X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=R%2FinitSmallEM.R;h=3f98e07ad2a1998791ccb53d8a42b3e5962b96e7;hb=ae4fa2cb5a036ed1c8b8b69f257c007e8894238d;hp=8cfb7e87c2fe65638e639c2b188a8bff0a00d4ab;hpb=f2a9120810d7e1e423c7b5c2c4320f4e27221f50;p=valse.git

diff --git a/R/initSmallEM.R b/R/initSmallEM.R
index 8cfb7e8..3f98e07 100644
--- a/R/initSmallEM.R
+++ b/R/initSmallEM.R
@@ -9,66 +9,66 @@
 #' @export
 initSmallEM = function(k,X,Y,tau)
 {
-  n = nrow(Y)
-  m = ncol(Y)
-  p = ncol(X)
+	n = nrow(Y)
+	m = ncol(Y)
+	p = ncol(X)
   
-  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))
-  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$classification
-    
-    for(r in 1:k)
-    {
-      Z = Zinit1[,repet]
-      Z_bin = vec_bin(Z,r)
-      Z_vec = Z_bin$Z #vecteur 0 et 1 aux endroits o? Z==r
-      Z_indice = Z_bin$indice #renvoit les indices o? Z==r
-      
-      betaInit1[,,r,repet] =
-        ginv(t(x[Z_indice,])%*%x[Z_indice,])%*%t(x[Z_indice,])%*%y[Z_indice,]
-      sigmaInit1[,,r,repet] = diag(m)
-      phiInit1[,,r,repet] = betaInit1[,,r,repet]/sigmaInit1[,,r,repet]
-      rhoInit1[,,r,repet] = solve(sigmaInit1[,,r,repet])
-      piInit1[repet,r] = sum(Z_vec)/n
-    }
-    
-    for(i in 1:n)
-    {
-      for(r in 1:k)
-      {
-        dotProduct = (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet]) %*%
-          (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet])
-        Gam[i,r] = piInit1[repet,r]*det(rhoInit1[,,r,repet])*exp(-0.5*dotProduct)
-      }
-      sumGamI = sum(gam[i,])
-      gamInit1[i,,repet]= Gam[i,] / sumGamI
-    }
-    
-    miniInit = 10
-    maxiInit = 11
-    
-    new_EMG = .Call("EMGLLF",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],
-                    gamInit1[,,repet],miniInit,maxiInit,1,0,x,y,tau)
-    LLFEessai = new_EMG$LLF
-    LLFinit1[repet] = LLFEessai[length(LLFEessai)]
-  }
-  
-  b = which.max(LLFinit1)
-  phiInit = phiInit1[,,,b]
-  rhoInit = rhoInit1[,,,b]
-  piInit = piInit1[b,]
-  gamInit = gamInit1[,,b]
-  
-  return (list(phiInit=phiInit, rhoInit=rhoInit, piInit=piInit, gamInit=gamInit))
+	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()
+
+	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$classification
+		
+		for(r in 1:k)
+		{
+			Z = Zinit1[,repet]
+			Z_bin = vec_bin(Z,r)
+			Z_vec = Z_bin$vec #vecteur 0 et 1 aux endroits o? Z==r
+			Z_indice = Z_bin$indice #renvoit les indices o? Z==r
+			
+			betaInit1[,,r,repet] = ginv(t(X[Z_indice,])%*%X[Z_indice,])%*%t(X[Z_indice,])%*%Y[Z_indice,]
+			sigmaInit1[,,r,repet] = diag(m)
+			phiInit1[,,r,repet] = betaInit1[,,r,repet]#/sigmaInit1[,,r,repet]
+			rhoInit1[,,r,repet] = solve(sigmaInit1[,,r,repet])
+			piInit1[repet,r] = sum(Z_vec)/n
+		}
+		
+		for(i in 1:n)
+		{
+			for(r in 1:k)
+			{
+				dotProduct = 3 #(Y[i,]%*%rhoInit1[,,r,repet]-X[i,]%*%phiInit1[,,r,repet]) %*% (Y[i,]%*%rhoInit1[,,r,repet]-X[i,]%*%phiInit1[,,r,repet])
+				Gam[i,r] = piInit1[repet,r]*det(rhoInit1[,,r,repet])*exp(-0.5*dotProduct)
+			}
+			sumGamI = sum(Gam[i,])
+			gamInit1[i,,repet]= Gam[i,] / sumGamI
+		}
+		
+		miniInit = 10
+		maxiInit = 11
+		
+		new_EMG = .Call("EMGLLF",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],
+										gamInit1[,,repet],miniInit,maxiInit,1,0,X,Y,tau)
+		LLFEessai = new_EMG$LLF
+		LLFinit1[repet] = LLFEessai[length(LLFEessai)]
+	}
+
+	b = which.max(LLFinit1)
+	phiInit = phiInit1[,,,b]
+	rhoInit = rhoInit1[,,,b]
+	piInit = piInit1[b,]
+	gamInit = gamInit1[,,b]
+
+	return (list(phiInit=phiInit, rhoInit=rhoInit, piInit=piInit, gamInit=gamInit))
 }