X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=src%2Fsources%2FconstructionModelesLassoMLE.c;fp=src%2Fsources%2FconstructionModelesLassoMLE.c;h=bcbfd3c23c66d9dbca2531e33ad4a69605486a5b;hb=493a35bfea6d1210c94ced8fbfe3e572f0389ea5;hp=0000000000000000000000000000000000000000;hpb=35b42a4bd37b162a3d579693b2b5fa4913a52ed5;p=valse.git

diff --git a/src/sources/constructionModelesLassoMLE.c b/src/sources/constructionModelesLassoMLE.c
new file mode 100644
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+++ b/src/sources/constructionModelesLassoMLE.c
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+#include "EMGLLF.h"
+#include "constructionModelesLassoMLE.h"
+#include <gsl/gsl_linalg.h>
+#include <omp.h>
+#include "omp_num_threads.h"
+
+// TODO: comment on constructionModelesLassoMLE purpose
+void constructionModelesLassoMLE(
+	// IN parameters 
+	const Real* phiInit, // parametre initial de moyenne renormalisé
+	const Real* rhoInit, // parametre initial de variance renormalisé
+	const Real* piInit,  // parametre initial des proportions
+	const Real* gamInit, // paramètre initial des probabilités a posteriori de chaque échantillon
+	Int mini,        // nombre minimal d'itérations dans l'algorithme EM	
+	Int maxi,        // nombre maximal d'itérations dans l'algorithme EM
+	Real gamma,    // valeur de gamma : puissance des proportions dans la pénalisation pour un Lasso adaptatif
+	const Real* glambda, // valeur des paramètres de régularisation du Lasso
+	const Real* X,       // régresseurs
+	const Real* Y,       // réponse
+	Real seuil,    // seuil pour prendre en compte une variable
+	Real tau,      // seuil pour accepter la convergence
+	const Int* A1,         // matrice des coefficients des parametres selectionnes
+	const Int* A2,         // matrice des coefficients des parametres non selectionnes
+	// OUT parameters
+	Real* phi,            // estimateur ainsi calculé par le Lasso
+    Real* rho,            // estimateur ainsi calculé par le Lasso
+	Real* pi,             // estimateur ainsi calculé par le Lasso
+    Real* lvraisemblance, // estimateur ainsi calculé par le Lasso
+	// additional size parameters
+	mwSize n,                 // taille de l'echantillon                
+	mwSize p,                 // nombre de covariables
+	mwSize m,                 // taille de Y (multivarié)
+	mwSize k,                 // nombre de composantes
+	mwSize L)                 // taille de glambda
+{
+	//preparation: phi = 0
+	for (mwSize u=0; u<p*m*k*L; u++)
+		phi[u] = 0.0;
+	
+	//initiate parallel section
+	mwSize lambdaIndex;
+	omp_set_num_threads(OMP_NUM_THREADS);
+	#pragma omp parallel default(shared) private(lambdaIndex)
+	{
+	#pragma omp for schedule(dynamic,CHUNK_SIZE) nowait
+	for (lambdaIndex=0; lambdaIndex<L; lambdaIndex++)
+	{
+		//~ a = A1(:,1,lambdaIndex);
+		//~ a(a==0) = [];
+		Int* a = (Int*)malloc(p*sizeof(Int));
+		mwSize lengthA = 0;
+		for (mwSize j=0; j<p; j++)
+		{
+			if (A1[j*(m+1)*L+0*L+lambdaIndex] != 0)
+				a[lengthA++] = A1[j*(m+1)*L+0*L+lambdaIndex] - 1;
+		}
+		if (lengthA == 0)
+			continue;
+		
+		//Xa = X(:,a)
+		Real* Xa = (Real*)malloc(n*lengthA*sizeof(Real));
+		for (mwSize i=0; i<n; i++)
+		{
+			for (mwSize j=0; j<lengthA; j++)
+				Xa[i*lengthA+j] = X[i*p+a[j]];
+		}
+		
+		//phia = phiInit(a,:,:)
+		Real* phia = (Real*)malloc(lengthA*m*k*sizeof(Real));
+		for (mwSize j=0; j<lengthA; j++)
+		{
+			for (mwSize mm=0; mm<m; mm++)
+			{
+				for (mwSize r=0; r<k; r++)
+					phia[j*m*k+mm*k+r] = phiInit[a[j]*m*k+mm*k+r];
+			}
+		}
+		
+		//[phiLambda,rhoLambda,piLambda,~,~] = EMGLLF(...
+		//	phiInit(a,:,:),rhoInit,piInit,gamInit,mini,maxi,gamma,0,X(:,a),Y,tau);
+		Real* phiLambda = (Real*)malloc(lengthA*m*k*sizeof(Real));
+		Real* rhoLambda = (Real*)malloc(m*m*k*sizeof(Real));
+		Real* piLambda = (Real*)malloc(k*sizeof(Real));
+		Real* LLF = (Real*)malloc((maxi+1)*sizeof(Real));
+		Real* S = (Real*)malloc(lengthA*m*k*sizeof(Real));
+		EMGLLF(phia,rhoInit,piInit,gamInit,mini,maxi,gamma,0.0,Xa,Y,tau,
+			phiLambda,rhoLambda,piLambda,LLF,S,
+			n,lengthA,m,k);
+		free(Xa);
+		free(phia);
+		free(LLF);
+		free(S);
+		
+		//~ for j=1:length(a)
+			//~ phi(a(j),:,:,lambdaIndex) = phiLambda(j,:,:);
+		//~ end
+		for (mwSize j=0; j<lengthA; j++)
+		{
+			for (mwSize mm=0; mm<m; mm++)
+			{
+				for (mwSize r=0; r<k; r++)
+					phi[a[j]*m*k*L+mm*k*L+r*L+lambdaIndex] = phiLambda[j*m*k+mm*k+r];
+			}
+		}
+		free(phiLambda);
+		//~ rho(:,:,:,lambdaIndex) = rhoLambda;
+		for (mwSize u=0; u<m; u++)
+		{
+			for (mwSize v=0; v<m; v++)
+			{
+				for (mwSize r=0; r<k; r++)
+					rho[u*m*k*L+v*k*L+r*L+lambdaIndex] = rhoLambda[u*m*k+v*k+r];
+			}
+		}
+		free(rhoLambda);
+		//~ pi(:,lambdaIndex) = piLambda;
+		for (mwSize r=0; r<k; r++)
+			pi[r*L+lambdaIndex] = piLambda[r];
+		free(piLambda);
+		
+		mwSize dimension = 0;
+		Int* b = (Int*)malloc(m*sizeof(Int));
+		for (mwSize j=0; j<p; j++)
+		{
+			//~ b = A2(j,2:end,lambdaIndex);
+			//~ b(b==0) = [];
+			mwSize lengthB = 0;
+			for (mwSize mm=0; mm<m; mm++)
+			{
+				if (A2[j*(m+1)*L+(mm+1)*L+lambdaIndex] != 0)
+					b[lengthB++] = A2[j*(m+1)*L+(mm+1)*L+lambdaIndex] - 1;
+			}
+			//~ if length(b) > 0
+				//~ phi(A2(j,1,lambdaIndex),b,:,lambdaIndex) = 0.0;
+			//~ end
+			if (lengthB > 0)
+			{
+				for (mwSize mm=0; mm<lengthB; mm++)
+				{
+					for (mwSize r=0; r<k; r++)
+						phi[(A2[j*(m+1)*L+0*L+lambdaIndex]-1)*m*k*L + b[mm]*k*L + r*L + lambdaIndex] = 0.0;
+				}
+			}
+			
+			//~ c = A1(j,2:end,lambdaIndex);
+			//~ c(c==0) = [];
+			//~ dimension = dimension + length(c);
+			for (mwSize mm=0; mm<m; mm++)
+			{
+				if (A1[j*(m+1)*L+(mm+1)*L+lambdaIndex] != 0)
+					dimension++;
+			}
+		}
+		free(b);
+		
+		int signum;
+		Real* densite = (Real*)calloc(L*n,sizeof(Real));
+		Real sumLogDensit = 0.0;
+		gsl_matrix* matrix = gsl_matrix_alloc(m, m);
+		gsl_permutation* permutation = gsl_permutation_alloc(m);
+		Real* YiRhoR = (Real*)malloc(m*sizeof(Real));
+		Real* XiPhiR = (Real*)malloc(m*sizeof(Real));
+		for (mwSize i=0; i<n; i++)
+		{
+			//~ for r=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(-dot(delta,delta)/2.0);
+			//~ end
+			for (mwSize r=0; r<k; r++)
+			{
+				//compute det(rho(:,:,r,lambdaIndex)) [TODO: avoid re-computations]
+				for (mwSize u=0; u<m; u++)
+				{
+					for (mwSize v=0; v<m; v++)
+						matrix->data[u*m+v] = rho[u*m*k*L+v*k*L+r*L+lambdaIndex];
+				}
+				gsl_linalg_LU_decomp(matrix, permutation, &signum);
+				Real detRhoR = gsl_linalg_LU_det(matrix, signum);
+				
+				//compute Y(i,:)*rho(:,:,r,lambdaIndex)
+				for (mwSize u=0; u<m; u++)
+				{
+					YiRhoR[u] = 0.0;
+					for (mwSize v=0; v<m; v++)
+						YiRhoR[u] += Y[i*m+v] * rho[v*m*k*L+u*k*L+r*L+lambdaIndex];
+				}
+				
+				//compute X(i,a)*phi(a,:,r,lambdaIndex)
+				for (mwSize u=0; u<m; u++)
+				{
+					XiPhiR[u] = 0.0;
+					for (mwSize v=0; v<lengthA; v++)
+						XiPhiR[u] += X[i*p+a[v]] * phi[a[v]*m*k*L+u*k*L+r*L+lambdaIndex];
+				}
+                // On peut remplacer X par Xa dans ce dernier calcul, mais je ne sais pas si c'est intéressant ...
+				
+				// compute dotProduct < delta . delta >
+				Real dotProduct = 0.0;
+				for (mwSize u=0; u<m; u++)
+					dotProduct += (YiRhoR[u]-XiPhiR[u]) * (YiRhoR[u]-XiPhiR[u]);
+				
+				densite[lambdaIndex*n+i] += (pi[r*L+lambdaIndex]*detRhoR/pow(sqrt(2.0*M_PI),m))*exp(-dotProduct/2.0);
+			}			
+			sumLogDensit += log(densite[lambdaIndex*n+i]);
+		}
+		lvraisemblance[lambdaIndex*2+0] = sumLogDensit;
+		lvraisemblance[lambdaIndex*2+1] = (dimension+m+1)*k-1;
+	
+		free(a);
+		free(YiRhoR);
+		free(XiPhiR);
+		free(densite);
+		gsl_matrix_free(matrix);
+		gsl_permutation_free(permutation);
+	}
+	}
+}