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Code Issues Releases Wiki Activity

Settings modules

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This commit is contained in:
Tasio Mendez
2017-04-21 13:55:42 +02:00
parent dd4ce15a3d
commit f29f5fa5bf
11 changed files with 262 additions and 210 deletions
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40
models/BigMarketModel/BigMarketModel.py
View File

@@ -1,9 +1,6 @@
import settings
import random
from ..BaseBehaviour import *
settings.init()
class BigMarketModel(BaseBehaviour):
"""
@@ -22,45 +19,44 @@ class BigMarketModel(BaseBehaviour):
sentiment_about [Array]
"""
def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
self.enterprises = settings.enterprises
self.enterprises = environment.enterprises
self.type = ""
self.number_of_enterprises = len(settings.enterprises)
self.number_of_enterprises = len(environment.enterprises)
if self.id < self.number_of_enterprises: # Enterprises
if self.id < self.number_of_enterprises: # Enterprises
self.state['id']=self.id
self.type="Enterprise"
self.tweet_probability = settings.tweet_probability_enterprises[self.id]
else: # normal users
self.tweet_probability = environment.tweet_probability_enterprises[self.id]
else: # normal users
self.state['id']=self.number_of_enterprises
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 # List
self.sentiment_about = settings.sentiment_about # List
self.tweet_probability = environment.tweet_probability_users
self.tweet_relevant_probability = environment.tweet_relevant_probability
self.tweet_probability_about = environment.tweet_probability_about # List
self.sentiment_about = environment.sentiment_about # List
def step(self, now):
if(self.id < self.number_of_enterprises): # Enterprise
if self.id < self.number_of_enterprises: # Enterprise
self.enterpriseBehaviour()
else: # Usuario
self.userBehaviour()
for i in range(self.number_of_enterprises): # So that it never is set to 0 if there are not changes (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: # Tweets
aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) #Nodes neighbour users
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 # Increments for enterprise
x.sentiment_about[self.id] += 0.1 # Increments for enterprise
else:
x.sentiment_about[self.id] -= 0.1 # Decrements for enterprise
x.sentiment_about[self.id] -= 0.1 # Decrements for enterprise
# Establecemos limites
if x.sentiment_about[self.id] > 1:
@@ -72,8 +68,8 @@ class BigMarketModel(BaseBehaviour):
def userBehaviour(self):
if random.random() < self.tweet_probability: # Tweets
if random.random() < self.tweet_relevant_probability: # Tweets something relevant
if random.random() < self.tweet_probability: # Tweets
if random.random() < self.tweet_relevant_probability: # Tweets something relevant
# Tweet probability per enterprise
for i in range(self.number_of_enterprises):
random_num = random.random()
@@ -90,7 +86,7 @@ class BigMarketModel(BaseBehaviour):
self.userTweets("positive",i)
def userTweets(self,sentiment,enterprise):
aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) #Nodes neighbours users
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

26
models/BigMarketModel/SISaModel.py
View File

@@ -1,10 +1,7 @@
import settings
import random
import numpy as np
from ..BaseBehaviour import *
settings.init()
class SISaModel(BaseBehaviour):
"""
@@ -35,19 +32,20 @@ class SISaModel(BaseBehaviour):
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.neutral_discontent_spon_prob = np.random.normal(environment.neutral_discontent_spon_prob,
environment.standard_variance)
self.neutral_discontent_infected_prob = np.random.normal(environment.neutral_discontent_infected_prob,
environment.standard_variance)
self.neutral_content_spon_prob = np.random.normal(environment.neutral_content_spon_prob,
environment.standard_variance)
self.neutral_content_infected_prob = np.random.normal(environment.neutral_content_infected_prob,
environment.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.discontent_neutral = np.random.normal(environment.discontent_neutral, environment.standard_variance)
self.discontent_content = np.random.normal(environment.discontent_content, environment.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)
self.content_discontent = np.random.normal(environment.content_discontent, environment.variance_c_d)
self.content_neutral = np.random.normal(environment.content_neutral, environment.standard_variance)
def step(self, now):
if self.state['id'] == 0: