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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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@ -0,0 +1,74 @@
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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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clase_base.pyc
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342
models.py
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342
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):
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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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class IndependentCascadeModel(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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self.time_awareness = 0
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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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aware_neighbors_1_time_step=[]
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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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self.time_awareness = self.env.now #Para saber cuando se han contagiado
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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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for x in aware_neighbors:
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if x.time_awareness == (self.env.now-1):
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aware_neighbors_1_time_step.append(x)
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num_neighbors_aware = len(aware_neighbors_1_time_step)
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if random.random() < (settings.imitation_prob*num_neighbors_aware):
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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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21
settings.py
21
settings.py
@ -19,34 +19,37 @@ def init():
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global tweet_probability_about
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global sentiment_about
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global tweet_probability_enterprises
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global enterprises
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network_type=1
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number_of_nodes=20
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max_time=1000
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number_of_nodes=50
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max_time=500
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num_trials=1
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timeout=10
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timeout=1
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#Zombie model
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bite_prob=0.01 # 0-1
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heal_prob=0.01 # 0-1
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#Bass model
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innovation_prob=0.01
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imitation_prob=0.01
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innovation_prob=0.001
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imitation_prob=0.005
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#Sentiment Correlation model
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outside_effects_prob = 0.2
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anger_prob = 0.08
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anger_prob = 0.06
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joy_prob = 0.05
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sadness_prob = 0.02
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disgust_prob = 0.02
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#Big Market model
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##Names
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enterprises = ["BBVA","Santander", "Bankia"]
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##Users
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tweet_probability_users = 0.44
|
||||
tweet_relevant_probability = 0.25
|
||||
tweet_probability_about = [0.25, 0.25]
|
||||
sentiment_about = [0, 0] #Valores por defecto
|
||||
tweet_probability_about = [0.15, 0.15, 0.15]
|
||||
sentiment_about = [0, 0, 0] #Valores por defecto
|
||||
##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 BaseNetworkAgent
|
||||
from nxsim import BaseLoggingAgent
|
||||
from random import randint
|
||||
from matplotlib import pyplot as plt
|
||||
import random
|
||||
import numpy as np
|
||||
import networkx as nx
|
||||
import settings
|
||||
import models
|
||||
import math
|
||||
import json
|
||||
|
||||
settings.init() # Loads all the data from settings
|
||||
models.init() # Loads the models and network variables
|
||||
|
||||
####################
|
||||
# Network creation #
|
||||
@ -24,397 +26,6 @@ if settings.network_type == 2:
|
||||
G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)
|
||||
# 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 #
|
||||
##############
|
||||
@ -429,8 +40,6 @@ sim.run_simulation()
|
||||
# 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)
|
||||
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 #
|
||||
#################
|
||||
|
||||
# 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):
|
||||
# emotionStatusAux2=[]
|
||||
# for tiempo in enterprise2Status[x]:
|
||||
# if tiempo != 'id':
|
||||
# prec = 2
|
||||
# output = math.floor(enterprise2Status[x][tiempo] * (10 ** prec)) / (10 ** prec) #Para tener 2 decimales solo
|
||||
# emotionStatusAux2.append((output,tiempo,None))
|
||||
# G.add_node(x, enterprise2emotion= emotionStatusAux2)
|
||||
for x in range(0, settings.number_of_nodes):
|
||||
for empresa in models.networkStatus["agente_%s"%x]:
|
||||
emotionStatusAux=[]
|
||||
for tiempo in models.networkStatus["agente_%s"%x][empresa]:
|
||||
prec = 2
|
||||
output = math.floor(models.networkStatus["agente_%s"%x][empresa][tiempo] * (10 ** prec)) / (10 ** prec) #Para tener 2 decimales solo
|
||||
emotionStatusAux.append((output,tiempo,None))
|
||||
attributes = {}
|
||||
attributes[empresa] = emotionStatusAux
|
||||
G.add_node(x, attributes)
|
||||
|
||||
|
||||
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:
|
||||
json.dump(allEnterprisesEmotionList, outfile)
|
||||
json.dump(models.networkStatus, outfile, sort_keys=True, indent=4, separators=(',', ': '))
|
||||
|
||||
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")
|
||||
|
||||
|
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status.png
BIN
status.png
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Before Width: | Height: | Size: 13 KiB After Width: | Height: | Size: 13 KiB |
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Reference in New Issue
Block a user