test auto-indenter

[valse.git] / pkg / R / constructionModelesLassoMLE.R

#' constructionModelesLassoMLE
#'
#' Construct a collection of models with the Lasso-MLE procedure.
#' 
#' @param phiInit an initialization for phi, get by initSmallEM.R
#' @param rhoInit an initialization for rho, get by initSmallEM.R
#' @param piInit an initialization for pi, get by initSmallEM.R
#' @param gamInit an initialization for gam, get by initSmallEM.R
#' @param mini integer, minimum number of iterations in the EM algorithm, by default = 10
#' @param maxi integer, maximum number of iterations in the EM algorithm, by default = 100
#' @param gamma integer for the power in the penaly, by default = 1
#' @param X matrix of covariates (of size n*p)
#' @param Y matrix of responses (of size n*m)
#' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4
#' @param S output of selectVariables.R
#' @param ncores Number of cores, by default = 3
#' @param fast TRUE to use compiled C code, FALSE for R code only
#' @param verbose TRUE to show some execution traces
#' 
#' @return a list with several models, defined by phi, rho, pi, llh
#'
#' @export
constructionModelesLassoMLE = function( phiInit, rhoInit, piInit, gamInit, mini, maxi,gamma, X, Y,
	 eps, S, ncores=3, fast=TRUE, verbose=FALSE)
{
	if (ncores > 1)
	{
		cl = parallel::makeCluster(ncores, outfile='')
		parallel::clusterExport( cl, envir=environment(),
			varlist=c("phiInit","rhoInit","gamInit","mini","maxi","gamma","X","Y","eps",
			"S","ncores","fast","verbose") )
	}

	# Individual model computation
	computeAtLambda <- function(lambda)
	{
		if (ncores > 1)
			require("valse") #nodes start with an empty environment

		if (verbose)
			print(paste("Computations for lambda=",lambda))

		n = dim(X)[1]
		p = dim(phiInit)[1]
		m = dim(phiInit)[2]
		k = dim(phiInit)[3]
		sel.lambda = S[[lambda]]$selected
#		col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
		col.sel <- which( sapply(sel.lambda,length) > 0 ) #if list of selected vars
		if (length(col.sel) == 0)
			return (NULL)

		# lambda == 0 because we compute the EMV: no penalization here
		res = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,
			X[,col.sel], Y, eps, fast)
		
		# Eval dimension from the result + selected
		phiLambda2 = res$phi
		rhoLambda = res$rho
		piLambda = res$pi
		phiLambda = array(0, dim = c(p,m,k))
		for (j in seq_along(col.sel))
			phiLambda[col.sel[j],sel.lambda[[j]],] = phiLambda2[j,sel.lambda[[j]],]
		dimension = length(unlist(sel.lambda))

		# Computation of the loglikelihood
		densite = vector("double",n)
		for (r in 1:k)
		{
		  if (length(col.sel)==1){
		    delta = (Y%*%rhoLambda[,,r] - (X[, col.sel]%*%t(phiLambda[col.sel,,r])))
		  } else delta = (Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r]))
			densite = densite + piLambda[r] *
				det(rhoLambda[,,r])/(sqrt(2*base::pi))^m * exp(-diag(tcrossprod(delta))/2.0)
		}
		llhLambda = c( sum(log(densite)), (dimension+m+1)*k-1 )
		list("phi"= phiLambda, "rho"= rhoLambda, "pi"= piLambda, "llh" = llhLambda)
	}

	# For each lambda, computation of the parameters
	out =
		if (ncores > 1)
			parLapply(cl, 1:length(S), computeAtLambda)
		else
			lapply(1:length(S), computeAtLambda)

	if (ncores > 1)
		parallel::stopCluster(cl)

	out
}