backward_connection!

backward_connection!([rng], reservoir_matrix, weight, shift;
    sampling_type=:no_sample, irrational=pi, start=1,
    p=0.5)

Adds a backward connection in the reservoir_matrix, with given shift and weight. The weight can be a single number or an array.

Arguments

• rng: Random number generator. Default is Utils.default_rng() from WeightInitializers.
• reservoir_matrix: matrix to be changed.
• weight: weight to add as a backward connection. Can be either a single number or an array.
• shift: How far the backward connection will be from the diagonal.

Keyword arguments

• sampling_type: Sampling that decides the distribution of weight negative numbers. If set to :no_sample the sign is unchanged. If set to :bernoulli_sample! then each weight can be positive with a probability set by positive_prob. If set to :irrational_sample! the weight is 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 chosen strides. strides can be a single number or an array. Default is :no_sample.
• positive_prob: probability of the weight being positive when sampling_type is set to :bernoulli_sample!. Default is 0.5.
• irrational: Irrational number whose decimals decide the sign of weight. Default is pi.
• start: Which place after the decimal point the counting starts for the irrational sign 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> backward_connection!(matrix, 3.0, 1)
5×5 Matrix{Float32}:
 0.0  3.0  0.0  0.0  0.0
 0.0  0.0  3.0  0.0  0.0
 0.0  0.0  0.0  3.0  0.0
 0.0  0.0  0.0  0.0  3.0
 0.0  0.0  0.0  0.0  0.0

julia> backward_connection!(matrix, 3.0, 1; sampling_type = :bernoulli_sample!)
5×5 Matrix{Float32}:
 0.0  3.0   0.0  0.0   0.0
 0.0  0.0  -3.0  0.0   0.0
 0.0  0.0   0.0  3.0   0.0
 0.0  0.0   0.0  0.0  -3.0
 0.0  0.0   0.0  0.0   0.0