ajout des commentaires Roxygen - anglicisme de certains noms de fonctions et de variables

[valse.git] / R / initSmallEM.R

diff --git a/R/initSmallEM.R b/R/initSmallEM.R
index d519766..1fa2d9b 100644 (file)
--- a/R/initSmallEM.R
+++ b/R/initSmallEM.R
@@ -1,84 +1,75 @@
-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=Z,indice=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)
-
-       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()
-
-       require(MASS) #Moore-Penrose generalized inverse of matrix
-       for(repet in 1:20)
-       {
-               clusters = hclust(dist(y)) #default distance : euclidean
-               #cutree retourne les indices (à quel cluster indiv_i appartient) d'un clustering hierarchique
-               clusterCut = cutree(clusters,k)
-               Zinit1[,repet] = clusterCut
-
-               for(r in 1:k)
-               {
-                       Z = Zinit1[,repet]
-                       Z_bin = vec_bin(Z,r)
-                       Z_vec = Z_bin$Z #vecteur 0 et 1 aux endroits où Z==r
-                       Z_indice = Z_bin$indice #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 = .Call("EMGLLF",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],
-                       gamInit1[,,repet],miniInit,maxiInit,1,0,x,y,tau)
-               LLFEessai = new_EMG$LLF
-               LLFinit1[repet] = LLFEessai[length(LLFEessai)]
-       }
-
-       b = which.max(LLFinit1)
-       phiInit = phiInit1[,,,b]
-       rhoInit = rhoInit1[,,,b]
-       piInit = piInit1[b,]
-       gamInit = gamInit1[,,b]
-
-       return (list(phiInit=phiInit, rhoInit=rhoInit, piInit=piInit, gamInit=gamInit))
+  n = nrow(Y)
+  m = ncol(Y)
+  p = ncol(X)
+  
+  betaInit1 = array(0, dim=c(p,m,k,20))
+  sigmaInit1 = array(0, dim = c(m,m,k,20))
+  phiInit1 = array(0, dim = c(p,m,k,20))
+  rhoInit1 = array(0, dim = c(m,m,k,20))
+  piInit1 = matrix(0,20,k)
+  gamInit1 = array(0, dim=c(n,k,20))
+  LLFinit1 = list()
+  
+  require(MASS) #Moore-Penrose generalized inverse of matrix
+  for(repet in 1:20)
+  {
+    clusters = hclust(dist(y)) #default distance : euclidean
+    #cutree retourne les indices (? quel cluster indiv_i appartient) d'un clustering hierarchique
+    clusterCut = cutree(clusters,k)
+    Zinit1[,repet] = clusterCut
+    
+    for(r in 1:k)
+    {
+      Z = Zinit1[,repet]
+      Z_bin = vec_bin(Z,r)
+      Z_vec = Z_bin$Z #vecteur 0 et 1 aux endroits o? Z==r
+      Z_indice = Z_bin$indice #renvoit les indices o? Z==r
+      
+      betaInit1[,,r,repet] =
+        ginv(t(x[Z_indice,])%*%x[Z_indice,])%*%t(x[Z_indice,])%*%y[Z_indice,]
+      sigmaInit1[,,r,repet] = diag(m)
+      phiInit1[,,r,repet] = betaInit1[,,r,repet]/sigmaInit1[,,r,repet]
+      rhoInit1[,,r,repet] = solve(sigmaInit1[,,r,repet])
+      piInit1[repet,r] = sum(Z_vec)/n
+    }
+    
+    for(i in 1:n)
+    {
+      for(r in 1:k)
+      {
+        dotProduct = (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet]) %*%
+          (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet])
+        Gam[i,r] = piInit1[repet,r]*det(rhoInit1[,,r,repet])*exp(-0.5*dotProduct)
+      }
+      sumGamI = sum(gam[i,])
+      gamInit1[i,,repet]= Gam[i,] / sumGamI
+    }
+    
+    miniInit = 10
+    maxiInit = 11
+    
+    new_EMG = .Call("EMGLLF",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],
+                    gamInit1[,,repet],miniInit,maxiInit,1,0,x,y,tau)
+    LLFEessai = new_EMG$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))
 }