delay_line
ReservoirComputing.delay_line — Functiondelay_line([rng], [T], dims...;
weight=0.1, return_sparse=false,
kwargs...)Create and return a delay line reservoir matrix (Rodan and Tino, 2011).
Arguments
rng: Random number generator. Default isUtils.default_rng()from WeightInitializers.T: Type of the elements in the reservoir matrix. Default isFloat32.dims: Dimensions of the reservoir matrix.
Keyword arguments
weight: Determines the value of all connections in the reservoir. This can be provided as a single value or an array. In case it is provided as an array please make sure that the length of the array matches the length of the sub-diagonal you want to populate. Default is 0.1.shift: delay line shift. Default is 1.return_sparse: flag for returning asparsematrix. Default isfalse.sampling_type: Sampling that decides the distribution ofweightnegative numbers. If set to:no_samplethe sign is unchanged. If set to:bernoulli_sample!then eachweightcan be positive with a probability set bypositive_prob. If set to:irrational_sample!theweightis negative if the decimal number of the irrational number chosen is odd. If set to:regular_sample!, each weight will be assigned a negative sign after the chosenstrides.stridescan be a single number or an array. Default is:no_sample.positive_prob: probability of theweightbeing positive whensampling_typeis set to:bernoulli_sample!. Default is 0.5.irrational: Irrational number whose decimals decide the sign ofweight. Default ispi.start: Which place after the decimal point the counting starts for theirrationalsign counting. Default is 1.strides: number of strides for assigning negative value to a weight. It can be an integer or an array. Default is 2.
Examples
julia> res_matrix = delay_line(5, 5)
5×5 Matrix{Float32}:
0.0 0.0 0.0 0.0 0.0
0.1 0.0 0.0 0.0 0.0
0.0 0.1 0.0 0.0 0.0
0.0 0.0 0.1 0.0 0.0
0.0 0.0 0.0 0.1 0.0
julia> res_matrix = delay_line(5, 5; weight = 1)
5×5 Matrix{Float32}:
0.0 0.0 0.0 0.0 0.0
1.0 0.0 0.0 0.0 0.0
0.0 1.0 0.0 0.0 0.0
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0References
- Rodan, A. and Tino, P. (2011). Minimum Complexity Echo State Network. IEEE Transactions on Neural Networks 22, 131–144.