progress in debug: fix LLF/llh mismatch, and length + add adapter test in test/

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

diff --git a/pkg/R/EMGrank.R b/pkg/R/EMGrank.R
index db40948..f2bf58e 100644 (file)
--- a/pkg/R/EMGrank.R
+++ b/pkg/R/EMGrank.R
@@ -8,7 +8,7 @@
 #' @param maxi Nombre maximal d'itérations dans l'algorithme EM
 #' @param X Régresseurs
 #' @param Y Réponse
-#' @param tau Seuil pour accepter la convergence
+#' @param eps Seuil pour accepter la convergence
 #' @param rank Vecteur des rangs possibles
 #'
 #' @return A list ...
@@ -16,12 +16,12 @@
 #'   LLF : log vraisemblance associé à cet échantillon, pour les valeurs estimées des paramètres
 #'
 #' @export
-EMGrank <- function(Pi, Rho, mini, maxi, X, Y, tau, rank, fast = TRUE)
+EMGrank <- function(Pi, Rho, mini, maxi, X, Y, eps, rank, fast = TRUE)
 {
   if (!fast)
   {
     # Function in R
-    return(.EMGrank_R(Pi, Rho, mini, maxi, X, Y, tau, rank))
+    return(.EMGrank_R(Pi, Rho, mini, maxi, X, Y, eps, rank))
   }
 
   # Function in C
@@ -29,7 +29,7 @@ EMGrank <- function(Pi, Rho, mini, maxi, X, Y, tau, rank, fast = TRUE)
   p <- ncol(X)  #nombre de covariables
   m <- ncol(Y)  #taille de Y (multivarié)
   k <- length(Pi)  #nombre de composantes dans le mélange
-  .Call("EMGrank", Pi, Rho, mini, maxi, X, Y, tau, rank, phi = double(p * m * k), 
+  .Call("EMGrank", Pi, Rho, mini, maxi, X, Y, eps, rank, phi = double(p * m * k), 
     LLF = double(1), n, p, m, k, PACKAGE = "valse")
 }
 
@@ -43,13 +43,13 @@ matricize <- function(X)
 }
 
 # R version - slow but easy to read
-.EMGrank_R <- function(Pi, Rho, mini, maxi, X, Y, tau, rank)
+.EMGrank_R <- function(Pi, Rho, mini, maxi, X, Y, eps, rank)
 {
   # matrix dimensions
-  n <- dim(X)[1]
-  p <- dim(X)[2]
-  m <- dim(Rho)[2]
-  k <- dim(Rho)[3]
+  n <- nrow(X)
+  p <- ncol(X)
+  m <- ncol(Y)
+  k <- length(Pi)
 
   # init outputs
   phi <- array(0, dim = c(p, m, k))
@@ -64,7 +64,7 @@ matricize <- function(X)
   
   # main loop
   ite <- 1
-  while (ite <= mini || (ite <= maxi && sumDeltaPhi > tau))
+  while (ite <= mini || (ite <= maxi && sumDeltaPhi > eps))
   {
     # M step: update for Beta ( and then phi)
     for (r in 1:k)
@@ -73,8 +73,8 @@ matricize <- function(X)
       if (length(Z_indice) == 0) 
         next
       # U,S,V = SVD of (t(Xr)Xr)^{-1} * t(Xr) * Yr
-      s <- svd(MASS::ginv(crossprod(matricize(X[Z_indice, ])))
-        %*% crossprod(matricize(X[Z_indice, ]), matricize(Y[Z_indice, ])))
+      s <- svd(MASS::ginv(crossprod(matricize(X[Z_indice, ]))) %*% 
+                 crossprod(matricize(X[Z_indice, ]), matricize(Y[Z_indice, ])))
       S <- s$d
       # Set m-rank(r) singular values to zero, and recompose best rank(r) approximation
       # of the initial product
@@ -92,7 +92,7 @@ matricize <- function(X)
       for (r in seq_len(k))
       {
         dotProduct <- tcrossprod(Y[i, ] %*% Rho[, , r] - X[i, ] %*% phi[, , r])
-        logGamIR <- log(Pi[r]) + log(det(Rho[, , r])) - 0.5 * dotProduct
+        logGamIR <- log(Pi[r]) + log(gdet(Rho[, , r])) - 0.5 * dotProduct
         # Z[i] = index of max (gam[i,])
         if (logGamIR > maxLogGamIR)
         {