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99 lines
4.1 KiB
Python
99 lines
4.1 KiB
Python
import random
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import numpy as np
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from models.BaseBehaviour import *
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class SISaModel(BaseBehaviour):
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"""
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Settings:
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neutral_discontent_spon_prob
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neutral_discontent_infected_prob
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neutral_content_spong_prob
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neutral_content_infected_prob
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discontent_neutral
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discontent_content
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variance_d_c
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content_discontent
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variance_c_d
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content_neutral
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standard_variance
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"""
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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.neutral_discontent_spon_prob = np.random.normal(environment.environment_params['neutral_discontent_spon_prob'],
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environment.environment_params['standard_variance'])
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self.neutral_discontent_infected_prob = np.random.normal(environment.environment_params['neutral_discontent_infected_prob'],
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environment.environment_params['standard_variance'])
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self.neutral_content_spon_prob = np.random.normal(environment.environment_params['neutral_content_spon_prob'],
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environment.environment_params['standard_variance'])
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self.neutral_content_infected_prob = np.random.normal(environment.environment_params['neutral_content_infected_prob'],
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environment.environment_params['standard_variance'])
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self.discontent_neutral = np.random.normal(environment.environment_params['discontent_neutral'],
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environment.environment_params['standard_variance'])
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self.discontent_content = np.random.normal(environment.environment_params['discontent_content'],
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environment.environment_params['variance_d_c'])
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self.content_discontent = np.random.normal(environment.environment_params['content_discontent'],
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environment.environment_params['variance_c_d'])
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self.content_neutral = np.random.normal(environment.environment_params['content_neutral'],
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environment.environment_params['standard_variance'])
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def step(self, now):
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if self.state['id'] == 0:
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self.neutral_behaviour()
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if self.state['id'] == 1:
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self.discontent_behaviour()
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if self.state['id'] == 2:
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self.content_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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# Spontaneous effects
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if random.random() < self.neutral_discontent_spon_prob:
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self.state['id'] = 1
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if random.random() < self.neutral_content_spon_prob:
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self.state['id'] = 2
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# Infected
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discontent_neighbors = self.get_neighboring_agents(state_id=1)
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if random.random() < len(discontent_neighbors) * self.neutral_discontent_infected_prob:
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self.state['id'] = 1
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content_neighbors = self.get_neighboring_agents(state_id=2)
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if random.random() < len(content_neighbors) * self.neutral_content_infected_prob:
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self.state['id'] = 2
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def discontent_behaviour(self):
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# Healing
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if random.random() < self.discontent_neutral:
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self.state['id'] = 0
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# Superinfected
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content_neighbors = self.get_neighboring_agents(state_id=2)
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if random.random() < len(content_neighbors) * self.discontent_content:
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self.state['id'] = 2
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def content_behaviour(self):
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# Healing
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if random.random() < self.content_neutral:
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self.state['id'] = 0
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# Superinfected
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discontent_neighbors = self.get_neighboring_agents(state_id=1)
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if random.random() < len(discontent_neighbors) * self.content_discontent:
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self.state['id'] = 1
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