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94 lines
3.9 KiB
Python
94 lines
3.9 KiB
Python
import random
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import numpy as np
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from . import FSM, state
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class SISaModel(FSM):
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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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@state
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def neutral(self):
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# Spontaneous effects
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if random.random() < self.neutral_discontent_spon_prob:
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return self.discontent
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if random.random() < self.neutral_content_spon_prob:
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return self.content
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# Infected
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discontent_neighbors = self.count_neighboring_agents(state_id=self.discontent)
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if random.random() < discontent_neighbors * self.neutral_discontent_infected_prob:
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return self.discontent
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content_neighbors = self.count_neighboring_agents(state_id=self.content.id)
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if random.random() < content_neighbors * self.neutral_content_infected_prob:
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return self.content
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return self.neutral
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@state
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def discontent(self):
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# Healing
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if random.random() < self.discontent_neutral:
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return self.neutral
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# Superinfected
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content_neighbors = self.count_neighboring_agents(state_id=self.content.id)
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if random.random() < content_neighbors * self.discontent_content:
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return self.content
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return self.discontent
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@state
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def content(self):
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# Healing
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if random.random() < self.content_neutral:
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return self.neutral
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# Superinfected
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discontent_neighbors = self.count_neighboring_agents(state_id=self.discontent.id)
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if random.random() < discontent_neighbors * self.content_discontent:
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self.discontent
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return self.content
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