Novel minimal reservoir computing

[MartinuzziFrancesco]

What kind of problems is it mostly used for?

Short time series modeling, showing improved results over NGRC (see #108)

Other implementations to know about

No open implementations available at the moment

References

Paper

[MartinuzziFrancesco]

quick sketch of a possible implementation of the proposed W_in

function structured_input(rng::AbstractRNG, ::Type{T}, dims::Integer...; scaling=T(0.1)) where {T <: Number}

    res_size, input_dim = dims
    n_f = 2^input_dim - 2
    if res_size % n_f != 0
        error("res_size must be divisible by 2^input_dim - 2 (got res_size=$res_size, input_dim=$input_dim)")
    end
    b = div(res_size, n_f)  # block size
    base_weights = range(one(T), stop=zero(T), length=b)
    w = sqrt.(collect(base_weights))

    features = Vector{Vector{T}}()
    for k in 1:(input_dim - 1)
        for subset in Iterators.combinations(1:input_dim, k)
            row = zeros(T, input_dim)
            for idx in subset
                row[idx] = one(T)
            end
            push!(features, row)
        end
    end

    A = vcat([reshape(f, 1, :) for f in features]...)
    blocks = [w * reshape(A[i, :], 1, :) for i in 1:size(A, 1)]
    W_in = vcat(blocks...)
    W_in .*= scaling
    return W_in
end