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First simple testing

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JesusMSM 2015-12-17 12:56:39 +01:00
parent 0a80da0eef
commit a8e4e2efe6
2 changed files with 76 additions and 39 deletions
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17
settings.py
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@ -14,6 +14,11 @@ def init():
global joy_prob
global sadness_prob
global disgust_prob
global tweet_probability_users
global tweet_relevant_probability
global tweet_probability_about
global sentiment_about
global tweet_probability_enterprises
network_type=1
number_of_nodes=200
@ -29,9 +34,19 @@ def init():
innovation_prob=0.01
imitation_prob=0.01
#SentimentCorrelation model
#Sentiment Correlation model
outside_effects_prob = 0.2
anger_prob = 0.08
joy_prob = 0.05
sadness_prob = 0.02
disgust_prob = 0.02
#Big Market model
##Users
tweet_probability_users = 0.44
tweet_relevant_probability = 0.25
tweet_probability_about = [0.25, 0.25]
sentiment_about = [0, 0]
##Enterprises
tweet_probability_enterprises = [0.5, 0.5]

96
soil.py
View File

@ -29,8 +29,10 @@ if settings.network_type == 2:
myList=[] # List just for debugging
networkStatus=[] # This list will contain the status of every node of the network
emotionStatus=[]
for x in range(0, settings.number_of_nodes):
networkStatus.append({'id':x})
emotionStatus.append({'id':x})
# Initialize agent states. Let's assume everyone is normal.
init_states = [{'id': 0, } for _ in range(settings.number_of_nodes)] # add keys as as necessary, but "id" must always refer to that state category
@ -47,66 +49,79 @@ class BigMarketModel(BaseNetworkAgent):
def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
self.time_awareness = 0
networkStatus[self.id][self.env.now]=0
self.type = ""
if self.id == 0: #Empresa 1
self.state['id']=0
self.tweet_probability = settings.tweet_probability_1
self.type="Enterprise"
self.tweet_probability = settings.tweet_probability_enterprises[0]
elif self.id == 1: #Empresa 2
self.state['id']=1
self.tweet_probability = settings.tweet_probability_2
self.type="Enterprise"
self.tweet_probability = settings.tweet_probability_enterprises[1]
else: #Usuarios normales
self.state['id']=2
self.type="User"
self.tweet_probability = settings.tweet_probability_users
self.tweet_relevant_probability = settings.tweet_relevant_probability
self.tweet_probability_about = settings.tweet_probability_about #Lista
self.sentiment_about = settings.sentiment_about #Lista
networkStatus[self.id][self.env.now]=self.state['id']
emotionStatus[self.id][self.env.now]=0
def run(self):
while True:
if(self.id == 0 or self.id == 1):
# Empresa
print("He entrado a empresa")
self.enterpriseBehaviour()
##Usuario
else:
# Usuario
print("He entrado a usuario")
self.userBehaviour()
yield self.env.timeout(settings.timeout)
def enterpriseBehaviour(self):
if random.random()< self.tweet_probability: #Twittea
aware_neighbors = self.get_neighboring_agents(state_id=2)
for x in aware_neighbors:
x.sentiment_about[0] += 0.01 #Aumenta para empresa 0
emotionStatus[x.id][self.env.now]=x.sentiment_about[0]
def userBehaviour(self):
if random.random() < self.tweet_probability: #Twittea
if random.random() < self.tweet_relevant_probability: #Twittea algo relevante
#Probabilidad de tweet para cada empresa
for i in range(len(self.tweet_probability_about)):
if random.random() < self.tweet_probability_about[i]:
random_num = random.random()
if random_num < self.tweet_probability_about[i]:
#Se ha cumplido la condicion, evaluo los sentimientos hacia esa empresa
if self.sentiment_about[i] < 0:
#NEGATIVO
print("Sentimiento negativo")
elif self.sentiment_about[i] == 0:
#NEUTRO
print("Sentimiento neutro")
else:
#POSITIVO
print("Sentimiento positivo")
aware_neighbors_1_time_step=[]
#Outside effects
if random.random() < settings.innovation_prob:
if self.state['id'] == 0:
self.state['id'] = 1
myList.append(self.id)
networkStatus[self.id][self.env.now]=1
self.time_awareness = self.env.now #Para saber cuando se han contagiado
yield self.env.timeout(settings.timeout)
else:
yield self.env.timeout(settings.timeout)
#Imitation effects
if self.state['id'] == 0:
aware_neighbors = self.get_neighboring_agents(state_id=1)
for x in aware_neighbors:
if x.time_awareness == (self.env.now-1):
aware_neighbors_1_time_step.append(x)
num_neighbors_aware = len(aware_neighbors_1_time_step)
if random.random() < (settings.imitation_prob*num_neighbors_aware):
myList.append(self.id)
self.state['id'] = 1
networkStatus[self.id][self.env.now]=1
yield self.env.timeout(settings.timeout)
else:
yield self.env.timeout(settings.timeout)
################################################
class SentimentCorrelationModel(BaseNetworkAgent):
def __init__(self, environment=None, agent_id=0, state=()):
@ -320,7 +335,7 @@ class ZombieOutbreak(BaseNetworkAgent):
# Simulation #
##############
sim = NetworkSimulation(topology=G, states=init_states, agent_type=SentimentCorrelationModel,
sim = NetworkSimulation(topology=G, states=init_states, agent_type=BigMarketModel,
max_time=settings.max_time, num_trials=settings.num_trials, logging_interval=1.0)
@ -341,14 +356,21 @@ status_census = [sum([1 for node_id, state in g.items() if state['id'] == 1]) fo
# Visualization #
#################
for x in range(0, settings.number_of_nodes):
emotionStatusAux=[]
for tiempo in emotionStatus[x]:
if tiempo != 'id':
emotionStatusAux.append((emotionStatus[x][tiempo],tiempo,None))
G.add_node(x, emotion= emotionStatusAux)
#lista = nx.nodes(G)
#print('Nodos: ' + str(lista))
for x in range(0, settings.number_of_nodes):
networkStatusAux=[]
for tiempo in networkStatus[x]:
if tiempo != 'id':
networkStatusAux.append((networkStatus[x][tiempo],tiempo,None))
G.add_node(x, status= networkStatusAux)
# for x in range(0, settings.number_of_nodes):
# networkStatusAux=[]
# for tiempo in networkStatus[x]:
# if tiempo != 'id':
# networkStatusAux.append((networkStatus[x][tiempo],tiempo,None))
# G.add_node(x, status= networkStatusAux)
#print(networkStatus)