#How to use the package
Introduction
The package can be sourced globaly using
source('Make.R')
Make()Then a simple example of use is
require(MASS)
####### Set what you want .... #################
K=2; #number of class
n=10; #
p=1000 # dimension
mu=array(0,c(p,K))
mu[1:4,2]= mu[1:4,2]+c(0.01,0.5,0.02,0.5)/3
Cdiag=array(0.01,2,p)
##################################################
#### Create the learning set
X=NULL; y=NULL;
for (k in 1:K)
{
X=cbind(X,array(C^(1/2),c(p,n))*(matrix(rnorm(p*n),nrow=p,ncol=n))+array(mu[,k],c(p,n)));
y=c(y,array(k,n))
}
######## Learn
LearnedLDAF=learnLinearRule(t(X),y,'Fisher')
LearnedLDAU=learnLinearRule(t(X),y,'UnivThresh',r=1)
LearnedLDAFDR=tune.LDA(t(X),y,'FDRThresh',q=10^(-2:-12)/log(p)) #Cross validation
#######
#### Create the testing set
X=NULL; y=NULL;
for (k in 1:K)
{
X=cbind(X,array(C^(1/2),c(p,n))*(matrix(rnorm(p*n),nrow=p,ncol=n))+array(mu[,k],c(p,n)));
y=c(y,array(k,n))
}
#### Predict ....
UnivScore=mean((y!=predict(LearnedLDAU,t(X)))) ;
FDRScore=mean((y!=predict(LearnedLDAFDR,t(X)))) ;
FisherScore= mean((y!=predict(LearnedLDAF,t(X)))) ;
Details