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Visualizacion aparte, codigo reestructurado
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__pycache__/clase_base.cpython-34.pyc
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__pycache__/clase_base.cpython-34.pyc
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clase_base.py
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clase_base.py
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import random
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import time
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settings = {
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"empresas": ["BBVA", "Santander"]
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}
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class BaseNetworkAgent:
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.id = random.random()
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@property
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def env(self):
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class T(object):
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pass
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temp = T()
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temp.now = time.time()
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return temp
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def agentes_a_json(agentes):
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final = {}
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for agente in agentes:
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for stamp, attrs in self._attrs.items():
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for a in attrs:
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if a not in final:
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final[a] = {}
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final[a][stamp] = attrs[a]
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return final
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class ComportamientoBase(BaseNetworkAgent):
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def __init__(self, *args, **kwargs):
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self._attrs = {}
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@property
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def attrs(self):
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now = self.env.now
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if now not in self._attrs:
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self._attrs[now] = {}
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return self._attrs[now]
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@attrs.setter
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def attrs(self, value):
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self._attrs[self.env.now] = value
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def run(self):
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while True:
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self.step(self.env.now)
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#yield self.env.timeout(settings.timeout)
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def step(self, now):
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pass
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def a_json(self):
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final = {}
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for stamp, attrs in self._attrs.items():
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for a in attrs:
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if a not in final:
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final[a] = {}
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final[a][stamp] = attrs[a]
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return final
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class NuevoComportamiento(ComportamientoBase):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.empresas = settings["empresas"]
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def step(self, now):
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for i in self.empresas:
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self.attrs['sentimiento_empresa_%s' % i] = random.random()
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clase_base.pyc
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models.py
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models.py
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from nxsim import NetworkSimulation
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from nxsim import BaseNetworkAgent
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from nxsim import BaseLoggingAgent
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from random import randint
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from pprint import pprint
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from matplotlib import pyplot as plt
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import random
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import numpy as np
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import networkx as nx
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import settings
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settings.init()
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####################
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# Network creation #
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####################
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if settings.network_type == 0:
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G = nx.complete_graph(settings.number_of_nodes)
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if settings.network_type == 1:
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G = nx.barabasi_albert_graph(settings.number_of_nodes,3)
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if settings.network_type == 2:
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G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)
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# More types of networks can be added here
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##############################
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# Variables initializitation #
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##############################
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def init():
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global networkStatus
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networkStatus={} # Dict that will contain the status of every agent in the network
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sentimentCorrelationNodeArray=[]
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for x in range(0, settings.number_of_nodes):
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sentimentCorrelationNodeArray.append({'id':x})
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# Initialize agent states. Let's assume everyone is normal.
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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
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####################
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# Available models #
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####################
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class ComportamientoBase(BaseNetworkAgent):
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def __init__(self, environment=None, agent_id=0, state=()):
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super().__init__(environment=environment, agent_id=agent_id, state=state)
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self._attrs = {}
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@property
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def attrs(self):
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now = self.env.now
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if now not in self._attrs:
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self._attrs[now] = {}
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return self._attrs[now]
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@attrs.setter
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def attrs(self, value):
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self._attrs[self.env.now] = value
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def run(self):
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while True:
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self.step(self.env.now)
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yield self.env.timeout(settings.timeout)
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def step(self, now):
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networkStatus['agente_%s'% self.id] = self.a_json()
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def a_json(self):
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final = {}
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for stamp, attrs in self._attrs.items():
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for a in attrs:
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if a not in final:
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final[a] = {}
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final[a][stamp] = attrs[a]
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return final
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class BigMarketModel(ComportamientoBase):
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def __init__(self, environment=None, agent_id=0, state=()):
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super().__init__(environment=environment, agent_id=agent_id, state=state)
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self.enterprises = settings.enterprises
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self.type = ""
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self.number_of_enterprises = len(settings.enterprises)
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if self.id < self.number_of_enterprises: #Empresas
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self.state['id']=self.id
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self.type="Enterprise"
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self.tweet_probability = settings.tweet_probability_enterprises[self.id]
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else: #Usuarios normales
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self.state['id']=self.number_of_enterprises
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self.type="User"
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self.tweet_probability = settings.tweet_probability_users
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self.tweet_relevant_probability = settings.tweet_relevant_probability
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self.tweet_probability_about = settings.tweet_probability_about #Lista
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self.sentiment_about = settings.sentiment_about #Lista
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def step(self, now):
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if(self.id < self.number_of_enterprises): # Empresa
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self.enterpriseBehaviour()
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else: # Usuario
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self.userBehaviour()
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super().step(now)
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def enterpriseBehaviour(self):
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if random.random()< self.tweet_probability: #Twittea
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aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) #Nodos vecinos usuarios
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for x in aware_neighbors:
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if random.uniform(0,10) < 5:
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x.sentiment_about[self.id] += 0.1 #Aumenta para empresa
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else:
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x.sentiment_about[self.id] -= 0.1 #Reduce para empresa
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# Establecemos limites
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if x.sentiment_about[self.id] > 1:
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x.sentiment_about[self.id] = 1
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if x.sentiment_about[self.id]< -1:
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x.sentiment_about[self.id] = -1
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x.attrs['sentiment_enterprise_%s'% self.enterprises[self.id]] = x.sentiment_about[self.id]
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def userBehaviour(self):
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if random.random() < self.tweet_probability: #Twittea
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if random.random() < self.tweet_relevant_probability: #Twittea algo relevante
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#Probabilidad de tweet para cada empresa
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for i in range(self.number_of_enterprises):
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random_num = random.random()
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if random_num < self.tweet_probability_about[i]:
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#Se ha cumplido la condicion, evaluo los sentimientos hacia esa empresa
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if self.sentiment_about[i] < 0:
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#NEGATIVO
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self.userTweets("negative",i)
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elif self.sentiment_about[i] == 0:
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#NEUTRO
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pass
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else:
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#POSITIVO
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self.userTweets("positive",i)
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def userTweets(self,sentiment,enterprise):
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aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) #Nodos vecinos usuarios
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for x in aware_neighbors:
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if sentiment == "positive":
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x.sentiment_about[enterprise] +=0.003
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elif sentiment == "negative":
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x.sentiment_about[enterprise] -=0.003
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else:
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pass
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# Establecemos limites
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if x.sentiment_about[enterprise] > 1:
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x.sentiment_about[enterprise] = 1
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if x.sentiment_about[enterprise] < -1:
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x.sentiment_about[enterprise] = -1
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x.attrs['sentiment_enterprise_%s'% self.enterprises[enterprise]] = x.sentiment_about[enterprise]
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class SentimentCorrelationModel(ComportamientoBase):
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def __init__(self, environment=None, agent_id=0, state=()):
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super().__init__(environment=environment, agent_id=agent_id, state=state)
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self.outside_effects_prob = settings.outside_effects_prob
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self.anger_prob = settings.anger_prob
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self.joy_prob = settings.joy_prob
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self.sadness_prob = settings.sadness_prob
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self.disgust_prob = settings.disgust_prob
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self.time_awareness=[]
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for i in range(4): #En este modelo tenemos 4 sentimientos
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self.time_awareness.append(0) #0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
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sentimentCorrelationNodeArray[self.id][self.env.now]=0
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def step(self, now):
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self.behaviour()
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super().step(now)
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def behaviour(self):
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angry_neighbors_1_time_step=[]
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joyful_neighbors_1_time_step=[]
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sad_neighbors_1_time_step=[]
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disgusted_neighbors_1_time_step=[]
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angry_neighbors = self.get_neighboring_agents(state_id=1)
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for x in angry_neighbors:
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if x.time_awareness[0] > (self.env.now-500):
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angry_neighbors_1_time_step.append(x)
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num_neighbors_angry = len(angry_neighbors_1_time_step)
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joyful_neighbors = self.get_neighboring_agents(state_id=2)
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for x in joyful_neighbors:
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if x.time_awareness[1] > (self.env.now-500):
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joyful_neighbors_1_time_step.append(x)
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num_neighbors_joyful = len(joyful_neighbors_1_time_step)
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sad_neighbors = self.get_neighboring_agents(state_id=3)
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for x in sad_neighbors:
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if x.time_awareness[2] > (self.env.now-500):
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sad_neighbors_1_time_step.append(x)
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num_neighbors_sad = len(sad_neighbors_1_time_step)
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disgusted_neighbors = self.get_neighboring_agents(state_id=4)
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for x in disgusted_neighbors:
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if x.time_awareness[3] > (self.env.now-500):
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disgusted_neighbors_1_time_step.append(x)
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num_neighbors_disgusted = len(disgusted_neighbors_1_time_step)
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anger_prob= settings.anger_prob+(len(angry_neighbors_1_time_step)*settings.anger_prob)
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joy_prob= settings.joy_prob+(len(joyful_neighbors_1_time_step)*settings.joy_prob)
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sadness_prob = settings.sadness_prob+(len(sad_neighbors_1_time_step)*settings.sadness_prob)
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disgust_prob = settings.disgust_prob+(len(disgusted_neighbors_1_time_step)*settings.disgust_prob)
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outside_effects_prob= settings.outside_effects_prob
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num = random.random()
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if(num<outside_effects_prob):
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self.state['id'] = random.randint(1,4)
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sentimentCorrelationNodeArray[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
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self.time_awareness[self.state['id']-1] = self.env.now
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self.attrs['sentiment'] = self.state['id']
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if(num<anger_prob):
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self.state['id'] = 1
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sentimentCorrelationNodeArray[self.id][self.env.now]=1
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self.time_awareness[self.state['id']-1] = self.env.now
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elif (num<joy_prob+anger_prob and num>anger_prob):
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self.state['id'] = 2
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sentimentCorrelationNodeArray[self.id][self.env.now]=2
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self.time_awareness[self.state['id']-1] = self.env.now
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elif (num<sadness_prob+anger_prob+joy_prob and num>joy_prob+anger_prob):
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self.state['id'] = 3
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sentimentCorrelationNodeArray[self.id][self.env.now]=3
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self.time_awareness[self.state['id']-1] = self.env.now
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elif (num<disgust_prob+sadness_prob+anger_prob+joy_prob and num>sadness_prob+anger_prob+joy_prob):
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self.state['id'] = 4
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sentimentCorrelationNodeArray[self.id][self.env.now]=4
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self.time_awareness[self.state['id']-1] = self.env.now
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self.attrs['sentiment'] = self.state['id']
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class BassModel(ComportamientoBase):
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def __init__(self, environment=None, agent_id=0, state=()):
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super().__init__(environment=environment, agent_id=agent_id, state=state)
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self.innovation_prob = settings.innovation_prob
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self.imitation_prob = settings.imitation_prob
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sentimentCorrelationNodeArray[self.id][self.env.now]=0
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def step(self, now):
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self.behaviour()
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super().step(now)
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def behaviour(self):
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#Outside effects
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if random.random() < settings.innovation_prob:
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if self.state['id'] == 0:
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self.state['id'] = 1
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sentimentCorrelationNodeArray[self.id][self.env.now]=1
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else:
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pass
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self.attrs['status'] = self.state['id']
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return
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#Imitation effects
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if self.state['id'] == 0:
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aware_neighbors = self.get_neighboring_agents(state_id=1)
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num_neighbors_aware = len(aware_neighbors)
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||||||
|
if random.random() < (settings.imitation_prob*num_neighbors_aware):
|
||||||
|
self.state['id'] = 1
|
||||||
|
sentimentCorrelationNodeArray[self.id][self.env.now]=1
|
||||||
|
|
||||||
|
else:
|
||||||
|
pass
|
||||||
|
self.attrs['status'] = self.state['id']
|
||||||
|
|
||||||
|
|
||||||
|
class IndependentCascadeModel(ComportamientoBase):
|
||||||
|
def __init__(self, environment=None, agent_id=0, state=()):
|
||||||
|
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||||
|
self.innovation_prob = settings.innovation_prob
|
||||||
|
self.imitation_prob = settings.imitation_prob
|
||||||
|
self.time_awareness = 0
|
||||||
|
sentimentCorrelationNodeArray[self.id][self.env.now]=0
|
||||||
|
|
||||||
|
def step(self,now):
|
||||||
|
self.behaviour()
|
||||||
|
super().step(now)
|
||||||
|
|
||||||
|
def behaviour(self):
|
||||||
|
aware_neighbors_1_time_step=[]
|
||||||
|
#Outside effects
|
||||||
|
if random.random() < settings.innovation_prob:
|
||||||
|
if self.state['id'] == 0:
|
||||||
|
self.state['id'] = 1
|
||||||
|
sentimentCorrelationNodeArray[self.id][self.env.now]=1
|
||||||
|
self.time_awareness = self.env.now #Para saber cuando se han contagiado
|
||||||
|
|
||||||
|
else:
|
||||||
|
pass
|
||||||
|
|
||||||
|
self.attrs['status'] = self.state['id']
|
||||||
|
return
|
||||||
|
|
||||||
|
#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):
|
||||||
|
self.state['id'] = 1
|
||||||
|
sentimentCorrelationNodeArray[self.id][self.env.now]=1
|
||||||
|
else:
|
||||||
|
pass
|
||||||
|
|
||||||
|
self.attrs['status'] = self.state['id']
|
||||||
|
return
|
21
settings.py
21
settings.py
@ -19,34 +19,37 @@ def init():
|
|||||||
global tweet_probability_about
|
global tweet_probability_about
|
||||||
global sentiment_about
|
global sentiment_about
|
||||||
global tweet_probability_enterprises
|
global tweet_probability_enterprises
|
||||||
|
global enterprises
|
||||||
|
|
||||||
network_type=1
|
network_type=1
|
||||||
number_of_nodes=20
|
number_of_nodes=50
|
||||||
max_time=1000
|
max_time=500
|
||||||
num_trials=1
|
num_trials=1
|
||||||
timeout=10
|
timeout=1
|
||||||
|
|
||||||
#Zombie model
|
#Zombie model
|
||||||
bite_prob=0.01 # 0-1
|
bite_prob=0.01 # 0-1
|
||||||
heal_prob=0.01 # 0-1
|
heal_prob=0.01 # 0-1
|
||||||
|
|
||||||
#Bass model
|
#Bass model
|
||||||
innovation_prob=0.01
|
innovation_prob=0.001
|
||||||
imitation_prob=0.01
|
imitation_prob=0.005
|
||||||
|
|
||||||
#Sentiment Correlation model
|
#Sentiment Correlation model
|
||||||
outside_effects_prob = 0.2
|
outside_effects_prob = 0.2
|
||||||
anger_prob = 0.08
|
anger_prob = 0.06
|
||||||
joy_prob = 0.05
|
joy_prob = 0.05
|
||||||
sadness_prob = 0.02
|
sadness_prob = 0.02
|
||||||
disgust_prob = 0.02
|
disgust_prob = 0.02
|
||||||
|
|
||||||
#Big Market model
|
#Big Market model
|
||||||
|
##Names
|
||||||
|
enterprises = ["BBVA","Santander", "Bankia"]
|
||||||
##Users
|
##Users
|
||||||
tweet_probability_users = 0.44
|
tweet_probability_users = 0.44
|
||||||
tweet_relevant_probability = 0.25
|
tweet_relevant_probability = 0.25
|
||||||
tweet_probability_about = [0.25, 0.25]
|
tweet_probability_about = [0.15, 0.15, 0.15]
|
||||||
sentiment_about = [0, 0] #Valores por defecto
|
sentiment_about = [0, 0, 0] #Valores por defecto
|
||||||
##Enterprises
|
##Enterprises
|
||||||
tweet_probability_enterprises = [0.3, 0.3]
|
tweet_probability_enterprises = [0.3, 0.3, 0.3]
|
||||||
|
|
||||||
|
Binary file not shown.
453
soil.py
453
soil.py
@ -1,16 +1,18 @@
|
|||||||
|
#from clase_base import *
|
||||||
|
from models import *
|
||||||
from nxsim import NetworkSimulation
|
from nxsim import NetworkSimulation
|
||||||
from nxsim import BaseNetworkAgent
|
from nxsim import BaseNetworkAgent
|
||||||
from nxsim import BaseLoggingAgent
|
from nxsim import BaseLoggingAgent
|
||||||
from random import randint
|
|
||||||
from matplotlib import pyplot as plt
|
|
||||||
import random
|
import random
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import networkx as nx
|
import networkx as nx
|
||||||
import settings
|
import settings
|
||||||
|
import models
|
||||||
import math
|
import math
|
||||||
import json
|
import json
|
||||||
|
|
||||||
settings.init() # Loads all the data from settings
|
settings.init() # Loads all the data from settings
|
||||||
|
models.init() # Loads the models and network variables
|
||||||
|
|
||||||
####################
|
####################
|
||||||
# Network creation #
|
# Network creation #
|
||||||
@ -24,397 +26,6 @@ if settings.network_type == 2:
|
|||||||
G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)
|
G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)
|
||||||
# More types of networks can be added here
|
# More types of networks can be added here
|
||||||
|
|
||||||
|
|
||||||
##############################
|
|
||||||
# Variables initializitation #
|
|
||||||
##############################
|
|
||||||
|
|
||||||
myList=[] # List just for debugging
|
|
||||||
networkStatus=[] # This list will contain the status of every node of the network
|
|
||||||
emotionStatus=[]
|
|
||||||
enterprise1Status=[]
|
|
||||||
enterprise2Status=[]
|
|
||||||
allEnterprisesEmotionList = {}
|
|
||||||
for x in range(0, settings.number_of_nodes):
|
|
||||||
networkStatus.append({'id':x})
|
|
||||||
emotionStatus.append({'id':x})
|
|
||||||
enterprise1Status.append({'id':x})
|
|
||||||
enterprise2Status.append({'id':x})
|
|
||||||
|
|
||||||
for enterpriseIndex in range(0,len(settings.tweet_probability_about)):
|
|
||||||
allEnterprisesEmotionList['enterprise'+str(enterpriseIndex)] = enterprise1Status
|
|
||||||
# for node in range(0, settings.number_of_nodes):
|
|
||||||
|
|
||||||
# allEnterprisesEmotionList['enterprise'+str(enterpriseIndex)].update({'id':node})
|
|
||||||
|
|
||||||
#print(allEnterprisesEmotionList)
|
|
||||||
|
|
||||||
# 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
|
|
||||||
|
|
||||||
# Seed a zombie, just for zombie model
|
|
||||||
#init_states[5] = {'id': 1}
|
|
||||||
#init_states[3] = {'id': 1}
|
|
||||||
|
|
||||||
####################
|
|
||||||
# Available models #
|
|
||||||
####################
|
|
||||||
|
|
||||||
class BigMarketModel(BaseNetworkAgent):
|
|
||||||
number_of_enterprises = 0
|
|
||||||
|
|
||||||
def __init__(self, environment=None, agent_id=0, state=()):
|
|
||||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
|
||||||
self.time_awareness = 0
|
|
||||||
self.type = ""
|
|
||||||
self.number_of_enterprises = len(settings.tweet_probability_about)
|
|
||||||
|
|
||||||
if self.id < self.number_of_enterprises: #Empresas
|
|
||||||
self.state['id']=self.id
|
|
||||||
self.type="Enterprise"
|
|
||||||
self.tweet_probability = settings.tweet_probability_enterprises[self.id]
|
|
||||||
else: #Usuarios normales
|
|
||||||
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 #Lista
|
|
||||||
self.sentiment_about = settings.sentiment_about #Lista
|
|
||||||
#Inicializacion de visualizacion
|
|
||||||
for enterpriseIndex in range(0,len(settings.tweet_probability_about)):
|
|
||||||
allEnterprisesEmotionList['enterprise'+str(enterpriseIndex)][self.id].update({0:self.sentiment_about[enterpriseIndex]})
|
|
||||||
#print(allEnterprisesEmotionList)
|
|
||||||
|
|
||||||
#networkStatus[self.id][self.env.now]=self.state['id']
|
|
||||||
#emotionStatus[self.id][self.env.now]=0
|
|
||||||
|
|
||||||
def run(self):
|
|
||||||
while True:
|
|
||||||
if(self.id < self.number_of_enterprises): # Empresa
|
|
||||||
self.enterpriseBehaviour()
|
|
||||||
else: # 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=self.number_of_enterprises) #Nodos vecinos usuarios
|
|
||||||
for x in aware_neighbors:
|
|
||||||
if random.uniform(0,10) < 5:
|
|
||||||
x.sentiment_about[self.id] += 0.1 #Aumenta para empresa
|
|
||||||
else:
|
|
||||||
x.sentiment_about[self.id] -= 0.1 #Reduce para empresa
|
|
||||||
|
|
||||||
# Establecemos limites
|
|
||||||
if x.sentiment_about[self.id] > 1:
|
|
||||||
x.sentiment_about[self.id] = 1
|
|
||||||
if x.sentiment_about[self.id] < -1:
|
|
||||||
x.sentiment_about[self.id] = -1
|
|
||||||
|
|
||||||
|
|
||||||
#Visualización
|
|
||||||
enterpriseEmotion=[]
|
|
||||||
if self.id < self.number_of_enterprises:
|
|
||||||
#try:
|
|
||||||
#enterpriseEmotion = allEnterprisesEmotionList[self.id] #Cogemos la lista si ya ha sido creada
|
|
||||||
#print (enterpriseEmotion)
|
|
||||||
#except IndexError: # Si no existe la inicializamos
|
|
||||||
# for y in range(0, settings.number_of_nodes):
|
|
||||||
# enterpriseEmotion.append({'id':y})
|
|
||||||
#enterpriseEmotion[x.id][self.env.now]=x.sentiment_about[self.id]
|
|
||||||
#allEnterprisesEmotionList.insert(self.id,enterpriseEmotion) #Guardamos el valor
|
|
||||||
#enterpriseEmotion[:] = [] #Vaciamos la lista
|
|
||||||
allEnterprisesEmotionList['enterprise'+str(self.id)][x.id].update({self.env.now:x.sentiment_about[self.id]})
|
|
||||||
|
|
||||||
#if self.id == 0:
|
|
||||||
# enterprise1Status[x.id][self.env.now]=x.sentiment_about[self.id]
|
|
||||||
#if self.id == 1:
|
|
||||||
# enterprise2Status[x.id][self.env.now]=x.sentiment_about[self.id]
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
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)):
|
|
||||||
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
|
|
||||||
self.userTweets("negative",i)
|
|
||||||
elif self.sentiment_about[i] == 0:
|
|
||||||
#NEUTRO
|
|
||||||
pass
|
|
||||||
else:
|
|
||||||
#POSITIVO
|
|
||||||
self.userTweets("positive",i)
|
|
||||||
|
|
||||||
|
|
||||||
def userTweets(self,sentiment,enterprise):
|
|
||||||
aware_neighbors = self.get_neighboring_agents(state_id=2) #Nodos vecinos usuarios
|
|
||||||
for x in aware_neighbors:
|
|
||||||
if sentiment == "positive":
|
|
||||||
x.sentiment_about[enterprise] +=0.003
|
|
||||||
elif sentiment == "negative":
|
|
||||||
x.sentiment_about[enterprise] -=0.003
|
|
||||||
else:
|
|
||||||
pass
|
|
||||||
|
|
||||||
# Establecemos limites
|
|
||||||
if x.sentiment_about[enterprise] > 1:
|
|
||||||
x.sentiment_about[enterprise] = 1
|
|
||||||
if x.sentiment_about[enterprise] < -1:
|
|
||||||
x.sentiment_about[enterprise] = -1
|
|
||||||
|
|
||||||
#Visualización
|
|
||||||
# enterpriseEmotion=[]
|
|
||||||
# try:
|
|
||||||
# enterpriseEmotion = allEnterprisesEmotionList[self.id] #Cogemos la lista si ya ha sido creada
|
|
||||||
# except IndexError: # Si no existe la inicializamos
|
|
||||||
# for y in range(0, settings.number_of_nodes):
|
|
||||||
# enterpriseEmotion.append({'id':y})
|
|
||||||
# enterpriseEmotion[x.id][self.env.now]=x.sentiment_about[enterprise]
|
|
||||||
# allEnterprisesEmotionList.insert(enterprise,enterpriseEmotion) #Guardamos el valor
|
|
||||||
# enterpriseEmotion[:] = [] #Vaciamos la lista
|
|
||||||
#if enterprise == 0:
|
|
||||||
# enterprise1Status[x.id][self.env.now]=x.sentiment_about[enterprise]
|
|
||||||
#if enterprise == 1:
|
|
||||||
# enterprise2Status[x.id][self.env.now]=x.sentiment_about[enterprise]
|
|
||||||
|
|
||||||
allEnterprisesEmotionList['enterprise'+str(enterprise)][x.id].update({self.env.now:x.sentiment_about[enterprise]})
|
|
||||||
|
|
||||||
|
|
||||||
def checkLimits(sentimentValue):
|
|
||||||
if sentimentValue > 1:
|
|
||||||
return 1
|
|
||||||
if sentimentValue < -1:
|
|
||||||
return -1
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
################################################
|
|
||||||
|
|
||||||
|
|
||||||
class SentimentCorrelationModel(BaseNetworkAgent):
|
|
||||||
def __init__(self, environment=None, agent_id=0, state=()):
|
|
||||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
|
||||||
self.outside_effects_prob = settings.outside_effects_prob
|
|
||||||
self.anger_prob = settings.anger_prob
|
|
||||||
self.joy_prob = settings.joy_prob
|
|
||||||
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
|
|
||||||
self.time_awareness.append(0) #0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
|
|
||||||
networkStatus[self.id][self.env.now]=0
|
|
||||||
|
|
||||||
|
|
||||||
def run(self):
|
|
||||||
while True:
|
|
||||||
|
|
||||||
angry_neighbors_1_time_step=[]
|
|
||||||
joyful_neighbors_1_time_step=[]
|
|
||||||
sad_neighbors_1_time_step=[]
|
|
||||||
disgusted_neighbors_1_time_step=[]
|
|
||||||
|
|
||||||
|
|
||||||
angry_neighbors = self.get_neighboring_agents(state_id=1)
|
|
||||||
for x in angry_neighbors:
|
|
||||||
if x.time_awareness[0] > (self.env.now-500):
|
|
||||||
angry_neighbors_1_time_step.append(x)
|
|
||||||
num_neighbors_angry = len(angry_neighbors_1_time_step)
|
|
||||||
|
|
||||||
|
|
||||||
joyful_neighbors = self.get_neighboring_agents(state_id=2)
|
|
||||||
for x in joyful_neighbors:
|
|
||||||
if x.time_awareness[1] > (self.env.now-500):
|
|
||||||
joyful_neighbors_1_time_step.append(x)
|
|
||||||
num_neighbors_joyful = len(joyful_neighbors_1_time_step)
|
|
||||||
|
|
||||||
|
|
||||||
sad_neighbors = self.get_neighboring_agents(state_id=3)
|
|
||||||
for x in sad_neighbors:
|
|
||||||
if x.time_awareness[2] > (self.env.now-500):
|
|
||||||
sad_neighbors_1_time_step.append(x)
|
|
||||||
num_neighbors_sad = len(sad_neighbors_1_time_step)
|
|
||||||
|
|
||||||
|
|
||||||
disgusted_neighbors = self.get_neighboring_agents(state_id=4)
|
|
||||||
for x in disgusted_neighbors:
|
|
||||||
if x.time_awareness[3] > (self.env.now-500):
|
|
||||||
disgusted_neighbors_1_time_step.append(x)
|
|
||||||
num_neighbors_disgusted = len(disgusted_neighbors_1_time_step)
|
|
||||||
|
|
||||||
|
|
||||||
anger_prob= settings.anger_prob+(len(angry_neighbors_1_time_step)*settings.anger_prob)
|
|
||||||
joy_prob= settings.joy_prob+(len(joyful_neighbors_1_time_step)*settings.joy_prob)
|
|
||||||
sadness_prob = settings.sadness_prob+(len(sad_neighbors_1_time_step)*settings.sadness_prob)
|
|
||||||
disgust_prob = settings.disgust_prob+(len(disgusted_neighbors_1_time_step)*settings.disgust_prob)
|
|
||||||
outside_effects_prob= settings.outside_effects_prob
|
|
||||||
|
|
||||||
|
|
||||||
num = random.random()
|
|
||||||
|
|
||||||
|
|
||||||
if(num<outside_effects_prob):
|
|
||||||
self.state['id'] = random.randint(1,4)
|
|
||||||
myList.append(self.id)
|
|
||||||
networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
|
|
||||||
self.time_awareness[self.state['id']-1] = self.env.now
|
|
||||||
yield self.env.timeout(settings.timeout)
|
|
||||||
|
|
||||||
|
|
||||||
if(num<anger_prob):
|
|
||||||
|
|
||||||
myList.append(self.id)
|
|
||||||
self.state['id'] = 1
|
|
||||||
networkStatus[self.id][self.env.now]=1
|
|
||||||
self.time_awareness[self.state['id']-1] = self.env.now
|
|
||||||
elif (num<joy_prob+anger_prob and num>anger_prob):
|
|
||||||
|
|
||||||
myList.append(self.id)
|
|
||||||
self.state['id'] = 2
|
|
||||||
networkStatus[self.id][self.env.now]=2
|
|
||||||
self.time_awareness[self.state['id']-1] = self.env.now
|
|
||||||
elif (num<sadness_prob+anger_prob+joy_prob and num>joy_prob+anger_prob):
|
|
||||||
|
|
||||||
myList.append(self.id)
|
|
||||||
self.state['id'] = 3
|
|
||||||
networkStatus[self.id][self.env.now]=3
|
|
||||||
self.time_awareness[self.state['id']-1] = self.env.now
|
|
||||||
elif (num<disgust_prob+sadness_prob+anger_prob+joy_prob and num>sadness_prob+anger_prob+joy_prob):
|
|
||||||
|
|
||||||
myList.append(self.id)
|
|
||||||
self.state['id'] = 4
|
|
||||||
networkStatus[self.id][self.env.now]=4
|
|
||||||
self.time_awareness[self.state['id']-1] = self.env.now
|
|
||||||
|
|
||||||
yield self.env.timeout(settings.timeout)
|
|
||||||
|
|
||||||
|
|
||||||
class BassModel(BaseNetworkAgent):
|
|
||||||
def __init__(self, environment=None, agent_id=0, state=()):
|
|
||||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
|
||||||
self.innovation_prob = settings.innovation_prob
|
|
||||||
self.imitation_prob = settings.imitation_prob
|
|
||||||
networkStatus[self.id][self.env.now]=0
|
|
||||||
|
|
||||||
def run(self):
|
|
||||||
while True:
|
|
||||||
|
|
||||||
|
|
||||||
#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
|
|
||||||
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)
|
|
||||||
num_neighbors_aware = len(aware_neighbors)
|
|
||||||
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 IndependentCascadeModel(BaseNetworkAgent):
|
|
||||||
def __init__(self, environment=None, agent_id=0, state=()):
|
|
||||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
|
||||||
self.innovation_prob = settings.innovation_prob
|
|
||||||
self.imitation_prob = settings.imitation_prob
|
|
||||||
self.time_awareness = 0
|
|
||||||
networkStatus[self.id][self.env.now]=0
|
|
||||||
|
|
||||||
def run(self):
|
|
||||||
while True:
|
|
||||||
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 ZombieOutbreak(BaseNetworkAgent):
|
|
||||||
def __init__(self, environment=None, agent_id=0, state=()):
|
|
||||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
|
||||||
self.bite_prob = settings.bite_prob
|
|
||||||
networkStatus[self.id][self.env.now]=0
|
|
||||||
|
|
||||||
|
|
||||||
def run(self):
|
|
||||||
while True:
|
|
||||||
if random.random() < settings.heal_prob:
|
|
||||||
if self.state['id'] == 1:
|
|
||||||
self.zombify()
|
|
||||||
yield self.env.timeout(settings.timeout)
|
|
||||||
else:
|
|
||||||
yield self.env.timeout(settings.timeout)
|
|
||||||
else:
|
|
||||||
if self.state['id'] == 1:
|
|
||||||
print("Soy el zombie " + str(self.id) + " y me voy a curar porque el num aleatorio ha sido " + str(num))
|
|
||||||
networkStatus[self.id][self.env.now]=0
|
|
||||||
if self.id in myList:
|
|
||||||
myList.remove(self.id)
|
|
||||||
self.state['id'] = 0
|
|
||||||
yield self.env.timeout(settings.timeout)
|
|
||||||
else:
|
|
||||||
yield self.env.timeout(settings.timeout)
|
|
||||||
|
|
||||||
|
|
||||||
def zombify(self):
|
|
||||||
normal_neighbors = self.get_neighboring_agents(state_id=0)
|
|
||||||
for neighbor in normal_neighbors:
|
|
||||||
if random.random() < self.bite_prob:
|
|
||||||
print("Soy el zombie " + str(self.id) + " y voy a contagiar a " + str(neighbor.id))
|
|
||||||
neighbor.state['id'] = 1 # zombie
|
|
||||||
myList.append(neighbor.id)
|
|
||||||
networkStatus[self.id][self.env.now]=1
|
|
||||||
networkStatus[neighbor.id][self.env.now]=1
|
|
||||||
print(self.env.now, "Soy el zombie: "+ str(self.id), "Mi vecino es: "+ str(neighbor.id), sep='\t')
|
|
||||||
break
|
|
||||||
|
|
||||||
|
|
||||||
##############
|
##############
|
||||||
# Simulation #
|
# Simulation #
|
||||||
##############
|
##############
|
||||||
@ -429,8 +40,6 @@ sim.run_simulation()
|
|||||||
# Results #
|
# Results #
|
||||||
###########
|
###########
|
||||||
|
|
||||||
myList = sorted(myList, key=int)
|
|
||||||
#print("Los zombies son: " + str(myList))
|
|
||||||
|
|
||||||
trial = BaseLoggingAgent.open_trial_state_history(dir_path='sim_01', trial_id=0)
|
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()]
|
status_census = [sum([1 for node_id, state in g.items() if state['id'] == 1]) for t,g in trial.items()]
|
||||||
@ -440,53 +49,23 @@ status_census = [sum([1 for node_id, state in g.items() if state['id'] == 1]) fo
|
|||||||
# Visualization #
|
# Visualization #
|
||||||
#################
|
#################
|
||||||
|
|
||||||
# print("Empresa1")
|
|
||||||
# print (enterprise1Status)
|
|
||||||
# print("Empresa2")
|
|
||||||
# print (enterprise2Status)
|
|
||||||
for y in allEnterprisesEmotionList:
|
|
||||||
for x in range(0, settings.number_of_nodes):
|
|
||||||
emotionStatusAux=[]
|
|
||||||
enterpriseStatus = allEnterprisesEmotionList[y]
|
|
||||||
for tiempo in enterpriseStatus[x]:
|
|
||||||
if tiempo != 'id':
|
|
||||||
prec = 2
|
|
||||||
output = math.floor(enterpriseStatus[x][tiempo] * (10 ** prec)) / (10 ** prec) #Para tener 2 decimales solo
|
|
||||||
emotionStatusAux.append((output,tiempo,None))
|
|
||||||
keyword = 'enterprise'+str(y)+'Emotion'
|
|
||||||
G.add_node(x, keyword = emotionStatusAux)
|
|
||||||
|
|
||||||
# for x in range(0, settings.number_of_nodes):
|
for x in range(0, settings.number_of_nodes):
|
||||||
# emotionStatusAux2=[]
|
for empresa in models.networkStatus["agente_%s"%x]:
|
||||||
# for tiempo in enterprise2Status[x]:
|
emotionStatusAux=[]
|
||||||
# if tiempo != 'id':
|
for tiempo in models.networkStatus["agente_%s"%x][empresa]:
|
||||||
# prec = 2
|
prec = 2
|
||||||
# output = math.floor(enterprise2Status[x][tiempo] * (10 ** prec)) / (10 ** prec) #Para tener 2 decimales solo
|
output = math.floor(models.networkStatus["agente_%s"%x][empresa][tiempo] * (10 ** prec)) / (10 ** prec) #Para tener 2 decimales solo
|
||||||
# emotionStatusAux2.append((output,tiempo,None))
|
emotionStatusAux.append((output,tiempo,None))
|
||||||
# G.add_node(x, enterprise2emotion= emotionStatusAux2)
|
attributes = {}
|
||||||
|
attributes[empresa] = emotionStatusAux
|
||||||
|
G.add_node(x, attributes)
|
||||||
|
|
||||||
|
|
||||||
print("Done!")
|
print("Done!")
|
||||||
|
|
||||||
#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)
|
|
||||||
#print(networkStatus)
|
|
||||||
|
|
||||||
print(allEnterprisesEmotionList)
|
|
||||||
with open('data.txt', 'w') as outfile:
|
with open('data.txt', 'w') as outfile:
|
||||||
json.dump(allEnterprisesEmotionList, outfile)
|
json.dump(models.networkStatus, outfile, sort_keys=True, indent=4, separators=(',', ': '))
|
||||||
|
|
||||||
nx.write_gexf(G,"test.gexf", version="1.2draft")
|
nx.write_gexf(G,"test.gexf", version="1.2draft")
|
||||||
plt.plot(status_census)
|
|
||||||
plt.draw() # pyplot draw()
|
|
||||||
plt.savefig("status.png")
|
|
||||||
#print(networkStatus)
|
|
||||||
#nx.draw(G)
|
|
||||||
#plt.show()
|
|
||||||
#plt.savefig("path.png")
|
|
||||||
|
|
||||||
|
BIN
status.png
BIN
status.png
Binary file not shown.
Before Width: | Height: | Size: 13 KiB After Width: | Height: | Size: 13 KiB |
Loading…
Reference in New Issue
Block a user