From f29f5fa5bfd307111469fd1bf651e9154e62c2a1 Mon Sep 17 00:00:00 2001 From: Tasio Mendez Date: Fri, 21 Apr 2017 13:55:42 +0200 Subject: [PATCH] Settings modules --- models/BaseBehaviour/BaseBehaviour.py | 6 +- models/BassModel/BassModel.py | 17 +-- models/BigMarketModel/BigMarketModel.py | 40 +++-- models/BigMarketModel/SISaModel.py | 26 ++-- .../IndependentCascadeModel.py | 23 ++- models/ModelM2/ControlModelM2.py | 24 +-- models/ModelM2/SpreadModelM2.py | 28 ++-- .../SentimentCorrelationModel.py | 45 +++--- settings.py | 139 ++++++------------ settings_org.py | 104 +++++++++++++ soil.py | 20 +-- 11 files changed, 262 insertions(+), 210 deletions(-) create mode 100644 settings_org.py diff --git a/models/BaseBehaviour/BaseBehaviour.py b/models/BaseBehaviour/BaseBehaviour.py index b0d7327..820658a 100644 --- a/models/BaseBehaviour/BaseBehaviour.py +++ b/models/BaseBehaviour/BaseBehaviour.py @@ -2,8 +2,6 @@ import settings from nxsim import BaseNetworkAgent from .. import networkStatus -settings.init() - class BaseBehaviour(BaseNetworkAgent): @@ -35,6 +33,6 @@ class BaseBehaviour(BaseNetworkAgent): for stamp, attrs in self._attrs.items(): for a in attrs: if a not in final: - final[a] = {} + final[a] = {} final[a][stamp] = attrs[a] - return final \ No newline at end of file + return final diff --git a/models/BassModel/BassModel.py b/models/BassModel/BassModel.py index d252613..c7467e6 100644 --- a/models/BassModel/BassModel.py +++ b/models/BassModel/BassModel.py @@ -1,10 +1,7 @@ -import settings import random from ..BaseBehaviour import * from .. import sentimentCorrelationNodeArray -settings.init() - class BassModel(BaseBehaviour): """ @@ -16,9 +13,9 @@ 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 + self.innovation_prob = environment.innovation_prob + self.imitation_prob = environment.imitation_prob + sentimentCorrelationNodeArray[self.id][self.env.now] = 0 def step(self, now): self.behaviour() @@ -26,10 +23,10 @@ class BassModel(BaseBehaviour): def behaviour(self): # Outside effects - if random.random() < settings.innovation_prob: + if random.random() < self.innovation_prob: if self.state['id'] == 0: self.state['id'] = 1 - sentimentCorrelationNodeArray[self.id][self.env.now]=1 + sentimentCorrelationNodeArray[self.id][self.env.now] = 1 else: pass @@ -40,9 +37,9 @@ class BassModel(BaseBehaviour): 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): + if random.random() < (self.imitation_prob*num_neighbors_aware): self.state['id'] = 1 - sentimentCorrelationNodeArray[self.id][self.env.now]=1 + sentimentCorrelationNodeArray[self.id][self.env.now] = 1 else: pass diff --git a/models/BigMarketModel/BigMarketModel.py b/models/BigMarketModel/BigMarketModel.py index 7d32db1..0e46a59 100644 --- a/models/BigMarketModel/BigMarketModel.py +++ b/models/BigMarketModel/BigMarketModel.py @@ -1,9 +1,6 @@ -import settings import random from ..BaseBehaviour import * -settings.init() - class BigMarketModel(BaseBehaviour): """ @@ -22,45 +19,44 @@ class BigMarketModel(BaseBehaviour): sentiment_about [Array] """ - def __init__(self, environment=None, agent_id=0, state=()): super().__init__(environment=environment, agent_id=agent_id, state=state) - self.enterprises = settings.enterprises + self.enterprises = environment.enterprises self.type = "" - self.number_of_enterprises = len(settings.enterprises) + self.number_of_enterprises = len(environment.enterprises) - if self.id < self.number_of_enterprises: # 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.tweet_probability = environment.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 + self.tweet_probability = environment.tweet_probability_users + self.tweet_relevant_probability = environment.tweet_relevant_probability + self.tweet_probability_about = environment.tweet_probability_about # List + self.sentiment_about = environment.sentiment_about # List def step(self, now): - if(self.id < self.number_of_enterprises): # Enterprise + if self.id < self.number_of_enterprises: # Enterprise 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) + 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 + 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 + x.sentiment_about[self.id] += 0.1 # Increments for enterprise else: - x.sentiment_about[self.id] -= 0.1 # Decrements for enterprise + x.sentiment_about[self.id] -= 0.1 # Decrements for enterprise # Establecemos limites if x.sentiment_about[self.id] > 1: @@ -72,8 +68,8 @@ class BigMarketModel(BaseBehaviour): def userBehaviour(self): - if random.random() < self.tweet_probability: # Tweets - if random.random() < self.tweet_relevant_probability: # Tweets something relevant + 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() @@ -90,7 +86,7 @@ class BigMarketModel(BaseBehaviour): self.userTweets("positive",i) def userTweets(self,sentiment,enterprise): - aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) #Nodes neighbours users + 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 diff --git a/models/BigMarketModel/SISaModel.py b/models/BigMarketModel/SISaModel.py index 5aeee8c..e432c02 100644 --- a/models/BigMarketModel/SISaModel.py +++ b/models/BigMarketModel/SISaModel.py @@ -1,10 +1,7 @@ -import settings import random import numpy as np from ..BaseBehaviour import * -settings.init() - class SISaModel(BaseBehaviour): """ @@ -35,19 +32,20 @@ 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.neutral_discontent_spon_prob = np.random.normal(environment.neutral_discontent_spon_prob, + environment.standard_variance) + self.neutral_discontent_infected_prob = np.random.normal(environment.neutral_discontent_infected_prob, + environment.standard_variance) + self.neutral_content_spon_prob = np.random.normal(environment.neutral_content_spon_prob, + environment.standard_variance) + self.neutral_content_infected_prob = np.random.normal(environment.neutral_content_infected_prob, + environment.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.discontent_neutral = np.random.normal(environment.discontent_neutral, environment.standard_variance) + self.discontent_content = np.random.normal(environment.discontent_content, environment.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) + self.content_discontent = np.random.normal(environment.content_discontent, environment.variance_c_d) + self.content_neutral = np.random.normal(environment.content_neutral, environment.standard_variance) def step(self, now): if self.state['id'] == 0: diff --git a/models/IndependentCascadeModel/IndependentCascadeModel.py b/models/IndependentCascadeModel/IndependentCascadeModel.py index 8a70c4c..f8dd87d 100644 --- a/models/IndependentCascadeModel/IndependentCascadeModel.py +++ b/models/IndependentCascadeModel/IndependentCascadeModel.py @@ -1,10 +1,7 @@ -import settings import random from ..BaseBehaviour import * from .. import sentimentCorrelationNodeArray -settings.init() - class IndependentCascadeModel(BaseBehaviour): """ @@ -16,23 +13,23 @@ 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.innovation_prob = environment.innovation_prob + self.imitation_prob = environment.imitation_prob self.time_awareness = 0 - sentimentCorrelationNodeArray[self.id][self.env.now]=0 + sentimentCorrelationNodeArray[self.id][self.env.now] = 0 - def step(self,now): + def step(self, now): self.behaviour() super().step(now) def behaviour(self): - aware_neighbors_1_time_step=[] + aware_neighbors_1_time_step = [] # Outside effects - if random.random() < settings.innovation_prob: + if random.random() < self.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 + sentimentCorrelationNodeArray[self.id][self.env.now] = 1 + self.time_awareness = self.env.now # To know when they have been infected else: pass @@ -46,9 +43,9 @@ class IndependentCascadeModel(BaseBehaviour): 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): + if random.random() < (self.imitation_prob*num_neighbors_aware): self.state['id'] = 1 - sentimentCorrelationNodeArray[self.id][self.env.now]=1 + sentimentCorrelationNodeArray[self.id][self.env.now] = 1 else: pass diff --git a/models/ModelM2/ControlModelM2.py b/models/ModelM2/ControlModelM2.py index 54170a5..cdf9b49 100644 --- a/models/ModelM2/ControlModelM2.py +++ b/models/ModelM2/ControlModelM2.py @@ -4,8 +4,6 @@ import numpy as np from ..BaseBehaviour import * from .. import init_states -settings.init() - class ControlModelM2(BaseBehaviour): """ @@ -36,16 +34,22 @@ class ControlModelM2(BaseBehaviour): 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_neutral_making_denier = np.random.normal(environment.prob_neutral_making_denier, + environment.standard_variance) - self.prob_infect = np.random.normal(settings.prob_infect, settings.standard_variance) + self.prob_infect = np.random.normal(environment.prob_infect, environment.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_cured_healing_infected = np.random.normal(environment.prob_cured_healing_infected, + environment.standard_variance) + self.prob_cured_vaccinate_neutral = np.random.normal(environment.prob_cured_vaccinate_neutral, + environment.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) + self.prob_vaccinated_healing_infected = np.random.normal(environment.prob_vaccinated_healing_infected, + environment.standard_variance) + self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.prob_vaccinated_vaccinate_neutral, + environment.standard_variance) + self.prob_generate_anti_rumor = np.random.normal(environment.prob_generate_anti_rumor, + environment.standard_variance) def step(self, now): @@ -69,7 +73,7 @@ class ControlModelM2(BaseBehaviour): # Infected infected_neighbors = self.get_neighboring_agents(state_id=1) - if len(infected_neighbors)>0: + if len(infected_neighbors) > 0: if random.random() < self.prob_neutral_making_denier: self.state['id'] = 3 # Vaccinated making denier diff --git a/models/ModelM2/SpreadModelM2.py b/models/ModelM2/SpreadModelM2.py index aac29a2..ed4f16e 100644 --- a/models/ModelM2/SpreadModelM2.py +++ b/models/ModelM2/SpreadModelM2.py @@ -4,8 +4,6 @@ import numpy as np from ..BaseBehaviour import * from .. import init_states -settings.init() - class SpreadModelM2(BaseBehaviour): """ @@ -25,22 +23,28 @@ class SpreadModelM2(BaseBehaviour): prob_generate_anti_rumor """ - init_states[random.randint(0, settings.number_of_nodes)] = {'id':1} - init_states[random.randint(0, settings.number_of_nodes)] = {'id':1} + 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_neutral_making_denier = np.random.normal(environment.prob_neutral_making_denier, + environment.standard_variance) - self.prob_infect = np.random.normal(settings.prob_infect, settings.standard_variance) + self.prob_infect = np.random.normal(environment.prob_infect, environment.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_cured_healing_infected = np.random.normal(environment.prob_cured_healing_infected, + environment.standard_variance) + self.prob_cured_vaccinate_neutral = np.random.normal(environment.prob_cured_vaccinate_neutral, + environment.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) + self.prob_vaccinated_healing_infected = np.random.normal(environment.prob_vaccinated_healing_infected, + environment.standard_variance) + self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.prob_vaccinated_vaccinate_neutral, + environment.standard_variance) + self.prob_generate_anti_rumor = np.random.normal(environment.prob_generate_anti_rumor, + environment.standard_variance) def step(self, now): @@ -60,7 +64,7 @@ class SpreadModelM2(BaseBehaviour): # Infected infected_neighbors = self.get_neighboring_agents(state_id=1) - if len(infected_neighbors)>0: + if len(infected_neighbors) > 0: if random.random() < self.prob_neutral_making_denier: self.state['id'] = 3 # Vaccinated making denier diff --git a/models/SentimentCorrelationModel/SentimentCorrelationModel.py b/models/SentimentCorrelationModel/SentimentCorrelationModel.py index a4b5021..8eae6a4 100644 --- a/models/SentimentCorrelationModel/SentimentCorrelationModel.py +++ b/models/SentimentCorrelationModel/SentimentCorrelationModel.py @@ -1,10 +1,7 @@ -import settings import random from ..BaseBehaviour import * from .. import sentimentCorrelationNodeArray -settings.init() - class SentimentCorrelationModel(BaseBehaviour): """ @@ -22,15 +19,15 @@ 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 + self.outside_effects_prob = environment.outside_effects_prob + self.anger_prob = environment.anger_prob + self.joy_prob = environment.joy_prob + self.sadness_prob = environment.sadness_prob + self.disgust_prob = environment.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() @@ -38,10 +35,10 @@ class SentimentCorrelationModel(BaseBehaviour): 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_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: @@ -67,18 +64,18 @@ class SentimentCorrelationModel(BaseBehaviour): 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 + 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(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 diff --git a/settings.py b/settings.py index 12c01ea..f36cc40 100644 --- a/settings.py +++ b/settings.py @@ -1,103 +1,62 @@ -# settings.py -def init(): - global number_of_nodes - global max_time - global num_trials - global bite_prob - global network_type - global heal_prob - global innovation_prob - global imitation_prob - global timeout - global outside_effects_prob - global anger_prob - global joy_prob - global sadness_prob - global disgust_prob - global tweet_probability_users - global tweet_relevant_probability - global tweet_probability_about - global sentiment_about - global tweet_probability_enterprises - global enterprises - global neutral_discontent_spon_prob - global neutral_discontent_infected_prob - global neutral_content_spon_prob - global neutral_content_infected_prob - global discontent_content - global discontent_neutral - global content_discontent - global content_neutral - global variance_d_c - global variance_c_d - global standard_variance - global prob_neutral_making_denier - global prob_infect - global prob_cured_healing_infected - global prob_cured_vaccinate_neutral - global prob_vaccinated_healing_infected - global prob_vaccinated_vaccinate_neutral - global prob_generate_anti_rumor +# General configuration - network_type=1 - number_of_nodes=1000 - max_time=50 - num_trials=1 - timeout=2 +# Network settings +network_type = 1 +number_of_nodes = 1000 +max_time = 50 +num_trials = 1 +timeout = 2 - #Zombie model - bite_prob=0.01 # 0-1 - heal_prob=0.01 # 0-1 +# Zombie model +bite_prob = 0.01 # 0-1 +heal_prob = 0.01 # 0-1 - #Bass model - innovation_prob=0.001 - imitation_prob=0.005 +# Bass model +innovation_prob = 0.001 +imitation_prob = 0.005 - #Sentiment Correlation model - outside_effects_prob = 0.2 - anger_prob = 0.06 - joy_prob = 0.05 - sadness_prob = 0.02 - disgust_prob = 0.02 +# Sentiment Correlation model +outside_effects_prob = 0.2 +anger_prob = 0.06 +joy_prob = 0.05 +sadness_prob = 0.02 +disgust_prob = 0.02 - #Big Market model - ##Names - enterprises = ["BBVA","Santander", "Bankia"] - ##Users - tweet_probability_users = 0.44 - tweet_relevant_probability = 0.25 - tweet_probability_about = [0.15, 0.15, 0.15] - sentiment_about = [0, 0, 0] #Default values - ##Enterprises - tweet_probability_enterprises = [0.3, 0.3, 0.3] +# Big Market model +## Names +enterprises = ["BBVA", "Santander", "Bankia"] +## Users +tweet_probability_users = 0.44 +tweet_relevant_probability = 0.25 +tweet_probability_about = [0.15, 0.15, 0.15] +sentiment_about = [0, 0, 0] # Default values +## Enterprises +tweet_probability_enterprises = [0.3, 0.3, 0.3] - #SISa - neutral_discontent_spon_prob = 0.04 - neutral_discontent_infected_prob = 0.04 - neutral_content_spon_prob = 0.18 - neutral_content_infected_prob = 0.02 +# SISa +neutral_discontent_spon_prob = 0.04 +neutral_discontent_infected_prob = 0.04 +neutral_content_spon_prob = 0.18 +neutral_content_infected_prob = 0.02 - discontent_neutral = 0.13 - discontent_content = 0.07 - variance_d_c = 0.02 +discontent_neutral = 0.13 +discontent_content = 0.07 +variance_d_c = 0.02 - content_discontent = 0.009 - variance_c_d = 0.003 - content_neutral = 0.088 +content_discontent = 0.009 +variance_c_d = 0.003 +content_neutral = 0.088 - standard_variance = 0.055 +standard_variance = 0.055 - #Spread Model M2 and Control Model M2 - prob_neutral_making_denier = 0.035 - - prob_infect = 0.075 - - prob_cured_healing_infected = 0.035 - prob_cured_vaccinate_neutral = 0.035 - - prob_vaccinated_healing_infected = 0.035 - prob_vaccinated_vaccinate_neutral = 0.035 - prob_generate_anti_rumor = 0.035 +# Spread Model M2 and Control Model M2 +prob_neutral_making_denier = 0.035 +prob_infect = 0.075 +prob_cured_healing_infected = 0.035 +prob_cured_vaccinate_neutral = 0.035 +prob_vaccinated_healing_infected = 0.035 +prob_vaccinated_vaccinate_neutral = 0.035 +prob_generate_anti_rumor = 0.035 diff --git a/settings_org.py b/settings_org.py new file mode 100644 index 0000000..2927aea --- /dev/null +++ b/settings_org.py @@ -0,0 +1,104 @@ +# settings.py +def init(): + global number_of_nodes + global max_time + global num_trials + global bite_prob + global timeout + global network_type + global heal_prob + global innovation_prob + global imitation_prob + global outside_effects_prob + global anger_prob + global joy_prob + global sadness_prob + global disgust_prob + global tweet_probability_users + global tweet_relevant_probability + global tweet_probability_about + global sentiment_about + global tweet_probability_enterprises + global enterprises + global neutral_discontent_spon_prob + global neutral_discontent_infected_prob + global neutral_content_spon_prob + global neutral_content_infected_prob + global discontent_content + global discontent_neutral + global content_discontent + global content_neutral + global variance_d_c + global variance_c_d + global standard_variance + global prob_neutral_making_denier + global prob_infect + global prob_cured_healing_infected + global prob_cured_vaccinate_neutral + global prob_vaccinated_healing_infected + global prob_vaccinated_vaccinate_neutral + global prob_generate_anti_rumor + + # Network settings + network_type = 1 + number_of_nodes = 1000 + max_time = 50 + num_trials = 1 + timeout = 2 + + # Zombie model + bite_prob = 0.01 # 0-1 + heal_prob = 0.01 # 0-1 + + # Bass model + innovation_prob = 0.001 + imitation_prob = 0.005 + + # Sentiment Correlation model + outside_effects_prob = 0.2 + anger_prob = 0.06 + joy_prob = 0.05 + sadness_prob = 0.02 + disgust_prob = 0.02 + + # Big Market model + ## Names + enterprises = ["BBVA", "Santander", "Bankia"] + ## Users + tweet_probability_users = 0.44 + tweet_relevant_probability = 0.25 + tweet_probability_about = [0.15, 0.15, 0.15] + sentiment_about = [0, 0, 0] # Default values + ## Enterprises + tweet_probability_enterprises = [0.3, 0.3, 0.3] + + # SISa + neutral_discontent_spon_prob = 0.04 + neutral_discontent_infected_prob = 0.04 + neutral_content_spon_prob = 0.18 + neutral_content_infected_prob = 0.02 + + discontent_neutral = 0.13 + discontent_content = 0.07 + variance_d_c = 0.02 + + content_discontent = 0.009 + variance_c_d = 0.003 + content_neutral = 0.088 + + standard_variance = 0.055 + + # Spread Model M2 and Control Model M2 + prob_neutral_making_denier = 0.035 + + prob_infect = 0.075 + + prob_cured_healing_infected = 0.035 + prob_cured_vaccinate_neutral = 0.035 + + prob_vaccinated_healing_infected = 0.035 + prob_vaccinated_vaccinate_neutral = 0.035 + prob_generate_anti_rumor = 0.035 + + + diff --git a/soil.py b/soil.py index 007ce2d..5a8ec4c 100644 --- a/soil.py +++ b/soil.py @@ -8,7 +8,6 @@ import models import math import json -settings.init() # Loads all the data from settings #################### # Network creation # @@ -17,11 +16,12 @@ settings.init() # Loads all the data from settings if settings.network_type == 0: G = nx.complete_graph(settings.number_of_nodes) if settings.network_type == 1: - G = nx.barabasi_albert_graph(settings.number_of_nodes,10) + G = nx.barabasi_albert_graph(settings.number_of_nodes, 10) if settings.network_type == 2: G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None) # More types of networks can be added here + ############## # Simulation # ############## @@ -29,12 +29,13 @@ if settings.network_type == 2: sim = NetworkSimulation(topology=G, states=init_states, agent_type=ControlModelM2, max_time=settings.max_time, num_trials=settings.num_trials, logging_interval=1.0) - sim.run_simulation() + ########### # Results # ########### + x_values = [] infected_values = [] neutral_values = [] @@ -52,16 +53,16 @@ for time in range(0, settings.max_time): for x in range(0, settings.number_of_nodes): if attribute_plot in models.networkStatus["agent_%s" % x]: if real_time in models.networkStatus["agent_%s" % x][attribute_plot]: - if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 1: ##Infected + if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 1: ## Infected value_infectados += 1 activity = True - if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 0: ##Neutral + if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 0: ## Neutral value_neutral += 1 activity = True - if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 2: ##Cured + if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 2: ## Cured value_cured += 1 activity = True - if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 3: ##Vaccinated + if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 3: ## Vaccinated value_vaccinated += 1 activity = True @@ -79,20 +80,19 @@ cured_line = plt.plot(x_values,cured_values, label='Cured') vaccinated_line = plt.plot(x_values,vaccinated_values, label='Vaccinated') plt.legend() plt.savefig('control_model.png') -#plt.show() +# plt.show() ################# # Visualization # ################# - for x in range(0, settings.number_of_nodes): for attribute in models.networkStatus["agent_%s"%x]: emotionStatusAux=[] for t_step in models.networkStatus["agent_%s"%x][attribute]: prec = 2 - output = math.floor(models.networkStatus["agent_%s"%x][attribute][t_step] * (10 ** prec)) / (10 ** prec) #2 decimals + output = math.floor(models.networkStatus["agent_%s"%x][attribute][t_step] * (10 ** prec)) / (10 ** prec) # 2 decimals emotionStatusAux.append((output,t_step,None)) attributes = {} attributes[attribute] = emotionStatusAux