basic test OK, but problem with 'fast' EMGLLF output in last EMilie test

[valse.git] / pkg / R / main.R

diff --git a/pkg/R/main.R b/pkg/R/main.R
index 64e0586..d29fe69 100644 (file)
--- 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
@@ -28,12 +29,12 @@
 #' @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)
 {
-  p <- dim(X)[2]
-  m <- dim(Y)[2]
-  n <- dim(X)[1]
+  n <- nrow(X)
+  p <- ncol(X)
+  m <- ncol(Y)
 
   if (verbose) 
     print("main loop: over all k and all lambda")
@@ -50,16 +51,19 @@ valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mi
   computeModels <- function(k)
   {
     if (ncores_outer > 1) 
-      require("valse")  #nodes start with an empty environment
+      require("valse") #nodes start with an empty environment
 
     if (verbose) 
       print(paste("Parameters initialization for k =", k))
     # smallEM initializes parameters by k-means and regression model in each
     # component, doing this 20 times, and keeping the values maximizing the
     # likelihood after 10 iterations of the EM algorithm.
-    P <- initSmallEM(k, X, Y)
-    grid_lambda <- computeGridLambda(P$phiInit, P$rhoInit, P$piInit, P$gamInit, 
-      X, Y, gamma, mini, maxi, eps, fast)
+    P <- initSmallEM(k, X, Y, 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)]
 
@@ -119,32 +123,29 @@ 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") 
-    as.numeric(modSel@Djump@model) else if (selecMod == "BIC") 
-    modSel@BIC_capushe$model else if (selecMod == "AIC") 
-    modSel@AIC_capushe$model
-
-  mod <- as.character(tableauRecap[indModSel, 1])
-  listMod <- as.integer(unlist(strsplit(mod, "[.]")))
-  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$pi[r] * exp(-0.5 * sqNorm2) * det(modelSel$rho[, , r])
-    }
+    as.numeric(modSel@DDSE@model)
+  } else if (selecMod == "Djump") 
+  {
+    as.numeric(modSel@Djump@model)
+  } else if (selecMod == "BIC") 
+  {
+    modSel@BIC_capushe$model
+  } else if (selecMod == "AIC") 
+  {
+    modSel@AIC_capushe$model
   }
-  Gam <- Gam/rowSums(Gam)
-  modelSel$affec <- apply(Gam, 1, which.max)
-  modelSel$proba <- Gam
-
+    
+  listMod <- as.integer(unlist(strsplit(as.character(indModSel), "[.]")))
+  modelSel <- models_list[[listMod[1]]][[listMod[2]]]
+  modelSel$tableau <- tableauRecap
+  
   if (plot)
     print(plot_valse(X, Y, modelSel, n))