Selects action from computed actions probabilities – used for stochastic action sampling

Creates an array of "Any" with the desired number of sub-arrays

Creates an array of "Any" as a uniform categorical distribution

Creates an array of "Any" with the desired number of sub-arrays filled with zeros

Calculate Expected Utility

Calculate Free Energy

Calculate observation to state info Gain

Calculate state to state info Gain

Calculate States Information Gain

Calculate Accuracy Term

Edited Compute Accuracy [Still needs to be nested within Fixed-Point Iteration]

Construct Policies

Dot-Product Function

Function for Calculating Entropy of A-Matrix

Run State Inference via Fixed-Point Iteration

Generate Random Generative Model as A and B matrices

Get Expected Observations

Get Expected States

Get Joint Likelihood

Function to get log marginal probabilities of actions

Get Model Dimensions from either A or B Matrix

Update the agents's beliefs over policies

Update the agents's beliefs over states

Initialize Active Inference Agent function initaif( A, B; C=nothing, D=nothing, E = nothing, pA = nothing, pB = nothing, pD = nothing, parameters::Union{Nothing, Dict{String,Real}} = nothing, settings::Union{Nothing, Dict} = nothing, savehistory::Bool = true)

Arguments

• 'A': Relationship between hidden states and observations.
• 'B': Transition probabilities.
• 'C = nothing': Prior preferences over observations.
• 'D = nothing': Prior over initial hidden states.
• 'E = nothing': Prior over policies. (habits)
• 'pA = nothing':
• 'pB = nothing':
• 'pD = nothing':
• 'parameters::Union{Nothing, Dict{String,Real}} = nothing':
• 'settings::Union{Nothing, Dict} = nothing':
• 'settings::Union{Nothing, Dict} = nothing':

Function for getting KL-divergence

Normalizes a Categorical probability distribution

Normalizes muliple arrays

Creates a onehot encoded vector

Process Observation to the Correct Format

Sample action from the beliefs over policies

Sample Action [Stochastic or Deterministic]

Sampling Function

Selects the highest value from Array – used for deterministic action sampling

Softmax Function

Softmax Function for array of arrays

Calculate Bayesian Surprise

Multi-dimensional outer product

Apply spm_log to array of arrays

SPM_wnorm

Equivalent to pymdp's "toobjarray"

Update A-matrix

Update B-matrix

Update D-matrix

Update obs likelihood matrix

Update Posterior over Policies

Update Posterior States

Update state likelihood matrix

Update prior D matrix