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### mgsim.cv.R (2006-01)
###
### Determination by Cross-validation of MGSIM
### hyper-parameters for categorical data
###
### Copyright 2006-01 Sophie Lambert-Lacroix and Julie Peyre
###
###
### This file is part of the `plsgenomics' library for R and related languages.
### It is made available under the terms of the GNU General Public
### License, version 2, or at your option, any later version,
### incorporated herein by reference.
###
### This program is distributed in the hope that it will be
### useful, but WITHOUT ANY WARRANTY; without even the implied
### warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
### PURPOSE. See the GNU General Public License for more
### details.
###
### You should have received a copy of the GNU General Public
### License along with this program; if not, write to the Free
### Software Foundation, Inc., 59 Temple Place - Suite 330, Boston,
### MA 02111-1307, USA
mgsim.cv <- function (Ytrain,Xtrain,LambdaRange,hRange,NbIterMax=50)
{
## INPUT VARIABLES
#########################
## Xtrain : matrix ntrain x p
## train data matrix
## Ytrain : vector ntrain
## response variable {1,...c+1}-valued vector
## LambdaRange : vector nLambda
## possible values for the regularization parameter Lambda
## NbIterMax : positive integer
## maximal number of iteration in the MWIRRLS part
## hRandge : vector nh
## possible values for the bandwidth parameter
## OUTPUT VARIABLES
##########################
## Lambda : optimal regularization parameter Lambda
## h : optimal bandwidth parameter
## TEST ON INPUT VARIABLES
##############################
#On Xtrain
if ((is.matrix(Xtrain)==FALSE)||(is.numeric(Xtrain)==FALSE)) {
stop("Message from mgsim.cv.R: Xtrain is not of valid type")}
if (dim(Xtrain)[2]==1) {
stop("Message from mgsim.cv.R: p=1 is not valid")}
ntrain <- dim(Xtrain)[1]
#On Ytrain
if ((is.vector(Ytrain)==FALSE)||(is.numeric(Ytrain)==FALSE)) {
stop("Message from mgsim.cv.R: Ytrain is not of valid type")}
if (length(Ytrain)!=ntrain) {
stop("Message from mgsim.cv.R: the length of Ytrain is not equal to the Xtrain row number")}
Ytrain <- Ytrain-1
if ((sum(floor(Ytrain)-Ytrain)!=0)||(sum(Ytrain<0)>0)){
stop("Message from mgsim.cv.R: Ytrain is not of valid type")}
c <- max(Ytrain)
eff<-rep(0,(c+1))
for (i in 0:c) {
eff[(i+1)]<-sum(Ytrain==i)}
if (sum(eff<=1)>0) {
stop("Message from mgsim.cv.R: there are not enough samples for each class")}
if (c==1) {
stop("Message from mgsim.cv.R: Ytrain is a binary vector, use gsim.cv.R")}
#On hyper parameters range
if ((is.numeric(LambdaRange)==FALSE)||(is.vector(LambdaRange)==FALSE)||(sum(LambdaRange<0)>0)){
stop("Message from mgsim.cv.R: LambdaRange is not of valid type")}
if ((is.numeric(hRange)==FALSE)||(is.vector(hRange)==FALSE)||(sum(hRange<=0)>0)){
stop("Message from mgsim.cv.R: hRange is not of valid type")}
if ((is.numeric(NbIterMax)==FALSE)||(round(NbIterMax)-NbIterMax!=0)||(NbIterMax<1)){
stop("Message from mgsim.cv.R: NbIterMax is not of valid type")}
## CV LOOP
############
#Some initializations
LambdaRange <- sort(LambdaRange)
hRange <- sort(hRange)
ntrainCV <- dim(Xtrain)[1]-1
nL <- length(LambdaRange)
nh <- length(hRange)
ResCV <- matrix(0,nrow=nL,ncol=nh)
CVG <- matrix(1,nrow=nL,ncol=nh)
for (ncv in 1:ntrain) {
# Determine the data matrix
cvXtrain <- Xtrain[-ncv,]
cvXtest <- matrix(Xtrain[ncv,],nrow=1)
p <- dim(cvXtrain)[2]
r <- min(p,ntrainCV)
# Standardize the cvXtrain matrix
Sigma2train <- apply(cvXtrain,2,var)*(ntrainCV-1)/ntrainCV
if (sum(Sigma2train==0)!=0){
if (sum(Sigma2train==0)>(p-2)){
stop("Message from mgsim.cv.R: the procedure stops because, after leaving one sample, number of predictor variables with no null variance is less than 1.")}
cvXtrain <- cvXtrain[,which(Sigma2train!=0)]
cvXtest <- matrix(cvXtest[,which(Sigma2train!=0)],nrow=1)
Sigma2train <-Sigma2train[which(Sigma2train!=0)]
p <- dim(cvXtrain)[2]
r <- min(p,ntrainCV)}
MeancvXtrain <- apply(cvXtrain,2,mean)
sXtrain <- sweep(cvXtrain,2,MeancvXtrain,FUN="-")
sXtrain <- sweep(sXtrain,2,sqrt(Sigma2train),FUN="/")
# Move in the reduced space when necessary
if (p>ntrainCV)
{svd.sXtrain <- svd(t(sXtrain))
r<-length(svd.sXtrain$d[abs(svd.sXtrain$d)>10^(-13)])
V <- svd.sXtrain$u[,1:r]
D <- diag(c(svd.sXtrain$d[1:r]))
U <- svd.sXtrain$v[,1:r]
sXtrain <- U%*%D
rm(D)
rm(U)
rm(svd.sXtrain)}
sXtest <- sweep(cvXtest,2,MeancvXtrain,FUN="-")
sXtest <- sweep(sXtest,2,sqrt(Sigma2train),FUN="/")
if (p>ntrainCV)
{sXtest <- sXtest%*%V
rm(V)
}
rm(cvXtrain)
#Compute Zblock
Z <- cbind(rep(1,ntrainCV),sXtrain)
Zt <- cbind(rep(1,1),sXtest)
Zbloc <- matrix(0,nrow=ntrainCV*c,ncol=c*(r+1))
Ztestbloc <- matrix(0,nrow=c,ncol=c*(r+1))
for (cc in 1:c) {
row <- (0:(ntrainCV-1))*c+cc
col <- (r+1)*(cc-1)+1:(r+1)
Zbloc[row,col] <- Z
row <- cc
Ztestbloc[row,col] <- Zt
}
rm(Z)
rm(Zt)
for (j in 1:nh) {
# Compute the Kernel matrix
WKernel <- matrix(0,ntrainCV,ntrainCV)
for (jj in 1:ntrainCV) {
WKernel[,jj] <- apply(exp(-(sXtrain-rep(1,ntrainCV)%*%t(sXtrain[jj,]))^2/(2*hRange[j]^2)),1,prod)
WKernel[,jj] <- WKernel[,jj]/sum(WKernel[,jj])
}
for (i in 1:nL) {
if (CVG[i,j]==1) {
res <- mgsimaux(Ytrain[-ncv],Zbloc,sXtrain,LambdaRange[i],WKernel,Ztestbloc,NbIterMax=NbIterMax)
if (res$Convergence==0)
{CVG[i,j] <- 0
ResCV[i,j] <- ntrain}
if (res$Convergence==1)
{ResCV[i,j] <- ResCV[i,j]+abs(res$hatY-Ytrain[ncv])}
}
}
}
}
## CONCLUDE
##############
if (sum(CVG)==0)
{stop("No optimal Lambda and h for the given Range")}
#else
# Determine optimal Lambda and h
aux <- which.min(ResCV)
if (nh==1) {
Lambda <- LambdaRange[aux]
h <- hRange[1]}
if (nh!=1) {
h <- hRange[(aux-1)%/%nL+1]
Lambda <- LambdaRange[(aux-1)%%nL+1]}
return(list(Lambda=Lambda,h=h))
}
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