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Settings models
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@ -13,8 +13,8 @@ class BassModel(BaseBehaviour):
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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.innovation_prob
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self.imitation_prob = environment.imitation_prob
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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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@ -21,21 +21,21 @@ class BigMarketModel(BaseBehaviour):
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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.enterprises
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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.enterprises)
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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.tweet_probability_enterprises[self.id]
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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.tweet_probability_users
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self.tweet_relevant_probability = environment.tweet_relevant_probability
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self.tweet_probability_about = environment.tweet_probability_about # List
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self.sentiment_about = environment.sentiment_about # List
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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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@ -13,8 +13,8 @@ class IndependentCascadeModel(BaseBehaviour):
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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.innovation_prob
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self.imitation_prob = environment.imitation_prob
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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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@ -24,8 +24,8 @@ class ControlModelM2(BaseBehaviour):
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"""
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# Init infected
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init_states[random.randint(0, settings.number_of_nodes-1)] = {'id':1}
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init_states[random.randint(0, settings.number_of_nodes-1)] = {'id':1}
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init_states[random.randint(0, settings.number_of_nodes-1)] = {'id': 1}
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init_states[random.randint(0, settings.number_of_nodes-1)] = {'id': 1}
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# Init beacons
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init_states[random.randint(0, settings.number_of_nodes-1)] = {'id': 4}
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@ -34,22 +34,23 @@ class ControlModelM2(BaseBehaviour):
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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.prob_neutral_making_denier,
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environment.standard_variance)
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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.prob_infect, environment.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.prob_cured_healing_infected,
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environment.standard_variance)
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self.prob_cured_vaccinate_neutral = np.random.normal(environment.prob_cured_vaccinate_neutral,
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environment.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.prob_vaccinated_healing_infected,
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environment.standard_variance)
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self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.prob_vaccinated_vaccinate_neutral,
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environment.standard_variance)
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self.prob_generate_anti_rumor = np.random.normal(environment.prob_generate_anti_rumor,
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environment.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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@ -29,22 +29,23 @@ class SpreadModelM2(BaseBehaviour):
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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.prob_neutral_making_denier,
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environment.standard_variance)
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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.prob_infect, environment.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.prob_cured_healing_infected,
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environment.standard_variance)
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self.prob_cured_vaccinate_neutral = np.random.normal(environment.prob_cured_vaccinate_neutral,
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environment.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.prob_vaccinated_healing_infected,
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environment.standard_variance)
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self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.prob_vaccinated_vaccinate_neutral,
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environment.standard_variance)
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self.prob_generate_anti_rumor = np.random.normal(environment.prob_generate_anti_rumor,
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environment.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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@ -32,20 +32,24 @@ class SISaModel(BaseBehaviour):
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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.neutral_discontent_spon_prob = np.random.normal(environment.neutral_discontent_spon_prob,
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environment.standard_variance)
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self.neutral_discontent_infected_prob = np.random.normal(environment.neutral_discontent_infected_prob,
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environment.standard_variance)
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self.neutral_content_spon_prob = np.random.normal(environment.neutral_content_spon_prob,
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environment.standard_variance)
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self.neutral_content_infected_prob = np.random.normal(environment.neutral_content_infected_prob,
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environment.standard_variance)
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self.neutral_discontent_spon_prob = np.random.normal(environment.environment_params['neutral_discontent_spon_prob'],
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environment.environment_params['standard_variance'])
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self.neutral_discontent_infected_prob = np.random.normal(environment.environment_params['neutral_discontent_infected_prob'],
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environment.environment_params['standard_variance'])
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self.neutral_content_spon_prob = np.random.normal(environment.environment_params['neutral_content_spon_prob'],
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environment.environment_params['standard_variance'])
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self.neutral_content_infected_prob = np.random.normal(environment.environment_params['neutral_content_infected_prob'],
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environment.environment_params['standard_variance'])
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self.discontent_neutral = np.random.normal(environment.discontent_neutral, environment.standard_variance)
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self.discontent_content = np.random.normal(environment.discontent_content, environment.variance_d_c)
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self.discontent_neutral = np.random.normal(environment.environment_params['discontent_neutral'],
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environment.environment_params['standard_variance'])
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self.discontent_content = np.random.normal(environment.environment_params['discontent_content'],
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environment.environment_params['variance_d_c'])
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self.content_discontent = np.random.normal(environment.content_discontent, environment.variance_c_d)
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self.content_neutral = np.random.normal(environment.content_neutral, environment.standard_variance)
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self.content_discontent = np.random.normal(environment.environment_params['content_discontent'],
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environment.environment_params['variance_c_d'])
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self.content_neutral = np.random.normal(environment.environment_params['content_neutral'],
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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:
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@ -19,11 +19,11 @@ class SentimentCorrelationModel(BaseBehaviour):
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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 = environment.outside_effects_prob
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self.anger_prob = environment.anger_prob
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self.joy_prob = environment.joy_prob
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self.sadness_prob = environment.sadness_prob
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self.disgust_prob = environment.disgust_prob
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self.outside_effects_prob = environment.environment_params['outside_effects_prob']
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self.anger_prob = environment.environment_params['anger_prob']
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self.joy_prob = environment.environment_params['joy_prob']
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self.sadness_prob = environment.environment_params['sadness_prob']
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self.disgust_prob = environment.environment_params['disgust_prob']
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self.time_awareness = []
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for i in range(4): # In this model we have 4 sentiments
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self.time_awareness.append(0) # 0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
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86
settings.py
86
settings.py
@ -7,56 +7,58 @@ max_time = 50
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num_trials = 1
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timeout = 2
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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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environment_params = {
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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 = 0.001
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imitation_prob = 0.005
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# Bass model
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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 = 0.06
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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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# Sentiment Correlation model
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'outside_effects_prob': 0.2,
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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
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tweet_relevant_probability = 0.25
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tweet_probability_about = [0.15, 0.15, 0.15]
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sentiment_about = [0, 0, 0] # Default values
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## Enterprises
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tweet_probability_enterprises = [0.3, 0.3, 0.3]
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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,
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'tweet_relevant_probability': 0.25,
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'tweet_probability_about': [0.15, 0.15, 0.15],
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'sentiment_about': [0, 0, 0], # Default values
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## Enterprises
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'tweet_probability_enterprises': [0.3, 0.3, 0.3],
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# SISa
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neutral_discontent_spon_prob = 0.04
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neutral_discontent_infected_prob = 0.04
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neutral_content_spon_prob = 0.18
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neutral_content_infected_prob = 0.02
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# SISa
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'neutral_discontent_spon_prob': 0.04,
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'neutral_discontent_infected_prob': 0.04,
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'neutral_content_spon_prob': 0.18,
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'neutral_content_infected_prob': 0.02,
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discontent_neutral = 0.13
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discontent_content = 0.07
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variance_d_c = 0.02
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'discontent_neutral': 0.13,
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'discontent_content': 0.07,
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'variance_d_c': 0.02,
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content_discontent = 0.009
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variance_c_d = 0.003
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content_neutral = 0.088
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'content_discontent': 0.009,
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'variance_c_d': 0.003,
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'content_neutral': 0.088,
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standard_variance = 0.055
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'standard_variance': 0.055,
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# Spread Model M2 and Control Model M2
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prob_neutral_making_denier = 0.035
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# Spread Model M2 and Control Model M2
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'prob_neutral_making_denier': 0.035,
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prob_infect = 0.075
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'prob_infect': 0.075,
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prob_cured_healing_infected = 0.035
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prob_cured_vaccinate_neutral = 0.035
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'prob_cured_healing_infected': 0.035,
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'prob_cured_vaccinate_neutral': 0.035,
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prob_vaccinated_healing_infected = 0.035
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prob_vaccinated_vaccinate_neutral = 0.035
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prob_generate_anti_rumor = 0.035
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'prob_vaccinated_healing_infected': 0.035,
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'prob_vaccinated_vaccinate_neutral': 0.035,
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'prob_generate_anti_rumor': 0.035
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}
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31
soil.py
31
soil.py
@ -26,8 +26,8 @@ if settings.network_type == 2:
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# Simulation #
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##############
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sim = NetworkSimulation(topology=G, states=init_states, agent_type=ControlModelM2,
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max_time=settings.max_time, num_trials=settings.num_trials, logging_interval=1.0)
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sim = NetworkSimulation(topology=G, states=init_states, agent_type=ControlModelM2, max_time=settings.max_time,
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num_trials=settings.num_trials, logging_interval=1.0, **settings.environment_params)
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sim.run_simulation()
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@ -49,14 +49,14 @@ for time in range(0, settings.max_time):
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value_cured = 0
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value_vaccinated = 0
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real_time = time * settings.timeout
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activity= False
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activity = False
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for x in range(0, settings.number_of_nodes):
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if attribute_plot in models.networkStatus["agent_%s" % x]:
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if real_time in models.networkStatus["agent_%s" % x][attribute_plot]:
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if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 1: ## Infected
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value_infectados += 1
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activity = True
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if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 0: ## Neutral
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if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 0: ## Neutral
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value_neutral += 1
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activity = True
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if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 2: ## Cured
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@ -72,12 +72,12 @@ for time in range(0, settings.max_time):
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neutral_values.append(value_neutral)
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cured_values.append(value_cured)
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vaccinated_values.append(value_vaccinated)
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activity=False
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activity = False
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infected_line = plt.plot(x_values,infected_values,label='Infected')
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neutral_line = plt.plot(x_values,neutral_values, label='Neutral')
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cured_line = plt.plot(x_values,cured_values, label='Cured')
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vaccinated_line = plt.plot(x_values,vaccinated_values, label='Vaccinated')
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infected_line = plt.plot(x_values, infected_values, label='Infected')
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neutral_line = plt.plot(x_values, neutral_values, label='Neutral')
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cured_line = plt.plot(x_values, cured_values, label='Cured')
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vaccinated_line = plt.plot(x_values, vaccinated_values, label='Vaccinated')
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plt.legend()
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plt.savefig('control_model.png')
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# plt.show()
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@ -88,12 +88,12 @@ plt.savefig('control_model.png')
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#################
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for x in range(0, settings.number_of_nodes):
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for attribute in models.networkStatus["agent_%s"%x]:
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emotionStatusAux=[]
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for t_step in models.networkStatus["agent_%s"%x][attribute]:
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for attribute in models.networkStatus["agent_%s" % x]:
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emotionStatusAux = []
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for t_step in models.networkStatus["agent_%s" % x][attribute]:
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prec = 2
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output = math.floor(models.networkStatus["agent_%s"%x][attribute][t_step] * (10 ** prec)) / (10 ** prec) # 2 decimals
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emotionStatusAux.append((output,t_step,None))
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output = math.floor(models.networkStatus["agent_%s" % x][attribute][t_step] * (10 ** prec)) / (10 ** prec) # 2 decimals
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emotionStatusAux.append((output, t_step,None))
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attributes = {}
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attributes[attribute] = emotionStatusAux
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G.add_node(x, attributes)
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@ -104,5 +104,4 @@ print("Done!")
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with open('data.txt', 'w') as outfile:
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json.dump(models.networkStatus, outfile, sort_keys=True, indent=4, separators=(',', ': '))
|
||||
|
||||
nx.write_gexf(G,"test.gexf", version="1.2draft")
|
||||
|
||||
nx.write_gexf(G, "test.gexf", version="1.2draft")
|
||||
|
Loading…
Reference in New Issue
Block a user