X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=R%2FinitSmallEM.R;h=e2157b254b6bfdbd5e369e02d304962e0fb44197;hp=d5197661087de2323e6475d24277e2d4605e04af;hb=31463ab809c0195273ff2760606ea65361d721ab;hpb=39046da6016f15d625bd99cf0303ea8beb838c79

diff --git a/R/initSmallEM.R b/R/initSmallEM.R
index d519766..e2157b2 100644
--- a/R/initSmallEM.R
+++ b/R/initSmallEM.R
@@ -1,31 +1,24 @@
-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)
 	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()
@@ -33,43 +26,39 @@ initSmallEM = function(k,X,Y,tau)
 	require(MASS) #Moore-Penrose generalized inverse of matrix
 	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
+	  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$Z #vecteur 0 et 1 aux endroits où Z==r
-			Z_indice = Z_bin$indice #renvoit les indices où Z==r
-
-			betaInit1[,,r,repet] =
-				ginv(t(x[Z_indice,])%*%x[Z_indice,])%*%t(x[Z_indice,])%*%y[Z_indice,]
+			Z_indice = seq_len(n)[Z == r] #renvoit les indices où Z==r
+			
+			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)]
 	}