fix initialization and made some update

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

diff --git a/pkg/R/initSmallEM.R b/pkg/R/initSmallEM.R
index 541d7e1..bd5ce17 100644 (file)
--- a/pkg/R/initSmallEM.R
+++ b/pkg/R/initSmallEM.R
@@ -8,26 +8,26 @@
 #' @export
 #' @importFrom methods new
 #' @importFrom stats cutree dist hclust runif
-initSmallEM = function(k,X,Y)
+initSmallEM = function(k,X,Y, fast=TRUE)
 {
        n = nrow(Y)
        m = ncol(Y)
        p = ncol(X)
-  
-       Zinit1 = array(0, dim=c(n,20))
-       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))
+  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(0,20,k)
-       gamInit1 = array(0, dim=c(n,k,20))
+       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 1:20)
+       #require(MASS) #Moore-Penrose generalized inverse of matrix
+       for(repet in 1:nIte)
        {
-         distance_clus = dist(X)
+         distance_clus = dist(cbind(X,Y))
          tree_hier = hclust(distance_clus)
          Zinit1[,repet] = cutree(tree_hier, k)
 
@@ -36,10 +36,10 @@ initSmallEM = function(k,X,Y)
                        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,]))) %*%
+                         betaInit1[,,r,repet] = MASS::ginv(crossprod(t(X[Z_indice,]))) %*%
                            crossprod(t(X[Z_indice,]), Y[Z_indice,])
                        } else {
-                       betaInit1[,,r,repet] = ginv(crossprod(X[Z_indice,])) %*%
+                       betaInit1[,,r,repet] = MASS::ginv(crossprod(X[Z_indice,])) %*%
                                crossprod(X[Z_indice,], Y[Z_indice,])
                        }
                        sigmaInit1[,,r,repet] = diag(m)
@@ -62,14 +62,12 @@ initSmallEM = function(k,X,Y)
                miniInit = 10
                maxiInit = 11
                
-               #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$LLF
+               init_EMG = EMGLLF(phiInit1[,,,repet], rhoInit1[,,,repet], piInit1[repet,],
+                       gamInit1[,,repet], miniInit, maxiInit, gamma=1, lambda=0, X, Y, eps=1e-4, fast)
+               LLFEessai = init_EMG$LLF
                LLFinit1[repet] = LLFEessai[length(LLFEessai)]
        }
-
-       b = which.max(LLFinit1)
+       b = which.min(LLFinit1)
        phiInit = phiInit1[,,,b]
        rhoInit = rhoInit1[,,,b]
        piInit = piInit1[b,]