X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=R%2FinitSmallEM.R;h=c24fca91162425ba01480f727c0bda026872c9e0;hb=23fff6d03a3b19a2ee5f70b71808d39ae9b4ef8a;hp=5044a38743f564c71cd8f2fb66536791412bbdd7;hpb=b4899af94061fca34163bdef9ae3ff2155038bb7;p=valse.git

diff --git a/R/initSmallEM.R b/R/initSmallEM.R
index 5044a38..c24fca9 100644
--- a/R/initSmallEM.R
+++ b/R/initSmallEM.R
@@ -12,11 +12,13 @@ initSmallEM = function(k,X,Y,tau)
 	n = nrow(Y)
 	m = ncol(Y)
 	p = ncol(X)
-
+  
+	Zinit1 = array(0, dim=c(n,20)) #doute sur la taille
 	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))
+	Gam = matrix(0, n, k)
 	piInit1 = matrix(0,20,k)
 	gamInit1 = array(0, dim=c(n,k,20))
 	LLFinit1 = list()
@@ -25,18 +27,17 @@ initSmallEM = function(k,X,Y,tau)
 	require(mclust) # K-means with selection of K
 	for(repet in 1:20)
 	{
-		clusters = Mclust(matrix(c(X,Y),nrow=n),k) #default distance : euclidean
+		clusters = Mclust(X,k) #default distance : euclidean  #Mclust(matrix(c(X,Y)),k)
 		Zinit1[,repet] = clusters$classification
 		
 		for(r in 1:k)
 		{
 			Z = Zinit1[,repet]
 			Z_bin = vec_bin(Z,r)
-			Z_vec = Z_bin$Z #vecteur 0 et 1 aux endroits o? Z==r
+			Z_vec = Z_bin$vec #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,]
+			betaInit1[,,r,repet] = ginv(t(X[Z_indice,])*X[Z_indice,])%*%t(X[Z_indice,])%*%Y[Z_indice,]
 			sigmaInit1[,,r,repet] = diag(m)
 			phiInit1[,,r,repet] = betaInit1[,,r,repet]/sigmaInit1[,,r,repet]
 			rhoInit1[,,r,repet] = solve(sigmaInit1[,,r,repet])
@@ -47,19 +48,18 @@ initSmallEM = function(k,X,Y,tau)
 		{
 			for(r in 1:k)
 			{
-				dotProduct = (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet]) %*%
-					(y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet])
+				dotProduct = 3 * (Y[i,]%*%rhoInit1[,,r,repet]-X[i,]%*%phiInit1[,,r,repet]) %*% (Y[i,]%*%rhoInit1[,,r,repet]-X[i,]%*%phiInit1[,,r,repet])
 				Gam[i,r] = piInit1[repet,r]*det(rhoInit1[,,r,repet])*exp(-0.5*dotProduct)
 			}
-			sumGamI = sum(gam[i,])
+			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)
+		new_EMG = .Call("EMGLLF_core",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],
+										gamInit1[,,repet],miniInit,maxiInit,1,0,X,Y,tau)
 		LLFEessai = new_EMG$LLF
 		LLFinit1[repet] = LLFEessai[length(LLFEessai)]
 	}