fix many problems (models appearing twice, irrelevant coefficients in a relevant...

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

diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
index e8013a2..b864985 100644 (file)
--- a/pkg/R/constructionModelesLassoMLE.R
+++ b/pkg/R/constructionModelesLassoMLE.R
@@ -8,11 +8,11 @@
 #'
 #' export
 constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini, maxi,
-       gamma, X, Y, thresh, tau, S, ncores=3, artefact = 1e3, verbose=FALSE)
+       gamma, X, Y, thresh, tau, S, ncores=3, fast=TRUE, verbose=FALSE)
 {
        if (ncores > 1)
        {
-               cl = parallel::makeCluster(ncores)
+               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") )
@@ -31,17 +31,15 @@ constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini,
                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,tau)
+                       X[,col.sel], Y, tau, fast)
                
                # Eval dimension from the result + selected
                phiLambda2 = res$phi
@@ -49,19 +47,20 @@ constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini,
                piLambda = res$pi
                phiLambda = array(0, dim = c(p,m,k))
                for (j in seq_along(col.sel))
-                       phiLambda[col.sel[j],,] = phiLambda2[j,,]
+                       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)
                {
-                       delta = (Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r]))/artefact
-                       print(max(delta))
+                 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(artefact^2 * log(densite)), (dimension+m+1)*k-1 )
+               llhLambda = c( sum(log(densite)), (dimension+m+1)*k-1 )
                list("phi"= phiLambda, "rho"= rhoLambda, "pi"= piLambda, "llh" = llhLambda)
        }
 
@@ -69,8 +68,8 @@ constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini,
        out =
                if (ncores > 1)
                        parLapply(cl, 1:length(S), computeAtLambda)
-       else
-               lapply(1:length(S), computeAtLambda)
+               else
+                       lapply(1:length(S), computeAtLambda)
 
        if (ncores > 1)
                parallel::stopCluster(cl)