RMKL (version 1.0)

prediction.Classification: Prediction from MKL model

Description

This function creates gramm matrix for traning set baed upon several types of kernel and specified hyper paremeters. Matrix corresponds to similarity betwween each sample in the training set.

Usage

prediction.Classification(model, ktest, train.outcome)

Arguments

model

MKL model

ktest

Gramm matrix of training data and test data

train.outcome

Outcome for the training data

Value

yhat Predicted value for each test point

predicted Sign of yhat, which is the final predicted outcome

Examples

# NOT RUN {
library(kernlab)
library(caret)
data(benchmark.data)
example.data=benchmark.data[[1]]
training.samples=sample(1:dim(example.data)[1],floor(0.7*dim(example.data)[1]),replace=FALSE)
C=100
kernels=rep('radial',3)
degree=rep(0,3)
scale=rep(0,3)
sigma=c(0,2^seq(-3:0))
K=kernels.gen(example.data[,1:2], training.samples, kernels, degree, scale, sigma)
K.train=K$K.train
K.test=K$K.test
SEMKL.model=SEMKL.classification(K.train,example.data[training.samples,3], C)
predicted=prediction.Classification(SEMKL.model, K.test, example.data[training.samples,3])
confusionMatrix(factor(predicted$predict, levels=c(-1,1)),
                factor(example.data[-training.samples,3],levels=c(-1,1)))
# }