fix the likelihood computation. fix some other few things

diff --git a/pkg/DESCRIPTION b/pkg/DESCRIPTION
index 3b33e25..c28f54b 100644 (file)
--- a/pkg/DESCRIPTION
+++ b/pkg/DESCRIPTION
@@ -22,8 +22,9 @@ Imports:
     parallel
 Suggests:
     capushe,
+    methods,
     roxygen2,
-    testhat
+    testthat
 URL: http://git.auder.net/?p=valse.git
 License: MIT + file LICENSE
 RoxygenNote: 5.0.1

diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
index 16f58b6..d2a16bc 100644 (file)
--- a/pkg/R/constructionModelesLassoMLE.R
+++ b/pkg/R/constructionModelesLassoMLE.R
@@ -63,25 +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) gdet(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)
-    list(phi = phiLambda, rho = rhoLambda, pi = piLambda, llh = llhLambda)
+    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)
+    # {
+    #   # 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(-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

diff --git a/pkg/R/initSmallEM.R b/pkg/R/initSmallEM.R
index 44b4b06..179822f 100644 (file)
--- a/pkg/R/initSmallEM.R
+++ b/pkg/R/initSmallEM.R
@@ -55,7 +55,7 @@ initSmallEM <- function(k, X, Y, fast)
         dotProduct <- tcrossprod(Y[i, ] %*% rhoInit1[, , r, repet]
           - X[i, ] %*% phiInit1[, , r, repet])
         Gam[i, r] <- piInit1[repet, r] * 
-          gdet(rhoInit1[, , r, repet]) * exp(-0.5 * dotProduct)
+          det(rhoInit1[, , r, repet]) * exp(-0.5 * dotProduct)
       }
       sumGamI <- sum(Gam[i, ])
       gamInit1[i, , repet] <- Gam[i, ]/sumGamI

diff --git a/pkg/R/main.R b/pkg/R/main.R
index 632d90b..bb1e3fe 100644 (file)
--- a/pkg/R/main.R
+++ b/pkg/R/main.R
@@ -123,6 +123,8 @@ valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mi
       complexity = sumPen, contrast = -LLH)
   }))
   tableauRecap <- tableauRecap[which(tableauRecap[, 4] != Inf), ]
+  
+  
   if (verbose == TRUE)
     print(tableauRecap)
   modSel <- capushe::capushe(tableauRecap, n)
@@ -140,26 +142,10 @@ valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mi
     modSel@AIC_capushe$model
   }
     
-
-  mod <- as.character(tableauRecap[indModSel, 1])
-  listMod <- as.integer(unlist(strsplit(mod, "[.]")))
+  listMod <- as.integer(unlist(strsplit(as.character(indModSel), "[.]")))
   modelSel <- models_list[[listMod[1]]][[listMod[2]]]
-
-  ## Affectations
-  Gam <- matrix(0, ncol = length(modelSel$pi), nrow = n)
-  for (i in 1:n)
-  {
-    for (r in 1:length(modelSel$pi))
-    {
-      sqNorm2 <- sum((Y[i, ] %*% modelSel$rho[, , r] - X[i, ] %*% modelSel$phi[, , r])^2)
-      Gam[i, r] <- modelSel$pi[r] * exp(-0.5 * sqNorm2) * gdet(modelSel$rho[, , r])
-    }
-  }
-  Gam <- Gam/rowSums(Gam)
-  modelSel$affec <- apply(Gam, 1, which.max)
-  modelSel$proba <- Gam
   modelSel$tableau <- tableauRecap
-
+  
   if (plot)
     print(plot_valse(X, Y, modelSel, n))
 

diff --git a/pkg/R/selectVariables.R b/pkg/R/selectVariables.R
index 39e54d2..bab45cc 100644 (file)
--- a/pkg/R/selectVariables.R
+++ b/pkg/R/selectVariables.R
@@ -67,6 +67,8 @@ selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma
     }
   if (ncores > 1) 
     parallel::stopCluster(cl)
+ 
+  print(out)
   # Suppress models which are computed twice En fait, ca ca fait la comparaison de
   # tous les parametres On veut juste supprimer ceux qui ont les memes variables
   # sélectionnées

diff --git a/pkg/data/script_data.R b/pkg/data/script_data.R
index d425a38..7479674 100644 (file)
--- a/pkg/data/script_data.R
+++ b/pkg/data/script_data.R
@@ -1,5 +1,5 @@
-m=11
-p=10
+m=6
+p=6
 
 covY = array(0,dim = c(m,m,2))
 covY[,,1] = diag(m)
@@ -9,7 +9,7 @@ Beta = array(0, dim = c(p, m, 2))
 Beta[1:4,1:4,1] = 3*diag(4)
 Beta[1:4,1:4,2] = -2*diag(4)
 
-#Data = generateXY(100, c(0.5,0.5), rep(0,p), Beta, diag(p), covY)
-
-#Res = valse(Data$X,Data$Y, fast=FALSE, plot=FALSE, verbose = TRUE, kmax=2, size_coll_mod = 100,
-#            selecMod = "BIC")
+#Data = generateXY(200, c(0.5,0.5), rep(0,p), Beta, diag(p), covY)
+#  
+#Res = valse(Data$X,Data$Y, fast=FALSE, plot=FALSE, verbose = TRUE, kmax=3, size_coll_mod = 50, selecMod = "DDSE", mini = 50, maxi=100)
+#plot(Res$tableau[,3], -Res$tableau[,4])