#
# Runs the code "IFSR.R"
#
# Written by:
# -- 
# John L. Weatherwax                2010-03-02
# 
# email: wax@alum.mit.edu
# 
# Please send comments and especially bug reports to the
# above email address.
#
#-----

source("load_prostate_data.R")
source("IFSR.R")

PD        = load_prostate_data(globalScale=FALSE,trainingScale=FALSE,responseScale=FALSE) # read in unscaled data 
XTraining = PD[[1]]
XTesting  = PD[[2]]

# this generates "statistics" on how well this method performd "out of sample"  
res       = IFSR(XTraining,XTesting, 500 )
betaHat   = res[[1]]
p         = dim(betaHat)[1]
nTS       = dim(betaHat)[2]
featNames = names(XTraining)

saveFig=FALSE
if( saveFig ) postscript("../../WriteUp/Graphics/Chapter3/dup_fig_3_19.eps", onefile=FALSE, horizontal=FALSE)

for( pi in 1:p ){
  if( pi==1 ){
    plot( betaHat[1,], xlim=range(1:round(1.075*nTS)+20), ylim=range(betaHat), type="l", xlab="Iteration", ylab="Coefficients" )
  }else{
    points( betaHat[pi,], ylim=range(betaHat), type="l" )
  }

  if( featNames[pi]=="svi" ){
    text( 1.075*nTS, 1.02*betaHat[pi,nTS], featNames[pi] )
  }else if( featNames[pi]=="lweight" ){
    text( 1.075*nTS, betaHat[pi,nTS], featNames[pi] )
  }else if( featNames[pi]=="pgg45" ){
    text( 1.075*nTS, 0.925*betaHat[pi,nTS], featNames[pi] )
  }else{
    text( 1.075*nTS, betaHat[pi,nTS], featNames[pi] )
  }
}

if( saveFig ) dev.off()