kernels.gen: Generate both training and test kernel matrices

In cwilso6/RMKL: Multiple Kernel Learning for Classification, Regression Problems, and Survival Settings

Description

This function creates gramm matrix for traning set baed upon several types of kernel and specified hyper paremeters. This function is essentially a wrappper functions that combines gramm and grammpred. Additionally this function divides each kernel matrix by it's trace, which is a common transformation used in MKL.

Usage

kernels.gen(data, train.samples, kernels, degree, scale, sigma)

Arguments

data	List of data matrices
train.samples	Vector of indices that will be used as training samples
kernels	Character vector of kernel types
degree	Degree of polynomial kernel matrix
scale	Leading coefficient on the polynomial kernel
sigma	Hyperparameter for the radial basis kernel

Value

K.train Gramm matricesfor training data

K.test Gramm matrices for test data

Examples

library(kernlab)
data(benchmark.data)
example.data=benchmark.data[[1]]
#Dividing the samples into a train set and test set.
training.samples=sample(1:dim(example.data)[1],floor(0.7*dim(example.data)[1]),replace=FALSE)
#Specifying the type and hyperparameters for each kernel.
kernels=c('linear',rep('radial',3))
degree=rep(0,4)
scale=rep(0,4)
sigma=c(0,2^seq(-3:0))
kernels.gen(example.data[,1:2], training.samples, kernels, degree, scale, sigma)

cwilso6/RMKL documentation built on May 18, 2021, 9:58 a.m.