mirror of
https://github.com/gsi-upm/soil
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feab0ba79e
The examples weren't being properly tested in the last commit. When we fixed that a lot of bugs in the new implementation of environment and agent were found, which accounts for most of these changes. The main difference is the mechanism to load simulations from a configuration file. For that to work, we had to rework our module loading code in `serialization` and add a `source_file` attribute to configurations (and simulations, for that matter).
340 lines
10 KiB
Python
340 lines
10 KiB
Python
import networkx as nx
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from soil.agents import Geo, NetworkAgent, FSM, custom, state, default_state
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from soil import Environment, Simulation
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from soil.parameters import *
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class TerroristEnvironment(Environment):
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n: Integer = 100
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radius: Float = 0.2
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information_spread_intensity: probability = 0.7
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terrorist_additional_influence: probability = 0.03
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terrorist_additional_influence: probability = 0.035
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max_vulnerability: probability = 0.7
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prob_interaction: probability = 0.5
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# TrainingAreaModel and HavenModel
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training_influence: probability = 0.20
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haven_influence: probability = 0.20
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# TerroristNetworkModel
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vision_range: Float = 0.30
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sphere_influence: Integer = 2
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weight_social_distance: Float = 0.035
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weight_link_distance: Float = 0.035
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ratio_civil: probability = 0.8
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ratio_leader: probability = 0.1
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ratio_training: probability = 0.05
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ratio_haven: probability = 0.05
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def init(self):
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self.create_network(generator=self.generator, n=self.n, radius=self.radius)
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self.populate_network([
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TerroristNetworkModel.w(state_id='civilian'),
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TerroristNetworkModel.w(state_id='leader'),
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TrainingAreaModel,
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HavenModel
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], [self.ratio_civil, self.ratio_leader, self.ratio_training, self.ratio_haven])
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@staticmethod
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def generator(*args, **kwargs):
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return nx.random_geometric_graph(*args, **kwargs)
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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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"""
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information_spread_intensity = 0.1
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terrorist_additional_influence = 0.1
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min_vulnerability = 0
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max_vulnerability = 1
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def init(self):
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if self.state_id == self.civilian.id: # Civilian
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self.mean_belief = self.model.random.uniform(0.00, 0.5)
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elif self.state_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.state_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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self.vulnerability = self.random.uniform(
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self.get("min_vulnerability", 0), self.get("max_vulnerability", 1)
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)
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@default_state
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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.model.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_id
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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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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[agent.node_id]
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def betweenness(self, agent, force=False):
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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[agent.node_id]
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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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training_influence = 0.1
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min_vulnerability = 0
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def init(self):
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self.mean_believe = 1
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self.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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min_vulnerability = 0
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haven_influence = 0.1
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max_vulnerability = 0.5
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def init(self):
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self.mean_believe = 0
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self.vulnerability = 0
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def get_occupants(self, **kwargs):
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return self.get_neighbors(agent_class=TerroristSpreadModel,
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**kwargs)
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@default_state
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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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sphere_influence: float
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vision_range: float
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weight_social_distance: float
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weight_link_distance: float
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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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sim = Simulation(
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model=TerroristEnvironment,
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num_trials=1,
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name="TerroristNetworkModel_sim",
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max_steps=150,
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skip_test=False,
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dump=False,
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)
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# TODO: integrate visualization
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# visualization_params:
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# # Icons downloaded from https://www.iconfinder.com/
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# shape_property: agent
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# shapes:
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# TrainingAreaModel: target
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# HavenModel: home
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# TerroristNetworkModel: person
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# colors:
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# - attr_id: civilian
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# color: '#40de40'
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# - attr_id: terrorist
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# color: red
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# - attr_id: leader
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# color: '#c16a6a'
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# background_image: 'map_4800x2860.jpg'
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# background_opacity: '0.9'
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# background_filter_color: 'blue' |