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https://github.com/gsi-upm/soil
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Probabilities and seed infection
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@ -57,6 +57,8 @@ class ComportamientoBase(BaseNetworkAgent):
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return final
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return final
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class SpreadModelM2(ComportamientoBase):
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class SpreadModelM2(ComportamientoBase):
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init_states[random.randint(0,settings.number_of_nodes)] = {'id':1}
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init_states[random.randint(0,settings.number_of_nodes)] = {'id':1}
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def __init__(self, environment=None, agent_id=0, state=()):
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def __init__(self, environment=None, agent_id=0, state=()):
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super().__init__(environment=environment, agent_id=agent_id, state=state)
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super().__init__(environment=environment, agent_id=agent_id, state=state)
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25
settings.py
25
settings.py
@ -31,9 +31,16 @@ def init():
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global variance_d_c
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global variance_d_c
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global variance_c_d
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global variance_c_d
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global standard_variance
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global standard_variance
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global prob_neutral_making_denier
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global prob_infect
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global prob_cured_healing_infected
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global prob_cured_vaccinate_neutral
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global prob_vaccinated_healing_infected
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global prob_vaccinated_vaccinate_neutral
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global prob_generate_anti_rumor
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network_type=1
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network_type=1
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number_of_nodes=50
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number_of_nodes=1000
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max_time=500
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max_time=500
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num_trials=1
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num_trials=1
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timeout=20
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timeout=20
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@ -78,5 +85,19 @@ def init():
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variance_c_d = 0.003
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variance_c_d = 0.003
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content_neutral = 0.088
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content_neutral = 0.088
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standard_variance = 0.02
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standard_variance = 0.055
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#Spread Model M2
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prob_neutral_making_denier = 0.055
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prob_infect = 0.1
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prob_cured_healing_infected = 0.055
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prob_cured_vaccinate_neutral = 0.055
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prob_vaccinated_healing_infected = 0.055
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prob_vaccinated_vaccinate_neutral = 0.055
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prob_generate_anti_rumor = 0.055
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22
soil.py
22
soil.py
@ -18,7 +18,7 @@ models.init() # Loads the models and network variables
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if settings.network_type == 0:
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if settings.network_type == 0:
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G = nx.complete_graph(settings.number_of_nodes)
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G = nx.complete_graph(settings.number_of_nodes)
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if settings.network_type == 1:
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if settings.network_type == 1:
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G = nx.barabasi_albert_graph(settings.number_of_nodes,3)
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G = nx.barabasi_albert_graph(settings.number_of_nodes,10)
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if settings.network_type == 2:
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if settings.network_type == 2:
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G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)
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G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)
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# More types of networks can be added here
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# More types of networks can be added here
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@ -27,7 +27,7 @@ if settings.network_type == 2:
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# Simulation #
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# Simulation #
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##############
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##############
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sim = NetworkSimulation(topology=G, states=init_states, agent_type=SISaModel,
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sim = NetworkSimulation(topology=G, states=init_states, agent_type=SpreadModelM2,
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max_time=settings.max_time, num_trials=settings.num_trials, logging_interval=1.0)
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max_time=settings.max_time, num_trials=settings.num_trials, logging_interval=1.0)
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@ -39,19 +39,25 @@ sim.run_simulation()
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x_values = []
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x_values = []
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y_values = []
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y_values = []
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attribute_plot = 'status'
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for time in range(0, settings.max_time):
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for time in range(0, settings.max_time):
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value = settings.sentiment_about[0]
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value = settings.sentiment_about[0]
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real_time = time * settings.timeout
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real_time = time * settings.timeout
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activity= False
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for x in range(0, settings.number_of_nodes):
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for x in range(0, settings.number_of_nodes):
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if "sentiment_enterprise_BBVA" in models.networkStatus["agente_%s" % x]:
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if attribute_plot in models.networkStatus["agente_%s" % x]:
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if real_time in models.networkStatus["agente_%s" % x]["sentiment_enterprise_BBVA"]:
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if real_time in models.networkStatus["agente_%s" % x][attribute_plot]:
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value += models.networkStatus["agente_%s" % x]["sentiment_enterprise_BBVA"][real_time]
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if models.networkStatus["agente_%s" % x][attribute_plot][real_time] == 1: ##Representar infectados
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value += 1
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activity = True
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x_values.append(real_time)
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if activity:
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y_values.append(value)
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x_values.append(real_time)
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y_values.append(value)
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activity=False
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plt.plot(x_values,y_values)
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plt.plot(x_values,y_values)
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#plt.show()
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plt.show()
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#################
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#################
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