#' constructionModelesLassoMLE
#'
-#' TODO: description
+#' 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
#'
-#' @param ...
-#'
-#' @return ...
-#'
-#' export
-constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini, maxi,
- gamma, X, Y, seuil, tau, selected, ncores=3, verbose=FALSE)
+#' @export
+constructionModelesLassoMLE = function( phiInit, rhoInit, piInit, gamInit, mini, maxi,gamma, X, Y,
+ eps, S, ncores=3, fast=TRUE, verbose=FALSE)
{
- if (ncores > 1)
+ if (ncores > 1)
{
- cl = parallel::makeCluster(ncores)
- parallel::clusterExport( cl, envir=environment(),
- varlist=c("phiInit","rhoInit","gamInit","mini","maxi","gamma","X","Y","seuil",
- "tau","selected","ncores","verbose") )
+ 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 ampty environment
+ require("valse") #nodes start with an empty environment
- if (verbose)
+ 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 = selected[[lambda]]
+ sel.lambda = S[[lambda]]$selected
# col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
col.sel <- which( sapply(sel.lambda,length) > 0 ) #if list of selected vars
-
if (length(col.sel) == 0)
return (NULL)
# lambda == 0 because we compute the EMV: no penalization here
res = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,
- X[,col.sel],Y,tau)
+ X[,col.sel], Y, eps, fast)
# Eval dimension from the result + selected
- phiLambda2 = res_EM$phi
- rhoLambda = res_EM$rho
- piLambda = res_EM$pi
- phiLambda = array(0, dim = c(p,m,k))
+ 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],,] = phiLambda2[j,,]
-
- dimension = 0
- for (j in 1:p)
- {
- b = setdiff(1:m, sel.lambda[,j])
- if (length(b) > 0)
- phiLambda[j,b,] = 0.0
- dimension = dimension + sum(sel.lambda[,j]!=0)
- }
+ phiLambda[col.sel[j],sel.lambda[[j]],] = phiLambda2[j,sel.lambda[[j]],]
+ dimension = length(unlist(sel.lambda))
- # on veut calculer la vraisemblance avec toutes nos estimations
+ # Computation of the loglikelihood
densite = vector("double",n)
for (r in 1:k)
{
- delta = Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r])
+ 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(-tcrossprod(delta)/2.0)
+ 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)
}
- #Pour chaque lambda de la grille, on calcule les coefficients
- out =
+ # For each lambda, computation of the parameters
+ out =
if (ncores > 1)
- parLapply(cl, seq_along(glambda), computeAtLambda)
+ parLapply(cl, 1:length(S), computeAtLambda)
else
- lapply(seq_along(glambda), computeAtLambda)
+ lapply(1:length(S), computeAtLambda)
if (ncores > 1)
- parallel::stopCluster(cl)
+ parallel::stopCluster(cl)
out
}
projects / valse.git / blobdiff