#'
#' export
constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini, maxi,
- gamma, X, Y, seuil, tau, selected, ncores=3, verbose=FALSE)
+ gamma, X, Y, thresh, tau, S, ncores=3, artefact = 1e3, 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","thresh",
+ "tau","S","ncores","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]
m = dim(phiInit)[2]
k = dim(phiInit)[3]
- sel.lambda = selected[[lambda]]
+ sel.lambda = S[[lambda]]$selected
# col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
col.sel <- which( sapply(sel.lambda,length) > 0 ) #if list of selected vars
X[,col.sel],Y,tau)
# Eval dimension from the result + selected
- phiLambda2 = res_EM$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 = length(unlist(sel.lambda))
- 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)
- }
-
- # 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])
+ delta = (Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r]))/artefact
+ print(max(delta))
densite = densite + piLambda[r] *
det(rhoLambda[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
}
- llhLambda = c( sum(log(densite)), (dimension+m+1)*k-1 )
+ llhLambda = c( sum(artefact^2 * 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, glambda, computeAtLambda)
+ parLapply(cl, 1:length(S), computeAtLambda)
else
- lapply(glambda, computeAtLambda)
+ lapply(1:length(S), computeAtLambda)
if (ncores > 1)
- parallel::stopCluster(cl)
+ parallel::stopCluster(cl)
out
}