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95 lines
3.4 KiB
Python
95 lines
3.4 KiB
Python
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_spon_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, unique_id=0, state=()):
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super().__init__(model=environment, unique_id=unique_id, state=state)
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random = np.random.default_rng(seed=self._seed)
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self.neutral_discontent_spon_prob = random.normal(self.env['neutral_discontent_spon_prob'],
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self.env['standard_variance'])
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self.neutral_discontent_infected_prob = random.normal(self.env['neutral_discontent_infected_prob'],
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self.env['standard_variance'])
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self.neutral_content_spon_prob = random.normal(self.env['neutral_content_spon_prob'],
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self.env['standard_variance'])
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self.neutral_content_infected_prob = random.normal(self.env['neutral_content_infected_prob'],
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self.env['standard_variance'])
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self.discontent_neutral = random.normal(self.env['discontent_neutral'],
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self.env['standard_variance'])
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self.discontent_content = random.normal(self.env['discontent_content'],
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self.env['variance_d_c'])
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self.content_discontent = random.normal(self.env['content_discontent'],
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self.env['variance_c_d'])
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self.content_neutral = random.normal(self.env['content_neutral'],
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self.env['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 self.prob(self.neutral_discontent_spon_prob):
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return self.discontent
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if self.prob(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 self.prob(scontent_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 self.prob(s * 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 self.prob(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 self.prob(s * 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 self.prob(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 self.prob(scontent_neighbors * self.content_discontent):
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self.discontent
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return self.content
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