re-apply a few undone updates; simplifiy a bit args in main

diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
index 9743f0c..75ae679 100644 (file)
--- 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
@@ -65,7 +65,7 @@ constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini,
 
     ## Computation of the loglikelihood
     # Precompute det(rhoLambda[,,r]) for r in 1...k
-    detRho <- sapply(1:k, function(r) det(rhoLambda[, , r]))
+    detRho <- sapply(1:k, function(r) gdet(rhoLambda[, , r]))
     sumLogLLH <- 0
     for (i in 1:n)
     {
@@ -82,17 +82,6 @@ constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini,
       sumLogLLH <- sumLogLLH + log(norm_fact) - log((2 * base::pi)^(m/2))
     }
     llhLambda <- c(sumLogLLH/n, (dimension + m + 1) * k - 1)
-    # densite <- vector("double", n)
-    # for (r in 1:k)
-    # {
-    #   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)
-    # }
-    # llhLambda <- c(mean(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 d710b7e..0a3487b 100644 (file)
--- a/pkg/R/main.R
+++ b/pkg/R/main.R
@@ -17,8 +17,7 @@
 #' @param ncores_outer Number of cores for the outer loop on k
 #' @param ncores_inner Number of cores for the inner loop on lambda
 #' @param thresh real, threshold to say a variable is relevant, by default = 1e-8
-#' @param compute_grid_lambda, TRUE to compute the grid, FALSE if known (in arguments)
-#' @param grid_lambda, a vector with regularization parameters if known, by default 0
+#' @param grid_lambda, a vector with regularization parameters if known, by default numeric(0)
 #' @param size_coll_mod (Maximum) size of a collection of models
 #' @param fast TRUE to use compiled C code, FALSE for R code only
 #' @param verbose TRUE to show some execution traces
@@ -30,8 +29,8 @@
 #' @export
 valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mini = 10, 
   maxi = 50, eps = 1e-04, kmin = 2, kmax = 3, rank.min = 1, rank.max = 5, ncores_outer = 1, 
-  ncores_inner = 1, thresh = 1e-08, compute_grid_lambda = TRUE, grid_lambda = 0, size_coll_mod = 10, fast = TRUE, verbose = FALSE, 
-  plot = TRUE)
+  ncores_inner = 1, thresh = 1e-08, grid_lambda = numeric(0), size_coll_mod = 10,
+  fast = TRUE, verbose = FALSE, plot = TRUE)
 {
   n <- nrow(X)
   p <- ncol(X)
@@ -60,7 +59,7 @@ valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mi
     # component, doing this 20 times, and keeping the values maximizing the
     # likelihood after 10 iterations of the EM algorithm.
     P <- initSmallEM(k, X, Y, fast)
-    if (compute_grid_lambda == TRUE)
+    if (length(grid_lambda) == 0)
     {
       grid_lambda <- computeGridLambda(P$phiInit, P$rhoInit, P$piInit, P$gamInit, 
                                        X, Y, gamma, mini, maxi, eps, fast)
@@ -125,9 +124,7 @@ valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mi
   }))
   tableauRecap <- tableauRecap[which(tableauRecap[, 4] != Inf), ]
   if (verbose == TRUE)
-  {
     print(tableauRecap)
-  }
   modSel <- capushe::capushe(tableauRecap, n)
   indModSel <- if (selecMod == "DDSE") 
     as.numeric(modSel@DDSE@model) else if (selecMod == "Djump")