essai fusion

[valse.git] / pkg / R / initSmallEM.R

diff --git a/pkg/R/initSmallEM.R b/pkg/R/initSmallEM.R
deleted file mode 100644 (file)
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))
-}