X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FEMGrank.R;h=b85a0faf4fd9b1b2cea2c4f7b11ccf5c9371a08d;hp=0e68cb4ecdf4dd9f9270557823d76a3875a7f72e;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=a3105972158da4773b33d41e1ead65a942c15f80

diff --git a/pkg/R/EMGrank.R b/pkg/R/EMGrank.R
deleted file mode 100644
index 0e68cb4..0000000
--- a/pkg/R/EMGrank.R
+++ /dev/null
@@ -1,124 +0,0 @@
-#' EMGrank
-#'
-#' Description de EMGrank
-#'
-#' @param phiInit ...
-#' @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 = dim(X)[1]
-  p = dim(X)[2]
-  m = dim(Rho)[2]
-  k = dim(Rho)[3]
-
-  #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: Mise à jour de Beta (et donc phi)
-    for(r in 1:k)
-		{
-      Z_indice = seq_len(n)[Z==r] #indices où Z == r
-      if (length(Z_indice) == 0)
-        next
-      #U,S,V = SVD of (t(Xr)Xr)^{-1} * t(Xr) * Yr
-      s = svd( 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]
-    }
-
-		#Etape E et calcul de LLF
-		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(det(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))
-}