X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FEMGrank.R;fp=pkg%2FR%2FEMGrank.R;h=0000000000000000000000000000000000000000;hp=b85a0faf4fd9b1b2cea2c4f7b11ccf5c9371a08d;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=ea5860f1b4fc91f06e371a0b26915198474a849d

diff --git a/pkg/R/EMGrank.R b/pkg/R/EMGrank.R
deleted file mode 100644
index b85a0fa..0000000
--- a/pkg/R/EMGrank.R
+++ /dev/null
@@ -1,120 +0,0 @@
-#' EMGrank
-#'
-#' Description de EMGrank
-#'
-#' @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 A list ...
-#'   phi : parametre de moyenne renormalisé, calculé par l'EM
-#'   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)
-{
-  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))
-}