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J. Fernando Sánchez
2022-10-06 15:49:10 +02:00
parent 0a9c6d8b19
commit f811ee18c5
53 changed files with 856 additions and 774 deletions
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28
docs/configuration.rst
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@@ -13,7 +13,7 @@ Here's an example (``example.yml``).
This example configuration will run three trials (``num_trials``) of a simulation containing a randomly generated network (``network_params``).
The 100 nodes in the network will be SISaModel agents (``network_agents.agent_type``), which is an agent behavior that is included in Soil.
The 100 nodes in the network will be SISaModel agents (``network_agents.agent_class``), which is an agent behavior that is included in Soil.
10% of the agents (``weight=1``) will start in the content state, 10% in the discontent state, and the remaining 80% (``weight=8``) in the neutral state.
All agents will have access to the environment (``environment_params``), which only contains one variable, ``prob_infected``.
The state of the agents will be updated every 2 seconds (``interval``).
@@ -116,7 +116,7 @@ Agents
======
Agents are a way of modelling behavior.
Agents can be characterized with two variables: agent type (``agent_type``) and state.
Agents can be characterized with two variables: agent type (``agent_class``) and state.
The agent type is a ``soil.Agent`` class, which contains the code that encapsulates the behavior of the agent.
The state is a set of variables, which may change during the simulation, and that the code may use to control the behavior.
All agents provide a ``step`` method either explicitly or implicitly (by inheriting it from a superclass), which controls how the agent will behave in each step of the simulation.
@@ -142,7 +142,7 @@ Hence, every node in the network will be associated to an agent of that type.
.. code:: yaml
agent_type: SISaModel
agent_class: SISaModel
It is also possible to add more than one type of agent to the simulation.
@@ -152,9 +152,9 @@ For instance, with following configuration, it is five times more likely for a n
.. code:: yaml
network_agents:
- agent_type: SISaModel
- agent_class: SISaModel
weight: 1
- agent_type: CounterModel
- agent_class: CounterModel
weight: 5
The third option is to specify the type of agent on the node itself, e.g.:
@@ -165,10 +165,10 @@ The third option is to specify the type of agent on the node itself, e.g.:
topology:
nodes:
- id: first
agent_type: BaseAgent
agent_class: BaseAgent
states:
first:
agent_type: SISaModel
agent_class: SISaModel
This would also work with a randomly generated network:
@@ -179,9 +179,9 @@ This would also work with a randomly generated network:
network:
generator: complete
n: 5
agent_type: BaseAgent
agent_class: BaseAgent
states:
- agent_type: SISaModel
- agent_class: SISaModel
@@ -192,11 +192,11 @@ e.g., to populate the network with SISaModel, roughly 10% of them with a discont
.. code:: yaml
network_agents:
- agent_type: SISaModel
- agent_class: SISaModel
weight: 9
state:
id: neutral
- agent_type: SISaModel
- agent_class: SISaModel
weight: 1
state:
id: discontent
@@ -206,7 +206,7 @@ For instance, to add a state for the two nodes in this configuration:
.. code:: yaml
agent_type: SISaModel
agent_class: SISaModel
network:
generator: complete_graph
n: 2
@@ -231,10 +231,10 @@ These agents are programmed in much the same way as network agents, the only dif
.. code::
environment_agents:
- agent_type: MyAgent
- agent_class: MyAgent
state:
mood: happy
- agent_type: DummyAgent
- agent_class: DummyAgent
You may use environment agents to model events that a normal agent cannot control, such as natural disasters or chance.

6
docs/example.yml
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@@ -8,15 +8,15 @@ network_params:
n: 100
m: 2
network_agents:
- agent_type: SISaModel
- agent_class: SISaModel
weight: 1
state:
id: content
- agent_type: SISaModel
- agent_class: SISaModel
weight: 1
state:
id: discontent
- agent_type: SISaModel
- agent_class: SISaModel
weight: 8
state:
id: neutral

4
docs/quickstart.yml
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@@ -3,11 +3,11 @@ name: quickstart
num_trials: 1
max_time: 1000
network_agents:
- agent_type: SISaModel
- agent_class: SISaModel
state:
id: neutral
weight: 1
- agent_type: SISaModel
- agent_class: SISaModel
state:
id: content
weight: 2

10
docs/soil_tutorial.rst
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@@ -211,11 +211,11 @@ nodes in that network. Notice how node 0 is the only one with a TV.
sim = soil.Simulation(topology=G,
num_trials=1,
max_time=MAX_TIME,
environment_agents=[{'agent_type': NewsEnvironmentAgent,
environment_agents=[{'agent_class': NewsEnvironmentAgent,
'state': {
'event_time': EVENT_TIME
}}],
network_agents=[{'agent_type': NewsSpread,
network_agents=[{'agent_class': NewsSpread,
'weight': 1}],
states={0: {'has_tv': True}},
default_state={'has_tv': False},
@@ -285,14 +285,14 @@ For this demo, we will use a python dictionary:
},
'network_agents': [
{
'agent_type': NewsSpread,
'agent_class': NewsSpread,
'weight': 1,
'state': {
'has_tv': False
}
},
{
'agent_type': NewsSpread,
'agent_class': NewsSpread,
'weight': 2,
'state': {
'has_tv': True
@@ -300,7 +300,7 @@ For this demo, we will use a python dictionary:
}
],
'environment_agents':[
{'agent_type': NewsEnvironmentAgent,
{'agent_class': NewsEnvironmentAgent,
'state': {
'event_time': 10
}