X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FinitSmallEM.R;h=bd5ce1779303f3d17bf727b30048d5768b7a12a6;hp=541d7e1cb922b590d1893eea5bba3472eeb1e4ed;hb=4d9db27f0d1749e5577038dedbc5f4d0826f2772;hpb=f33f35efc9a01f93bb61959522d90ee6a76b892e

diff --git a/pkg/R/initSmallEM.R b/pkg/R/initSmallEM.R
index 541d7e1..bd5ce17 100644
--- a/pkg/R/initSmallEM.R
+++ b/pkg/R/initSmallEM.R
@@ -8,26 +8,26 @@
 #' @export
 #' @importFrom methods new
 #' @importFrom stats cutree dist hclust runif
-initSmallEM = function(k,X,Y)
+initSmallEM = function(k,X,Y, fast=TRUE)
 {
 	n = nrow(Y)
 	m = ncol(Y)
 	p = ncol(X)
-  
-	Zinit1 = array(0, dim=c(n,20))
-	betaInit1 = array(0, dim=c(p,m,k,20))
-	sigmaInit1 = array(0, dim = c(m,m,k,20))
-	phiInit1 = array(0, dim = c(p,m,k,20))
-	rhoInit1 = array(0, dim = c(m,m,k,20))
+  nIte = 20
+	Zinit1 = array(0, dim=c(n,nIte))
+	betaInit1 = array(0, dim=c(p,m,k,nIte))
+	sigmaInit1 = array(0, dim = c(m,m,k,nIte))
+	phiInit1 = array(0, dim = c(p,m,k,nIte))
+	rhoInit1 = array(0, dim = c(m,m,k,nIte))
 	Gam = matrix(0, n, k)
-	piInit1 = matrix(0,20,k)
-	gamInit1 = array(0, dim=c(n,k,20))
+	piInit1 = matrix(0,nIte,k)
+	gamInit1 = array(0, dim=c(n,k,nIte))
 	LLFinit1 = list()
 
-	require(MASS) #Moore-Penrose generalized inverse of matrix
-	for(repet in 1:20)
+	#require(MASS) #Moore-Penrose generalized inverse of matrix
+	for(repet in 1:nIte)
 	{
-	  distance_clus = dist(X)
+	  distance_clus = dist(cbind(X,Y))
 	  tree_hier = hclust(distance_clus)
 	  Zinit1[,repet] = cutree(tree_hier, k)
 
@@ -36,10 +36,10 @@ initSmallEM = function(k,X,Y)
 			Z = Zinit1[,repet]
 			Z_indice = seq_len(n)[Z == r] #renvoit les indices où Z==r
 			if (length(Z_indice) == 1) {
-			  betaInit1[,,r,repet] = ginv(crossprod(t(X[Z_indice,]))) %*%
+			  betaInit1[,,r,repet] = MASS::ginv(crossprod(t(X[Z_indice,]))) %*%
 			    crossprod(t(X[Z_indice,]), Y[Z_indice,])
 			} else {
-			betaInit1[,,r,repet] = ginv(crossprod(X[Z_indice,])) %*%
+			betaInit1[,,r,repet] = MASS::ginv(crossprod(X[Z_indice,])) %*%
 				crossprod(X[Z_indice,], Y[Z_indice,])
 			}
 			sigmaInit1[,,r,repet] = diag(m)
@@ -62,14 +62,12 @@ initSmallEM = function(k,X,Y)
 		miniInit = 10
 		maxiInit = 11
 		
-		#new_EMG = .Call("EMGLLF_core",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],
-#			gamInit1[,,repet],miniInit,maxiInit,1,0,X,Y,1e-4)
-		new_EMG = EMGLLF(phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],gamInit1[,,repet],miniInit,maxiInit,1,0,X,Y,1e-4)
-		LLFEessai = new_EMG$LLF
+		init_EMG = EMGLLF(phiInit1[,,,repet], rhoInit1[,,,repet], piInit1[repet,],
+			gamInit1[,,repet], miniInit, maxiInit, gamma=1, lambda=0, X, Y, eps=1e-4, fast)
+		LLFEessai = init_EMG$LLF
 		LLFinit1[repet] = LLFEessai[length(LLFEessai)]
 	}
-
-	b = which.max(LLFinit1)
+	b = which.min(LLFinit1)
 	phiInit = phiInit1[,,,b]
 	rhoInit = rhoInit1[,,,b]
 	piInit = piInit1[b,]