X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=R%2FinitSmallEM.R;h=e2157b254b6bfdbd5e369e02d304962e0fb44197;hp=5044a38743f564c71cd8f2fb66536791412bbdd7;hb=31463ab809c0195273ff2760606ea65361d721ab;hpb=e166ed4e1370aa7961f0d8609936591cfc6808cc

diff --git a/R/initSmallEM.R b/R/initSmallEM.R
index 5044a38..e2157b2 100644
--- a/R/initSmallEM.R
+++ b/R/initSmallEM.R
@@ -12,54 +12,53 @@ initSmallEM = function(k,X,Y,tau)
 	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))
+	Gam = matrix(0, n, k)
 	piInit1 = matrix(0,20,k)
 	gamInit1 = array(0, dim=c(n,k,20))
 	LLFinit1 = list()
 
 	require(MASS) #Moore-Penrose generalized inverse of matrix
-	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
-		Zinit1[,repet] = clusters$classification
-		
+	  distance_clus = dist(X)
+	  tree_hier = hclust(distance_clus)
+	  Zinit1[,repet] = cutree(tree_hier, k)
+
 		for(r in 1:k)
 		{
 			Z = Zinit1[,repet]
-			Z_bin = vec_bin(Z,r)
-			Z_vec = Z_bin$Z #vecteur 0 et 1 aux endroits o? Z==r
-			Z_indice = Z_bin$indice #renvoit les indices o? Z==r
+			Z_indice = seq_len(n)[Z == r] #renvoit les indices où Z==r
 			
-			betaInit1[,,r,repet] =
-				ginv(t(x[Z_indice,])%*%x[Z_indice,])%*%t(x[Z_indice,])%*%y[Z_indice,]
+			betaInit1[,,r,repet] = ginv(crossprod(X[Z_indice,])) %*%
+				crossprod(X[Z_indice,], Y[Z_indice,])
 			sigmaInit1[,,r,repet] = diag(m)
-			phiInit1[,,r,repet] = betaInit1[,,r,repet]/sigmaInit1[,,r,repet]
+			phiInit1[,,r,repet] = betaInit1[,,r,repet] #/ sigmaInit1[,,r,repet]
 			rhoInit1[,,r,repet] = solve(sigmaInit1[,,r,repet])
-			piInit1[repet,r] = sum(Z_vec)/n
+			piInit1[repet,r] = mean(Z == r)
 		}
 		
 		for(i in 1:n)
 		{
 			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 = tcrossprod(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)]
 	}