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Apply JuliaFormatter to fix code formatting #297

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Merged
merged 1 commit into from
Jul 31, 2025
Merged

Apply JuliaFormatter to fix code formatting #297

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4 changes: 3 additions & 1 deletion src/esn/esn_inits.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -305,7 +305,8 @@ function informed_init(rng::AbstractRNG, ::Type{T}, dims::Integer...;
idxs = findall(Bool[zero_connections .== input_matrix[jdx, :]
for jdx in axes(input_matrix, 1)])
random_row_idx = idxs[DeviceAgnostic.rand(rng, T, 1:end)]
random_clm_idx = range(1, state_size; step = 1)[DeviceAgnostic.rand(rng, T, 1:end)]
random_clm_idx = range(1, state_size; step = 1)[DeviceAgnostic.rand(
rng, T, 1:end)]
input_matrix[random_row_idx, random_clm_idx] = (DeviceAgnostic.rand(rng, T) -
T(0.5)) .* (T(2) * T(scaling))
end
Expand Down Expand Up @@ -943,6 +944,7 @@ function digital_chaotic_adjacency(rng::AbstractRNG, bit_precision::Integer;
end
adjacency_matrix[matrix_order, 1] = 1
for row_index in 1:matrix_order, column_index in 1:matrix_order

if row_index != column_index && rand(rng) < extra_edge_probability
adjacency_matrix[row_index, column_index] = 1
end
Expand Down
6 changes: 4 additions & 2 deletions src/esn/esn_predict.jl
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Expand Up @@ -18,7 +18,8 @@ function obtain_esn_prediction(esn,
x_new = esn.states_type(esn.nla_type, x, out_pad)

for i in 1:prediction_len
x, x_new = next_state_prediction!(esn, x, x_new, out, out_pad, i, tmp_array,
x,
x_new = next_state_prediction!(esn, x, x_new, out, out_pad, i, tmp_array,
args...)
out_tmp = get_prediction(output_layer.training_method, output_layer, x_new)
out = store_results!(output_layer.training_method, out_tmp, output, i)
Expand Down Expand Up @@ -48,7 +49,8 @@ function obtain_esn_prediction(esn,
x_new = esn.states_type(esn.nla_type, x, out_pad)

for i in 1:prediction_len
x, x_new = next_state_prediction!(esn, x, x_new, prediction.prediction_data[:, i],
x,
x_new = next_state_prediction!(esn, x, x_new, prediction.prediction_data[:, i],
out_pad, i, tmp_array, args...)
out_tmp = get_prediction(training_method, output_layer, x_new)
out = store_results!(training_method, out_tmp, output, i)
Expand Down
2 changes: 1 addition & 1 deletion src/reca/reca_input_encodings.jl
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Expand Up @@ -70,7 +70,7 @@ function encoding(rm::RandomMaps, input_vector, tot_encoded_vector)
input_vector,
new_tot_enc_vec[((i - 1) * rm.expansion_size + 1):(i * rm.expansion_size)],
rm.maps[i,
:])
:])
end

return new_tot_enc_vec
Expand Down
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