X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=R%2FinitSmallEM.R;h=5044a38743f564c71cd8f2fb66536791412bbdd7;hb=09ab3c164abb566764e86a175b5973241e708fd6;hp=d5197661087de2323e6475d24277e2d4605e04af;hpb=39046da6016f15d625bd99cf0303ea8beb838c79;p=valse.git

diff --git a/R/initSmallEM.R b/R/initSmallEM.R
index d519766..5044a38 100644
--- a/R/initSmallEM.R
+++ b/R/initSmallEM.R
@@ -1,21 +1,12 @@
-vec_bin = function(X,r)
-{
-	Z = c()
-	indice = c()
-	j = 1
-	for (i in 1:length(X))
-	{
-		if(X[i] == r)
-		{
-			Z[i] = 1
-			indice[j] = i
-			j=j+1
-		} else
-			Z[i] = 0
-	}
-	return (list(Z=Z,indice=indice))
-}
-
+#' initialization of the EM algorithm
+#'
+#' @param k number of components
+#' @param X matrix of covariates (of size n*p)
+#' @param Y matrix of responses (of size n*m)
+#' @param tau threshold to stop EM algorithm
+#'
+#' @return a list with phiInit, rhoInit, piInit, gamInit
+#' @export
 initSmallEM = function(k,X,Y,tau)
 {
 	n = nrow(Y)
@@ -31,20 +22,19 @@ initSmallEM = function(k,X,Y,tau)
 	LLFinit1 = list()
 
 	require(MASS) #Moore-Penrose generalized inverse of matrix
+	require(mclust) # K-means with selection of K
 	for(repet in 1:20)
 	{
-		clusters = hclust(dist(y)) #default distance : euclidean
-		#cutree retourne les indices (à quel cluster indiv_i appartient) d'un clustering hierarchique
-		clusterCut = cutree(clusters,k)
-		Zinit1[,repet] = clusterCut
-
+		clusters = Mclust(matrix(c(X,Y),nrow=n),k) #default distance : euclidean
+		Zinit1[,repet] = clusters$classification
+		
 		for(r in 1:k)
 		{
 			Z = Zinit1[,repet]
 			Z_bin = vec_bin(Z,r)
-			Z_vec = Z_bin$Z #vecteur 0 et 1 aux endroits où Z==r
-			Z_indice = Z_bin$indice #renvoit les indices où Z==r
-
+			Z_vec = Z_bin$Z #vecteur 0 et 1 aux endroits o? Z==r
+			Z_indice = Z_bin$indice #renvoit les indices o? Z==r
+			
 			betaInit1[,,r,repet] =
 				ginv(t(x[Z_indice,])%*%x[Z_indice,])%*%t(x[Z_indice,])%*%y[Z_indice,]
 			sigmaInit1[,,r,repet] = diag(m)
@@ -52,7 +42,7 @@ initSmallEM = function(k,X,Y,tau)
 			rhoInit1[,,r,repet] = solve(sigmaInit1[,,r,repet])
 			piInit1[repet,r] = sum(Z_vec)/n
 		}
-
+		
 		for(i in 1:n)
 		{
 			for(r in 1:k)
@@ -64,12 +54,12 @@ initSmallEM = function(k,X,Y,tau)
 			sumGamI = sum(gam[i,])
 			gamInit1[i,,repet]= Gam[i,] / sumGamI
 		}
-
+		
 		miniInit = 10
 		maxiInit = 11
-
+		
 		new_EMG = .Call("EMGLLF",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],
-			gamInit1[,,repet],miniInit,maxiInit,1,0,x,y,tau)
+										gamInit1[,,repet],miniInit,maxiInit,1,0,x,y,tau)
 		LLFEessai = new_EMG$LLF
 		LLFinit1[repet] = LLFEessai[length(LLFEessai)]
 	}