X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=src%2Ftest%2Fgenerate_test_data%2Fhelpers%2FconstructionModelesLassoMLE.R;h=ed05b2a1c625a1735c5f99b031664bf5241e61ac;hp=359bada51e08e8750f4da6f8c37b9f77db65835f;hb=46a2e676b2d85eef5a1811a6e623b65327fc451d;hpb=ef67d338c7f28ba041abe40ca9a8ab69f8365a90

diff --git a/src/test/generate_test_data/helpers/constructionModelesLassoMLE.R b/src/test/generate_test_data/helpers/constructionModelesLassoMLE.R
index 359bada..ed05b2a 100644
--- a/src/test/generate_test_data/helpers/constructionModelesLassoMLE.R
+++ b/src/test/generate_test_data/helpers/constructionModelesLassoMLE.R
@@ -1,58 +1,56 @@
-constructionModelesLassoMLE = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,X,Y,seuil,tau,A1,A2){
-  #get matrix sizes
+constructionModelesLassoMLE = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,
+	X,Y,seuil,tau,A1,A2)
+{
   n = dim(X)[1];
   p = dim(phiInit)[1]
   m = dim(phiInit)[2]
-  k  = dim(phiInit)[3]
+  k = dim(phiInit)[3]
   L = length(glambda)
-  
+
   #output parameters
   phi = array(0, dim=c(p,m,k,L))
   rho = array(0, dim=c(m,m,k,L))
-  Pi = matrix(0, k, L)
+  pi = matrix(0, k, L)
   llh = matrix(0, L, 2) #log-likelihood
 
-  for(lambdaIndex in 1:L){
-    a = A1[, 1, lambdaIndex]
-    a[a==0] = c()
-    if(length(a)==0){
+  for(lambdaIndex in 1:L)
+	{
+    a = A1[,1,lambdaIndex]
+    a = a[a!=0]
+    if(length(a)==0)
       next
-    }
-    EMGLLf = EMGLLF(phiInit[a,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,X[,a],Y,tau)
-    
-    phiLambda = EMGLLf$phi
-    rhoLambda = EMGLLf$rho
-    piLambda = EMGLLf$Pi
-    
-    for(j in 1:length(a)){
-      phi[a[j],,,lambdaIndex] = phiLambda[j,,]
-    }
-    rho[,,,lambdaIndex] = rhoLambda
-    Pi[,lambdaIndex] = piLambda
-    
+
+    res = EMGLLF(phiInit[a,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0.,X[,a],Y,tau)
+
+    for (j in 1:length(a))
+      phi[a[j],,,lambdaIndex] = res$phi[j,,]
+    rho[,,,lambdaIndex] = res$rho
+    pi[,lambdaIndex] = res$pi
+
     dimension = 0
-    for(j in 1:p){
-      vec =  c(2, dim(A2)[2])
-      b = A2[j,vec,lambdaIndex]
-      b[b==0] = c()
-      if(length(b) > 0){
-        phi[A2[j,1,lambdaIndex],b,,lambdaIndex] = 0
-      }
-      c = A1[j,vec,lambdaIndex]
-      c[c==0] = c()
-      dimension = dimension + length(c)
+    for (j in 1:p)
+		{
+      b = A2[j,2:dim(A2)[2],lambdaIndex]
+      b = b[b!=0]
+      if (length(b) > 0)
+        phi[A2[j,1,lambdaIndex],b,,lambdaIndex] = 0.
+      c = A1[j,2:dim(A1)[2],lambdaIndex]
+      dimension = dimension + sum(c!=0)
     }
-    
+
     #on veut calculer l'EMV avec toutes nos estimations
-		densite = matrix(0, n, L)
-		for(i in 1:n){
-			for( r in 1:k){
+		densite = matrix(0, nrow=n, ncol=L)
+		for (i in 1:n)
+		{
+			for (r in 1:k)
+			{
 				delta = Y[i,]%*%rho[,,r,lambdaIndex] - (X[i,a]%*%phi[a,,r,lambdaIndex]);
-				densite[i,lambdaIndex] = densite[i,lambdaIndex] +	Pi[r,lambdaIndex]*det(rho[,,r,lambdaIndex])/(sqrt(2*pi))^m*exp(-tcrossprod(delta)/2.0)
+				densite[i,lambdaIndex] = densite[i,lambdaIndex] + pi[r,lambdaIndex] *
+					det(rho[,,r,lambdaIndex])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
 			}
 		}
 		llh[lambdaIndex,1] = sum(log(densite[,lambdaIndex]))
 		llh[lambdaIndex,2] = (dimension+m+1)*k-1
   }
-  return(list(phi=phi, rho=rho, Pi=Pi, llh = llh))
+  return (list("phi"=phi, "rho"=rho, "pi"=pi, "llh" = llh))
 }