fix for m==1

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

diff --git a/pkg/R/EMGrank.R b/pkg/R/EMGrank.R
index 71f7c5b..b85a0fa 100644 (file)
--- a/pkg/R/EMGrank.R
+++ b/pkg/R/EMGrank.R
@@ -2,15 +2,119 @@
 #'
 #' Description de EMGrank
 #'
-#' @param Pi ...
+#' @param Pi Parametre de proportion
+#' @param Rho Parametre initial de variance renormalisé
+#' @param mini Nombre minimal d'itérations dans l'algorithme EM
+#' @param maxi Nombre maximal d'itérations dans l'algorithme EM
+#' @param X Régresseurs
+#' @param Y Réponse
+#' @param tau Seuil pour accepter la convergence
+#' @param rank Vecteur des rangs possibles
 #'
-#' @return ...
+#' @return A list ...
+#'   phi : parametre de moyenne renormalisé, calculé par l'EM
+#'   LLF : log vraisemblance associé à cet échantillon, pour les valeurs estimées des paramètres
 #'
-#' @examples
-#' ...
-#' ...
 #' @export
-EMGrank <- function(Pi, Rho, mini, maxi, X, Y, tau, rank)
+EMGrank <- function(Pi, Rho, mini, maxi, X, Y, tau, rank, fast = TRUE)
 {
-       .Call("EMGrank", Pi, Rho, mini, maxi, X, Y, tau, rank, PACKAGE="valse")
+  if (!fast)
+  {
+    # Function in R
+    return(.EMGrank_R(Pi, Rho, mini, maxi, X, Y, tau, rank))
+  }
+
+  # Function in C
+  n <- nrow(X)  #nombre d'echantillons
+  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), 
+    LLF = double(1), n, p, m, k, PACKAGE = "valse")
+}
+
+# helper to always have matrices as arg (TODO: put this elsewhere? improve?)  -->
+# Yes, we should use by-columns storage everywhere... [later!]
+matricize <- function(X)
+{
+  if (!is.matrix(X)) 
+    return(t(as.matrix(X)))
+  return(X)
+}
+
+# R version - slow but easy to read
+.EMGrank_R <- function(Pi, Rho, mini, maxi, X, Y, tau, rank)
+{
+  # matrix dimensions
+  n <- nrow(X)
+  p <- ncol(X)
+  m <- ncol(Y)
+  k <- length(Pi)
+
+  # init outputs
+  phi <- array(0, dim = c(p, m, k))
+  Z <- rep(1, n)
+  LLF <- 0
+
+  # local variables
+  Phi <- array(0, dim = c(p, m, k))
+  deltaPhi <- c()
+  sumDeltaPhi <- 0
+  deltaPhiBufferSize <- 20
+  
+  # main loop
+  ite <- 1
+  while (ite <= mini || (ite <= maxi && sumDeltaPhi > tau))
+  {
+    # M step: update for Beta ( and then phi)
+    for (r in 1:k)
+    {
+      Z_indice <- seq_len(n)[Z == r] #indices where Z == r
+      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 <- s$d
+      # Set m-rank(r) singular values to zero, and recompose best rank(r) approximation
+      # of the initial product
+      if (rank[r] < length(S)) 
+        S[(rank[r] + 1):length(S)] <- 0
+      phi[, , r] <- s$u %*% diag(S) %*% t(s$v) %*% Rho[, , r]
+    }
+
+    # Step E and computation of the loglikelihood
+    sumLogLLF2 <- 0
+    for (i in seq_len(n))
+    {
+      sumLLF1 <- 0
+      maxLogGamIR <- -Inf
+      for (r in seq_len(k))
+      {
+        dotProduct <- tcrossprod(Y[i, ] %*% Rho[, , r] - X[i, ] %*% phi[, , r])
+        logGamIR <- log(Pi[r]) + log(gdet(Rho[, , r])) - 0.5 * dotProduct
+        # Z[i] = index of max (gam[i,])
+        if (logGamIR > maxLogGamIR)
+        {
+          Z[i] <- r
+          maxLogGamIR <- logGamIR
+        }
+        sumLLF1 <- sumLLF1 + exp(logGamIR)/(2 * pi)^(m/2)
+      }
+      sumLogLLF2 <- sumLogLLF2 + log(sumLLF1)
+    }
+
+    LLF <- -1/n * sumLogLLF2
+
+    # update distance parameter to check algorithm convergence (delta(phi, Phi))
+    deltaPhi <- c(deltaPhi, max((abs(phi - Phi))/(1 + abs(phi)))) #TODO: explain?
+    if (length(deltaPhi) > deltaPhiBufferSize) 
+      deltaPhi <- deltaPhi[2:length(deltaPhi)]
+    sumDeltaPhi <- sum(abs(deltaPhi))
+
+    # update other local variables
+    Phi <- phi
+    ite <- ite + 1
+  }
+  return(list(phi = phi, LLF = LLF))
 }