From: emilie 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/variants/current/doc/css/app_dev.php/%7B%7B%20targetUrl%20%7D%7D?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')