From: Benjamin Goehry <benjamin.goehry@math.u-psud.fr>
Date: Tue, 17 Jan 2017 08:07:16 +0000 (+0100)
Subject: constructionModelLassoLME.R
X-Git-Url: https://git.auder.net/doc/html/css/%7B%7B?a=commitdiff_plain;h=b05e34dc0c44a4329f3535790e5741091407e461;p=valse.git

constructionModelLassoLME.R
---

diff --git a/src/test/generate_test_data/helpers/constructionModelesLassoMLE.R b/src/test/generate_test_data/helpers/constructionModelesLassoMLE.R
new file mode 100644
index 0000000..3eac5d1
--- /dev/null
+++ b/src/test/generate_test_data/helpers/constructionModelesLassoMLE.R
@@ -0,0 +1,58 @@
+constructionModelesLassoMLE = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,X,Y,seuil,tau,A1,A2){
+  #get matrix sizes
+  n = dim(X)[1];
+  p = dim(phiInit)[1]
+  m = dim(phiInit)[2]
+  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)
+  lvraisemblance = matrix(0, L, 2)
+
+  for(lambdaIndex in 1:L){
+    a = A1[, 1, lambdaIndex]
+    a[a==0] = c()
+    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
+    
+    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)
+    }
+    
+    #on veut calculer l'EMV avec toutes nos estimations
+		densite = matrix(0, n, 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)
+			}
+		}
+		lvraisemblance[lambdaIndex,1] = sum(log(densite[,lambdaIndex]))
+		lvraisemblance[lambdaIndex,2] = (dimension+m+1)*k-1
+  }
+  return(list(phi=phi, rho=rho, Pi=Pi, lvraisemblance = lvraisemblance))
+}
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