prepare EMGLLF / EMGrank wrappers, simplify folder generateTestData

[valse.git] / R / initSmallEM.R

diff --git a/R/initSmallEM.R b/R/initSmallEM.R
index 8f3c86b..e2157b2 100644 (file)
--- a/R/initSmallEM.R
+++ b/R/initSmallEM.R
@@ -1,80 +1,73 @@
-library(MASS) #generalized inverse of matrix Monroe-Penrose
-
-vec_bin = function(X,r){
-  Z = c()
-  indice = c()
-  j=1
-  for(i in 1:length(X)){
-    if(X[i] == r){
-      Z[i] = 1
-      indice[j] = i
-      j=j+1
-    }
-    else{
-      Z[i] = 0
-    }
-  }
-  return(list(Z,indice))
-}
+#' 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)
+#' @param tau threshold to stop EM algorithm
+#'
+#' @return a list with phiInit, rhoInit, piInit, gamInit
+#' @export
+initSmallEM = function(k,X,Y,tau)
+{
+       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))
+       Gam = matrix(0, n, k)
+       piInit1 = matrix(0,20,k)
+       gamInit1 = array(0, dim=c(n,k,20))
+       LLFinit1 = list()
 
-initSmallEM = function(k,X,Y,tau){
-  n = nrow(Y)
-  m = ncol(Y)
-  p = ncol(X)
+       require(MASS) #Moore-Penrose generalized inverse of matrix
+       for(repet in 1:20)
+       {
+         distance_clus = dist(X)
+         tree_hier = hclust(distance_clus)
+         Zinit1[,repet] = cutree(tree_hier, k)
 
-  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))
-  piInit1 = matrix(0,20,k)
-  gamInit1 = array(0, dim=c(n,k,20))
-  LLFinit1 = list()
-  
-  
-  for(repet in 1:20){
-    clusters = hclust(dist(y)) #default distance : euclidean
-    clusterCut = cutree(clusters,k)
-    Zinit1[,repet] = clusterCut #retourne les indices (à quel cluster indiv_i appartient) d'un clustering hierarchique (nb de cluster = k)
-    
-    for(r in 1:k){
-      Z = Zinit1[,repet]
-      Z_bin = vec_bin(Z,r)
-      Z_vec = Z_bin[[1]] #vecteur 0 et 1 aux endroits où Z==r
-      Z_indice = Z_bin[[2]] #renvoit les indices où Z==r
-      
-      betaInit1[,,r,repet] = ginv(t(x[Z_indice,])%*%x[Z_indice,])%*%t(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] = sum(Z_vec)/n
-    }
-    
-    for(i in 1:n){
-      for(r in 1:k){
-        dotProduct = (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet]) %*% (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
-    
-    new_EMG = EMGLLF(phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],gamInit1[,,repet],miniInit,maxiInit,1,0,x,y,tau)
-    ##.C("EMGLLF", phiInit = phiInit, rhoInit = rhoInit, ...)
-    LLFEessai = new_EMG[[4]]
-    LLFinit1[[repet]] = LLFEessai[[length(LLFEessai)]]
-  }
-  
-  b = which.max(LLFinit1)
-  
-  phiInit = phiInit1[,,,b]
-  rhoInit = rhoInit1[,,,b]
-  piInit = piInit1[b,]
-  gamInit = gamInit1[,,b]
-  
-  return(list(phiInit, rhoInit, piInit, gamInit))
-}
+               for(r in 1:k)
+               {
+                       Z = Zinit1[,repet]
+                       Z_indice = seq_len(n)[Z == r] #renvoit les indices où Z==r
+                       
+                       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]
+                       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
+               
+               new_EMG = .Call("EMGLLF_core",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],
+                       gamInit1[,,repet],miniInit,maxiInit,1,0,X,Y,tau)
+               LLFEessai = new_EMG$LLF
+               LLFinit1[repet] = LLFEessai[length(LLFEessai)]
+       }
 
+       b = which.max(LLFinit1)
+       phiInit = phiInit1[,,,b]
+       rhoInit = rhoInit1[,,,b]
+       piInit = piInit1[b,]
+       gamInit = gamInit1[,,b]
 
+       return (list(phiInit=phiInit, rhoInit=rhoInit, piInit=piInit, gamInit=gamInit))
+}