add_jumps!
ReservoirComputing.add_jumps! — Functionadd_jumps!([rng], reservoir_matrix, weight, jump_size;
sampling_type=:no_sample, irrational=pi, start=1,
positive_prob=0.5)Adds jumps to a given reservoir_matrix with chosen weight and determined jump_size. weight can be either a number or an array.
Arguments
rng: Random number generator. Default isUtils.default_rng()from WeightInitializers.reservoir_matrix: matrix to be changed.weight: weight to add as a simple cycle. Can be either a single number or an array.jump_size: size of the jump's distance.
Keyword arguments
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> matrix = zeros(Float32, 5, 5)
5×5 Matrix{Float32}:
0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0
julia> add_jumps!(matrix, 1.0)
5×5 Matrix{Float32}:
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0
1.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 1.0
0.0 0.0 1.0 0.0 0.0