From: emilie <emilie@devijver.org>
Date: Wed, 12 Apr 2017 10:21:09 +0000 (+0200)
Subject: fix the problem with the likelihood for the R code
X-Git-Url: https://git.auder.net/doc/current/app_dev.php/pieces/rpsls.js?a=commitdiff_plain;h=bb11d873bee8f9560b4b77a304d035be6a69f440;p=valse.git

fix the problem with the likelihood for the R code
---

diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
index b251135..227dfdc 100644
--- a/pkg/R/constructionModelesLassoMLE.R
+++ b/pkg/R/constructionModelesLassoMLE.R
@@ -8,7 +8,7 @@
 #'
 #' export
 constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini, maxi,
-	gamma, X, Y, thresh, tau, S, ncores=3, artefact = 1e3, fast=TRUE, verbose=FALSE)
+	gamma, X, Y, thresh, tau, S, ncores=3, fast=TRUE, verbose=FALSE)
 {
 	if (ncores > 1)
 	{
@@ -56,11 +56,11 @@ constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini,
 		densite = vector("double",n)
 		for (r in 1:k)
 		{
-			delta = (Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r]))/artefact
+			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)
 	}
 
diff --git a/pkg/R/main.R b/pkg/R/main.R
index a2e5697..695a23f 100644
--- a/pkg/R/main.R
+++ b/pkg/R/main.R
@@ -26,7 +26,7 @@
 #' #TODO: a few examples
 #' @export
 valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10, maxi=50,
-	eps=1e-4, kmin=2, kmax=4, rang.min=1, rang.max=10, ncores_outer=1, ncores_inner=1,
+	eps=1e-4, kmin=2, kmax=4, rank.min=1, rank.max=10, ncores_outer=1, ncores_inner=1,
 	size_coll_mod=50, fast=TRUE, verbose=FALSE, plot = TRUE)
 {
   p = dim(X)[2]
@@ -75,7 +75,7 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
       #compute parameter estimations, with the Maximum Likelihood
       #Estimator, restricted on selected variables.
       models <- constructionModelesLassoMLE(P$phiInit, P$rhoInit, P$piInit, P$gamInit,
-				mini, maxi, gamma, X, Y, thresh, eps, S, ncores_inner, artefact=1e3, fast, verbose)
+				mini, maxi, gamma, X, Y, thresh, eps, S, ncores_inner, fast, verbose)
     }
 		else
 		{
@@ -83,7 +83,7 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
 				print('run the procedure Lasso-Rank')
       #compute parameter estimations, with the Low Rank
       #Estimator, restricted on selected variables.
-      models <- constructionModelesLassoRank(S$Pi, S$Rho, mini, maxi, X, Y, eps, A1,
+      models <- constructionModelesLassoRank(S$Pi, S$Rho, mini, maxi, X, Y, eps, S,
 				rank.min, rank.max, ncores_inner, fast, verbose)
     }
 		#attention certains modeles sont NULL après selectVariables
@@ -112,18 +112,12 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
 		#Pour un groupe de modeles (même k, différents lambda):
 		LLH <- sapply( models, function(model) model$llh[1] )
 		k = length(models[[1]]$pi)
-		# TODO: chuis pas sûr du tout des lignes suivantes...
-		#       J'ai l'impression qu'il manque des infos 
-		## C'est surtout que la pénalité est la mauvaise, la c'est celle du Lasso, nous on veut ici
-		##celle de l'heuristique de pentes
-		#sumPen = sapply( models, function(model)
-		#	sum( model$pi^gamma * sapply(1:k, function(r) sum(abs(model$phi[,,r]))) ) )
 		sumPen = sapply(models, function(model)
 		  k*(dim(model$rho)[1]+sum(model$phi[,,1]!=0)+1)-1)
 		data.frame(model=paste(i,".",seq_along(models),sep=""),
-			pen=sumPen/n, complexity=sumPen, contrast=LLH)
+			pen=sumPen/n, complexity=sumPen, contrast=-LLH)
 	} ) )
-
+print(tableauRecap)
   modSel = capushe::capushe(tableauRecap, n)
   indModSel <-
 		if (selecMod == 'DDSE')