X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=R%2FinitSmallEM.R;h=541d7e1cb922b590d1893eea5bba3472eeb1e4ed;hb=53fa233d8fbeaf4d51a4874ba69d8472d01d04ba;hp=3f98e07ad2a1998791ccb53d8a42b3e5962b96e7;hpb=ae4fa2cb5a036ed1c8b8b69f257c007e8894238d;p=valse.git

diff --git a/R/initSmallEM.R b/R/initSmallEM.R
index 3f98e07..541d7e1 100644
--- a/R/initSmallEM.R
+++ b/R/initSmallEM.R
@@ -3,17 +3,18 @@
 #' @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)
+#' @importFrom methods new
+#' @importFrom stats cutree dist hclust runif
+initSmallEM = function(k,X,Y)
 {
 	n = nrow(Y)
 	m = ncol(Y)
 	p = ncol(X)
   
-	Zinit1 = array(0, dim=c(n,20)) #doute sur la taille
+	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))
@@ -24,31 +25,34 @@ 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 = Mclust(X,k) #default distance : euclidean  #Mclust(matrix(c(X,Y)),k)
-		Zinit1[,repet] = clusters$classification
-		
+	  distance_clus = dist(X)
+	  tree_hier = hclust(distance_clus)
+	  Zinit1[,repet] = cutree(tree_hier, k)
+
 		for(r in 1:k)
 		{
 			Z = Zinit1[,repet]
-			Z_bin = vec_bin(Z,r)
-			Z_vec = Z_bin$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,]
+			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,]))) %*%
+			    crossprod(t(X[Z_indice,]), Y[Z_indice,])
+			} else {
+			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 = 3 #(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,])
@@ -58,8 +62,9 @@ initSmallEM = function(k,X,Y,tau)
 		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,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
 		LLFinit1[repet] = LLFEessai[length(LLFEessai)]
 	}