HGF

Custom error type which will result in rejection of a sample

"" fitmodel(agent::Agent,inputs::Array,actions::Vector,parampriors::Dict,fixedparameters::Dict = Dict(); sampler = NUTS(),niterations = 1000, n_chains = 1,verbose = true,)

Returns a summary of the fitted parameters (parameters specified with param_prios).

Arguments

• 'agent::Agent': a specified agent created with either premade agent or init_agent.
• 'inputs:Array': array of inputs.
• 'actions::Array': array of actions.
• 'param_priors::Dict': priors (written as distributions) for the parameters you wish to fit.
• 'fixed_parameters::Dict = Dict()': fixed parameters.
• 'imputemissingactions = false': if true, include missing actions in the fitting process.
• 'sampler = NUTS()': specify the type of sampler.
• 'n_iterations = 1000': iterations pr. chain.
• 'n_chains = 1': amount of chains.
• 'verbose = true': set to false to hide warnings

get_parameters(agent::Agent, target_param::Union{String,Tuple})

Get out target parameter from agent

get_parameters(agent::Agent, target_parameters::Vector)

Returns a vector of the target parameters specefied in target_parameters

get_parameters(agent::Agent)

Returns all parameters from agent

get_posteriors(chain::Chains; type::String = "median")

Extracting parameters from a Turing chain

get_states(agent::Agent, target_state::Union{String,Tuple})

extract target state from agent's states.

get_states(agent::Agent, target_states::Vector)

specify a vector of target states you wish to extract.

Get_states(agent::Agent)

Get all target states from an agent.

give_inputs!(agent::Agent, inputs::Array)

Function for inputting multiple observations to an agent. Input is structured as an Array, with each row being a single input consisting of multiple numbers.

give_inputs!(agent::Agent, inputs::Real)

Convenience method for inputting multiple observations to an agent. Input is here just a single value.

give_inputs!(agent::Agent, inputs::Vector)

Function for inputting multiple observations to an agent. Input is structured as an Array, with each row being a single input consisting of multiple numbers.

multiple_actions(agent::Agent, input::Any)

If an agent is specified with multiple action models

Runs each action model sequentially and returns the action distributions for each action model given agent and input.

plotpredictivesimulation(paramdistributions::Union{Chains,Dict}, agent::Agent, inputs::Array, targetstate::Union{String,Tuple}; fixedparameters::Dict = Dict(), nsimulations::Int = 100, verbose::Bool = true, mediancolor::Union{String,Symbol} = :red, title::String = "Sampled trajectories", label::Union{String,Tuple} = targetstate, alpha::Real = 0.1, linewidth::Real = 2, )

compute predictive simulation of target state/states given a set of parameter distributions.

Arguments

• 'param_distributions::Union{Chains,Dict}': The parameter distributions you want to sample from during the predictive simulation. This can be a turing chain of posteriors or a regular parameter distribution.
• 'agent::Agent': specified premade agent or custom made agent.
• 'inputs::Array': input observations to your agent
• 'target_state::Union{String,Tuple}': the state(s) or actions you want to do simulate. Note that the target state(s) need to be in the agents history.
• 'fixed_parameters::Dict = Dict()': The fixed parameters which do not vary during the simulation.
• 'n_simulations::Int = 100': amount of simulations you want to run.
• 'verbose::Bool = true': if you wish to hide warnings set to false
• 'median_color::Union{String,Symbol} = :red': specify color of median value in the plot
• 'label::Union{String,Tuple} = target_state': label on graph
• 'alpha::Real = 0.1':
• 'linewidth::Real = 2': specify linewidth on your plot

function premade_agent(
    model_name::String, parameters_list::NamedTuple = (;)
)

Making a premade agent consisting of a model (a premade agent), and a list of configuations (parameter values) for the agent.

reset!(agent::Agent)

reset an agent to initial state parameters.

set_parameters!(agent::Agent, parameters::Dict)

Setting multiple parameters in dictionary where parameter name is specified followed by parameter value.

setparameters!(agent::Agent, targetparam::Union{String,Tuple}, param_value::Any)

Function for setting a single parameter in an agent. Input to the function is an agent, the parameter name and the parameter value.

single_input!(agent::Agent, input)

Function for giving an input to an Agent.