from nxsim import BaseNetworkAgent import numpy as np import random import settings settings.init() ############################## # Variables initialization # ############################## def init(): global networkStatus networkStatus = {} # Dict that will contain the status of every agent in the network sentimentCorrelationNodeArray=[] for x in range(0, settings.number_of_nodes): sentimentCorrelationNodeArray.append({'id':x}) # 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 #################### # Available models # #################### class BaseBehaviour(BaseNetworkAgent): def __init__(self, environment=None, agent_id=0, state=()): super().__init__(environment=environment, agent_id=agent_id, state=state) self._attrs = {} @property def attrs(self): now = self.env.now if now not in self._attrs: self._attrs[now] = {} return self._attrs[now] @attrs.setter def attrs(self, value): self._attrs[self.env.now] = value def run(self): while True: self.step(self.env.now) yield self.env.timeout(settings.timeout) def step(self, now): networkStatus['agent_%s'% self.id] = self.to_json() def to_json(self): final = {} for stamp, attrs in self._attrs.items(): for a in attrs: if a not in final: final[a] = {} final[a][stamp] = attrs[a] return final class ControlModelM2(BaseBehaviour): #Init infected init_states[random.randint(0,settings.number_of_nodes-1)] = {'id':1} init_states[random.randint(0,settings.number_of_nodes-1)] = {'id':1} # Init beacons init_states[random.randint(0, settings.number_of_nodes-1)] = {'id': 4} init_states[random.randint(0, settings.number_of_nodes-1)] = {'id': 4} def __init__(self, environment=None, agent_id=0, state=()): super().__init__(environment=environment, agent_id=agent_id, state=state) self.prob_neutral_making_denier = np.random.normal(settings.prob_neutral_making_denier, settings.standard_variance) self.prob_infect = np.random.normal(settings.prob_infect, settings.standard_variance) self.prob_cured_healing_infected = np.random.normal(settings.prob_cured_healing_infected, settings.standard_variance) self.prob_cured_vaccinate_neutral = np.random.normal(settings.prob_cured_vaccinate_neutral, settings.standard_variance) self.prob_vaccinated_healing_infected = np.random.normal(settings.prob_vaccinated_healing_infected, settings.standard_variance) self.prob_vaccinated_vaccinate_neutral = np.random.normal(settings.prob_vaccinated_vaccinate_neutral, settings.standard_variance) self.prob_generate_anti_rumor = np.random.normal(settings.prob_generate_anti_rumor, settings.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() elif self.state['id'] == 4: #Beacon-off self.beacon_off_behaviour() elif self.state['id'] == 5: #Beacon-on self.beacon_on_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 def beacon_off_behaviour(self): infected_neighbors = self.get_neighboring_agents(state_id=1) if len(infected_neighbors) > 0: self.state['id'] == 5 #Beacon on def beacon_on_behaviour(self): # Cure (M2 feature added) infected_neighbors = self.get_neighboring_agents(state_id=1) for neighbor in infected_neighbors: if random.random() < self.prob_generate_anti_rumor: neighbor.state['id'] = 2 # Cured neutral_neighbors_infected = neighbor.get_neighboring_agents(state_id=0) for neighbor in neutral_neighbors_infected: if random.random() < self.prob_generate_anti_rumor: neighbor.state['id'] = 3 # Vaccinated infected_neighbors_infected = neighbor.get_neighboring_agents(state_id=1) for neighbor in infected_neighbors_infected: if random.random() < self.prob_generate_anti_rumor: 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 class SpreadModelM2(BaseBehaviour): init_states[random.randint(0,settings.number_of_nodes)] = {'id':1} init_states[random.randint(0,settings.number_of_nodes)] = {'id':1} def __init__(self, environment=None, agent_id=0, state=()): super().__init__(environment=environment, agent_id=agent_id, state=state) self.prob_neutral_making_denier = np.random.normal(settings.prob_neutral_making_denier, settings.standard_variance) self.prob_infect = np.random.normal(settings.prob_infect, settings.standard_variance) self.prob_cured_healing_infected = np.random.normal(settings.prob_cured_healing_infected, settings.standard_variance) self.prob_cured_vaccinate_neutral = np.random.normal(settings.prob_cured_vaccinate_neutral, settings.standard_variance) self.prob_vaccinated_healing_infected = np.random.normal(settings.prob_vaccinated_healing_infected, settings.standard_variance) self.prob_vaccinated_vaccinate_neutral = np.random.normal(settings.prob_vaccinated_vaccinate_neutral, settings.standard_variance) self.prob_generate_anti_rumor = np.random.normal(settings.prob_generate_anti_rumor, settings.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 class SISaModel(BaseBehaviour): def __init__(self, environment=None, agent_id=0, state=()): super().__init__(environment=environment, agent_id=agent_id, state=state) self.neutral_discontent_spon_prob = np.random.normal(settings.neutral_discontent_spon_prob, settings.standard_variance) self.neutral_discontent_infected_prob = np.random.normal(settings.neutral_discontent_infected_prob,settings.standard_variance) self.neutral_content_spon_prob = np.random.normal(settings.neutral_content_spon_prob,settings.standard_variance) self.neutral_content_infected_prob = np.random.normal(settings.neutral_content_infected_prob,settings.standard_variance) self.discontent_neutral = np.random.normal(settings.discontent_neutral,settings.standard_variance) self.discontent_content = np.random.normal(settings.discontent_content,settings.variance_d_c) self.content_discontent = np.random.normal(settings.content_discontent,settings.variance_c_d) self.content_neutral = np.random.normal(settings.content_neutral,settings.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): #Spontaneus 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 class BigMarketModel(BaseBehaviour): def __init__(self, environment=None, agent_id=0, state=()): super().__init__(environment=environment, agent_id=agent_id, state=state) self.enterprises = settings.enterprises self.type = "" self.number_of_enterprises = len(settings.enterprises) if self.id < self.number_of_enterprises: #Enterprises self.state['id']=self.id self.type="Enterprise" self.tweet_probability = settings.tweet_probability_enterprises[self.id] else: #normal users self.state['id']=self.number_of_enterprises self.type="User" self.tweet_probability = settings.tweet_probability_users self.tweet_relevant_probability = settings.tweet_relevant_probability self.tweet_probability_about = settings.tweet_probability_about #List self.sentiment_about = settings.sentiment_about #List def step(self, now): if(self.id < self.number_of_enterprises): # Ennterprise self.enterpriseBehaviour() else: # Usuario self.userBehaviour() for i in range(self.number_of_enterprises): # So that it never is set to 0 if there are not changes (logs) self.attrs['sentiment_enterprise_%s'% self.enterprises[i]] = self.sentiment_about[i] super().step(now) def enterpriseBehaviour(self): if random.random()< self.tweet_probability: #Tweets aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) #Nodes neighbour users for x in aware_neighbors: if random.uniform(0,10) < 5: x.sentiment_about[self.id] += 0.1 #Increments for enterprise else: x.sentiment_about[self.id] -= 0.1 #Decrements for enterprise # 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 x.attrs['sentiment_enterprise_%s'% self.enterprises[self.id]] = x.sentiment_about[self.id] def userBehaviour(self): if random.random() < self.tweet_probability: #Tweets if random.random() < self.tweet_relevant_probability: #Tweets something relevant #Tweet probability per enterprise for i in range(self.number_of_enterprises): random_num = random.random() if random_num < self.tweet_probability_about[i]: #The condition is fulfilled, sentiments are evaluated towards that enterprise 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=self.number_of_enterprises) #Nodes neighbours users 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 x.attrs['sentiment_enterprise_%s'% self.enterprises[enterprise]] = x.sentiment_about[enterprise] class SentimentCorrelationModel(BaseBehaviour): 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): #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= 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(numanger_prob): self.state['id'] = 2 sentimentCorrelationNodeArray[self.id][self.env.now]=2 self.time_awareness[self.state['id']-1] = self.env.now elif (numjoy_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 (numsadness_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'] class BassModel(BaseBehaviour): 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 sentimentCorrelationNodeArray[self.id][self.env.now]=0 def step(self, now): self.behaviour() super().step(now) def behaviour(self): #Outside effects if random.random() < settings.innovation_prob: if self.state['id'] == 0: self.state['id'] = 1 sentimentCorrelationNodeArray[self.id][self.env.now]=1 else: pass self.attrs['status'] = self.state['id'] return #Imitation effects if self.state['id'] == 0: aware_neighbors = self.get_neighboring_agents(state_id=1) num_neighbors_aware = len(aware_neighbors) if random.random() < (settings.imitation_prob*num_neighbors_aware): self.state['id'] = 1 sentimentCorrelationNodeArray[self.id][self.env.now]=1 else: pass self.attrs['status'] = self.state['id'] class IndependentCascadeModel(BaseBehaviour): def __init__(self, environment=None, agent_id=0, state=()): super().__init__(environment=environment, agent_id=agent_id, state=state) self.innovation_prob = settings.innovation_prob self.imitation_prob = settings.imitation_prob self.time_awareness = 0 sentimentCorrelationNodeArray[self.id][self.env.now]=0 def step(self,now): self.behaviour() super().step(now) def behaviour(self): aware_neighbors_1_time_step=[] #Outside effects if random.random() < settings.innovation_prob: if self.state['id'] == 0: self.state['id'] = 1 sentimentCorrelationNodeArray[self.id][self.env.now]=1 self.time_awareness = self.env.now #To know when they have been infected else: pass self.attrs['status'] = self.state['id'] return #Imitation effects if self.state['id'] == 0: aware_neighbors = self.get_neighboring_agents(state_id=1) for x in aware_neighbors: if x.time_awareness == (self.env.now-1): aware_neighbors_1_time_step.append(x) num_neighbors_aware = len(aware_neighbors_1_time_step) if random.random() < (settings.imitation_prob*num_neighbors_aware): self.state['id'] = 1 sentimentCorrelationNodeArray[self.id][self.env.now]=1 else: pass self.attrs['status'] = self.state['id'] return