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Code Issues Releases Wiki Activity

WIP: all tests pass

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This commit is contained in:
J. Fernando Sánchez
2022-10-13 22:43:16 +02:00
parent f811ee18c5
commit cd62c23cb9
46 changed files with 1720 additions and 1434 deletions
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81
examples/complete.yml
View File

@@ -1,46 +1,54 @@
---
version: '2'
general:
id: simple
group: tests
dir_path: "/tmp/"
num_trials: 3
max_time: 100
interval: 1
seed: "CompleteSeed!"
topologies:
default:
params:
generator: complete_graph
n: 10
another_graph:
params:
generator: complete_graph
n: 2
environment:
environment_class: Environment
params:
am_i_complete: true
agents:
# Agents are split several groups, each with its own definition
default: # This is a special group. Its values will be used as default values for the rest of the groups
name: simple
group: tests
dir_path: "/tmp/"
num_trials: 3
max_steps: 100
interval: 1
seed: "CompleteSeed!"
model_class: Environment
model_params:
am_i_complete: true
topologies:
default:
params:
generator: complete_graph
n: 10
another_graph:
params:
generator: complete_graph
n: 2
environment:
agents:
agent_class: CounterModel
topology: default
state:
times: 1
environment:
# In this group we are not specifying any topology
topology: False
# In this group we are not specifying any topology
fixed:
- name: 'Environment Agent 1'
agent_class: CounterModel
agent_class: BaseAgent
group: environment
topology: null
hidden: true
state:
times: 10
general_counters:
topology: default
- agent_class: CounterModel
id: 0
group: other_counters
topology: another_graph
state:
times: 1
total: 0
- agent_class: CounterModel
topology: another_graph
group: other_counters
id: 1
distribution:
- agent_class: CounterModel
weight: 1
group: general_counters
state:
times: 3
- agent_class: AggregatedCounter
@@ -51,16 +59,3 @@ agents:
n: 2
state:
times: 5
other_counters:
topology: another_graph
fixed:
- agent_class: CounterModel
id: 0
state:
times: 1
total: 0
- agent_class: CounterModel
id: 1
# If not specified, it will use the state set in the default
# state:

63
examples/complete_opt2.yml Normal file
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@@ -0,0 +1,63 @@
---
version: '2'
id: simple
group: tests
dir_path: "/tmp/"
num_trials: 3
max_steps: 100
interval: 1
seed: "CompleteSeed!"
model_class: "soil.Environment"
model_params:
topologies:
default:
params:
generator: complete_graph
n: 10
another_graph:
params:
generator: complete_graph
n: 2
agents:
# The values here will be used as default values for any agent
agent_class: CounterModel
topology: default
state:
times: 1
# This specifies a distribution of agents, each with a `weight` or an explicit number of agents
distribution:
- agent_class: CounterModel
weight: 1
# This is inherited from the default settings
#topology: default
state:
times: 3
- agent_class: AggregatedCounter
topology: default
weight: 0.2
fixed:
- name: 'Environment Agent 1'
# All the other agents will assigned to the 'default' group
group: environment
# Do not count this agent towards total limits
hidden: true
agent_class: soil.BaseAgent
topology: null
state:
times: 10
- agent_class: CounterModel
topology: another_graph
id: 0
state:
times: 1
total: 0
- agent_class: CounterModel
topology: another_graph
id: 1
override:
# 2 agents that match this filter will be updated to match the state {times: 5}
- filter:
agent_class: AggregatedCounter
n: 2
state:
times: 5

2
examples/custom_generator/custom_generator.yml
View File

@@ -2,7 +2,7 @@
name: custom-generator
description: Using a custom generator for the network
num_trials: 3
max_time: 100
max_steps: 100
interval: 1
network_params:
generator: mymodule.mygenerator

5
examples/custom_generator/mymodule.py
View File

@@ -1,4 +1,5 @@
from networkx import Graph
import random
import networkx as nx
def mygenerator(n=5, n_edges=5):
@@ -13,9 +14,9 @@ def mygenerator(n=5, n_edges=5):
for i in range(n_edges):
nodes = list(G.nodes)
n_in = self.random.choice(nodes)
n_in = random.choice(nodes)
nodes.remove(n_in) # Avoid loops
n_out = self.random.choice(nodes)
n_out = random.choice(nodes)
G.add_edge(n_in, n_out)
return G

22
examples/mesa/mesa.yml
View File

@@ -3,17 +3,21 @@ name: mesa_sim
group: tests
dir_path: "/tmp"
num_trials: 3
max_time: 100
max_steps: 100
interval: 1
seed: '1'
network_params:
generator: social_wealth.graph_generator
n: 5
network_agents:
- agent_class: social_wealth.SocialMoneyAgent
weight: 1
environment_class: social_wealth.MoneyEnv
environment_params:
model_class: social_wealth.MoneyEnv
model_params:
topologies:
default:
params:
generator: social_wealth.graph_generator
n: 5
agents:
distribution:
- agent_class: social_wealth.SocialMoneyAgent
topology: default
weight: 1
mesa_agent_class: social_wealth.MoneyAgent
N: 10
width: 50

10
examples/newsspread/NewsSpread.yml
View File

@@ -5,7 +5,7 @@ environment_params:
prob_neighbor_spread: 0.0
prob_tv_spread: 0.01
interval: 1
max_time: 300
max_steps: 300
name: Sim_all_dumb
network_agents:
- agent_class: newsspread.DumbViewer
@@ -28,7 +28,7 @@ environment_params:
prob_neighbor_spread: 0.0
prob_tv_spread: 0.01
interval: 1
max_time: 300
max_steps: 300
name: Sim_half_herd
network_agents:
- agent_class: newsspread.DumbViewer
@@ -59,7 +59,7 @@ environment_params:
prob_neighbor_spread: 0.0
prob_tv_spread: 0.01
interval: 1
max_time: 300
max_steps: 300
name: Sim_all_herd
network_agents:
- agent_class: newsspread.HerdViewer
@@ -85,7 +85,7 @@ environment_params:
prob_tv_spread: 0.01
prob_neighbor_cure: 0.1
interval: 1
max_time: 300
max_steps: 300
name: Sim_wise_herd
network_agents:
- agent_class: newsspread.HerdViewer
@@ -110,7 +110,7 @@ environment_params:
prob_tv_spread: 0.01
prob_neighbor_cure: 0.1
interval: 1
max_time: 300
max_steps: 300
name: Sim_all_wise
network_agents:
- agent_class: newsspread.WiseViewer

16
examples/newsspread/newsspread.py
View File

@@ -16,13 +16,13 @@ class DumbViewer(FSM, NetworkAgent):
@state
def neutral(self):
if self['has_tv']:
if prob(self.env['prob_tv_spread']):
if self.prob(self.model['prob_tv_spread']):
return self.infected
@state
def infected(self):
for neighbor in self.get_neighboring_agents(state_id=self.neutral.id):
if prob(self.env['prob_neighbor_spread']):
if self.prob(self.model['prob_neighbor_spread']):
neighbor.infect()
def infect(self):
@@ -44,9 +44,9 @@ class HerdViewer(DumbViewer):
'''Notice again that this is NOT a state. See DumbViewer.infect for reference'''
infected = self.count_neighboring_agents(state_id=self.infected.id)
total = self.count_neighboring_agents()
prob_infect = self.env['prob_neighbor_spread'] * infected/total
prob_infect = self.model['prob_neighbor_spread'] * infected/total
self.debug('prob_infect', prob_infect)
if prob(prob_infect):
if self.prob(prob_infect):
self.set_state(self.infected)
@@ -63,9 +63,9 @@ class WiseViewer(HerdViewer):
@state
def cured(self):
prob_cure = self.env['prob_neighbor_cure']
prob_cure = self.model['prob_neighbor_cure']
for neighbor in self.get_neighboring_agents(state_id=self.infected.id):
if prob(prob_cure):
if self.prob(prob_cure):
try:
neighbor.cure()
except AttributeError:
@@ -80,7 +80,7 @@ class WiseViewer(HerdViewer):
1.0)
infected = max(self.count_neighboring_agents(self.infected.id),
1.0)
prob_cure = self.env['prob_neighbor_cure'] * (cured/infected)
if prob(prob_cure):
prob_cure = self.model['prob_neighbor_cure'] * (cured/infected)
if self.prob(prob_cure):
return self.cured
return self.set_state(super().infected)

18
examples/pubcrawl/pubcrawl.py
View File

@@ -60,12 +60,10 @@ class Patron(FSM, NetworkAgent):
'''
level = logging.DEBUG
defaults = {
'pub': None,
'drunk': False,
'pints': 0,
'max_pints': 3,
}
pub = None
drunk = False
pints = 0
max_pints = 3
@default_state
@state
@@ -89,9 +87,9 @@ class Patron(FSM, NetworkAgent):
return self.sober_in_pub
self.debug('I am looking for a pub')
group = list(self.get_neighboring_agents())
for pub in self.env.available_pubs():
for pub in self.model.available_pubs():
self.debug('We\'re trying to get into {}: total: {}'.format(pub, len(group)))
if self.env.enter(pub, self, *group):
if self.model.enter(pub, self, *group):
self.info('We\'re all {} getting in {}!'.format(len(group), pub))
return self.sober_in_pub
@@ -128,7 +126,7 @@ class Patron(FSM, NetworkAgent):
success depend on both agents' openness.
'''
if force or self['openness'] > self.random.random():
self.env.add_edge(self, other_agent)
self.model.add_edge(self, other_agent)
self.info('Made some friend {}'.format(other_agent))
return True
return False
@@ -150,7 +148,7 @@ class Patron(FSM, NetworkAgent):
return befriended
class Police(FSM, NetworkAgent):
class Police(FSM):
'''Simple agent to take drunk people out of pubs.'''
level = logging.INFO

2
examples/pubcrawl/pubcrawl.yml
View File

@@ -1,7 +1,7 @@
---
name: pubcrawl
num_trials: 3
max_time: 10
max_steps: 10
dump: false
network_params:
# Generate 100 empty nodes. They will be assigned a network agent

4
examples/rabbits/README.md Normal file
View File

@@ -0,0 +1,4 @@
There are two similar implementations of this simulation.
- `basic`. Using simple primites
- `improved`. Using more advanced features such as the `time` module to avoid unnecessary computations (i.e., skip steps), and generator functions.

130
examples/rabbits/basic/rabbit_agents.py Normal file
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@@ -0,0 +1,130 @@
from soil.agents import FSM, state, default_state, BaseAgent, NetworkAgent
from soil.time import Delta
from enum import Enum
from collections import Counter
import logging
import math
class RabbitModel(FSM, NetworkAgent):
sexual_maturity = 30
life_expectancy = 300
@default_state
@state
def newborn(self):
self.info('I am a newborn.')
self.age = 0
self.offspring = 0
return self.youngling
@state
def youngling(self):
self.age += 1
if self.age >= self.sexual_maturity:
self.info(f'I am fertile! My age is {self.age}')
return self.fertile
@state
def fertile(self):
raise Exception("Each subclass should define its fertile state")
@state
def dead(self):
self.die()
class Male(RabbitModel):
max_females = 5
mating_prob = 0.001
@state
def fertile(self):
self.age += 1
if self.age > self.life_expectancy:
return self.dead
# Males try to mate
for f in self.model.agents(agent_class=Female,
state_id=Female.fertile.id,
limit=self.max_females):
self.debug('FOUND A FEMALE: ', repr(f), self.mating_prob)
if self.prob(self['mating_prob']):
f.impregnate(self)
break # Take a break
class Female(RabbitModel):
gestation = 100
@state
def fertile(self):
# Just wait for a Male
self.age += 1
if self.age > self.life_expectancy:
return self.dead
def impregnate(self, male):
self.info(f'{repr(male)} impregnating female {repr(self)}')
self.mate = male
self.pregnancy = -1
self.set_state(self.pregnant, when=self.now)
self.number_of_babies = int(8+4*self.random.random())
self.debug('I am pregnant')
@state
def pregnant(self):
self.age += 1
self.pregnancy += 1
if self.prob(self.age / self.life_expectancy):
return self.die()
if self.pregnancy >= self.gestation:
self.info('Having {} babies'.format(self.number_of_babies))
for i in range(self.number_of_babies):
state = {}
agent_class = self.random.choice([Male, Female])
child = self.model.add_node(agent_class=agent_class,
topology=self.topology,
**state)
child.add_edge(self)
try:
child.add_edge(self.mate)
self.model.agents[self.mate].offspring += 1
except ValueError:
self.debug('The father has passed away')
self.offspring += 1
self.mate = None
return self.fertile
@state
def dead(self):
super().dead()
if 'pregnancy' in self and self['pregnancy'] > -1:
self.info('A mother has died carrying a baby!!')
class RandomAccident(BaseAgent):
level = logging.INFO
def step(self):
rabbits_alive = self.model.topology.number_of_nodes()
if not rabbits_alive:
return self.die()
prob_death = self.model.get('prob_death', 1e-100)*math.floor(math.log10(max(1, rabbits_alive)))
self.debug('Killing some rabbits with prob={}!'.format(prob_death))
for i in self.iter_agents(agent_class=RabbitModel):
if i.state.id == i.dead.id:
continue
if self.prob(prob_death):
self.info('I killed a rabbit: {}'.format(i.id))
rabbits_alive -= 1
i.set_state(i.dead)
self.debug('Rabbits alive: {}'.format(rabbits_alive))

41
examples/rabbits/basic/rabbits.yml Normal file
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@@ -0,0 +1,41 @@
---
version: '2'
name: rabbits_basic
num_trials: 1
seed: MySeed
description: null
group: null
interval: 1.0
max_time: 100
model_class: soil.environment.Environment
model_params:
agents:
topology: default
agent_class: rabbit_agents.RabbitModel
distribution:
- agent_class: rabbit_agents.Male
topology: default
weight: 1
- agent_class: rabbit_agents.Female
topology: default
weight: 1
fixed:
- agent_class: rabbit_agents.RandomAccident
topology: null
hidden: true
state:
group: environment
state:
group: network
mating_prob: 0.1
prob_death: 0.001
topologies:
default:
topology:
directed: true
links: []
nodes:
- id: 1
- id: 0
extra:
visualization_params: {}

130
examples/rabbits/improved/rabbit_agents.py Normal file
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@@ -0,0 +1,130 @@
from soil.agents import FSM, state, default_state, BaseAgent, NetworkAgent
from soil.time import Delta, When, NEVER
from enum import Enum
import logging
import math
class RabbitModel(FSM, NetworkAgent):
mating_prob = 0.005
offspring = 0
birth = None
sexual_maturity = 3
life_expectancy = 30
@default_state
@state
def newborn(self):
self.birth = self.now
self.info(f'I am a newborn.')
self.model['rabbits_alive'] = self.model.get('rabbits_alive', 0) + 1
# Here we can skip the `youngling` state by using a coroutine/generator.
while self.age < self.sexual_maturity:
interval = self.sexual_maturity - self.age
yield Delta(interval)
self.info(f'I am fertile! My age is {self.age}')
return self.fertile
@property
def age(self):
return self.now - self.birth
@state
def fertile(self):
raise Exception("Each subclass should define its fertile state")
def step(self):
super().step()
if self.prob(self.age / self.life_expectancy):
return self.die()
class Male(RabbitModel):
max_females = 5
@state
def fertile(self):
# Males try to mate
for f in self.model.agents(agent_class=Female,
state_id=Female.fertile.id,
limit=self.max_females):
self.debug('Found a female:', repr(f))
if self.prob(self['mating_prob']):
f.impregnate(self)
break # Take a break, don't try to impregnate the rest
class Female(RabbitModel):
due_date = None
age_of_pregnancy = None
gestation = 10
mate = None
@state
def fertile(self):
return self.fertile, NEVER
@state
def pregnant(self):
self.info('I am pregnant')
if self.age > self.life_expectancy:
return self.dead
self.due_date = self.now + self.gestation
number_of_babies = int(8+4*self.random.random())
while self.now < self.due_date:
yield When(self.due_date)
self.info('Having {} babies'.format(number_of_babies))
for i in range(number_of_babies):
agent_class = self.random.choice([Male, Female])
child = self.model.add_node(agent_class=agent_class,
topology=self.topology)
self.model.add_edge(self, child)
self.model.add_edge(self.mate, child)
self.offspring += 1
self.model.agents[self.mate].offspring += 1
self.mate = None
self.due_date = None
return self.fertile
@state
def dead(self):
super().dead()
if self.due_date is not None:
self.info('A mother has died carrying a baby!!')
def impregnate(self, male):
self.info(f'{repr(male)} impregnating female {repr(self)}')
self.mate = male
self.set_state(self.pregnant, when=self.now)
class RandomAccident(BaseAgent):
level = logging.INFO
def step(self):
rabbits_total = self.model.topology.number_of_nodes()
if 'rabbits_alive' not in self.model:
self.model['rabbits_alive'] = 0
rabbits_alive = self.model.get('rabbits_alive', rabbits_total)
prob_death = self.model.get('prob_death', 1e-100)*math.floor(math.log10(max(1, rabbits_alive)))
self.debug('Killing some rabbits with prob={}!'.format(prob_death))
for i in self.model.network_agents:
if i.state.id == i.dead.id:
continue
if self.prob(prob_death):
self.info('I killed a rabbit: {}'.format(i.id))
rabbits_alive = self.model['rabbits_alive'] = rabbits_alive -1
i.set_state(i.dead)
self.debug('Rabbits alive: {}/{}'.format(rabbits_alive, rabbits_total))
if self.model.count_agents(state_id=RabbitModel.dead.id) == self.model.topology.number_of_nodes():
self.die()

41
examples/rabbits/improved/rabbits.yml Normal file
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@@ -0,0 +1,41 @@
---
version: '2'
name: rabbits_improved
num_trials: 1
seed: MySeed
description: null
group: null
interval: 1.0
max_time: 100
model_class: soil.environment.Environment
model_params:
agents:
topology: default
agent_class: rabbit_agents.RabbitModel
distribution:
- agent_class: rabbit_agents.Male
topology: default
weight: 1
- agent_class: rabbit_agents.Female
topology: default
weight: 1
fixed:
- agent_class: rabbit_agents.RandomAccident
topology: null
hidden: true
state:
group: environment
state:
group: network
mating_prob: 0.1
prob_death: 0.001
topologies:
default:
topology:
directed: true
links: []
nodes:
- id: 1
- id: 0
extra:
visualization_params: {}

133
examples/rabbits/rabbit_agents.py
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@@ -1,133 +0,0 @@
from soil.agents import FSM, state, default_state, BaseAgent, NetworkAgent
from enum import Enum
import logging
import math
class Genders(Enum):
male = 'male'
female = 'female'
class RabbitModel(FSM, NetworkAgent):
defaults = {
'age': 0,
'gender': Genders.male.value,
'mating_prob': 0.001,
'offspring': 0,
}
sexual_maturity = 3 #4*30
life_expectancy = 365 * 3
gestation = 33
pregnancy = -1
max_females = 5
@default_state
@state
def newborn(self):
self.debug(f'I am a newborn at age {self["age"]}')
self['age'] += 1
if self['age'] >= self.sexual_maturity:
self.debug('I am fertile!')
return self.fertile
@state
def fertile(self):
raise Exception("Each subclass should define its fertile state")
@state
def dead(self):
self.info('Agent {} is dying'.format(self.id))
self.die()
class Male(RabbitModel):
@state
def fertile(self):
self['age'] += 1
if self['age'] > self.life_expectancy:
return self.dead
if self['gender'] == Genders.female.value:
return
# Males try to mate
for f in self.get_agents(state_id=Female.fertile.id,
agent_class=Female,
limit_neighbors=False,
limit=self.max_females):
r = self.random.random()
if r < self['mating_prob']:
self.impregnate(f)
break # Take a break
def impregnate(self, whom):
whom['pregnancy'] = 0
whom['mate'] = self.id
whom.set_state(whom.pregnant)
self.debug('{} impregnating: {}. {}'.format(self.id, whom.id, whom.state))
class Female(RabbitModel):
@state
def fertile(self):
# Just wait for a Male
pass
@state
def pregnant(self):
self['age'] += 1
if self['age'] > self.life_expectancy:
return self.dead
self['pregnancy'] += 1
self.debug('Pregnancy: {}'.format(self['pregnancy']))
if self['pregnancy'] >= self.gestation:
number_of_babies = int(8+4*self.random.random())
self.info('Having {} babies'.format(number_of_babies))
for i in range(number_of_babies):
state = {}
state['gender'] = self.random.choice(list(Genders)).value
child = self.env.add_node(self.__class__, state)
self.env.add_edge(self.id, child.id)
self.env.add_edge(self['mate'], child.id)
# self.add_edge()
self.debug('A BABY IS COMING TO LIFE')
self.env['rabbits_alive'] = self.env.get('rabbits_alive', self.topology.number_of_nodes())+1
self.debug('Rabbits alive: {}'.format(self.env['rabbits_alive']))
self['offspring'] += 1
self.env.get_agent(self['mate'])['offspring'] += 1
del self['mate']
self['pregnancy'] = -1
return self.fertile
@state
def dead(self):
super().dead()
if 'pregnancy' in self and self['pregnancy'] > -1:
self.info('A mother has died carrying a baby!!')
class RandomAccident(BaseAgent):
level = logging.DEBUG
def step(self):
rabbits_total = self.env.topology.number_of_nodes()
if 'rabbits_alive' not in self.env:
self.env['rabbits_alive'] = 0
rabbits_alive = self.env.get('rabbits_alive', rabbits_total)
prob_death = self.env.get('prob_death', 1e-100)*math.floor(math.log10(max(1, rabbits_alive)))
self.debug('Killing some rabbits with prob={}!'.format(prob_death))
for i in self.env.network_agents:
if i.state['id'] == i.dead.id:
continue
if self.prob(prob_death):
self.debug('I killed a rabbit: {}'.format(i.id))
rabbits_alive = self.env['rabbits_alive'] = rabbits_alive -1
self.log('Rabbits alive: {}'.format(self.env['rabbits_alive']))
i.set_state(i.dead)
self.log('Rabbits alive: {}/{}'.format(rabbits_alive, rabbits_total))
if self.env.count_agents(state_id=RabbitModel.dead.id) == self.env.topology.number_of_nodes():
self.die()

20
examples/rabbits/rabbits.yml
View File

@@ -1,20 +0,0 @@
---
name: rabbits_example
max_time: 100
interval: 1
seed: MySeed
agent_class: rabbit_agents.RabbitModel
environment_agents:
- agent_class: rabbit_agents.RandomAccident
environment_params:
prob_death: 0.001
default_state:
mating_prob: 0.1
topology:
nodes:
- id: 1
agent_class: rabbit_agents.Male
- id: 0
agent_class: rabbit_agents.Female
directed: true
links: []

24
examples/template.yml
View File

@@ -6,20 +6,20 @@ template:
group: simple
num_trials: 1
interval: 1
max_time: 2
max_steps: 2
seed: "CompleteSeed!"
dump: false
network_params:
generator: complete_graph
n: 10
network_agents:
- agent_class: CounterModel
weight: "{{ x1 }}"
state:
state_id: 0
- agent_class: AggregatedCounter
weight: "{{ 1 - x1 }}"
environment_params:
model_params:
network_params:
generator: complete_graph
n: 10
network_agents:
- agent_class: CounterModel
weight: "{{ x1 }}"
state:
state_id: 0
- agent_class: AggregatedCounter
weight: "{{ 1 - x1 }}"
name: "{{ x3 }}"
skip_test: true
vars:

26
examples/terrorism/TerroristNetworkModel.py
View File

@@ -81,6 +81,26 @@ class TerroristSpreadModel(FSM, Geo):
return
return self.leader
def ego_search(self, steps=1, center=False, node=None, **kwargs):
'''Get a list of nodes in the ego network of *node* of radius *steps*'''
node = as_node(node if node is not None else self)
G = self.subgraph(**kwargs)
return nx.ego_graph(G, node, center=center, radius=steps).nodes()
def degree(self, node, force=False):
node = as_node(node)
if force or (not hasattr(self.model, '_degree')) or getattr(self.model, '_last_step', 0) < self.now:
self.model._degree = nx.degree_centrality(self.G)
self.model._last_step = self.now
return self.model._degree[node]
def betweenness(self, node, force=False):
node = as_node(node)
if force or (not hasattr(self.model, '_betweenness')) or getattr(self.model, '_last_step', 0) < self.now:
self.model._betweenness = nx.betweenness_centrality(self.G)
self.model._last_step = self.now
return self.model._betweenness[node]
class TrainingAreaModel(FSM, Geo):
"""
@@ -194,14 +214,14 @@ class TerroristNetworkModel(TerroristSpreadModel):
break
def get_distance(self, target):
source_x, source_y = nx.get_node_attributes(self.topology, 'pos')[self.id]
target_x, target_y = nx.get_node_attributes(self.topology, 'pos')[target]
source_x, source_y = nx.get_node_attributes(self.G, 'pos')[self.id]
target_x, target_y = nx.get_node_attributes(self.G, 'pos')[target]
dx = abs( source_x - target_x )
dy = abs( source_y - target_y )
return ( dx ** 2 + dy ** 2 ) ** ( 1 / 2 )
def shortest_path_length(self, target):
try:
return nx.shortest_path_length(self.topology, self.id, target)
return nx.shortest_path_length(self.G, self.id, target)
except nx.NetworkXNoPath:
return float('inf')

50
examples/terrorism/TerroristNetworkModel.yml
View File

@@ -1,31 +1,31 @@
name: TerroristNetworkModel_sim
max_time: 150
max_steps: 150
num_trials: 1
network_params:
generator: random_geometric_graph
radius: 0.2
# generator: geographical_threshold_graph
# theta: 20
n: 100
network_agents:
- agent_class: TerroristNetworkModel.TerroristNetworkModel
weight: 0.8
state:
id: civilian # Civilians
- agent_class: TerroristNetworkModel.TerroristNetworkModel
weight: 0.1
state:
id: leader # Leaders
- agent_class: TerroristNetworkModel.TrainingAreaModel
weight: 0.05
state:
id: terrorist # Terrorism
- agent_class: TerroristNetworkModel.HavenModel
weight: 0.05
state:
id: civilian # Civilian
model_params:
network_params:
generator: random_geometric_graph
radius: 0.2
# generator: geographical_threshold_graph
# theta: 20
n: 100
network_agents:
- agent_class: TerroristNetworkModel.TerroristNetworkModel
weight: 0.8
state:
id: civilian # Civilians
- agent_class: TerroristNetworkModel.TerroristNetworkModel
weight: 0.1
state:
id: leader # Leaders
- agent_class: TerroristNetworkModel.TrainingAreaModel
weight: 0.05
state:
id: terrorist # Terrorism
- agent_class: TerroristNetworkModel.HavenModel
weight: 0.05
state:
id: civilian # Civilian
environment_params:
# TerroristSpreadModel
information_spread_intensity: 0.7
terrorist_additional_influence: 0.035

23
examples/torvalds.yml
View File

@@ -1,14 +1,15 @@
---
name: torvalds_example
max_time: 10
max_steps: 10
interval: 2
agent_class: CounterModel
default_state:
skill_level: 'beginner'
network_params:
path: 'torvalds.edgelist'
states:
Torvalds:
skill_level: 'God'
balkian:
skill_level: 'developer'
model_params:
agent_class: CounterModel
default_state:
skill_level: 'beginner'
network_params:
path: 'torvalds.edgelist'
states:
Torvalds:
skill_level: 'God'
balkian:
skill_level: 'developer'