update the model selection step. Beginning for the plots

[valse.git] / reports / essai16mars.R

diff --git a/reports/essai16mars.R b/reports/essai16mars.R
index d835599..416f895 100644 (file)
--- a/reports/essai16mars.R
+++ b/reports/essai16mars.R
@@ -1,88 +1,26 @@
-meanX = rep(0,6)
-covX = 0.1*diag(6)
+p = 10
+q = 15
+k = 2
+D = 20
+
+meanX = rep(0,p)
+covX = 0.1*diag(p)
 
-covY = array(dim = c(5,5,2))
-covY[,,1] = 0.1*diag(5)
-covY[,,2] = 0.2*diag(5)
+covY = array(dim = c(q,q,k))
+covY[,,1] = 0.1*diag(q)
+covY[,,2] = 0.2*diag(q)
 
-beta = array(dim = c(6,5,2))
-beta[,,2] = matrix(c(rep(2,12),rep(0, 18)))
-beta[,,1] = matrix(c(rep(1,12),rep(0, 18)))
+beta = array(dim = c(p,q,2))
+beta[,,2] = matrix(c(rep(2,(D)),rep(0, p*q-D)))
+beta[,,1] = matrix(c(rep(1,D),rep(0, p*q-D)))
 
-n = 500
+n = 100
 
 pi = c(0.4,0.6)
 
-source('~/valse/R/generateSampleInputs.R')
 data = generateXY(meanX,covX,covY, pi, beta, n)
 
 X = data$X
 Y = data$Y
 
-k = 2
-
-n = nrow(Y)
-m = ncol(Y)
-p = ncol(X)
-
-Zinit1 = array(0, dim=c(n))
-betaInit1 = array(0, dim=c(p,m,k))
-sigmaInit1 = array(0, dim = c(m,m,k))
-phiInit1 = array(0, dim = c(p,m,k))
-rhoInit1 = array(0, dim = c(m,m,k))
-Gam = matrix(0, n, k)
-piInit1 = matrix(0,k)
-gamInit1 = array(0, dim=c(n,k))
-LLFinit1 = list()
-
-require(MASS) #Moore-Penrose generalized inverse of matrix
-
-  distance_clus = dist(X)
-  tree_hier = hclust(distance_clus)
-  Zinit1 = cutree(tree_hier, k)
-  sum(Zinit1==1)
-  
-  for(r in 1:k)
-  {
-    Z = Zinit1
-    Z_indice = seq_len(n)[Z == r] #renvoit les indices où Z==r
-    if (length(Z_indice) == 1) {
-      betaInit1[,,r] = ginv(crossprod(t(X[Z_indice,]))) %*%
-        crossprod(t(X[Z_indice,]), Y[Z_indice,])
-    } else {
-      betaInit1[,,r] = ginv(crossprod(X[Z_indice,])) %*%
-        crossprod(X[Z_indice,], Y[Z_indice,])
-    }
-    sigmaInit1[,,r] = diag(m)
-    phiInit1[,,r] = betaInit1[,,r] #/ sigmaInit1[,,r]
-    rhoInit1[,,r] = solve(sigmaInit1[,,r])
-    piInit1[r] = mean(Z == r)
-  }
-  
-  for(i in 1:n)
-  {
-    for(r in 1:k)
-    {
-      dotProduct = tcrossprod(Y[i,]%*%rhoInit1[,,r]-X[i,]%*%phiInit1[,,r])
-      Gam[i,r] = piInit1[r]*det(rhoInit1[,,r])*exp(-0.5*dotProduct)
-    }
-    sumGamI = sum(Gam[i,])
-    gamInit1[i,]= Gam[i,] / sumGamI
-  }
-  
-  miniInit = 10
-  maxiInit = 101
-  
-  new_EMG = EMGLLF(phiInit1,rhoInit1,piInit1,gamInit1,miniInit,maxiInit,1,0,X,Y,1e-6)
-  
-  new_EMG$phi
-  new_EMG$pi
-  LLFEessai = new_EMG$LLF
-  LLFinit1 = LLFEessai[length(LLFEessai)]
-
-
-b = which.max(LLFinit1)
-phiInit = phiInit1[,,,b]
-rhoInit = rhoInit1[,,,b]
-piInit = piInit1[b,]
-gamInit = gamInit1[,,b]
+res_valse = valse(X,Y)