A great deal of efforts in the ESNs field are devoted to finding an ideal construction for the reservoir matrices. With a simple interface using ReservoirComputing.jl is possible to leverage the currently implemented matrix constructions methods for both the reservoir and the input layer. In this page it is showcased how it is possible to change both of these layers.
input_init keyword argument provided with the
ESN constructor allows for changing the input layer. The layers provided in ReservoirComputing.jl are the following:
InformedLayer(model_in_size; scaling=0.1, gamma=0.5)
In addition the user can define a custom layer following this workflow:
#creation of the new struct for the layer struct MyNewLayer <: AbstractLayer #the layer params go here end #dispatch over the function to actually build the layer matrix function create_layer(input_layer::MyNewLayer, res_size, in_size) #the new algorithm to build the input layer goes here end
reservoir_init keyword argument provides the possibility to change the construction for the reservoir matrix. The available reservoir are:
RandSparseReservoir(res_size, radius, sparsity)
PseudoSVDReservoir(res_size, max_value, sparsity, sorted, reverse_sort)
DelayLineBackwardReservoir(res_size, weight, fb_weight)
CycleJumpsReservoir(res_size, cycle_weight, jump_weight, jump_size)
And, like before, it is possible to build a custom reservoir by following this workflow:
#creation of the new struct for the reservoir struct MyNewReservoir <: AbstractReservoir #the reservoir params go here end #dispatch over the function to build the reservoir matrix function create_reservoir(reservoir::AbstractReservoir, res_size) #the new algorithm to build the reservoir matrix goes here end
Using  and  as references this section will provide an example on how to change both the input layer and the reservoir for ESNs. The full script for this example can be found here. This example was run on Julia v1.7.2.
The task for this example will be the one step ahead prediction of the Henon map. To obtain the data one can leverage the package DynamicalSystems.jl. The data is scaled to be between -1 and 1.
using DynamicalSystems train_len = 3000 predict_len = 2000 ds = Systems.henon() traj = trajectory(ds, 7000) data = Matrix(traj)' data = (data .-0.5) .* 2 shift = 200 training_input = data[:, shift:shift+train_len-1] training_target = data[:, shift+1:shift+train_len] testing_input = data[:,shift+train_len:shift+train_len+predict_len-1] testing_target = data[:,shift+train_len+1:shift+train_len+predict_len]
Now it is possible to define the input layers and reservoirs we want to compare and run the comparison in a simple for loop. The accuracy will be tested using the mean squared deviation
msd from StatsBase.
using ReservoirComputing, StatsBase res_size = 300 input_layer = [MinimumLayer(0.85, IrrationalSample()), MinimumLayer(0.95, IrrationalSample())] reservoirs = [SimpleCycleReservoir(res_size, 0.7), CycleJumpsReservoir(res_size, cycle_weight=0.7, jump_weight=0.2, jump_size=5)] for i=1:length(reservoirs) esn = ESN(training_input; input_init = input_layer[i], reservoir_init = reservoirs[i]) wout = train(esn, training_target, StandardRidge(0.001)) output = esn(Predictive(testing_input), wout) println(msd(testing_target, output)) end
As it is possible to see, changing layers in ESN models is straightforward. Be sure to check the API documentation for a full list of reservoir and layers.