X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FconstructionModelesLassoMLE.R;h=d2a16bc0f40a3e7e9f679035fd3404e0912448d8;hb=923ed737d0fa335b858204b813c964432488abbe;hp=0197e1a32d785a74efeda4efa7e6b666deb389e6;hpb=57a8adeb0e558a54f9481e0256e258525aeb8cd1;p=valse.git

diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
index 0197e1a..d2a16bc 100644
--- a/pkg/R/constructionModelesLassoMLE.R
+++ b/pkg/R/constructionModelesLassoMLE.R
@@ -40,10 +40,10 @@ constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini,
     if (verbose) 
       print(paste("Computations for lambda=", lambda))
 
-    n <- dim(X)[1]
-    p <- dim(phiInit)[1]
-    m <- dim(phiInit)[2]
-    k <- dim(phiInit)[3]
+    n <- nrow(X)
+    p <- ncol(X)
+    m <- ncol(Y)
+    k <- length(piInit)
     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
@@ -51,8 +51,8 @@ constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini,
       return(NULL)
 
     # lambda == 0 because we compute the EMV: no penalization here
-    res <- EMGLLF(array(phiInit[col.sel, , ],dim=c(length(col.sel),m,k)), rhoInit,
-      piInit, gamInit, mini, maxi, gamma, 0, as.matrix(X[, col.sel]), Y, eps, fast)
+    res <- EMGLLF(array(phiInit,dim=c(p,m,k))[col.sel, , ], rhoInit, piInit, gamInit,
+      mini, maxi, gamma, 0, as.matrix(X[, col.sel]), Y, eps, fast)
 
     # Eval dimension from the result + selected
     phiLambda2 <- res$phi
@@ -63,36 +63,39 @@ constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini,
       phiLambda[col.sel[j], sel.lambda[[j]], ] <- phiLambda2[j, sel.lambda[[j]], ]
     dimension <- length(unlist(sel.lambda))
 
-    ## Computation of the loglikelihood
-    # Precompute det(rhoLambda[,,r]) for r in 1...k
-    detRho <- sapply(1:k, function(r) det(rhoLambda[, , r]))
-    sumLogLLH <- 0
+    ## Affectations
+    Gam <- matrix(0, ncol = length(piLambda), nrow = n)
     for (i in 1:n)
     {
-      # Update gam[,]; use log to avoid numerical problems
-      logGam <- sapply(1:k, function(r) {
-        log(piLambda[r]) + log(detRho[r]) - 0.5 *
-          sum((Y[i, ] %*% rhoLambda[, , r] - X[i, ] %*% phiLambda[, , r])^2)
-      })
-      
-      logGam <- logGam - max(logGam) #adjust without changing proportions
-      gam <- exp(logGam)
-      norm_fact <- sum(gam)
-      sumLogLLH <- sumLogLLH + log(norm_fact) - log((2 * base::pi)^(m/2))
+      for (r in 1:length(piLambda))
+      {
+        sqNorm2 <- sum((Y[i, ] %*% rhoLambda[, , r] - X[i, ] %*% phiLambda[, , r])^2)
+        Gam[i, r] <- piLambda[r] * exp(-0.5 * sqNorm2) * det(rhoLambda[, , r])
+      }
     }
-    llhLambda <- c(-sumLogLLH/n, (dimension + m + 1) * k - 1)
-    # densite <- vector("double", n)
-    # for (r in 1:k)
+    Gam2 <- Gam/rowSums(Gam)
+    affec <- apply(Gam2, 1, which.max)
+    proba <- Gam2
+    LLH <- c(sum(log(apply(Gam,1,sum))), (dimension + m + 1) * k - 1)
+    # ## Computation of the loglikelihood
+    # # Precompute det(rhoLambda[,,r]) for r in 1...k
+    # detRho <- sapply(1:k, function(r) gdet(rhoLambda[, , r]))
+    # sumLogLLH <- 0
+    # for (i in 1:n)
     # {
-    #   if (length(col.sel) == 1)
-    #   {
-    #     delta <- (Y %*% rhoLambda[, , r] - (X[, col.sel] %*% t(phiLambda[col.sel, , r])))
-    #   } else delta <- (Y %*% rhoLambda[, , r] - (X[, col.sel] %*% phiLambda[col.sel, , r]))
-    #   densite <- densite + piLambda[r] * det(rhoLambda[, , r])/(sqrt(2 * base::pi))^m * 
-    #     exp(-rowSums(delta^2)/2)
+    #   # Update gam[,]; use log to avoid numerical problems
+    #   logGam <- sapply(1:k, function(r) {
+    #     log(piLambda[r]) + log(detRho[r]) - 0.5 *
+    #       sum((Y[i, ] %*% rhoLambda[, , r] - X[i, ] %*% phiLambda[, , r])^2)
+    #   })
+    #   
+    #   #logGam <- logGam - max(logGam) #adjust without changing proportions -> change the LLH
+    #   gam <- exp(logGam)
+    #   norm_fact <- sum(gam)
+    #   sumLogLLH <- sumLogLLH + log(norm_fact) - m/2* log(2 * base::pi)
     # }
-    # llhLambda <- c(mean(log(densite)), (dimension + m + 1) * k - 1)
-    list(phi = phiLambda, rho = rhoLambda, pi = piLambda, llh = llhLambda)
+    #llhLambda <- c(-sumLogLLH/n, (dimension + m + 1) * k - 1)
+    list(phi = phiLambda, rho = rhoLambda, pi = piLambda, llh = LLH, affec = affec, proba = proba)
   }
 
   # For each lambda, computation of the parameters