'update'

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

diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
index 50879c9..e8013a2 100644 (file)
--- a/pkg/R/constructionModelesLassoMLE.R
+++ b/pkg/R/constructionModelesLassoMLE.R
@@ -1,37 +1,79 @@
-constructionModelesLassoMLE = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,
-       X,Y,seuil,tau,selected)
+#' constructionModelesLassoMLE
+#'
+#' TODO: description
+#'
+#' @param ...
+#'
+#' @return ...
+#'
+#' export
+constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini, maxi,
+       gamma, X, Y, thresh, tau, S, ncores=3, artefact = 1e3, verbose=FALSE)
 {
-       #TODO: parameter ncores (chaque tâche peut aussi demander du parallélisme...)
-       cl = parallel::makeCluster( parallel::detectCores() / 4 )
-       parallel::clusterExport(cl=cl,
-               varlist=c("phiInit","rhoInit","gamInit","mini","maxi","glambda","X","Y","seuil","tau"),
-               envir=environment())
-       #Pour chaque lambda de la grille, on calcule les coefficients
-       out = parLapply( seq_along(glambda), function(lambdaindex)
+       if (ncores > 1)
        {
+               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 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]
 
-               #TODO: phiInit[selected] et X[selected] sont bien sûr faux; par quoi remplacer ?
-               #lambda == 0 c'est normal ? -> ED : oui, ici on calcule le maximum de vraisembance, donc on ne pénalise plus
-    res = EMGLLF(phiInit[selected],rhoInit,piInit,gamInit,mini,maxi,gamma,0.,X[selected],Y,tau)
+               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
 
-               #comment évaluer la dimension à partir du résultat et de [not]selected ?
-    #dimension = ...
+               if (length(col.sel) == 0)
+                       return (NULL)
 
-    #on veut calculer la vraisemblance avec toutes nos estimations
+               # 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)
+               
+               # Eval dimension from the result + selected
+               phiLambda2 = res$phi
+               rhoLambda = res$rho
+               piLambda = res$pi
+               phiLambda = array(0, dim = c(p,m,k))
+               for (j in seq_along(col.sel))
+                       phiLambda[col.sel[j],,] = phiLambda2[j,,]
+               dimension = length(unlist(sel.lambda))
+
+               # Computation of the loglikelihood
                densite = vector("double",n)
                for (r in 1:k)
                {
-                       delta = Y%*%rho[,,r] - (X[selected]%*%res$phi[selected,,r])
-                       densite = densite + pi[r] *
-                               det(rho[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
+                       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)
                }
-               llh = c( sum(log(densite[,lambdaIndex])), (dimension+m+1)*k-1 )
-               list("phi"=res$phi, "rho"=res$rho, "pi"=res$pi, "llh" = llh)
-       })
-       parallel::stopCluster(cl)
+               llhLambda = c( sum(artefact^2 * 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
 }