Echo State Networks

The core component of an ESN is the ESN type. It represents the entire Echo State Network and includes parameters for configuring the reservoir, input scaling, and output weights. Here's the documentation for the ESN type:

ReservoirComputing.ESN — Type

ESN(train_data; kwargs...) -> ESN

Creates an Echo State Network (ESN).

Arguments

train_data: Matrix of training data num_features x time_steps.
variation: Variation of ESN (default: Default()).
input_layer: Input layer of ESN.
reservoir: Reservoir of the ESN.
bias: Bias vector for each time step.
rng: Random number generator used for initializing weights. Default is Utils.default_rng().
reservoir_driver: Mechanism for evolving reservoir states (default: RNN()).
nla_type: Non-linear activation type (default: NLADefault()).
states_type: Format for storing states (default: StandardStates()).
washout: Initial time steps to discard (default: 0).
matrix_type: Type of matrices used internally (default: type of train_data).

Examples

julia> train_data = rand(Float32, 10, 100)  # 10 features, 100 time steps
10×100 Matrix{Float32}:
 0.567676   0.154756  0.584611  0.294015   …  0.573946    0.894333    0.429133
 0.327073   0.729521  0.804667  0.263944      0.559342    0.020167    0.897862
 0.453606   0.800058  0.568311  0.749441      0.0713146   0.464795    0.532854
 0.0173253  0.536959  0.722116  0.910328      0.00224048  0.00202501  0.631075
 0.366744   0.119761  0.100593  0.125122      0.700562    0.675474    0.102947
 0.539737   0.768351  0.54681   0.648672   …  0.256738    0.223784    0.94327
 0.558099   0.42676   0.1948    0.735625      0.0989234   0.119342    0.624182
 0.0603135  0.929999  0.263439  0.0372732     0.066125    0.332769    0.25562
 0.4463     0.334423  0.444679  0.311695      0.0494497   0.27171     0.214925
 0.987182   0.898593  0.295241  0.233098      0.789699    0.453692    0.759205

julia> esn = ESN(train_data, 10, 300; washout = 10)
ESN(10 => 300)

source

Training

To train an ESN model, you can use the train function. It takes the ESN model, training data, and other optional parameters as input and returns a trained model. Here's the documentation for the train function:

ReservoirComputing.train — Function

train(esn::AbstractEchoStateNetwork, target_data, training_method = StandardRidge(0.0))

Trains an Echo State Network (ESN) using the provided target data and a specified training method.

Parameters

esn::AbstractEchoStateNetwork: The ESN instance to be trained.
target_data: Supervised training data for the ESN.
training_method: The method for training the ESN (default: StandardRidge(0.0)).

Example

julia> train_data = rand(Float32, 10, 100)  # 10 features, 100 time steps
10×100 Matrix{Float32}:
 0.11437   0.425367  0.585867   0.34078   …  0.0531493  0.761425  0.883164
 0.301373  0.497806  0.279603   0.802417     0.49873    0.270156  0.333333
 0.135224  0.660179  0.394233   0.512753     0.901221   0.784377  0.687691
 0.510203  0.877234  0.614245   0.978405     0.332775   0.768826  0.527077
 0.955027  0.398322  0.312156   0.981938     0.473357   0.156704  0.476101
 0.353024  0.997632  0.164328   0.470783  …  0.745613   0.85797   0.465201
 0.966044  0.194299  0.599167   0.040475     0.0996013  0.325959  0.770103
 0.292068  0.495138  0.481299   0.214566     0.819573   0.155951  0.227168
 0.133498  0.451058  0.0761995  0.90421      0.994212   0.332164  0.545112
 0.214467  0.791524  0.124105   0.951805     0.947166   0.954244  0.889733

julia> esn = ESN(train_data, 10, 300; washout = 10)
ESN(10 => 300)

julia> output_layer = train(esn, rand(Float32, 3, 90))
OutputLayer successfully trained with output size: 3

source

With these components and variations, you can configure and train ESN models for various time series and sequential data prediction tasks.