get_scalar_function(rf::RandomFeature) -> Any

gets the scalar_function field

get_feature_sampler(rf::RandomFeature) -> Any

gets the feature_sampler field

get_feature_sample(rf::RandomFeature) -> Any

gets the feature_sample field

get_n_features(rf::RandomFeature) -> Any

gets the n_features field

get_feature_parameters(rf::RandomFeature) -> Any

gets the feature_parameters field

get_output_dim(rf::ScalarFeature) -> Int64

gets the output dimension (equals 1 for scalar-valued features)

get_output_dim(rf::VectorFeature) -> Int64

gets the output dimension (equals 1 for scalar-valued features)

sample(rf::RandomFeature) -> Any

samples the random feature distribution

struct ScalarFeature{S<:AbstractString, SF<:ScalarFunction} <: RandomFeature

Contains information to build and sample RandomFeatures mapping from N-D -> 1-D

• n_features::Int64: Number of features

• feature_sampler::Sampler: Sampler of the feature distribution

• scalar_function::ScalarFunction: ScalarFunction mapping R -> R

• feature_sample::EnsembleKalmanProcesses.ParameterDistributions.ParameterDistribution: Current Sample from sampler

• feature_parameters::Union{Nothing, Dict{S}} where S<:AbstractString: hyperparameters in Feature (and not in Sampler)

ScalarFourierFeature(
    n_features::Int64,
    sampler::Sampler;
    feature_parameters
) -> ScalarFeature{String, Cosine}

Constructor for a ScalarFeature with cosine features

ScalarNeuronFeature(
    n_features::Int64,
    sampler::Sampler;
    activation_fun,
    kwargs...
) -> ScalarFeature{String, Relu}

Constructor for a ScalarFeature with activation-function features (default ReLU)

build_features(
    rf::ScalarFeature,
    inputs::AbstractMatrix,
    batch_feature_idx::AbstractVector
) -> Any

builds features (possibly batched) from an input matrix of size (input dimension, number of samples) output of dimension (number of samples, 1, number features)

struct VectorFeature{S<:AbstractString, SF<:ScalarFunction} <: RandomFeature

Contains information to build and sample RandomFeatures mapping from N-D -> M-D

• n_features::Int64: Number of features

• output_dim::Int64: Dimension of output

• feature_sampler::Sampler: Sampler of the feature distribution

• scalar_function::ScalarFunction: ScalarFunction mapping R -> R

• feature_sample::EnsembleKalmanProcesses.ParameterDistributions.ParameterDistribution: Current Sample from sampler

• feature_parameters::Union{Nothing, Dict{String}}: hyperparameters in Feature (and not in Sampler)

VectorFourierFeature(
    n_features::Int64,
    output_dim::Int64,
    sampler::Sampler;
    feature_parameters
) -> VectorFeature{String, Cosine}

Constructor for a VectorFeature with cosine features

VectorNeuronFeature(
    n_features::Int64,
    output_dim::Int64,
    sampler::Sampler;
    activation_fun,
    kwargs...
) -> VectorFeature{String, Relu}

Constructor for a VectorFeature with activation-function features (default ReLU)

build_features(
    rf::VectorFeature,
    inputs::AbstractMatrix,
    batch_feature_idx::AbstractVector
) -> Any

builds features (possibly batched) from an input matrix of size (input dimension,number of samples) output of dimension (number of samples, output dimension, number features)

abstract type ScalarFunction

Type of a function mapping 1D -> 1D

abstract type ScalarActivation <: ScalarFunction

Type of scalar activation functions

apply_scalar_function(
    sf::ScalarFunction,
    r::AbstractArray
) -> Any

apply the scalar function sf pointwise to vectors or matrices

struct Cosine <: ScalarFunction

struct Relu <: ScalarActivation

struct Lrelu{FT<:AbstractFloat} <: ScalarActivation

struct Gelu <: ScalarActivation

struct Elu{FT<:AbstractFloat} <: ScalarActivation

struct Selu{FT<:AbstractFloat} <: ScalarActivation

struct Heaviside <: ScalarActivation

struct SmoothHeaviside{FT<:AbstractFloat} <: ScalarActivation

struct Sawtooth <: ScalarActivation

struct Softplus <: ScalarActivation

struct Tansig <: ScalarActivation

struct Sigmoid <: ScalarActivation