X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FinitSmallEM.R;h=44b4b06afa909893bf44a91f8c699af4cbb9aa99;hp=bd5ce1779303f3d17bf727b30048d5768b7a12a6;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=4d9db27f0d1749e5577038dedbc5f4d0826f2772

diff --git a/pkg/R/initSmallEM.R b/pkg/R/initSmallEM.R
deleted file mode 100644
index bd5ce17..0000000
--- a/pkg/R/initSmallEM.R
+++ /dev/null
@@ -1,77 +0,0 @@
-#' 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)
-#'
-#' @return a list with phiInit, rhoInit, piInit, gamInit
-#' @export
-#' @importFrom methods new
-#' @importFrom stats cutree dist hclust runif
-initSmallEM = function(k,X,Y, fast=TRUE)
-{
-	n = nrow(Y)
-	m = ncol(Y)
-	p = ncol(X)
-  nIte = 20
-	Zinit1 = array(0, dim=c(n,nIte))
-	betaInit1 = array(0, dim=c(p,m,k,nIte))
-	sigmaInit1 = array(0, dim = c(m,m,k,nIte))
-	phiInit1 = array(0, dim = c(p,m,k,nIte))
-	rhoInit1 = array(0, dim = c(m,m,k,nIte))
-	Gam = matrix(0, n, k)
-	piInit1 = matrix(0,nIte,k)
-	gamInit1 = array(0, dim=c(n,k,nIte))
-	LLFinit1 = list()
-
-	#require(MASS) #Moore-Penrose generalized inverse of matrix
-	for(repet in 1:nIte)
-	{
-	  distance_clus = dist(cbind(X,Y))
-	  tree_hier = hclust(distance_clus)
-	  Zinit1[,repet] = cutree(tree_hier, k)
-
-		for(r in 1:k)
-		{
-			Z = Zinit1[,repet]
-			Z_indice = seq_len(n)[Z == r] #renvoit les indices où Z==r
-			if (length(Z_indice) == 1) {
-			  betaInit1[,,r,repet] = MASS::ginv(crossprod(t(X[Z_indice,]))) %*%
-			    crossprod(t(X[Z_indice,]), Y[Z_indice,])
-			} else {
-			betaInit1[,,r,repet] = MASS::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])
-			piInit1[repet,r] = mean(Z == r)
-		}
-		
-		for(i in 1:n)
-		{
-			for(r in 1:k)
-			{
-				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,])
-			gamInit1[i,,repet]= Gam[i,] / sumGamI
-		}
-		
-		miniInit = 10
-		maxiInit = 11
-		
-		init_EMG = EMGLLF(phiInit1[,,,repet], rhoInit1[,,,repet], piInit1[repet,],
-			gamInit1[,,repet], miniInit, maxiInit, gamma=1, lambda=0, X, Y, eps=1e-4, fast)
-		LLFEessai = init_EMG$LLF
-		LLFinit1[repet] = LLFEessai[length(LLFEessai)]
-	}
-	b = which.min(LLFinit1)
-	phiInit = phiInit1[,,,b]
-	rhoInit = rhoInit1[,,,b]
-	piInit = piInit1[b,]
-	gamInit = gamInit1[,,b]
-
-	return (list(phiInit=phiInit, rhoInit=rhoInit, piInit=piInit, gamInit=gamInit))
-}