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Code Issues Releases Wiki Activity

Settings modules

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This commit is contained in:
Tasio Mendez
2017-04-21 13:55:42 +02:00
parent dd4ce15a3d
commit f29f5fa5bf
11 changed files with 262 additions and 210 deletions
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6
models/BaseBehaviour/BaseBehaviour.py
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@@ -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
return final

17
models/BassModel/BassModel.py
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@@ -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

40
models/BigMarketModel/BigMarketModel.py
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@@ -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

26
models/BigMarketModel/SISaModel.py
View File

@@ -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:

23
models/IndependentCascadeModel/IndependentCascadeModel.py
View File

@@ -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

24
models/ModelM2/ControlModelM2.py
View File

@@ -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

28
models/ModelM2/SpreadModelM2.py
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@@ -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

45
models/SentimentCorrelationModel/SentimentCorrelationModel.py
View File

@@ -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(num<outside_effects_prob):
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
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']
@@ -95,13 +92,11 @@ class SentimentCorrelationModel(BaseBehaviour):
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