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SciML · Jan 8, 2025 · 136addf · 136addf
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diff --git a/src/esn/esn.jl b/src/esn/esn.jl
@@ -50,7 +50,6 @@ julia> train_data = rand(Float32, 10, 100)  # 10 features, 100 time steps
 
 julia> esn = ESN(train_data, 10, 300; washout=10)
 ESN(10 => 300)
-
 ```
 """
 function ESN(train_data,
@@ -95,8 +94,9 @@ function (esn::AbstractEchoStateNetwork)(prediction::AbstractPrediction,
         kwargs...)
 end
 
-Base.show(io::IO, esn::ESN) =
+function Base.show(io::IO, esn::ESN)
     print(io, "ESN(", size(esn.train_data, 1), " => ", size(esn.reservoir_matrix, 1), ")")
+end
 
 #training dispatch on esn
 """
@@ -110,7 +110,6 @@ Trains an Echo State Network (ESN) using the provided target data and a specifie
   - `target_data`: Supervised training data for the ESN.
   - `training_method`: The method for training the ESN (default: `StandardRidge(0.0)`).
 
-
 # Example
 
 ```jldoctest
@@ -132,7 +131,6 @@ ESN(10 => 300)
 
 julia> output_layer = train(esn, rand(Float32, 3, 90))
 OutputLayer successfully trained with output size: 3
-
 ```
 """
 function train(esn::AbstractEchoStateNetwork,

diff --git a/src/esn/esn_reservoir_drivers.jl b/src/esn/esn_reservoir_drivers.jl
@@ -20,7 +20,6 @@ specified reservoir driver.
     and reservoir nodes.
   - `bias_vector`: The bias vector to be added at each time step during the reservoir
     update.
-
 """
 function create_states(reservoir_driver::AbstractReservoirDriver,
         train_data,
@@ -108,8 +107,6 @@ echo state networks (`ESN`).
     Defaults to `tanh_fast`.
   - `leaky_coefficient`: The leaky coefficient used in the RNN.
     Defaults to 1.0.
-
-
 """
 function RNN(; activation_function=NNlib.fast_act(tanh), leaky_coefficient=1.0)
     RNN(activation_function, leaky_coefficient)
@@ -185,7 +182,6 @@ This function creates an MRNN object with the specified activation functions,
 leaky coefficient, and scaling factors, which can be used as a reservoir driver
 in the ESN.
 
-
 [^Lun2015]: Lun, Shu-Xian, et al.
     "_A novel model of leaky integrator echo state network for
     time-series prediction._" Neurocomputing 159 (2015): 58-66.
@@ -234,10 +230,9 @@ end
 Returns a Fully Gated Recurrent Unit (FullyGated) initializer
 for the Echo State Network (ESN).
 
-Returns the standard gated recurrent unit [^Cho2014] as a driver for the 
+Returns the standard gated recurrent unit [^Cho2014] as a driver for the
 echo state network (`ESN`).
 
-
 [^Cho2014]: Cho, Kyunghyun, et al.
     "_Learning phrase representations using RNN encoder-decoder
     for statistical machine translation._"
@@ -281,7 +276,6 @@ This driver is based on the GRU architecture [^Cho2014].
   - `variant`: The GRU variant to use.
     By default, it uses the "FullyGated" variant.
 
-
 [^Cho2014]: Cho, Kyunghyun, et al.
     "_Learning phrase representations using RNN encoder-decoder for statistical machine translation._"
     arXiv preprint arXiv:1406.1078 (2014).

diff --git a/src/predict.jl b/src/predict.jl
@@ -9,8 +9,9 @@ struct OutputLayer{T, I, S, L} <: AbstractOutputLayer
     last_value::L
 end
 
-Base.show(io::IO, ol::OutputLayer) = 
+function Base.show(io::IO, ol::OutputLayer)
     print(io, "OutputLayer successfully trained with output size: ", ol.out_size)
+end
 
 #prediction types
 """
@@ -58,14 +59,13 @@ of input features (`prediction_data`).
 
 The `Predictive` prediction method uses the provided input data
 (`prediction_data`) to produce corresponding labels or outputs based
-on the learned relationships in the model. 
+on the learned relationships in the model.
 """
 function Predictive(prediction_data)
     prediction_len = size(prediction_data, 2)
     Predictive(prediction_data, prediction_len)
 end
 
-
 function obtain_prediction(rc::AbstractReservoirComputer,
         prediction::Generative,
         x,