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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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2
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):

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

24
models/BigMarketModel/BigMarketModel.py
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@ -1,9 +1,6 @@
import settings
import random
from ..BaseBehaviour import *
settings.init()
class BigMarketModel(BaseBehaviour):
"""
@ -22,28 +19,27 @@ 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
self.state['id']=self.id
self.type="Enterprise"
self.tweet_probability = settings.tweet_probability_enterprises[self.id]
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()
@ -55,7 +51,7 @@ class BigMarketModel(BaseBehaviour):
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
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
@ -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
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@ -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
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@ -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
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@ -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
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@ -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

139
settings.py
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@ -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

104
settings_org.py Normal file
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@ -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

20
soil.py
View File

@ -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