balkian/soil
1
0
Fork 0
mirror of https://github.com/gsi-upm/soil synced 2024-11-13 06:52:28 +00:00
Code Issues Releases Wiki Activity

Update models.py

Browse Source
This commit is contained in:
Carlos A. Iglesias 2017-01-19 15:41:46 +01:00 committed by GitHub
parent 286b2489cf
commit b8c4204f5c
1 changed files with 8 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

16
models.py
View File

@ -347,8 +347,8 @@ class BigMarketModel(BaseBehaviour):
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
self.tweet_probability_about = settings.tweet_probability_about #List
self.sentiment_about = settings.sentiment_about #List
def step(self, now):
@ -356,15 +356,15 @@ class BigMarketModel(BaseBehaviour):
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)
for i in range(self.number_of_enterprises): # So that it never is set to 0 if there are not changes (logs)
self.attrs['sentiment_enterprise_%s'% self.enterprises[i]] = self.sentiment_about[i]
super().step(now)
def enterpriseBehaviour(self):
if random.random()< self.tweet_probability: #Twittea
aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) #Nodos vecinos usuarios
if random.random()< self.tweet_probability: #Tweets
aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) #Nodes neighbour users
for x in aware_neighbors:
if random.uniform(0,10) < 5:
x.sentiment_about[self.id] += 0.1 #Aumenta para empresa
@ -400,7 +400,7 @@ class BigMarketModel(BaseBehaviour):
self.userTweets("positive",i)
def userTweets(self,sentiment,enterprise):
aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) #Nodos vecinos usuarios
aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) #Nodes neighbours users
for x in aware_neighbors:
if sentiment == "positive":
x.sentiment_about[enterprise] +=0.003
@ -427,7 +427,7 @@ class SentimentCorrelationModel(BaseBehaviour):
self.sadness_prob = settings.sadness_prob
self.disgust_prob = settings.disgust_prob
self.time_awareness=[]
for i in range(4): #En este modelo tenemos 4 sentimientos
for i in range(4): #In this model we have 4 sentiments
self.time_awareness.append(0) #0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
sentimentCorrelationNodeArray[self.id][self.env.now]=0
@ -485,7 +485,7 @@ class SentimentCorrelationModel(BaseBehaviour):
if(num<outside_effects_prob):
self.state['id'] = random.randint(1,4)
sentimentCorrelationNodeArray[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
sentimentCorrelationNodeArray[self.id][self.env.now]=self.state['id'] #It is stored when it has been infected for the dynamic network
self.time_awareness[self.state['id']-1] = self.env.now
self.attrs['sentiment'] = self.state['id']