X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2Fmain.R;h=0a3487b0cb430097425ddeb9f563df8dfc9febd0;hb=c3bf2821bce67c75504e303fae23dd41c00f06c8;hp=e741d65f4ed3b43037dc8978a96f20e76cda0523;hpb=ea5860f1b4fc91f06e371a0b26915198474a849d;p=valse.git

diff --git a/pkg/R/main.R b/pkg/R/main.R
index e741d65..0a3487b 100644
--- a/pkg/R/main.R
+++ b/pkg/R/main.R
@@ -17,6 +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 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
@@ -28,8 +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, 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)
@@ -58,8 +59,11 @@ 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)
-    grid_lambda <- computeGridLambda(P$phiInit, P$rhoInit, P$piInit, P$gamInit, 
-      X, Y, gamma, mini, maxi, eps, fast)
+    if (length(grid_lambda) == 0)
+    {
+      grid_lambda <- computeGridLambda(P$phiInit, P$rhoInit, P$piInit, P$gamInit, 
+                                       X, Y, gamma, mini, maxi, eps, fast)
+    }
     if (length(grid_lambda) > size_coll_mod) 
       grid_lambda <- grid_lambda[seq(1, length(grid_lambda), length.out = size_coll_mod)]
 
@@ -119,7 +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)
   indModSel <- if (selecMod == "DDSE") 
     as.numeric(modSel@DDSE@model) else if (selecMod == "Djump") 
@@ -144,6 +149,7 @@ valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mi
   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))