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290 lines
10 KiB
Python
290 lines
10 KiB
Python
import networkx as nx
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from soil.agents import Geo, NetworkAgent, FSM, state, default_state
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from soil import Environment
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class TerroristSpreadModel(FSM, Geo):
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"""
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Settings:
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information_spread_intensity
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terrorist_additional_influence
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min_vulnerability (optional else zero)
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max_vulnerability
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prob_interaction
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"""
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def __init__(self, model=None, unique_id=0, state=()):
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super().__init__(model=model, unique_id=unique_id, state=state)
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self.information_spread_intensity = model.environment_params[
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"information_spread_intensity"
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]
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self.terrorist_additional_influence = model.environment_params[
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"terrorist_additional_influence"
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]
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self.prob_interaction = model.environment_params["prob_interaction"]
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if self["id"] == self.civilian.id: # Civilian
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self.mean_belief = self.random.uniform(0.00, 0.5)
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elif self["id"] == self.terrorist.id: # Terrorist
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self.mean_belief = self.random.uniform(0.8, 1.00)
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elif self["id"] == self.leader.id: # Leader
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self.mean_belief = 1.00
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else:
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raise Exception("Invalid state id: {}".format(self["id"]))
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if "min_vulnerability" in model.environment_params:
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self.vulnerability = self.random.uniform(
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model.environment_params["min_vulnerability"],
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model.environment_params["max_vulnerability"],
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)
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else:
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self.vulnerability = self.random.uniform(
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0, model.environment_params["max_vulnerability"]
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)
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@state
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def civilian(self):
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neighbours = list(self.get_neighbors(agent_class=TerroristSpreadModel))
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if len(neighbours) > 0:
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# Only interact with some of the neighbors
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interactions = list(
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n for n in neighbours if self.random.random() <= self.prob_interaction
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)
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influence = sum(self.degree(i) for i in interactions)
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mean_belief = sum(
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i.mean_belief * self.degree(i) / influence for i in interactions
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)
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mean_belief = (
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mean_belief * self.information_spread_intensity
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+ self.mean_belief * (1 - self.information_spread_intensity)
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)
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self.mean_belief = mean_belief * self.vulnerability + self.mean_belief * (
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1 - self.vulnerability
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)
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if self.mean_belief >= 0.8:
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return self.terrorist
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@state
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def leader(self):
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self.mean_belief = self.mean_belief ** (1 - self.terrorist_additional_influence)
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for neighbour in self.get_neighbors(
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state_id=[self.terrorist.id, self.leader.id]
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):
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if self.betweenness(neighbour) > self.betweenness(self):
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return self.terrorist
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@state
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def terrorist(self):
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neighbours = self.get_agents(
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state_id=[self.terrorist.id, self.leader.id],
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agent_class=TerroristSpreadModel,
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limit_neighbors=True,
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)
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if len(neighbours) > 0:
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influence = sum(self.degree(n) for n in neighbours)
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mean_belief = sum(
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n.mean_belief * self.degree(n) / influence for n in neighbours
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)
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mean_belief = mean_belief * self.vulnerability + self.mean_belief * (
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1 - self.vulnerability
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)
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self.mean_belief = self.mean_belief ** (
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1 - self.terrorist_additional_influence
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)
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# Check if there are any leaders in the group
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leaders = list(filter(lambda x: x.state.id == self.leader.id, neighbours))
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if not leaders:
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# Check if this is the potential leader
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# Stop once it's found. Otherwise, set self as leader
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for neighbour in neighbours:
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if self.betweenness(self) < self.betweenness(neighbour):
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return
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return self.leader
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def ego_search(self, steps=1, center=False, agent=None, **kwargs):
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"""Get a list of nodes in the ego network of *node* of radius *steps*"""
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node = agent.node
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G = self.subgraph(**kwargs)
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return nx.ego_graph(G, node, center=center, radius=steps).nodes()
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def degree(self, agent, force=False):
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node = agent.node
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if (
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force
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or (not hasattr(self.model, "_degree"))
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or getattr(self.model, "_last_step", 0) < self.now
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):
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self.model._degree = nx.degree_centrality(self.G)
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self.model._last_step = self.now
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return self.model._degree[node]
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def betweenness(self, agent, force=False):
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node = agent.node
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if (
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force
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or (not hasattr(self.model, "_betweenness"))
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or getattr(self.model, "_last_step", 0) < self.now
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):
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self.model._betweenness = nx.betweenness_centrality(self.G)
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self.model._last_step = self.now
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return self.model._betweenness[node]
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class TrainingAreaModel(FSM, Geo):
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"""
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Settings:
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training_influence
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min_vulnerability
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Requires TerroristSpreadModel.
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"""
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def __init__(self, model=None, unique_id=0, state=()):
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super().__init__(model=model, unique_id=unique_id, state=state)
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self.training_influence = model.environment_params["training_influence"]
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if "min_vulnerability" in model.environment_params:
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self.min_vulnerability = model.environment_params["min_vulnerability"]
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else:
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self.min_vulnerability = 0
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@default_state
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@state
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def terrorist(self):
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for neighbour in self.get_neighbors(agent_class=TerroristSpreadModel):
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if neighbour.vulnerability > self.min_vulnerability:
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neighbour.vulnerability = neighbour.vulnerability ** (
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1 - self.training_influence
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)
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class HavenModel(FSM, Geo):
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"""
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Settings:
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haven_influence
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min_vulnerability
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max_vulnerability
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Requires TerroristSpreadModel.
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"""
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def __init__(self, model=None, unique_id=0, state=()):
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super().__init__(model=model, unique_id=unique_id, state=state)
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self.haven_influence = model.environment_params["haven_influence"]
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if "min_vulnerability" in model.environment_params:
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self.min_vulnerability = model.environment_params["min_vulnerability"]
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else:
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self.min_vulnerability = 0
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self.max_vulnerability = model.environment_params["max_vulnerability"]
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def get_occupants(self, **kwargs):
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return self.get_neighbors(agent_class=TerroristSpreadModel, **kwargs)
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@state
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def civilian(self):
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civilians = self.get_occupants(state_id=self.civilian.id)
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if not civilians:
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return self.terrorist
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for neighbour in self.get_occupants():
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if neighbour.vulnerability > self.min_vulnerability:
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neighbour.vulnerability = neighbour.vulnerability * (
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1 - self.haven_influence
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)
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return self.civilian
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@state
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def terrorist(self):
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for neighbour in self.get_occupants():
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if neighbour.vulnerability < self.max_vulnerability:
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neighbour.vulnerability = neighbour.vulnerability ** (
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1 - self.haven_influence
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)
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return self.terrorist
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class TerroristNetworkModel(TerroristSpreadModel):
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"""
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Settings:
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sphere_influence
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vision_range
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weight_social_distance
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weight_link_distance
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"""
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def __init__(self, model=None, unique_id=0, state=()):
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super().__init__(model=model, unique_id=unique_id, state=state)
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self.vision_range = model.environment_params["vision_range"]
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self.sphere_influence = model.environment_params["sphere_influence"]
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self.weight_social_distance = model.environment_params["weight_social_distance"]
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self.weight_link_distance = model.environment_params["weight_link_distance"]
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@state
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def terrorist(self):
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self.update_relationships()
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return super().terrorist()
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@state
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def leader(self):
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self.update_relationships()
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return super().leader()
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def update_relationships(self):
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if self.count_neighbors(state_id=self.civilian.id) == 0:
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close_ups = set(
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self.geo_search(
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radius=self.vision_range, agent_class=TerroristNetworkModel
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)
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)
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step_neighbours = set(
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self.ego_search(
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self.sphere_influence,
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agent_class=TerroristNetworkModel,
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center=False,
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)
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)
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neighbours = set(
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agent.id
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for agent in self.get_neighbors(agent_class=TerroristNetworkModel)
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)
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search = (close_ups | step_neighbours) - neighbours
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for agent in self.get_agents(search):
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social_distance = 1 / self.shortest_path_length(agent.id)
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spatial_proximity = 1 - self.get_distance(agent.id)
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prob_new_interaction = (
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self.weight_social_distance * social_distance
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+ self.weight_link_distance * spatial_proximity
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)
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if (
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agent["id"] == agent.civilian.id
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and self.random.random() < prob_new_interaction
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):
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self.add_edge(agent)
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break
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def get_distance(self, target):
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source_x, source_y = nx.get_node_attributes(self.G, "pos")[self.id]
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target_x, target_y = nx.get_node_attributes(self.G, "pos")[target]
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dx = abs(source_x - target_x)
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dy = abs(source_y - target_y)
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return (dx**2 + dy**2) ** (1 / 2)
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def shortest_path_length(self, target):
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try:
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return nx.shortest_path_length(self.G, self.id, target)
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except nx.NetworkXNoPath:
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return float("inf")
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