X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=R%2FinitSmallEM.R;h=541d7e1cb922b590d1893eea5bba3472eeb1e4ed;hb=f227455a1604906b255ef366d64c10a93e796983;hp=e2157b254b6bfdbd5e369e02d304962e0fb44197;hpb=ef67d338c7f28ba041abe40ca9a8ab69f8365a90;p=valse.git

diff --git a/R/initSmallEM.R b/R/initSmallEM.R
index e2157b2..541d7e1 100644
--- a/R/initSmallEM.R
+++ b/R/initSmallEM.R
@@ -3,11 +3,12 @@
 #' @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)
@@ -34,9 +35,13 @@ initSmallEM = function(k,X,Y,tau)
 		{
 			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,]))) %*%
+			    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]
 			rhoInit1[,,r,repet] = solve(sigmaInit1[,,r,repet])
@@ -57,8 +62,9 @@ initSmallEM = function(k,X,Y,tau)
 		miniInit = 10
 		maxiInit = 11
 		
-		new_EMG = .Call("EMGLLF_core",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)]
 	}