fix some errors

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

diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
index f86e816..a49529c 100644 (file)
--- a/pkg/R/constructionModelesLassoMLE.R
+++ b/pkg/R/constructionModelesLassoMLE.R
@@ -1,87 +1,89 @@
-constructionModelesLassoMLE = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,
-                                       X,Y,seuil,tau,selected, parallel = FALSE)
+#' 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)
 {
-  if (parallel) {
-    #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","X","Y","seuil","tau"),
-                            envir=environment())
-    #Pour chaque lambda de la grille, on calcule les coefficients
-    out = parLapply( seq_along(glambda), function(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)
-      
-      #comment évaluer la dimension à partir du résultat et de [not]selected ?
-      #dimension = ...
-      
-      #on veut calculer la vraisemblance avec toutes nos estimations
-      densite = vector("double",n)
-      for (r in 1:k)
-      {
-        delta = Y%*%rho[,,r] - (X[selected]%*%res$phi[selected,,r])
-        densite = densite + pi[r] *
-          det(rho[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
-      }
-      llh = c( sum(log(densite[,lambda])), (dimension+m+1)*k-1 )
-      list("phi"=res$phi, "rho"=res$rho, "pi"=res$pi, "llh" = llh)
-    })
-    parallel::stopCluster(cl)
-    out
+  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") )
   }
-  else {
-    #Pour chaque lambda de la grille, on calcule les coefficients
+  
+  # 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]
-    L = length(selected)
-    phi = list()
-    phiLambda = array(0, dim = c(p,m,k))
-    rho = list()
-    pi = list()
-    llh = list()
     
-    out = lapply( seq_along(selected), function(lambda)
-    {
-      sel.lambda = selected[[lambda]]
-      col.sel = which(colSums(sel.lambda)!=0)
-      res_EM = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0.,X[,col.sel],Y,tau)
+    sel.lambda = S[[lambda]]$selected
+    #          col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
+    col.sel <- which( sapply(sel.lambda,length) > 0 ) #if list of selected vars
+    
+    if (length(col.sel) == 0)
+    {return (NULL)} else {
+      
+      # lambda == 0 because we compute the EMV: no penalization here
+      res_EM = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,
+                      X[,col.sel],Y,tau)
+      
+      # Eval dimension from the result + selected
       phiLambda2 = res_EM$phi
       rhoLambda = res_EM$rho
       piLambda = res_EM$pi
-      for (j in 1:length(col.sel)){
+      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){
+      for (j in 1:p)
+      {
+        b = setdiff(1:m, sel.lambda[[j]])## je confonds un peu ligne et colonne : est-ce dans le bon sens ? 
+        ## moi pour la dimension, j'aurai juste mis length(unlist(sel.lambda)) mais je sais pas si c'est rapide
+        if (length(b) > 0)
           phiLambda[j,b,] = 0.0
-        }
-        dimension = dimension + sum(sel.lambda[,j]!=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)
     }
-    )
-    return(out)
   }
+  
+  # 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
 }