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mirror of https://github.com/gsi-upm/soil synced 2024-11-21 18:52:28 +00:00

Codigo ordenado

This commit is contained in:
JesusMSM 2015-12-04 12:32:24 +01:00
parent fe6942ce0d
commit 133730ad97
7 changed files with 13686 additions and 132 deletions

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sim_01/log.0.state.pickled Normal file

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soil.py
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@ -8,8 +8,11 @@ import numpy as np
import networkx as nx
import settings
settings.init() # Loads all the data from settings
settings.init()
####################
# Network creation #
####################
if settings.network_type == 0:
G = nx.complete_graph(settings.number_of_nodes)
@ -17,26 +20,28 @@ if settings.network_type == 1:
G = nx.barabasi_albert_graph(settings.number_of_nodes,3)
if settings.network_type == 2:
G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)
# More types of networks can be added here
myList=[]
networkStatus=[]
##############################
# Variables initializitation #
##############################
myList=[] # List just for debugging
networkStatus=[] # This list will contain the status of every node of the network
for x in range(0, settings.number_of_nodes):
networkStatus.append({'id':x})
# Initialize agent states. Let's assume everyone is normal.
init_states = [{'id': 0, } for _ in range(settings.number_of_nodes)] # add keys as as necessary, but "id" must always refer to that state category
# Seed a zombie, just for zombie model
#init_states[5] = {'id': 1}
#init_states[3] = {'id': 1}
# # Just like subclassing a process in SimPy
# class MyAgent(BaseNetworkAgent):
# def __init__(self, environment=None, agent_id=0, state=()): # Make sure to have these three keyword arguments
# super().__init__(environment=environment, agent_id=agent_id, state=state)
# # Add your own attributes here
# def run(self):
# # Add your behaviors here
####################
# Available models #
####################
class SentimentCorrelationModel(BaseNetworkAgent):
def __init__(self, environment=None, agent_id=0, state=()):
@ -47,16 +52,14 @@ class SentimentCorrelationModel(BaseNetworkAgent):
self.sadness_prob = settings.sadness_prob
self.disgust_prob = settings.disgust_prob
self.time_awareness=[]
for i in range(4):
for i in range(4): #En este modelo tenemos 4 sentimientos
self.time_awareness.append(0) #0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
networkStatus[self.id][self.env.now]=0
def run(self):
while True:
if self.env.now > 10:
G.add_node(205)
G.add_edge(205,0)
angry_neighbors_1_time_step=[]
joyful_neighbors_1_time_step=[]
sad_neighbors_1_time_step=[]
@ -90,67 +93,12 @@ class SentimentCorrelationModel(BaseNetworkAgent):
disgusted_neighbors_1_time_step.append(x)
num_neighbors_disgusted = len(disgusted_neighbors_1_time_step)
# #Outside effects. Asignamos un estado aleatorio
# if random.random() < settings.outside_effects_prob:
# if self.state['id'] == 0:
# self.state['id'] = random.randint(1,4)
# myList.append(self.id)
# networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
# self.time_awareness = self.env.now #Para saber cuando se han contagiado
# yield self.env.timeout(settings.timeout)
# else:
# yield self.env.timeout(settings.timeout)
# #Imitation effects-Joy
# if random.random() < (settings.joy_prob*(num_neighbors_joyful)/10):
# myList.append(self.id)
# self.state['id'] = 2
# networkStatus[self.id][self.env.now]=2
# yield self.env.timeout(settings.timeout)
# #Imitation effects-Sadness
# if random.random() < (settings.sadness_prob*(num_neighbors_sad)/10):
# myList.append(self.id)
# self.state['id'] = 3
# networkStatus[self.id][self.env.now]=3
# yield self.env.timeout(settings.timeout)
# #Imitation effects-Disgust
# if random.random() < (settings.disgust_prob*(num_neighbors_disgusted)/10):
# myList.append(self.id)
# self.state['id'] = 4
# networkStatus[self.id][self.env.now]=4
# yield self.env.timeout(settings.timeout)
# #Imitation effects-Anger
# if random.random() < (settings.anger_prob*(num_neighbors_angry)/10):
# myList.append(self.id)
# self.state['id'] = 1
# networkStatus[self.id][self.env.now]=1
# yield self.env.timeout(settings.timeout)
# yield self.env.timeout(settings.timeout)
###########################################
anger_prob= settings.anger_prob+(len(angry_neighbors_1_time_step)*settings.anger_prob)
print("anger_prob " + str(anger_prob))
joy_prob= settings.joy_prob+(len(joyful_neighbors_1_time_step)*settings.joy_prob)
print("joy_prob " + str(joy_prob))
sadness_prob = settings.sadness_prob+(len(sad_neighbors_1_time_step)*settings.sadness_prob)
print("sadness_prob "+ str(sadness_prob))
disgust_prob = settings.disgust_prob+(len(disgusted_neighbors_1_time_step)*settings.disgust_prob)
print("disgust_prob " + str(disgust_prob))
outside_effects_prob= settings.outside_effects_prob
print("outside_effects_prob " + str(outside_effects_prob))
num = random.random()
@ -192,52 +140,6 @@ class SentimentCorrelationModel(BaseNetworkAgent):
yield self.env.timeout(settings.timeout)
# anger_propagation = settings.anger_prob*num_neighbors_angry/10
# joy_propagation = anger_propagation + (settings.joy_prob*num_neighbors_joyful/10)
# sadness_propagation = joy_propagation + (settings.sadness_prob*num_neighbors_sad/10)
# disgust_propagation = sadness_propagation + (settings.disgust_prob*num_neighbors_disgusted/10)
# outside_effects_propagation = disgust_propagation + settings.outside_effects_prob
# if (num<anger_propagation):
# if(self.state['id'] !=0):
# myList.append(self.id)
# self.state['id'] = 1
# networkStatus[self.id][self.env.now]=1
# yield self.env.timeout(settings.timeout)
# if (num<joy_propagation):
# if(self.state['id'] !=0):
# myList.append(self.id)
# self.state['id'] = 2
# networkStatus[self.id][self.env.now]=2
# yield self.env.timeout(settings.timeout)
# if(num<sadness_propagation):
# if(self.state['id'] !=0):
# myList.append(self.id)
# self.state['id'] = 3
# networkStatus[self.id][self.env.now]=3
# yield self.env.timeout(settings.timeout)
# # if(num<disgust_propagation):
# # if(self.state['id'] !=0):
# # myList.append(self.id)
# # self.state['id'] = 4
# # networkStatus[self.id][self.env.now]=4
# # yield self.env.timeout(settings.timeout)
# if(num <outside_effects_propagation):
# if self.state['id'] == 0:
# self.state['id'] = random.randint(1,4)
# myList.append(self.id)
# networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
# self.time_awareness = self.env.now #Para saber cuando se han contagiado
# yield self.env.timeout(settings.timeout)
# else:
# yield self.env.timeout(settings.timeout)
# else:
# yield self.env.timeout(settings.timeout)
class BassModel(BaseNetworkAgent):
def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
@ -349,12 +251,9 @@ class ZombieOutbreak(BaseNetworkAgent):
break
# Initialize agent states. Let's assume everyone is normal.
init_states = [{'id': 0, } for _ in range(settings.number_of_nodes)] # add keys as as necessary, but "id" must always refer to that state category
# Seed a zombie
#init_states[5] = {'id': 1}
#init_states[3] = {'id': 1}
##############
# Simulation #
##############
sim = NetworkSimulation(topology=G, states=init_states, agent_type=SentimentCorrelationModel,
max_time=settings.max_time, num_trials=settings.num_trials, logging_interval=1.0)
@ -362,16 +261,20 @@ sim = NetworkSimulation(topology=G, states=init_states, agent_type=SentimentCorr
sim.run_simulation()
###########
# Results #
###########
myList = sorted(myList, key=int)
#print("Los zombies son: " + str(myList))
trial = BaseLoggingAgent.open_trial_state_history(dir_path='sim_01', trial_id=0)
zombie_census = [sum([1 for node_id, state in g.items() if state['id'] == 1]) for t,g in trial.items()]
status_census = [sum([1 for node_id, state in g.items() if state['id'] == 1]) for t,g in trial.items()]
#for x in range(len(myList)):
# G.node[myList[x]]['viz'] = {'color': {'r': 255, 'g': 0, 'b': 0, 'a': 0}}
#G.node[1]['viz'] = {'color': {'r': 255, 'g': 0, 'b': 0, 'a': 0}}
#################
# Visualization #
#################
#lista = nx.nodes(G)
#print('Nodos: ' + str(lista))
@ -380,15 +283,16 @@ for x in range(0, settings.number_of_nodes):
for tiempo in networkStatus[x]:
if tiempo != 'id':
networkStatusAux.append((networkStatus[x][tiempo],tiempo,None))
G.add_node(x, zombie= networkStatusAux)
G.add_node(x, status= networkStatusAux)
#print(networkStatus)
nx.write_gexf(G,"test.gexf", version="1.2draft")
plt.plot(zombie_census)
plt.plot(status_census)
plt.draw() # pyplot draw()
plt.savefig("zombie.png")
plt.savefig("status.png")
#print(networkStatus)
#nx.draw(G)
#plt.show()
#plt.savefig("path.png")

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@ -0,0 +1,394 @@
from nxsim import NetworkSimulation
from nxsim import BaseNetworkAgent
from nxsim import BaseLoggingAgent
from random import randint
from matplotlib import pyplot as plt
import random
import numpy as np
import networkx as nx
import settings
settings.init()
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,3)
if settings.network_type == 2:
G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)
myList=[]
networkStatus=[]
for x in range(0, settings.number_of_nodes):
networkStatus.append({'id':x})
# # Just like subclassing a process in SimPy
# class MyAgent(BaseNetworkAgent):
# def __init__(self, environment=None, agent_id=0, state=()): # Make sure to have these three keyword arguments
# super().__init__(environment=environment, agent_id=agent_id, state=state)
# # Add your own attributes here
# def run(self):
# # Add your behaviors here
class SentimentCorrelationModel(BaseNetworkAgent):
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):
self.time_awareness.append(0) #0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
networkStatus[self.id][self.env.now]=0
def run(self):
while True:
if self.env.now > 10:
G.add_node(205)
G.add_edge(205,0)
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:
if x.time_awareness[0] > (self.env.now-500):
angry_neighbors_1_time_step.append(x)
num_neighbors_angry = len(angry_neighbors_1_time_step)
joyful_neighbors = self.get_neighboring_agents(state_id=2)
for x in joyful_neighbors:
if x.time_awareness[1] > (self.env.now-500):
joyful_neighbors_1_time_step.append(x)
num_neighbors_joyful = len(joyful_neighbors_1_time_step)
sad_neighbors = self.get_neighboring_agents(state_id=3)
for x in sad_neighbors:
if x.time_awareness[2] > (self.env.now-500):
sad_neighbors_1_time_step.append(x)
num_neighbors_sad = len(sad_neighbors_1_time_step)
disgusted_neighbors = self.get_neighboring_agents(state_id=4)
for x in disgusted_neighbors:
if x.time_awareness[3] > (self.env.now-500):
disgusted_neighbors_1_time_step.append(x)
num_neighbors_disgusted = len(disgusted_neighbors_1_time_step)
# #Outside effects. Asignamos un estado aleatorio
# if random.random() < settings.outside_effects_prob:
# if self.state['id'] == 0:
# self.state['id'] = random.randint(1,4)
# myList.append(self.id)
# networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
# self.time_awareness = self.env.now #Para saber cuando se han contagiado
# yield self.env.timeout(settings.timeout)
# else:
# yield self.env.timeout(settings.timeout)
# #Imitation effects-Joy
# if random.random() < (settings.joy_prob*(num_neighbors_joyful)/10):
# myList.append(self.id)
# self.state['id'] = 2
# networkStatus[self.id][self.env.now]=2
# yield self.env.timeout(settings.timeout)
# #Imitation effects-Sadness
# if random.random() < (settings.sadness_prob*(num_neighbors_sad)/10):
# myList.append(self.id)
# self.state['id'] = 3
# networkStatus[self.id][self.env.now]=3
# yield self.env.timeout(settings.timeout)
# #Imitation effects-Disgust
# if random.random() < (settings.disgust_prob*(num_neighbors_disgusted)/10):
# myList.append(self.id)
# self.state['id'] = 4
# networkStatus[self.id][self.env.now]=4
# yield self.env.timeout(settings.timeout)
# #Imitation effects-Anger
# if random.random() < (settings.anger_prob*(num_neighbors_angry)/10):
# myList.append(self.id)
# self.state['id'] = 1
# networkStatus[self.id][self.env.now]=1
# yield self.env.timeout(settings.timeout)
# yield self.env.timeout(settings.timeout)
###########################################
anger_prob= settings.anger_prob+(len(angry_neighbors_1_time_step)*settings.anger_prob)
print("anger_prob " + str(anger_prob))
joy_prob= settings.joy_prob+(len(joyful_neighbors_1_time_step)*settings.joy_prob)
print("joy_prob " + str(joy_prob))
sadness_prob = settings.sadness_prob+(len(sad_neighbors_1_time_step)*settings.sadness_prob)
print("sadness_prob "+ str(sadness_prob))
disgust_prob = settings.disgust_prob+(len(disgusted_neighbors_1_time_step)*settings.disgust_prob)
print("disgust_prob " + str(disgust_prob))
outside_effects_prob= settings.outside_effects_prob
print("outside_effects_prob " + str(outside_effects_prob))
num = random.random()
if(num<outside_effects_prob):
self.state['id'] = random.randint(1,4)
myList.append(self.id)
networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
self.time_awareness[self.state['id']-1] = self.env.now
yield self.env.timeout(settings.timeout)
if(num<anger_prob):
myList.append(self.id)
self.state['id'] = 1
networkStatus[self.id][self.env.now]=1
self.time_awareness[self.state['id']-1] = self.env.now
elif (num<joy_prob+anger_prob and num>anger_prob):
myList.append(self.id)
self.state['id'] = 2
networkStatus[self.id][self.env.now]=2
self.time_awareness[self.state['id']-1] = self.env.now
elif (num<sadness_prob+anger_prob+joy_prob and num>joy_prob+anger_prob):
myList.append(self.id)
self.state['id'] = 3
networkStatus[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):
myList.append(self.id)
self.state['id'] = 4
networkStatus[self.id][self.env.now]=4
self.time_awareness[self.state['id']-1] = self.env.now
yield self.env.timeout(settings.timeout)
# anger_propagation = settings.anger_prob*num_neighbors_angry/10
# joy_propagation = anger_propagation + (settings.joy_prob*num_neighbors_joyful/10)
# sadness_propagation = joy_propagation + (settings.sadness_prob*num_neighbors_sad/10)
# disgust_propagation = sadness_propagation + (settings.disgust_prob*num_neighbors_disgusted/10)
# outside_effects_propagation = disgust_propagation + settings.outside_effects_prob
# if (num<anger_propagation):
# if(self.state['id'] !=0):
# myList.append(self.id)
# self.state['id'] = 1
# networkStatus[self.id][self.env.now]=1
# yield self.env.timeout(settings.timeout)
# if (num<joy_propagation):
# if(self.state['id'] !=0):
# myList.append(self.id)
# self.state['id'] = 2
# networkStatus[self.id][self.env.now]=2
# yield self.env.timeout(settings.timeout)
# if(num<sadness_propagation):
# if(self.state['id'] !=0):
# myList.append(self.id)
# self.state['id'] = 3
# networkStatus[self.id][self.env.now]=3
# yield self.env.timeout(settings.timeout)
# # if(num<disgust_propagation):
# # if(self.state['id'] !=0):
# # myList.append(self.id)
# # self.state['id'] = 4
# # networkStatus[self.id][self.env.now]=4
# # yield self.env.timeout(settings.timeout)
# if(num <outside_effects_propagation):
# if self.state['id'] == 0:
# self.state['id'] = random.randint(1,4)
# myList.append(self.id)
# networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
# self.time_awareness = self.env.now #Para saber cuando se han contagiado
# yield self.env.timeout(settings.timeout)
# else:
# yield self.env.timeout(settings.timeout)
# else:
# yield self.env.timeout(settings.timeout)
class BassModel(BaseNetworkAgent):
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
networkStatus[self.id][self.env.now]=0
def run(self):
while True:
#Outside effects
if random.random() < settings.innovation_prob:
if self.state['id'] == 0:
self.state['id'] = 1
myList.append(self.id)
networkStatus[self.id][self.env.now]=1
yield self.env.timeout(settings.timeout)
else:
yield self.env.timeout(settings.timeout)
#Imitation effects
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):
myList.append(self.id)
self.state['id'] = 1
networkStatus[self.id][self.env.now]=1
yield self.env.timeout(settings.timeout)
else:
yield self.env.timeout(settings.timeout)
class IndependentCascadeModel(BaseNetworkAgent):
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.time_awareness = 0
networkStatus[self.id][self.env.now]=0
def run(self):
while True:
aware_neighbors_1_time_step=[]
#Outside effects
if random.random() < settings.innovation_prob:
if self.state['id'] == 0:
self.state['id'] = 1
myList.append(self.id)
networkStatus[self.id][self.env.now]=1
self.time_awareness = self.env.now #Para saber cuando se han contagiado
yield self.env.timeout(settings.timeout)
else:
yield self.env.timeout(settings.timeout)
#Imitation effects
if self.state['id'] == 0:
aware_neighbors = self.get_neighboring_agents(state_id=1)
for x in aware_neighbors:
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):
myList.append(self.id)
self.state['id'] = 1
networkStatus[self.id][self.env.now]=1
yield self.env.timeout(settings.timeout)
else:
yield self.env.timeout(settings.timeout)
class ZombieOutbreak(BaseNetworkAgent):
def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
self.bite_prob = settings.bite_prob
networkStatus[self.id][self.env.now]=0
def run(self):
while True:
if random.random() < settings.heal_prob:
if self.state['id'] == 1:
self.zombify()
yield self.env.timeout(settings.timeout)
else:
yield self.env.timeout(settings.timeout)
else:
if self.state['id'] == 1:
print("Soy el zombie " + str(self.id) + " y me voy a curar porque el num aleatorio ha sido " + str(num))
networkStatus[self.id][self.env.now]=0
if self.id in myList:
myList.remove(self.id)
self.state['id'] = 0
yield self.env.timeout(settings.timeout)
else:
yield self.env.timeout(settings.timeout)
def zombify(self):
normal_neighbors = self.get_neighboring_agents(state_id=0)
for neighbor in normal_neighbors:
if random.random() < self.bite_prob:
print("Soy el zombie " + str(self.id) + " y voy a contagiar a " + str(neighbor.id))
neighbor.state['id'] = 1 # zombie
myList.append(neighbor.id)
networkStatus[self.id][self.env.now]=1
networkStatus[neighbor.id][self.env.now]=1
print(self.env.now, "Soy el zombie: "+ str(self.id), "Mi vecino es: "+ str(neighbor.id), sep='\t')
break
# Initialize agent states. Let's assume everyone is normal.
init_states = [{'id': 0, } for _ in range(settings.number_of_nodes)] # add keys as as necessary, but "id" must always refer to that state category
# Seed a zombie
#init_states[5] = {'id': 1}
#init_states[3] = {'id': 1}
sim = NetworkSimulation(topology=G, states=init_states, agent_type=SentimentCorrelationModel,
max_time=settings.max_time, num_trials=settings.num_trials, logging_interval=1.0)
sim.run_simulation()
myList = sorted(myList, key=int)
#print("Los zombies son: " + str(myList))
trial = BaseLoggingAgent.open_trial_state_history(dir_path='sim_01', trial_id=0)
zombie_census = [sum([1 for node_id, state in g.items() if state['id'] == 1]) for t,g in trial.items()]
#for x in range(len(myList)):
# G.node[myList[x]]['viz'] = {'color': {'r': 255, 'g': 0, 'b': 0, 'a': 0}}
#G.node[1]['viz'] = {'color': {'r': 255, 'g': 0, 'b': 0, 'a': 0}}
#lista = nx.nodes(G)
#print('Nodos: ' + str(lista))
for x in range(0, settings.number_of_nodes):
networkStatusAux=[]
for tiempo in networkStatus[x]:
if tiempo != 'id':
networkStatusAux.append((networkStatus[x][tiempo],tiempo,None))
G.add_node(x, zombie= networkStatusAux)
#print(networkStatus)
nx.write_gexf(G,"test.gexf", version="1.2draft")
plt.plot(zombie_census)
plt.draw() # pyplot draw()
plt.savefig("zombie.png")
#print(networkStatus)
#nx.draw(G)
#plt.show()
#plt.savefig("path.png")

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