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< h1 > Developing new models< a class = "headerlink" href = "#developing-new-models" title = "Permalink to this headline" > ¶< / a > < / h1 >
< p > This document describes how to develop a new analysis model.< / p >
< div class = "section" id = "what-is-a-model" >
< h2 > What is a model?< a class = "headerlink" href = "#what-is-a-model" title = "Permalink to this headline" > ¶< / a > < / h2 >
< p > A model defines the behaviour of the agents with a view to assessing their effects on the system as a whole.
In practice, a model consists of at least two parts:< / p >
< ul class = "simple" >
< li > Python module: the actual code that describes the behaviour.< / li >
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< li > Setting up the variables in the Settings JSON file.< / li >
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< / ul >
< p > This separation allows us to run the simulation with different agents.< / p >
< / div >
< div class = "section" id = "models-code" >
< h2 > Models Code< a class = "headerlink" href = "#models-code" title = "Permalink to this headline" > ¶< / a > < / h2 >
< p > All the models are imported to the main file. The initialization look like this:< / p >
< div class = "code python highlight-default" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > settings< / span >
< span class = "n" > networkStatus< / span > < span class = "o" > =< / span > < span class = "p" > {}< / span > < span class = "c1" > # Dict that will contain the status of every agent in the network< / span >
< span class = "n" > sentimentCorrelationNodeArray< / span > < span class = "o" > =< / span > < span class = "p" > []< / span >
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< span class = "k" > for< / span > < span class = "n" > x< / span > < span class = "ow" > in< / span > < span class = "nb" > range< / span > < span class = "p" > (< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span > < span class = "n" > settings< / span > < span class = "o" > .< / span > < span class = "n" > network_params< / span > < span class = "p" > [< / span > < span class = "s2" > " number_of_nodes" < / span > < span class = "p" > ]):< / span >
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< span class = "n" > sentimentCorrelationNodeArray< / span > < span class = "o" > .< / span > < span class = "n" > append< / span > < span class = "p" > ({< / span > < span class = "s1" > ' id' < / span > < span class = "p" > :< / span > < span class = "n" > x< / span > < span class = "p" > })< / span >
< span class = "c1" > # Initialize agent states. Let' s assume everyone is normal.< / span >
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< span class = "n" > init_states< / span > < span class = "o" > =< / span > < span class = "p" > [{< / span > < span class = "s1" > ' id' < / span > < span class = "p" > :< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span > < span class = "p" > }< / span > < span class = "k" > for< / span > < span class = "n" > _< / span > < span class = "ow" > in< / span > < span class = "nb" > range< / span > < span class = "p" > (< / span > < span class = "n" > settings< / span > < span class = "o" > .< / span > < span class = "n" > network_params< / span > < span class = "p" > [< / span > < span class = "s2" > " number_of_nodes" < / span > < span class = "p" > ])]< / span >
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< span class = "c1" > # add keys as as necessary, but " id" must always refer to that state category< / span >
< / pre > < / div >
< / div >
< p > A new model have to inherit the BaseBehaviour class which is in the same module.
There are two basics methods:< / p >
< ul class = "simple" >
< li > __init__< / li >
< li > step: used to define the behaviour over time.< / li >
< / ul >
< / div >
< div class = "section" id = "variable-initialization" >
< h2 > Variable Initialization< a class = "headerlink" href = "#variable-initialization" title = "Permalink to this headline" > ¶< / a > < / h2 >
< p > The different parameters of the model have to be initialize in the Simulation Settings JSON file which will be
passed as a parameter to the simulation.< / p >
< div class = "code json highlight-default" > < div class = "highlight" > < pre > < span > < / span > < span class = "p" > {< / span >
< span class = "s2" > " agent" < / span > < span class = "p" > :< / span > < span class = "p" > [< / span > < span class = "s2" > " SISaModel" < / span > < span class = "p" > ,< / span > < span class = "s2" > " ControlModelM2" < / span > < span class = "p" > ],< / span >
< span class = "s2" > " neutral_discontent_spon_prob" < / span > < span class = "p" > :< / span > < span class = "mf" > 0.04< / span > < span class = "p" > ,< / span >
< span class = "s2" > " neutral_discontent_infected_prob" < / span > < span class = "p" > :< / span > < span class = "mf" > 0.04< / span > < span class = "p" > ,< / span >
< span class = "s2" > " neutral_content_spon_prob" < / span > < span class = "p" > :< / span > < span class = "mf" > 0.18< / span > < span class = "p" > ,< / span >
< span class = "s2" > " neutral_content_infected_prob" < / span > < span class = "p" > :< / span > < span class = "mf" > 0.02< / span > < span class = "p" > ,< / span >
< span class = "s2" > " discontent_neutral" < / span > < span class = "p" > :< / span > < span class = "mf" > 0.13< / span > < span class = "p" > ,< / span >
< span class = "s2" > " discontent_content" < / span > < span class = "p" > :< / span > < span class = "mf" > 0.07< / span > < span class = "p" > ,< / span >
< span class = "s2" > " variance_d_c" < / span > < span class = "p" > :< / span > < span class = "mf" > 0.02< / span > < span class = "p" > ,< / span >
< span class = "s2" > " content_discontent" < / span > < span class = "p" > :< / span > < span class = "mf" > 0.009< / span > < span class = "p" > ,< / span >
< span class = "s2" > " variance_c_d" < / span > < span class = "p" > :< / span > < span class = "mf" > 0.003< / span > < span class = "p" > ,< / span >
< span class = "s2" > " content_neutral" < / span > < span class = "p" > :< / span > < span class = "mf" > 0.088< / span > < span class = "p" > ,< / span >
< span class = "s2" > " standard_variance" < / span > < span class = "p" > :< / span > < span class = "mf" > 0.055< / span > < span class = "p" > ,< / span >
< span class = "s2" > " prob_neutral_making_denier" < / span > < span class = "p" > :< / span > < span class = "mf" > 0.035< / span > < span class = "p" > ,< / span >
< span class = "s2" > " prob_infect" < / span > < span class = "p" > :< / span > < span class = "mf" > 0.075< / span > < span class = "p" > ,< / span >
< span class = "s2" > " prob_cured_healing_infected" < / span > < span class = "p" > :< / span > < span class = "mf" > 0.035< / span > < span class = "p" > ,< / span >
< span class = "s2" > " prob_cured_vaccinate_neutral" < / span > < span class = "p" > :< / span > < span class = "mf" > 0.035< / span > < span class = "p" > ,< / span >
< span class = "s2" > " prob_vaccinated_healing_infected" < / span > < span class = "p" > :< / span > < span class = "mf" > 0.035< / span > < span class = "p" > ,< / span >
< span class = "s2" > " prob_vaccinated_vaccinate_neutral" < / span > < span class = "p" > :< / span > < span class = "mf" > 0.035< / span > < span class = "p" > ,< / span >
< span class = "s2" > " prob_generate_anti_rumor" < / span > < span class = "p" > :< / span > < span class = "mf" > 0.035< / span >
< span class = "p" > }< / span >
< / pre > < / div >
< / div >
< p > In this file you will also define the models you are going to simulate. You can simulate as many models as you want.
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The simulation returns one result for each model, executing each model separately. For the usage, see < a class = "reference internal" href = "usage.html" > < span class = "doc" > Usage< / span > < / a > .< / p >
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< / div >
< div class = "section" id = "example-model" >
< h2 > Example Model< a class = "headerlink" href = "#example-model" title = "Permalink to this headline" > ¶< / a > < / h2 >
< p > In this section, we will implement a Sentiment Correlation Model.< / p >
< p > The class would look like this:< / p >
< div class = "code python highlight-default" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > ..BaseBehaviour< / span > < span class = "k" > import< / span > < span class = "o" > *< / span >
< span class = "kn" > from< / span > < span class = "nn" > ..< / span > < span class = "k" > import< / span > < span class = "n" > sentimentCorrelationNodeArray< / span >
< span class = "k" > class< / span > < span class = "nc" > SentimentCorrelationModel< / span > < span class = "p" > (< / span > < span class = "n" > BaseBehaviour< / span > < span class = "p" > ):< / span >
< span class = "k" > def< / span > < span class = "nf" > __init__< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > environment< / span > < span class = "o" > =< / span > < span class = "kc" > None< / span > < span class = "p" > ,< / span > < span class = "n" > agent_id< / span > < span class = "o" > =< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span > < span class = "n" > state< / span > < span class = "o" > =< / span > < span class = "p" > ()):< / span >
< span class = "nb" > super< / span > < span class = "p" > ()< / span > < span class = "o" > .< / span > < span class = "n" > __init__< / span > < span class = "p" > (< / span > < span class = "n" > environment< / span > < span class = "o" > =< / span > < span class = "n" > environment< / span > < span class = "p" > ,< / span > < span class = "n" > agent_id< / span > < span class = "o" > =< / span > < span class = "n" > agent_id< / span > < span class = "p" > ,< / span > < span class = "n" > state< / span > < span class = "o" > =< / span > < span class = "n" > state< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > outside_effects_prob< / span > < span class = "o" > =< / span > < span class = "n" > environment< / span > < span class = "o" > .< / span > < span class = "n" > environment_params< / span > < span class = "p" > [< / span > < span class = "s1" > ' outside_effects_prob' < / span > < span class = "p" > ]< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > anger_prob< / span > < span class = "o" > =< / span > < span class = "n" > environment< / span > < span class = "o" > .< / span > < span class = "n" > environment_params< / span > < span class = "p" > [< / span > < span class = "s1" > ' anger_prob' < / span > < span class = "p" > ]< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > joy_prob< / span > < span class = "o" > =< / span > < span class = "n" > environment< / span > < span class = "o" > .< / span > < span class = "n" > environment_params< / span > < span class = "p" > [< / span > < span class = "s1" > ' joy_prob' < / span > < span class = "p" > ]< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > sadness_prob< / span > < span class = "o" > =< / span > < span class = "n" > environment< / span > < span class = "o" > .< / span > < span class = "n" > environment_params< / span > < span class = "p" > [< / span > < span class = "s1" > ' sadness_prob' < / span > < span class = "p" > ]< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > disgust_prob< / span > < span class = "o" > =< / span > < span class = "n" > environment< / span > < span class = "o" > .< / span > < span class = "n" > environment_params< / span > < span class = "p" > [< / span > < span class = "s1" > ' disgust_prob' < / span > < span class = "p" > ]< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > time_awareness< / span > < span class = "o" > =< / span > < span class = "p" > []< / span >
< span class = "k" > for< / span > < span class = "n" > i< / span > < span class = "ow" > in< / span > < span class = "nb" > range< / span > < span class = "p" > (< / span > < span class = "mi" > 4< / span > < span class = "p" > ):< / span > < span class = "c1" > # In this model we have 4 sentiments< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > time_awareness< / span > < span class = "o" > .< / span > < span class = "n" > append< / span > < span class = "p" > (< / span > < span class = "mi" > 0< / span > < span class = "p" > )< / span > < span class = "c1" > # 0-> Anger, 1-> joy, 2-> sadness, 3 -> disgust< / span >
< span class = "n" > sentimentCorrelationNodeArray< / span > < span class = "p" > [< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > id< / span > < span class = "p" > ][< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > env< / span > < span class = "o" > .< / span > < span class = "n" > now< / span > < span class = "p" > ]< / span > < span class = "o" > =< / span > < span class = "mi" > 0< / span >
< span class = "k" > def< / span > < span class = "nf" > step< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > now< / span > < span class = "p" > ):< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > behaviour< / span > < span class = "p" > ()< / span > < span class = "c1" > # Method which define the behaviour< / span >
< span class = "nb" > super< / span > < span class = "p" > ()< / span > < span class = "o" > .< / span > < span class = "n" > step< / span > < span class = "p" > (< / span > < span class = "n" > now< / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p > The variables will be modified by the user, so you have to include them in the Simulation Settings JSON file.< / p >
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< h3 > < a href = "index.html" > Table Of Contents< / a > < / h3 >
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< li > < a class = "reference internal" href = "#" > Developing new models< / a > < ul >
< li > < a class = "reference internal" href = "#what-is-a-model" > What is a model?< / a > < / li >
< li > < a class = "reference internal" href = "#models-code" > Models Code< / a > < / li >
< li > < a class = "reference internal" href = "#variable-initialization" > Variable Initialization< / a > < / li >
< li > < a class = "reference internal" href = "#example-model" > Example Model< / a > < / li >
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< / li >
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< li > Previous: < a href = "usage.html" title = "previous chapter" > Usage< / a > < / li >
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