# 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)
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.