X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=R%2FinitSmallEM.R;h=1fa2d9b9a5daf0b366b3314f35eba412200f2675;hp=d5197661087de2323e6475d24277e2d4605e04af;hb=d1531659214edd6eaef0ac9ec835455614bba16c;hpb=6bd2e869a17f3980d52820643c1c1b5f3725738e

diff --git a/R/initSmallEM.R b/R/initSmallEM.R
index d519766..1fa2d9b 100644
--- a/R/initSmallEM.R
+++ b/R/initSmallEM.R
@@ -1,84 +1,75 @@
-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)
-
-	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
-	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
-
-		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,]
-			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$LLF
-		LLFinit1[repet] = LLFEessai[length(LLFEessai)]
-	}
-
-	b = which.max(LLFinit1)
-	phiInit = phiInit1[,,,b]
-	rhoInit = rhoInit1[,,,b]
-	piInit = piInit1[b,]
-	gamInit = gamInit1[,,b]
-
-	return (list(phiInit=phiInit, rhoInit=rhoInit, piInit=piInit, gamInit=gamInit))
+  n = nrow(Y)
+  m = ncol(Y)
+  p = ncol(X)
+  
+  betaInit1 = array(0, dim=c(p,m,k,20))
+  sigmaInit1 = array(0, dim = c(m,m,k,20))
+  phiInit1 = array(0, dim = c(p,m,k,20))
+  rhoInit1 = array(0, dim = c(m,m,k,20))
+  piInit1 = matrix(0,20,k)
+  gamInit1 = array(0, dim=c(n,k,20))
+  LLFinit1 = list()
+  
+  require(MASS) #Moore-Penrose generalized inverse of matrix
+  for(repet in 1:20)
+  {
+    clusters = hclust(dist(y)) #default distance : euclidean
+    #cutree retourne les indices (? quel cluster indiv_i appartient) d'un clustering hierarchique
+    clusterCut = cutree(clusters,k)
+    Zinit1[,repet] = clusterCut
+    
+    for(r in 1:k)
+    {
+      Z = Zinit1[,repet]
+      Z_bin = vec_bin(Z,r)
+      Z_vec = Z_bin$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))
 }