R/RcppExports.R

Defines functions bitWiseAnd sumSQuaredDiffsPartial sumSQuaredDiffs kahanSum navigateRFTree getTreeHeight generalNeuralNetworkKernelGrad generalNeuralNetworkKernel neuralNetworkKernelHess neuralNetworkKernelGrad neuralNetworkKernel randomForestKernelHess randomForestKernelGrad randomForestKernel homogeneousPolynomialKernelHess homogeneousPolynomialKernelGrad homogeneousPolynomialKernel polynomialKernelHess polynomialKernelGrad polynomialKernel generalisedPolynomialKernelHess generalisedPolynomialKernelGrad generalisedPolynomialKernel constantKernelHess constantKernelGrad constantKernel periodicKernelHess periodicKernelGrad periodicKernel rationalQuadraticKernelHess rationalQuadraticKernelGrad rationalQuadraticKernel stablePow shiftedLog inverseARDKernelHess inverseARDKernelGrad inverseARDKernel ARDKernelHess ARDKernelGrad ARDKernel squaredExponentialKernelHess squaredExponentialKernelGrad squaredExponentialKernel getKernelHyperparamNames callKernelHessByString callKernelGradByString callKernelByString getCovarianceMatrixHessianArray getCovarianceMatrixGradArray getCovarianceMatrixBuiltInCpp getCovarianceMatrixCpp

Documented in callKernelByString

# Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393

getCovarianceMatrixCpp <- function(x, k, sigma_n, hyperParams) {
    .Call('_gaussianProcess_getCovarianceMatrixCpp', PACKAGE = 'gaussianProcess', x, k, sigma_n, hyperParams)
}

getCovarianceMatrixBuiltInCpp <- function(x, k, sigma_n, hyperParams, additionalParams) {
    .Call('_gaussianProcess_getCovarianceMatrixBuiltInCpp', PACKAGE = 'gaussianProcess', x, k, sigma_n, hyperParams, additionalParams)
}

getCovarianceMatrixGradArray <- function(x, k, sigma_n, hyperParams, additionalParams) {
    .Call('_gaussianProcess_getCovarianceMatrixGradArray', PACKAGE = 'gaussianProcess', x, k, sigma_n, hyperParams, additionalParams)
}

getCovarianceMatrixHessianArray <- function(x, k, sigma_n, hyperParams, additionalParams) {
    .Call('_gaussianProcess_getCovarianceMatrixHessianArray', PACKAGE = 'gaussianProcess', x, k, sigma_n, hyperParams, additionalParams)
}

#' Select Built-in C++ Kernels by Name
#'
#' Built in kernels include:
#' \itemize{
#'   \item squaredExponential
#'   \item rationalQuadratic
#'   \item periodic
#'   \item constant
#'   \item generalisedLinear
#'   \item oneDLinear
#'   \item changepoint
#'   \item randomForest
#'   \item neuralNetwork
#'   \item generalisedPolynomial
#'   \item polynomial
#'   \item homogeneousPolynomial
#' }
#'
#' @param kernelName the kernel's name (as a string)
#' @param a the first data point
#' @param b the second data point
#' @param hyperParams the kernel's hyperparameters as a named numeric vector
#' @param additionalParams a list of any additional parameters
#'
#' @export
callKernelByString <- function(kernelName, a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_callKernelByString', PACKAGE = 'gaussianProcess', kernelName, a, b, hyperParams, additionalParams)
}

callKernelGradByString <- function(kernelName, a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_callKernelGradByString', PACKAGE = 'gaussianProcess', kernelName, a, b, hyperParams, additionalParams)
}

callKernelHessByString <- function(kernelName, a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_callKernelHessByString', PACKAGE = 'gaussianProcess', kernelName, a, b, hyperParams, additionalParams)
}

getKernelHyperparamNames <- function(kernelName, additionalParams) {
    .Call('_gaussianProcess_getKernelHyperparamNames', PACKAGE = 'gaussianProcess', kernelName, additionalParams)
}

squaredExponentialKernel <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_squaredExponentialKernel', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

squaredExponentialKernelGrad <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_squaredExponentialKernelGrad', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

squaredExponentialKernelHess <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_squaredExponentialKernelHess', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

ARDKernel <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_ARDKernel', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

ARDKernelGrad <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_ARDKernelGrad', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

ARDKernelHess <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_ARDKernelHess', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

inverseARDKernel <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_inverseARDKernel', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

inverseARDKernelGrad <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_inverseARDKernelGrad', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

inverseARDKernelHess <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_inverseARDKernelHess', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

shiftedLog <- function(x) {
    .Call('_gaussianProcess_shiftedLog', PACKAGE = 'gaussianProcess', x)
}

stablePow <- function(x, a) {
    .Call('_gaussianProcess_stablePow', PACKAGE = 'gaussianProcess', x, a)
}

rationalQuadraticKernel <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_rationalQuadraticKernel', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

rationalQuadraticKernelGrad <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_rationalQuadraticKernelGrad', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

rationalQuadraticKernelHess <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_rationalQuadraticKernelHess', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

periodicKernel <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_periodicKernel', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

periodicKernelGrad <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_periodicKernelGrad', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

periodicKernelHess <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_periodicKernelHess', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

constantKernel <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_constantKernel', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

constantKernelGrad <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_constantKernelGrad', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

constantKernelHess <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_constantKernelHess', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

generalisedPolynomialKernel <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_generalisedPolynomialKernel', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

generalisedPolynomialKernelGrad <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_generalisedPolynomialKernelGrad', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

generalisedPolynomialKernelHess <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_generalisedPolynomialKernelHess', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

polynomialKernel <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_polynomialKernel', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

polynomialKernelGrad <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_polynomialKernelGrad', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

polynomialKernelHess <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_polynomialKernelHess', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

homogeneousPolynomialKernel <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_homogeneousPolynomialKernel', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

homogeneousPolynomialKernelGrad <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_homogeneousPolynomialKernelGrad', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

homogeneousPolynomialKernelHess <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_homogeneousPolynomialKernelHess', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

randomForestKernel <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_randomForestKernel', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

randomForestKernelGrad <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_randomForestKernelGrad', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

randomForestKernelHess <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_randomForestKernelHess', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

neuralNetworkKernel <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_neuralNetworkKernel', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

neuralNetworkKernelGrad <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_neuralNetworkKernelGrad', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

neuralNetworkKernelHess <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_neuralNetworkKernelHess', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

generalNeuralNetworkKernel <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_generalNeuralNetworkKernel', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

generalNeuralNetworkKernelGrad <- function(a, b, hyperParams, additionalParams) {
    .Call('_gaussianProcess_generalNeuralNetworkKernelGrad', PACKAGE = 'gaussianProcess', a, b, hyperParams, additionalParams)
}

getTreeHeight <- function(tree) {
    .Call('_gaussianProcess_getTreeHeight', PACKAGE = 'gaussianProcess', tree)
}

navigateRFTree <- function(tree, data, isFactor, inputHeight) {
    .Call('_gaussianProcess_navigateRFTree', PACKAGE = 'gaussianProcess', tree, data, isFactor, inputHeight)
}

kahanSum <- function(summands) {
    .Call('_gaussianProcess_kahanSum', PACKAGE = 'gaussianProcess', summands)
}

sumSQuaredDiffs <- function(a, b) {
    .Call('_gaussianProcess_sumSQuaredDiffs', PACKAGE = 'gaussianProcess', a, b)
}

sumSQuaredDiffsPartial <- function(a, b, additionalParams) {
    .Call('_gaussianProcess_sumSQuaredDiffsPartial', PACKAGE = 'gaussianProcess', a, b, additionalParams)
}

bitWiseAnd <- function(a, b) {
    .Call('_gaussianProcess_bitWiseAnd', PACKAGE = 'gaussianProcess', a, b)
}
mattdneal/gaussianProcess documentation built on May 21, 2019, 12:58 p.m.