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 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 asparse
matrix. Default isfalse
.sampling_type
: Sampling that decides the distribution ofweight
negative numbers. If set to:no_sample
the sign is unchanged. If set to:bernoulli_sample!
then eachweight
can be positive with a probability set bypositive_prob
. If set to:irrational_sample!
theweight
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 chosenstrides
.strides
can be a single number or an array. Default is:no_sample
.positive_prob
: probability of theweight
being positive whensampling_type
is 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 theirrational
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
References
- Rodan, A. and Tino, P. (2011). Minimum Complexity Echo State Network. IEEE Transactions on Neural Networks 22, 131–144.