range(0, settings.max_time):
-    value = settings.sentiment_about[0]
+    value_infectados = 0
+    value_neutral = 0
+    value_cured = 0
+    value_vaccinated = 0
     real_time = time * settings.timeout
     activity= False
     for x in range(0, settings.number_of_nodes):
         if attribute_plot in models.networkStatus["agente_%s" % x]:
             if real_time in models.networkStatus["agente_%s" % x][attribute_plot]:
                 if models.networkStatus["agente_%s" % x][attribute_plot][real_time] == 1: ##Representar infectados
-                    value += 1
+                    value_infectados += 1
+                    activity = True
+                if models.networkStatus["agente_%s" % x][attribute_plot][real_time] == 0:  ##Representar neutrales
+                    value_neutral += 1
+                    activity = True
+                if models.networkStatus["agente_%s" % x][attribute_plot][real_time] == 2:  ##Representar cured
+                    value_cured += 1
+                    activity = True
+                if models.networkStatus["agente_%s" % x][attribute_plot][real_time] == 3:  ##Representar vaccinated
+                    value_vaccinated += 1
                     activity = True
 
     if activity:
         x_values.append(real_time)
-        y_values.append(value)
+        infected_values.append(value_infectados)
+        neutral_values.append(value_neutral)
+        cured_values.append(value_cured)
+        vaccinated_values.append(value_vaccinated)
         activity=False
 
-plt.plot(x_values,y_values)
+infected_line = plt.plot(x_values,infected_values,label='Infected')
+neutral_line = plt.plot(x_values,neutral_values, label='Neutral')
+cured_line = plt.plot(x_values,cured_values, label='Cured')
+vaccinated_line = plt.plot(x_values,vaccinated_values, label='Vaccinated')
+plt.legend()
 plt.show()