Merge pull request #297 from ChrisRackauckas/fix-formatting

Apply JuliaFormatter to fix code formatting

Author: ChrisRackauckas

src/esn/esn_inits.jl

@@ -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
@@ -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

src/esn/esn_predict.jl

@@ -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)
@@ -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)

src/reca/reca_input_encodings.jl

@@ -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