delay_line

ReservoirComputing.delay_lineFunction
delay_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 is Utils.default_rng() from WeightInitializers.
  • T: Type of the elements in the reservoir matrix. Default is Float32.
  • 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 lenght of the array matches the lenght of the sub-diagonal you want to populate. Default is 0.1.
  • shift: delay line shift. Default is 1.
  • return_sparse: flag for returning a sparse matrix. Default is false.
  • 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> 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  0.0
source

References