'update'

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

diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
index 6c37751..e8013a2 100644 (file)
--- a/pkg/R/constructionModelesLassoMLE.R
+++ b/pkg/R/constructionModelesLassoMLE.R
@@ -8,23 +8,23 @@
 #'
 #' 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)
+               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]
@@ -32,7 +32,7 @@ constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini,
                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
 
@@ -44,43 +44,36 @@ constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini,
                        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, seq_along(glambda), computeAtLambda)
-               else
-                       lapply(seq_along(glambda), computeAtLambda)
+                       parLapply(cl, 1:length(S), computeAtLambda)
+       else
+               lapply(1:length(S), computeAtLambda)
 
        if (ncores > 1)
-    parallel::stopCluster(cl)
+               parallel::stopCluster(cl)
 
        out
 }