SISa model implemented

models.py

@@ -1,4 +1,5 @@
 from nxsim import BaseNetworkAgent
+import numpy as np
 import random
 import settings
 
@@ -55,6 +56,73 @@ class ComportamientoBase(BaseNetworkAgent):
                 final[a][stamp] = attrs[a]
         return final
 
+class SISaModel(ComportamientoBase):
+    def __init__(self, environment=None, agent_id=0, state=()):
+        super().__init__(environment=environment, agent_id=agent_id, state=state)
+
+        self.neutral_discontent_spon_prob = np.random.normal(settings.neutral_discontent_spon_prob, settings.standard_variance)
+        self.neutral_discontent_infected_prob = np.random.normal(settings.neutral_discontent_infected_prob,settings.standard_variance)
+        self.neutral_content_spon_prob = np.random.normal(settings.neutral_content_spon_prob,settings.standard_variance)
+        self.neutral_content_infected_prob = np.random.normal(settings.neutral_content_infected_prob,settings.standard_variance)
+
+        self.discontent_neutral = np.random.normal(settings.discontent_neutral,settings.standard_variance)
+        self.discontent_content = np.random.normal(settings.discontent_content,settings.variance_d_c)
+
+        self.content_discontent = np.random.normal(settings.content_discontent,settings.variance_c_d)
+        self.content_neutral = np.random.normal(settings.content_neutral,settings.standard_variance)
+
+    def step(self, now):
+
+        if self.state['id'] == 0:
+            self.neutral_behaviour()
+        if self.state['id'] == 1:
+            self.discontent_behaviour()
+        if self.state['id'] == 2:
+            self.content_behaviour()
+
+        self.attrs['status'] = self.state['id']
+        super().step(now)
+
+
+    def neutral_behaviour(self):
+
+        #Spontaneus effects
+        if random.random() < self.neutral_discontent_spon_prob:
+            self.state['id'] = 1
+        if random.random() < self.neutral_content_spon_prob:
+            self.state['id'] = 2
+
+        #Infected
+        discontent_neighbors = self.get_neighboring_agents(state_id=1)
+        if random.random() < len(discontent_neighbors)*self.neutral_discontent_infected_prob:
+            self.state['id'] = 1
+        content_neighbors = self.get_neighboring_agents(state_id=2)
+        if random.random() < len(content_neighbors)*self.neutral_content_infected_prob:
+            self.state['id'] = 2
+
+    def discontent_behaviour(self):
+
+        #Healing
+        if random.random() < self.discontent_neutral:
+            self.state['id'] = 0
+
+        #Superinfected
+        content_neighbors = self.get_neighboring_agents(state_id=2)
+        if random.random() < len(content_neighbors)*self.discontent_content:
+            self.state['id'] = 2
+
+    def content_behaviour(self):
+
+        #Healing
+        if random.random() < self.content_neutral:
+            self.state['id'] = 0
+
+        #Superinfected
+        discontent_neighbors = self.get_neighboring_agents(state_id=1)
+        if random.random() < len(discontent_neighbors)*self.content_discontent:
+            self.state['id'] = 1
+
+
 class BigMarketModel(ComportamientoBase):
 
     def __init__(self, environment=None, agent_id=0, state=()):
@@ -81,6 +149,8 @@ class BigMarketModel(ComportamientoBase):
             self.enterpriseBehaviour()
         else:  # Usuario
             self.userBehaviour()
+            for i in range(self.number_of_enterprises):       # Para que nunca este a 0 si no ha habido cambios(logs)
+                self.attrs['sentiment_enterprise_%s'% self.enterprises[i]] = self.sentiment_about[i]
 
         super().step(now)
 

settings.py

@@ -20,12 +20,23 @@ def init():
     global sentiment_about
     global tweet_probability_enterprises
     global enterprises
+    global neutral_discontent_spon_prob
+    global neutral_discontent_infected_prob
+    global neutral_content_spon_prob
+    global neutral_content_infected_prob
+    global discontent_content
+    global discontent_neutral
+    global content_discontent
+    global content_neutral
+    global variance_d_c
+    global variance_c_d
+    global standard_variance
 
     network_type=1
     number_of_nodes=50
     max_time=500
     num_trials=1
-    timeout=1
+    timeout=20
 
     #Zombie model
     bite_prob=0.01 # 0-1
@@ -53,3 +64,19 @@ def init():
     ##Enterprises
     tweet_probability_enterprises = [0.3, 0.3, 0.3]
 
+    #SISa
+    neutral_discontent_spon_prob = 0.04
+    neutral_discontent_infected_prob = 0.04
+    neutral_content_spon_prob = 0.18
+    neutral_content_infected_prob = 0.02
+
+    discontent_neutral = 0.13
+    discontent_content = 0.07
+    variance_d_c = 0.02
+
+    content_discontent = 0.009
+    variance_c_d = 0.003
+    content_neutral = 0.088
+
+    standard_variance = 0.02
+

soil.py

@@ -1,6 +1,7 @@
 from models import *
 from nxsim import NetworkSimulation
-from nxsim import BaseLoggingAgent
+import numpy
+from matplotlib import pyplot as plt
 import networkx as nx
 import settings
 import models
@@ -26,7 +27,7 @@ if settings.network_type == 2:
 # Simulation #
 ##############
 
-sim = NetworkSimulation(topology=G, states=init_states, agent_type=BigMarketModel,
+sim = NetworkSimulation(topology=G, states=init_states, agent_type=SISaModel,
                         max_time=settings.max_time, num_trials=settings.num_trials, logging_interval=1.0)
 
 
@@ -35,10 +36,22 @@ sim.run_simulation()
 ###########
 # Results #
 ###########
+x_values = []
+y_values = []
 
+for time in range(0, settings.max_time):
+    value = settings.sentiment_about[0]
+    real_time = time * settings.timeout
+    for x in range(0, settings.number_of_nodes):
+        if "sentiment_enterprise_BBVA" in models.networkStatus["agente_%s" % x]:
+            if real_time in models.networkStatus["agente_%s" % x]["sentiment_enterprise_BBVA"]:
+                value += models.networkStatus["agente_%s" % x]["sentiment_enterprise_BBVA"][real_time]
 
-trial = BaseLoggingAgent.open_trial_state_history(dir_path='sim_01', trial_id=0)
-status_census = [sum([1 for node_id, state in g.items() if state['id'] == 1]) for t,g in trial.items()]
+    x_values.append(real_time)
+    y_values.append(value)
+
+plt.plot(x_values,y_values)
+#plt.show()
 
 
 #################