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SISa model implemented

This commit is contained in:
JesusMSM 2016-04-18 12:23:24 +02:00
parent 77f2f1c91b
commit 9aebacd4c7
6 changed files with 2818 additions and 16846 deletions

9764
data.txt

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@ -1,4 +1,5 @@
from nxsim import BaseNetworkAgent from nxsim import BaseNetworkAgent
import numpy as np
import random import random
import settings import settings
@ -55,6 +56,73 @@ class ComportamientoBase(BaseNetworkAgent):
final[a][stamp] = attrs[a] final[a][stamp] = attrs[a]
return final return final
class SISaModel(ComportamientoBase):
def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
self.neutral_discontent_spon_prob = np.random.normal(settings.neutral_discontent_spon_prob, settings.standard_variance)
self.neutral_discontent_infected_prob = np.random.normal(settings.neutral_discontent_infected_prob,settings.standard_variance)
self.neutral_content_spon_prob = np.random.normal(settings.neutral_content_spon_prob,settings.standard_variance)
self.neutral_content_infected_prob = np.random.normal(settings.neutral_content_infected_prob,settings.standard_variance)
self.discontent_neutral = np.random.normal(settings.discontent_neutral,settings.standard_variance)
self.discontent_content = np.random.normal(settings.discontent_content,settings.variance_d_c)
self.content_discontent = np.random.normal(settings.content_discontent,settings.variance_c_d)
self.content_neutral = np.random.normal(settings.content_neutral,settings.standard_variance)
def step(self, now):
if self.state['id'] == 0:
self.neutral_behaviour()
if self.state['id'] == 1:
self.discontent_behaviour()
if self.state['id'] == 2:
self.content_behaviour()
self.attrs['status'] = self.state['id']
super().step(now)
def neutral_behaviour(self):
#Spontaneus effects
if random.random() < self.neutral_discontent_spon_prob:
self.state['id'] = 1
if random.random() < self.neutral_content_spon_prob:
self.state['id'] = 2
#Infected
discontent_neighbors = self.get_neighboring_agents(state_id=1)
if random.random() < len(discontent_neighbors)*self.neutral_discontent_infected_prob:
self.state['id'] = 1
content_neighbors = self.get_neighboring_agents(state_id=2)
if random.random() < len(content_neighbors)*self.neutral_content_infected_prob:
self.state['id'] = 2
def discontent_behaviour(self):
#Healing
if random.random() < self.discontent_neutral:
self.state['id'] = 0
#Superinfected
content_neighbors = self.get_neighboring_agents(state_id=2)
if random.random() < len(content_neighbors)*self.discontent_content:
self.state['id'] = 2
def content_behaviour(self):
#Healing
if random.random() < self.content_neutral:
self.state['id'] = 0
#Superinfected
discontent_neighbors = self.get_neighboring_agents(state_id=1)
if random.random() < len(discontent_neighbors)*self.content_discontent:
self.state['id'] = 1
class BigMarketModel(ComportamientoBase): class BigMarketModel(ComportamientoBase):
def __init__(self, environment=None, agent_id=0, state=()): def __init__(self, environment=None, agent_id=0, state=()):
@ -81,6 +149,8 @@ class BigMarketModel(ComportamientoBase):
self.enterpriseBehaviour() self.enterpriseBehaviour()
else: # Usuario else: # Usuario
self.userBehaviour() self.userBehaviour()
for i in range(self.number_of_enterprises): # Para que nunca este a 0 si no ha habido cambios(logs)
self.attrs['sentiment_enterprise_%s'% self.enterprises[i]] = self.sentiment_about[i]
super().step(now) super().step(now)

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@ -20,12 +20,23 @@ def init():
global sentiment_about global sentiment_about
global tweet_probability_enterprises global tweet_probability_enterprises
global 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
network_type=1 network_type=1
number_of_nodes=50 number_of_nodes=50
max_time=500 max_time=500
num_trials=1 num_trials=1
timeout=1 timeout=20
#Zombie model #Zombie model
bite_prob=0.01 # 0-1 bite_prob=0.01 # 0-1
@ -53,3 +64,19 @@ def init():
##Enterprises ##Enterprises
tweet_probability_enterprises = [0.3, 0.3, 0.3] 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 = 0.02

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21
soil.py
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@ -1,6 +1,7 @@
from models import * from models import *
from nxsim import NetworkSimulation from nxsim import NetworkSimulation
from nxsim import BaseLoggingAgent import numpy
from matplotlib import pyplot as plt
import networkx as nx import networkx as nx
import settings import settings
import models import models
@ -26,7 +27,7 @@ if settings.network_type == 2:
# Simulation # # Simulation #
############## ##############
sim = NetworkSimulation(topology=G, states=init_states, agent_type=BigMarketModel, sim = NetworkSimulation(topology=G, states=init_states, agent_type=SISaModel,
max_time=settings.max_time, num_trials=settings.num_trials, logging_interval=1.0) max_time=settings.max_time, num_trials=settings.num_trials, logging_interval=1.0)
@ -35,10 +36,22 @@ sim.run_simulation()
########### ###########
# Results # # Results #
########### ###########
x_values = []
y_values = []
for time in range(0, settings.max_time):
value = settings.sentiment_about[0]
real_time = time * settings.timeout
for x in range(0, settings.number_of_nodes):
if "sentiment_enterprise_BBVA" in models.networkStatus["agente_%s" % x]:
if real_time in models.networkStatus["agente_%s" % x]["sentiment_enterprise_BBVA"]:
value += models.networkStatus["agente_%s" % x]["sentiment_enterprise_BBVA"][real_time]
trial = BaseLoggingAgent.open_trial_state_history(dir_path='sim_01', trial_id=0) x_values.append(real_time)
status_census = [sum([1 for node_id, state in g.items() if state['id'] == 1]) for t,g in trial.items()] y_values.append(value)
plt.plot(x_values,y_values)
#plt.show()
################# #################

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test.gexf

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