prepare EMGLLF / EMGrank wrappers, simplify folder generateTestData

[valse.git] / R / initSmallEM.R

diff --git a/R/initSmallEM.R b/R/initSmallEM.R
index c24fca9..e2157b2 100644 (file)
--- a/R/initSmallEM.R
+++ b/R/initSmallEM.R
@@ -13,7 +13,7 @@ initSmallEM = function(k,X,Y,tau)
        m = ncol(Y)
        p = ncol(X)
   
-       Zinit1 = array(0, dim=c(n,20)) #doute sur la taille
+       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))
@@ -24,31 +24,30 @@ initSmallEM = function(k,X,Y,tau)
        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$classification
-               
+         distance_clus = dist(X)
+         tree_hier = hclust(distance_clus)
+         Zinit1[,repet] = cutree(tree_hier, k)
+
                for(r in 1:k)
                {
                        Z = Zinit1[,repet]
-                       Z_bin = vec_bin(Z,r)
-                       Z_vec = Z_bin$vec #vecteur 0 et 1 aux endroits o? Z==r
-                       Z_indice = Z_bin$indice #renvoit les indices o? Z==r
+                       Z_indice = seq_len(n)[Z == r] #renvoit les indices où Z==r
                        
-                       betaInit1[,,r,repet] = ginv(t(X[Z_indice,])*X[Z_indice,])%*%t(X[Z_indice,])%*%Y[Z_indice,]
+                       betaInit1[,,r,repet] = 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]
+                       phiInit1[,,r,repet] = betaInit1[,,r,repet] #/ sigmaInit1[,,r,repet]
                        rhoInit1[,,r,repet] = solve(sigmaInit1[,,r,repet])
-                       piInit1[repet,r] = sum(Z_vec)/n
+                       piInit1[repet,r] = mean(Z == r)
                }
                
                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])
+                               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,])
@@ -59,7 +58,7 @@ initSmallEM = function(k,X,Y,tau)
                maxiInit = 11
                
                new_EMG = .Call("EMGLLF_core",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],
-                                                                               gamInit1[,,repet],miniInit,maxiInit,1,0,X,Y,tau)
+                       gamInit1[,,repet],miniInit,maxiInit,1,0,X,Y,tau)
                LLFEessai = new_EMG$LLF
                LLFinit1[repet] = LLFEessai[length(LLFEessai)]
        }