Redundant function array_of_any() fully removed

src/ActiveInference.jl

@@ -32,7 +32,6 @@ export # utils/create_matrix_templates.jl
        normalize_arrays,
 
        # utils/utils.jl
-       array_of_any, 
        array_of_any_zeros, 
        array_of_any_uniform, 
        onehot,

src/Environments/TMazeEnv.jl

@@ -85,7 +85,7 @@ function reset_TMaze!(env::TMazeEnv; state=nothing)
         env.reward_condition = onehot(env._reward_condition_idx, env.num_reward_conditions)
 
         # Initialize the full state array
-        full_state = array_of_any(env.num_factors)
+        full_state = Vector{Any}(undef, env.num_factors)
         full_state[env.location_factor_id] = loc_state
         full_state[env.trial_factor_id] = env.reward_condition
 
@@ -182,7 +182,7 @@ end
 
 function construct_state(env::TMazeEnv, state_tuple)
     # Create an array of any
-    state = array_of_any(env.num_factors)
+    state = Vector{Any}(undef, env.num_factors)
 
     # Populate the state array with one-hot encoded vectors
     for (f, ns) in enumerate(env.num_states)

src/pomdp/POMDP.jl

@@ -14,7 +14,7 @@ function action_pomdp!(agent::Agent, obs::Vector{Int64})
     n_factors = length(aif.settings["num_controls"])
 
     # Initialize empty arrays for action distribution per factor
-    action_p = array_of_any(n_factors)
+    action_p = Vector{Any}(undef, n_factors)
     action_distribution = Vector(undef, n_factors)
 
     #If there was a previous action
@@ -59,7 +59,7 @@ function action_pomdp!(aif::AIF, obs::Vector{Int64})
     n_factors = length(aif.settings["num_controls"])
 
     # Initialize an empty arrays for action distribution per factor
-    action_p = array_of_any(n_factors)
+    action_p = Vector{Any}(undef, n_factors)
     action_distribution = Vector(undef, n_factors)
 
     ### Infer states & policies

src/pomdp/inference.jl

@@ -248,7 +248,7 @@ function get_expected_obs(qs_pi, A::Vector{Array{<:Real}})
     qo_pi = []
 
     for t in 1:n_steps
-        qo_pi_t = array_of_any(length(A))
+        qo_pi_t = Vector{Any}(undef, length(A))
         qo_pi = push!(qo_pi, qo_pi_t)
     end
 
@@ -305,7 +305,7 @@ function calc_pA_info_gain(pA, qo_pi, qs_pi)
     n_steps = length(qo_pi)
     num_modalities = length(pA)
 
-    wA = array_of_any(num_modalities)
+    wA = Vector{Any}(undef, num_modalities)
     for (modality, pA_m) in enumerate(pA)
         wA[modality] = spm_wnorm(pA[modality])
     end
@@ -327,7 +327,7 @@ function calc_pB_info_gain(pB, qs_pi, qs_prev, policy)
     n_steps = length(qs_pi)
     num_factors = length(pB)
 
-    wB = array_of_any(num_factors)
+    wB = Vector{Any}(undef, num_factors)
     for (factor, pB_f) in enumerate(pB)
         wB[factor] = spm_wnorm(pB_f)
     end

src/utils/utils.jl

@@ -1,10 +1,5 @@
 """ -------- Utility Functions -------- """
 
-""" Creates an array of "Any" with the desired number of sub-arrays"""
-function array_of_any(num_arr::Int) 
-    return Array{Any}(undef, num_arr) #saves it as {Any} e.g. can be any kind of data type.
-end
-
 """ Creates an array of "Any" with the desired number of sub-arrays filled with zeros"""
 function array_of_any_zeros(shape_list)
     arr = Array{Any}(undef, length(shape_list))
@@ -79,7 +74,7 @@ end
 function get_log_action_marginals(aif)
     num_factors = length(aif.num_controls)
     action_marginals = create_matrix_templates(aif.num_controls, "zeros")
-    log_action_marginals = array_of_any(num_factors)
+    log_action_marginals = Vector{Any}(undef, num_factors)
     q_pi = get_states(aif, "posterior_policies")
     policies = get_states(aif, "policies")