mirror of
https://github.com/gsi-upm/soil
synced 2025-08-23 19:52:19 +00:00
WIP soil
* Pandas integration * Improved environment * Logging and data dumps * Tests * Added Finite State Machine models * Rewritten ipython notebook and documentation
This commit is contained in:
J. Fernando Sánchez
@@ -1,38 +0,0 @@
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import settings
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from nxsim import BaseNetworkAgent
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from .. import networkStatus
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class BaseBehaviour(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.network_params["timeout"])
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def step(self, now):
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networkStatus['agent_%s'% self.id] = self.to_json()
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def to_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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@@ -1 +0,0 @@
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from .BaseBehaviour import BaseBehaviour
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@@ -1,46 +0,0 @@
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import random
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from ..BaseBehaviour import *
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from .. import sentimentCorrelationNodeArray
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class BassModel(BaseBehaviour):
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"""
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Settings:
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innovation_prob
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imitation_prob
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"""
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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 = environment.environment_params['innovation_prob']
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self.imitation_prob = environment.environment_params['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() < self.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() < (self.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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@@ -1 +0,0 @@
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from .BassModel import BassModel
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@@ -1,104 +0,0 @@
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import random
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from ..BaseBehaviour import *
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class BigMarketModel(BaseBehaviour):
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"""
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Settings:
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Names:
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enterprises [Array]
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tweet_probability_enterprises [Array]
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Users:
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tweet_probability_users
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tweet_relevant_probability
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tweet_probability_about [Array]
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sentiment_about [Array]
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"""
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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 = environment.environment_params['enterprises']
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self.type = ""
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self.number_of_enterprises = len(environment.environment_params['enterprises'])
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if self.id < self.number_of_enterprises: # Enterprises
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self.state['id'] = self.id
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self.type = "Enterprise"
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self.tweet_probability = environment.environment_params['tweet_probability_enterprises'][self.id]
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else: # normal users
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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 = environment.environment_params['tweet_probability_users']
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self.tweet_relevant_probability = environment.environment_params['tweet_relevant_probability']
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self.tweet_probability_about = environment.environment_params['tweet_probability_about'] # List
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self.sentiment_about = environment.environment_params['sentiment_about'] # List
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def step(self, now):
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if self.id < self.number_of_enterprises: # Enterprise
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self.enterpriseBehaviour()
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else: # Usuario
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self.userBehaviour()
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for i in range(self.number_of_enterprises): # So that it never is set to 0 if there are not changes (logs)
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self.attrs['sentiment_enterprise_%s'% self.enterprises[i]] = self.sentiment_about[i]
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super().step(now)
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def enterpriseBehaviour(self):
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if random.random()< self.tweet_probability: # Tweets
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aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) # Nodes neighbour users
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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 # Increments for enterprise
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else:
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x.sentiment_about[self.id] -= 0.1 # Decrements for enterprise
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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: # Tweets
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if random.random() < self.tweet_relevant_probability: # Tweets something relevant
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# Tweet probability per enterprise
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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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# The condition is fulfilled, sentiments are evaluated towards that enterprise
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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) # Nodes neighbours users
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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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@@ -1 +0,0 @@
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from .BigMarketModel import BigMarketModel
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@@ -1,53 +0,0 @@
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import random
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from ..BaseBehaviour import *
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from .. import sentimentCorrelationNodeArray
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class IndependentCascadeModel(BaseBehaviour):
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"""
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Settings:
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innovation_prob
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imitation_prob
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"""
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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 = environment.environment_params['innovation_prob']
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self.imitation_prob = environment.environment_params['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() < self.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 # To know when they have been infected
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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() < (self.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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@@ -1 +0,0 @@
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from .IndependentCascadeModel import IndependentCascadeModel
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@@ -1,153 +0,0 @@
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import settings
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import random
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import numpy as np
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from ..BaseBehaviour import *
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from .. import init_states
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class ControlModelM2(BaseBehaviour):
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"""
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Settings:
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prob_neutral_making_denier
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prob_infect
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prob_cured_healing_infected
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prob_cured_vaccinate_neutral
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prob_vaccinated_healing_infected
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prob_vaccinated_vaccinate_neutral
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prob_generate_anti_rumor
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"""
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# Init infected
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init_states[random.randint(0, settings.network_params["number_of_nodes"]-1)] = {'id': 1}
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init_states[random.randint(0, settings.network_params["number_of_nodes"]-1)] = {'id': 1}
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# Init beacons
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init_states[random.randint(0, settings.network_params["number_of_nodes"]-1)] = {'id': 4}
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init_states[random.randint(0, settings.network_params["number_of_nodes"]-1)] = {'id': 4}
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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.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],
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environment.environment_params['standard_variance'])
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self.prob_infect = np.random.normal(environment.environment_params['prob_infect'],
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environment.environment_params['standard_variance'])
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self.prob_cured_healing_infected = np.random.normal(environment.environment_params['prob_cured_healing_infected'],
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environment.environment_params['standard_variance'])
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self.prob_cured_vaccinate_neutral = np.random.normal(environment.environment_params['prob_cured_vaccinate_neutral'],
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environment.environment_params['standard_variance'])
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self.prob_vaccinated_healing_infected = np.random.normal(environment.environment_params['prob_vaccinated_healing_infected'],
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environment.environment_params['standard_variance'])
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self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.environment_params['prob_vaccinated_vaccinate_neutral'],
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environment.environment_params['standard_variance'])
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self.prob_generate_anti_rumor = np.random.normal(environment.environment_params['prob_generate_anti_rumor'],
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environment.environment_params['standard_variance'])
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def step(self, now):
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if self.state['id'] == 0: # Neutral
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self.neutral_behaviour()
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elif self.state['id'] == 1: # Infected
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self.infected_behaviour()
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elif self.state['id'] == 2: # Cured
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self.cured_behaviour()
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elif self.state['id'] == 3: # Vaccinated
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self.vaccinated_behaviour()
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elif self.state['id'] == 4: # Beacon-off
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self.beacon_off_behaviour()
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elif self.state['id'] == 5: # Beacon-on
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self.beacon_on_behaviour()
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self.attrs['status'] = self.state['id']
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super().step(now)
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def neutral_behaviour(self):
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self.attrs['visible'] = False
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# Infected
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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if len(infected_neighbors) > 0:
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if random.random() < self.prob_neutral_making_denier:
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self.state['id'] = 3 # Vaccinated making denier
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def infected_behaviour(self):
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# Neutral
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neutral_neighbors = self.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors:
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if random.random() < self.prob_infect:
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neighbor.state['id'] = 1 # Infected
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self.attrs['visible'] = False
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def cured_behaviour(self):
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self.attrs['visible'] = True
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# Vaccinate
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neutral_neighbors = self.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors:
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if random.random() < self.prob_cured_vaccinate_neutral:
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neighbor.state['id'] = 3 # Vaccinated
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# Cure
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors:
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if random.random() < self.prob_cured_healing_infected:
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neighbor.state['id'] = 2 # Cured
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def vaccinated_behaviour(self):
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self.attrs['visible'] = True
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# Cure
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors:
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if random.random() < self.prob_cured_healing_infected:
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neighbor.state['id'] = 2 # Cured
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# Vaccinate
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neutral_neighbors = self.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors:
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if random.random() < self.prob_cured_vaccinate_neutral:
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neighbor.state['id'] = 3 # Vaccinated
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# Generate anti-rumor
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infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors_2:
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if random.random() < self.prob_generate_anti_rumor:
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neighbor.state['id'] = 2 # Cured
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def beacon_off_behaviour(self):
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self.attrs['visible'] = False
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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if len(infected_neighbors) > 0:
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self.state['id'] == 5 # Beacon on
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def beacon_on_behaviour(self):
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self.attrs['visible'] = False
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# Cure (M2 feature added)
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors:
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if random.random() < self.prob_generate_anti_rumor:
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neighbor.state['id'] = 2 # Cured
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neutral_neighbors_infected = neighbor.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors_infected:
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if random.random() < self.prob_generate_anti_rumor:
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neighbor.state['id'] = 3 # Vaccinated
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infected_neighbors_infected = neighbor.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors_infected:
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if random.random() < self.prob_generate_anti_rumor:
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neighbor.state['id'] = 2 # Cured
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# Vaccinate
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neutral_neighbors = self.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors:
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if random.random() < self.prob_cured_vaccinate_neutral:
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neighbor.state['id'] = 3 # Vaccinated
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@@ -1,112 +0,0 @@
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import settings
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import random
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import numpy as np
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from ..BaseBehaviour import *
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from .. import init_states
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class SpreadModelM2(BaseBehaviour):
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"""
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Settings:
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prob_neutral_making_denier
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prob_infect
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|
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prob_cured_healing_infected
|
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prob_cured_vaccinate_neutral
|
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prob_vaccinated_healing_infected
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prob_vaccinated_vaccinate_neutral
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prob_generate_anti_rumor
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"""
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init_states[random.randint(0, settings.network_params["number_of_nodes"])] = {'id': 1}
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init_states[random.randint(0, settings.network_params["number_of_nodes"])] = {'id': 1}
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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.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],
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environment.environment_params['standard_variance'])
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self.prob_infect = np.random.normal(environment.environment_params['prob_infect'],
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environment.environment_params['standard_variance'])
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self.prob_cured_healing_infected = np.random.normal(environment.environment_params['prob_cured_healing_infected'],
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environment.environment_params['standard_variance'])
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self.prob_cured_vaccinate_neutral = np.random.normal(environment.environment_params['prob_cured_vaccinate_neutral'],
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environment.environment_params['standard_variance'])
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self.prob_vaccinated_healing_infected = np.random.normal(environment.environment_params['prob_vaccinated_healing_infected'],
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environment.environment_params['standard_variance'])
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self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.environment_params['prob_vaccinated_vaccinate_neutral'],
|
||||
environment.environment_params['standard_variance'])
|
||||
self.prob_generate_anti_rumor = np.random.normal(environment.environment_params['prob_generate_anti_rumor'],
|
||||
environment.environment_params['standard_variance'])
|
||||
|
||||
def step(self, now):
|
||||
|
||||
if self.state['id'] == 0: # Neutral
|
||||
self.neutral_behaviour()
|
||||
elif self.state['id'] == 1: # Infected
|
||||
self.infected_behaviour()
|
||||
elif self.state['id'] == 2: # Cured
|
||||
self.cured_behaviour()
|
||||
elif self.state['id'] == 3: # Vaccinated
|
||||
self.vaccinated_behaviour()
|
||||
|
||||
self.attrs['status'] = self.state['id']
|
||||
super().step(now)
|
||||
|
||||
def neutral_behaviour(self):
|
||||
|
||||
# Infected
|
||||
infected_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
if len(infected_neighbors) > 0:
|
||||
if random.random() < self.prob_neutral_making_denier:
|
||||
self.state['id'] = 3 # Vaccinated making denier
|
||||
|
||||
def infected_behaviour(self):
|
||||
|
||||
# Neutral
|
||||
neutral_neighbors = self.get_neighboring_agents(state_id=0)
|
||||
for neighbor in neutral_neighbors:
|
||||
if random.random() < self.prob_infect:
|
||||
neighbor.state['id'] = 1 # Infected
|
||||
|
||||
def cured_behaviour(self):
|
||||
|
||||
# Vaccinate
|
||||
neutral_neighbors = self.get_neighboring_agents(state_id=0)
|
||||
for neighbor in neutral_neighbors:
|
||||
if random.random() < self.prob_cured_vaccinate_neutral:
|
||||
neighbor.state['id'] = 3 # Vaccinated
|
||||
|
||||
# Cure
|
||||
infected_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
for neighbor in infected_neighbors:
|
||||
if random.random() < self.prob_cured_healing_infected:
|
||||
neighbor.state['id'] = 2 # Cured
|
||||
|
||||
def vaccinated_behaviour(self):
|
||||
|
||||
# Cure
|
||||
infected_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
for neighbor in infected_neighbors:
|
||||
if random.random() < self.prob_cured_healing_infected:
|
||||
neighbor.state['id'] = 2 # Cured
|
||||
|
||||
# Vaccinate
|
||||
neutral_neighbors = self.get_neighboring_agents(state_id=0)
|
||||
for neighbor in neutral_neighbors:
|
||||
if random.random() < self.prob_cured_vaccinate_neutral:
|
||||
neighbor.state['id'] = 3 # Vaccinated
|
||||
|
||||
# Generate anti-rumor
|
||||
infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
|
||||
for neighbor in infected_neighbors_2:
|
||||
if random.random() < self.prob_generate_anti_rumor:
|
||||
neighbor.state['id'] = 2 # Cured
|
||||
@@ -1,2 +0,0 @@
|
||||
from .ControlModelM2 import ControlModelM2
|
||||
from .SpreadModelM2 import SpreadModelM2
|
||||
@@ -1,98 +0,0 @@
|
||||
import random
|
||||
import numpy as np
|
||||
from models.BaseBehaviour import *
|
||||
|
||||
|
||||
class SISaModel(BaseBehaviour):
|
||||
"""
|
||||
Settings:
|
||||
neutral_discontent_spon_prob
|
||||
|
||||
neutral_discontent_infected_prob
|
||||
|
||||
neutral_content_spong_prob
|
||||
|
||||
neutral_content_infected_prob
|
||||
|
||||
discontent_neutral
|
||||
|
||||
discontent_content
|
||||
|
||||
variance_d_c
|
||||
|
||||
content_discontent
|
||||
|
||||
variance_c_d
|
||||
|
||||
content_neutral
|
||||
|
||||
standard_variance
|
||||
"""
|
||||
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
|
||||
self.neutral_discontent_spon_prob = np.random.normal(environment.environment_params['neutral_discontent_spon_prob'],
|
||||
environment.environment_params['standard_variance'])
|
||||
self.neutral_discontent_infected_prob = np.random.normal(environment.environment_params['neutral_discontent_infected_prob'],
|
||||
environment.environment_params['standard_variance'])
|
||||
self.neutral_content_spon_prob = np.random.normal(environment.environment_params['neutral_content_spon_prob'],
|
||||
environment.environment_params['standard_variance'])
|
||||
self.neutral_content_infected_prob = np.random.normal(environment.environment_params['neutral_content_infected_prob'],
|
||||
environment.environment_params['standard_variance'])
|
||||
|
||||
self.discontent_neutral = np.random.normal(environment.environment_params['discontent_neutral'],
|
||||
environment.environment_params['standard_variance'])
|
||||
self.discontent_content = np.random.normal(environment.environment_params['discontent_content'],
|
||||
environment.environment_params['variance_d_c'])
|
||||
|
||||
self.content_discontent = np.random.normal(environment.environment_params['content_discontent'],
|
||||
environment.environment_params['variance_c_d'])
|
||||
self.content_neutral = np.random.normal(environment.environment_params['content_neutral'],
|
||||
environment.environment_params['standard_variance'])
|
||||
|
||||
def step(self, now):
|
||||
if self.state['id'] == 0:
|
||||
self.neutral_behaviour()
|
||||
if self.state['id'] == 1:
|
||||
self.discontent_behaviour()
|
||||
if self.state['id'] == 2:
|
||||
self.content_behaviour()
|
||||
|
||||
self.attrs['status'] = self.state['id']
|
||||
super().step(now)
|
||||
|
||||
def neutral_behaviour(self):
|
||||
# Spontaneous effects
|
||||
if random.random() < self.neutral_discontent_spon_prob:
|
||||
self.state['id'] = 1
|
||||
if random.random() < self.neutral_content_spon_prob:
|
||||
self.state['id'] = 2
|
||||
|
||||
# Infected
|
||||
discontent_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
if random.random() < len(discontent_neighbors) * self.neutral_discontent_infected_prob:
|
||||
self.state['id'] = 1
|
||||
content_neighbors = self.get_neighboring_agents(state_id=2)
|
||||
if random.random() < len(content_neighbors) * self.neutral_content_infected_prob:
|
||||
self.state['id'] = 2
|
||||
|
||||
def discontent_behaviour(self):
|
||||
# Healing
|
||||
if random.random() < self.discontent_neutral:
|
||||
self.state['id'] = 0
|
||||
|
||||
# Superinfected
|
||||
content_neighbors = self.get_neighboring_agents(state_id=2)
|
||||
if random.random() < len(content_neighbors) * self.discontent_content:
|
||||
self.state['id'] = 2
|
||||
|
||||
def content_behaviour(self):
|
||||
# Healing
|
||||
if random.random() < self.content_neutral:
|
||||
self.state['id'] = 0
|
||||
|
||||
# Superinfected
|
||||
discontent_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
if random.random() < len(discontent_neighbors) * self.content_discontent:
|
||||
self.state['id'] = 1
|
||||
@@ -1 +0,0 @@
|
||||
from .SISaModel import SISaModel
|
||||
@@ -1,104 +0,0 @@
|
||||
import random
|
||||
from ..BaseBehaviour import *
|
||||
from .. import sentimentCorrelationNodeArray
|
||||
|
||||
|
||||
class SentimentCorrelationModel(BaseBehaviour):
|
||||
"""
|
||||
Settings:
|
||||
outside_effects_prob
|
||||
|
||||
anger_prob
|
||||
|
||||
joy_prob
|
||||
|
||||
sadness_prob
|
||||
|
||||
disgust_prob
|
||||
"""
|
||||
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
self.outside_effects_prob = environment.environment_params['outside_effects_prob']
|
||||
self.anger_prob = environment.environment_params['anger_prob']
|
||||
self.joy_prob = environment.environment_params['joy_prob']
|
||||
self.sadness_prob = environment.environment_params['sadness_prob']
|
||||
self.disgust_prob = environment.environment_params['disgust_prob']
|
||||
self.time_awareness = []
|
||||
for i in range(4): # In this model we have 4 sentiments
|
||||
self.time_awareness.append(0) # 0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
|
||||
sentimentCorrelationNodeArray[self.id][self.env.now] = 0
|
||||
|
||||
def step(self, now):
|
||||
self.behaviour()
|
||||
super().step(now)
|
||||
|
||||
def behaviour(self):
|
||||
|
||||
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 = self.anger_prob+(len(angry_neighbors_1_time_step)*self.anger_prob)
|
||||
joy_prob = self.joy_prob+(len(joyful_neighbors_1_time_step)*self.joy_prob)
|
||||
sadness_prob = self.sadness_prob+(len(sad_neighbors_1_time_step)*self.sadness_prob)
|
||||
disgust_prob = self.disgust_prob+(len(disgusted_neighbors_1_time_step)*self.disgust_prob)
|
||||
outside_effects_prob = self.outside_effects_prob
|
||||
|
||||
num = random.random()
|
||||
|
||||
if num<outside_effects_prob:
|
||||
self.state['id'] = random.randint(1,4)
|
||||
|
||||
sentimentCorrelationNodeArray[self.id][self.env.now]=self.state['id'] # It is stored when it has been infected for the dynamic network
|
||||
self.time_awareness[self.state['id']-1] = self.env.now
|
||||
self.attrs['sentiment'] = self.state['id']
|
||||
|
||||
|
||||
if(num<anger_prob):
|
||||
|
||||
self.state['id'] = 1
|
||||
sentimentCorrelationNodeArray[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):
|
||||
|
||||
self.state['id'] = 2
|
||||
sentimentCorrelationNodeArray[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):
|
||||
|
||||
self.state['id'] = 3
|
||||
sentimentCorrelationNodeArray[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):
|
||||
|
||||
self.state['id'] = 4
|
||||
sentimentCorrelationNodeArray[self.id][self.env.now]=4
|
||||
self.time_awareness[self.state['id']-1] = self.env.now
|
||||
|
||||
self.attrs['sentiment'] = self.state['id']
|
||||
@@ -1 +0,0 @@
|
||||
from .SentimentCorrelationModel import SentimentCorrelationModel
|
||||
@@ -1,8 +0,0 @@
|
||||
from .models import *
|
||||
from .BaseBehaviour import *
|
||||
from .BassModel import *
|
||||
from .BigMarketModel import *
|
||||
from .IndependentCascadeModel import *
|
||||
from .ModelM2 import *
|
||||
from .SentimentCorrelationModel import *
|
||||
from .SISaModel import *
|
||||
@@ -1,10 +0,0 @@
|
||||
import settings
|
||||
|
||||
networkStatus = {} # Dict that will contain the status of every agent in the network
|
||||
|
||||
sentimentCorrelationNodeArray = []
|
||||
for x in range(0, settings.network_params["number_of_nodes"]):
|
||||
sentimentCorrelationNodeArray.append({'id': x})
|
||||
# Initialize agent states. Let's assume everyone is normal.
|
||||
init_states = [{'id': 0, } for _ in range(settings.network_params["number_of_nodes"])]
|
||||
# add keys as as necessary, but "id" must always refer to that state category
|
||||
Reference in New Issue
Block a user