fix memory leaks on EMGLLF, test OK for EMGrank

[valse.git] / src / test / generate_test_data / helpers / EMGrank.R

diff --git a/src/test/generate_test_data/helpers/EMGrank.R b/src/test/generate_test_data/helpers/EMGrank.R
index a1e3df7..d419813 100644 (file)
--- a/src/test/generate_test_data/helpers/EMGrank.R
+++ b/src/test/generate_test_data/helpers/EMGrank.R
@@ -1,3 +1,11 @@
+#helper to always have matrices as arg (TODO: put this elsewhere? improve?)
+matricize <- function(X)
+{
+       if (!is.matrix(X))
+               return (t(as.matrix(X)))
+       return (X)
+}
+
 require(MASS)
 EMGrank = function(Pi, Rho, mini, maxi, X, Y, tau, rank){
   #matrix dimensions
@@ -9,7 +17,7 @@ EMGrank = function(Pi, Rho, mini, maxi, X, Y, tau, rank){
   #init outputs
   phi = array(0, dim=c(p,m,k))
   Z = rep(1, n)
-  Pi = piInit
+#  Pi = piInit
   LLF = 0
   
   #local variables
@@ -20,65 +28,58 @@ EMGrank = function(Pi, Rho, mini, maxi, X, Y, tau, rank){
   
   #main loop
   ite = 1
-  while(ite<=mini || (ite<=maxi && sumDeltaPhi>tau)){
+  while(ite<=mini || (ite<=maxi && sumDeltaPhi>tau))
+       {
     #M step: Mise à jour de Beta (et donc phi)
-    for(r in 1:k){
-      Z_bin = valse:::vec_bin(Z,r)
-      Z_vec = Z_bin$vec #vecteur 0 et 1 aux endroits o? Z==r
-      Z_indice = Z_bin$indice 
-      if(sum(Z_indice) == 0){
+    for(r in 1:k)
+               {
+      Z_indice = seq_len(n)[Z==r] #indices où Z == r
+      if (length(Z_indice) == 0)
         next
-      }
       #U,S,V = SVD of (t(Xr)Xr)^{-1} * t(Xr) * Yr
-      sv = svd(ginv( crossprod(X[Z_indice,]) )   %*%   crossprod(X[Z_indice,], Y[Z_indice,])     )
-      S = diag(sv$d)
-      U = sv$u
-      V = sv$v
+      s = svd( ginv(crossprod(matricize(X[Z_indice,]))) %*%
+                               crossprod(matricize(X[Z_indice,]),matricize(Y[Z_indice,])) )
+      S = s$d
+      U = s$u
+      V = s$v
       #Set m-rank(r) singular values to zero, and recompose
       #best rank(r) approximation of the initial product
-      if(r==k){
-        j_r_1 = length(S)
-      }
-      else{
-        j_r_1 = c(rank[r]+1:length(S))
-      }
-      S[j_r_1] = 0
-      S = diag(S, nrow = ncol(U))
-      phi[,,r] = U %*% S %*% t(V) %*% Rho[,,r]
+      if(rank[r] < length(S))
+        S[(rank[r]+1):length(S)] = 0
+      phi[,,r] = U %*% diag(S) %*% t(V) %*% Rho[,,r]
     }
   
-  #Etape E et calcul de LLF
-  sumLogLLF2 = 0
-  for(i in 1:n){
-    sumLLF1 = 0
-    maxLogGamIR = -Inf
-    for(r in 1:k){
-      dotProduct = tcrossprod(Y[i,]%*%Rho[,,r]-X[i,]%*%phi[,,r])
-      logGamIR = log(Pi[r]) + log(det(Rho[,,r])) - 0.5*dotProduct
-      #Z[i] = index of max (gam[i,])
-      if(logGamIR > maxLogGamIR){
-        Z[i] = r
-        maxLogGamIR = logGamIR
-      }
-    sumLLF1 = sumLLF1 + exp(logGamIR) / (2*pi)^(m/2)
-    }
-    sumLogLLF2 = sumLogLLF2 + log(sumLLF1)
-  }
+               #Etape E et calcul de LLF
+               sumLogLLF2 = 0
+               for(i in 1:n){
+                       sumLLF1 = 0
+                       maxLogGamIR = -Inf
+                       for(r in 1:k){
+                               dotProduct = tcrossprod(Y[i,]%*%Rho[,,r]-X[i,]%*%phi[,,r])
+                               logGamIR = log(Pi[r]) + log(det(Rho[,,r])) - 0.5*dotProduct
+                               #Z[i] = index of max (gam[i,])
+                               if(logGamIR > maxLogGamIR){
+                                       Z[i] = r
+                                       maxLogGamIR = logGamIR
+                               }
+                       sumLLF1 = sumLLF1 + exp(logGamIR) / (2*pi)^(m/2)
+                       }
+                       sumLogLLF2 = sumLogLLF2 + log(sumLLF1)
+               }
   
-  LLF = -1/n * sumLogLLF2
-  
-  #update distance parameter to check algorithm convergence (delta(phi, Phi))
-  deltaPhi = c(deltaPhi, max(max(max((abs(phi-Phi))/(1+abs(phi))))) )
-  if(length(deltaPhi) > deltaPhiBufferSize){
-    l_1 = c(2:length(deltaPhi))
-    deltaPhi = deltaPhi[l_1]
-  }
-  sumDeltaPhi = sum(abs(deltaPhi))
+               LLF = -1/n * sumLogLLF2
   
-  #update other local variables
-  Phi = phi
-  ite = ite+1
+               #update distance parameter to check algorithm convergence (delta(phi, Phi))
+               deltaPhi = c(deltaPhi, max(max(max((abs(phi-Phi))/(1+abs(phi))))) )
+               if(length(deltaPhi) > deltaPhiBufferSize){
+                 l_1 = c(2:length(deltaPhi))
+                 deltaPhi = deltaPhi[l_1]
+               }
+               sumDeltaPhi = sum(abs(deltaPhi))
   
+               #update other local variables
+               Phi = phi
+               ite = ite+1
   }
   return(list(phi=phi, LLF=LLF))
 }