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Control model M2 implemented
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control_model.png
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112
models.py
112
models.py
@ -56,6 +56,118 @@ class ComportamientoBase(BaseNetworkAgent):
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final[a][stamp] = attrs[a]
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final[a][stamp] = attrs[a]
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return final
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return final
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class ControlModelM2(ComportamientoBase):
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#Init infected
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init_states[random.randint(0,settings.number_of_nodes-1)] = {'id':1}
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init_states[random.randint(0,settings.number_of_nodes-1)] = {'id':1}
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# Init beacons
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init_states[random.randint(0, settings.number_of_nodes-1)] = {'id': 4}
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init_states[random.randint(0, settings.number_of_nodes-1)] = {'id': 4}
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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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self.prob_neutral_making_denier = np.random.normal(settings.prob_neutral_making_denier, settings.standard_variance)
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self.prob_infect = np.random.normal(settings.prob_infect, settings.standard_variance)
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self.prob_cured_healing_infected = np.random.normal(settings.prob_cured_healing_infected, settings.standard_variance)
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self.prob_cured_vaccinate_neutral = np.random.normal(settings.prob_cured_vaccinate_neutral, settings.standard_variance)
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self.prob_vaccinated_healing_infected = np.random.normal(settings.prob_vaccinated_healing_infected, settings.standard_variance)
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self.prob_vaccinated_vaccinate_neutral = np.random.normal(settings.prob_vaccinated_vaccinate_neutral, settings.standard_variance)
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self.prob_generate_anti_rumor = np.random.normal(settings.prob_generate_anti_rumor, settings.standard_variance)
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def step(self, now):
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if self.state['id'] == 0: #Neutral
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self.neutral_behaviour()
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elif self.state['id'] == 1: #Infected
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self.infected_behaviour()
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elif self.state['id'] == 2: #Cured
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self.cured_behaviour()
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elif self.state['id'] == 3: #Vaccinated
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self.vaccinated_behaviour()
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elif self.state['id'] == 4: #Beacon-off
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self.beacon_off_behaviour()
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elif self.state['id'] == 5: #Beacon-on
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self.beacon_on_behaviour()
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self.attrs['status'] = self.state['id']
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super().step(now)
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def neutral_behaviour(self):
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# Infected
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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if len(infected_neighbors)>0:
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if random.random() < self.prob_neutral_making_denier:
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self.state['id'] = 3 # Vaccinated making denier
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def infected_behaviour(self):
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# Neutral
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neutral_neighbors = self.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors:
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if random.random() < self.prob_infect:
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neighbor.state['id'] = 1 # Infected
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def cured_behaviour(self):
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# Vaccinate
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neutral_neighbors = self.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors:
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if random.random() < self.prob_cured_vaccinate_neutral:
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neighbor.state['id'] = 3 # Vaccinated
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# Cure
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors:
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if random.random() < self.prob_cured_healing_infected:
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neighbor.state['id'] = 2 # Cured
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def vaccinated_behaviour(self):
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# Cure
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors:
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if random.random() < self.prob_cured_healing_infected:
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neighbor.state['id'] = 2 # Cured
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# Vaccinate
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neutral_neighbors = self.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors:
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if random.random() < self.prob_cured_vaccinate_neutral:
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neighbor.state['id'] = 3 # Vaccinated
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# Generate anti-rumor
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infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors_2:
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if random.random() < self.prob_generate_anti_rumor:
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neighbor.state['id'] = 2 # Cured
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def beacon_off_behaviour(self):
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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if len(infected_neighbors) > 0:
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self.state['id'] == 5 #Beacon on
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def beacon_on_behaviour(self):
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors:
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if random.random() < self.prob_generate_anti_rumor:
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neighbor.state['id'] = 2 # Cured
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# Vaccinate
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neutral_neighbors = self.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors:
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if random.random() < self.prob_cured_vaccinate_neutral:
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neighbor.state['id'] = 3 # Vaccinated
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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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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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@ -40,10 +40,10 @@ def init():
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global prob_generate_anti_rumor
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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=20
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number_of_nodes=1000
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max_time=50
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max_time=50
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num_trials=1
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num_trials=1
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timeout=1
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timeout=2
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#Zombie model
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#Zombie model
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bite_prob=0.01 # 0-1
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bite_prob=0.01 # 0-1
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@ -87,7 +87,7 @@ def init():
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standard_variance = 0.055
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standard_variance = 0.055
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#Spread Model M2
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#Spread Model M2 and Control Model M2
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prob_neutral_making_denier = 0.035
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prob_neutral_making_denier = 0.035
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prob_infect = 0.075
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prob_infect = 0.075
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3
soil.py
3
soil.py
@ -79,7 +79,8 @@ neutral_line = plt.plot(x_values,neutral_values, label='Neutral')
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cured_line = plt.plot(x_values,cured_values, label='Cured')
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cured_line = plt.plot(x_values,cured_values, label='Cured')
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vaccinated_line = plt.plot(x_values,vaccinated_values, label='Vaccinated')
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vaccinated_line = plt.plot(x_values,vaccinated_values, label='Vaccinated')
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plt.legend()
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plt.legend()
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plt.show()
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plt.savefig('spread_model.png')
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#plt.show()
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#################
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#################
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BIN
spread_model.png
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BIN
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After Width: | Height: | Size: 37 KiB |
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