X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=src%2Fsources%2FEMGrank.c;h=2422fc0ba6fafb6b322615fbf5d621ab919865a1;hp=3d5a9c760b807f978033b68ff3b661e22e861662;hb=9ff729fb6afff5bed327fa8619138fd3b6f6f13b;hpb=e8bb47649b9f4e625ef050aae096d23cfc058163

diff --git a/src/sources/EMGrank.c b/src/sources/EMGrank.c
index 3d5a9c7..2422fc0 100644
--- a/src/sources/EMGrank.c
+++ b/src/sources/EMGrank.c
@@ -3,13 +3,13 @@
 #include "utils.h"
 
 // Compute pseudo-inverse of a square matrix
-static float* pinv(const float* matrix, int dim)
+static Real* pinv(const Real* matrix, int dim)
 {
 	gsl_matrix* U = gsl_matrix_alloc(dim,dim);
 	gsl_matrix* V = gsl_matrix_alloc(dim,dim);
 	gsl_vector* S = gsl_vector_alloc(dim);
 	gsl_vector* work = gsl_vector_alloc(dim);
-	float EPS = 1e-10; //threshold for singular value "== 0"
+	Real EPS = 1e-10; //threshold for singular value "== 0"
 	
 	//copy matrix into U
 	copyArray(matrix, U->data, dim*dim);
@@ -19,12 +19,12 @@ static float* pinv(const float* matrix, int dim)
 	gsl_vector_free(work);
 
 	// Obtain pseudo-inverse by V*S^{-1}*t(U)
-	float* inverse = (float*)malloc(dim*dim*sizeof(float));
+	Real* inverse = (Real*)malloc(dim*dim*sizeof(Real));
 	for (int i=0; i<dim; i++)
 	{
 		for (int ii=0; ii<dim; ii++)
 		{
-			float dotProduct = 0.0;
+			Real dotProduct = 0.0;
 			for (int j=0; j<dim; j++)
 				dotProduct += V->data[i*dim+j] * (S->data[j] > EPS ? 1.0/S->data[j] : 0.0) * U->data[ii*dim+j];
 			inverse[i*dim+ii] = dotProduct;
@@ -40,17 +40,17 @@ static float* pinv(const float* matrix, int dim)
 // TODO: comment EMGrank purpose
 void EMGrank_core(
 	// IN parameters
-	const float* Pi, // parametre de proportion
-	const float* Rho, // parametre initial de variance renormalisé
+	const Real* Pi, // parametre de proportion
+	const Real* Rho, // parametre initial de variance renormalisé
 	int mini, // nombre minimal d'itérations dans l'algorithme EM
 	int maxi, // nombre maximal d'itérations dans l'algorithme EM
-	const float* X, // régresseurs
-	const float* Y, // réponse
-	float tau, // seuil pour accepter la convergence
+	const Real* X, // régresseurs
+	const Real* Y, // réponse
+	Real tau, // seuil pour accepter la convergence
 	const int* rank, // vecteur des rangs possibles
 	// OUT parameters
-	float* phi, // parametre de moyenne renormalisé, calculé par l'EM
-	float* LLF, // log vraisemblance associé à cet échantillon, pour les valeurs estimées des paramètres
+	Real* phi, // parametre de moyenne renormalisé, calculé par l'EM
+	Real* LLF, // log vraisemblance associé à cet échantillon, pour les valeurs estimées des paramètres
 	// additional size parameters
 	int n, // taille de l'echantillon
 	int p, // nombre de covariables
@@ -58,20 +58,20 @@ void EMGrank_core(
 	int k) // nombre de composantes
 {
 	// Allocations, initializations
-	float* Phi = (float*)calloc(p*m*k,sizeof(float));
-	float* hatBetaR = (float*)malloc(p*m*sizeof(float));
+	Real* Phi = (Real*)calloc(p*m*k,sizeof(Real));
+	Real* hatBetaR = (Real*)malloc(p*m*sizeof(Real));
 	int signum;
-	float invN = 1.0/n;
+	Real invN = 1.0/n;
 	int deltaPhiBufferSize = 20;
-	float* deltaPhi = (float*)malloc(deltaPhiBufferSize*sizeof(float));
+	Real* deltaPhi = (Real*)malloc(deltaPhiBufferSize*sizeof(Real));
 	int ite = 0;
-	float sumDeltaPhi = 0.0;
-	float* YiRhoR = (float*)malloc(m*sizeof(float));
-	float* XiPhiR = (float*)malloc(m*sizeof(float));
-	float* Xr = (float*)malloc(n*p*sizeof(float));
-	float* Yr = (float*)malloc(n*m*sizeof(float));
-	float* tXrXr = (float*)malloc(p*p*sizeof(float));
-	float* tXrYr = (float*)malloc(p*m*sizeof(float));
+	Real sumDeltaPhi = 0.0;
+	Real* YiRhoR = (Real*)malloc(m*sizeof(Real));
+	Real* XiPhiR = (Real*)malloc(m*sizeof(Real));
+	Real* Xr = (Real*)malloc(n*p*sizeof(Real));
+	Real* Yr = (Real*)malloc(n*m*sizeof(Real));
+	Real* tXrXr = (Real*)malloc(p*p*sizeof(Real));
+	Real* tXrYr = (Real*)malloc(p*m*sizeof(Real));
 	gsl_matrix* matrixM = gsl_matrix_alloc(p, m);
 	gsl_matrix* matrixE = gsl_matrix_alloc(m, m);
 	gsl_permutation* permutation = gsl_permutation_alloc(m);
@@ -115,7 +115,7 @@ void EMGrank_core(
 			{
 				for (int jj=0; jj<p; jj++)
 				{
-					float dotProduct = 0.0;
+					Real dotProduct = 0.0;
 					for (int u=0; u<cardClustR; u++)
 						dotProduct += Xr[mi(u,j,n,p)] * Xr[mi(u,jj,n,p)];
 					tXrXr[mi(j,jj,p,p)] = dotProduct;
@@ -123,14 +123,14 @@ void EMGrank_core(
 			}
 
 			//Get pseudo inverse = (t(Xr)*Xr)^{-1}
-			float* invTXrXr = pinv(tXrXr, p);
+			Real* invTXrXr = pinv(tXrXr, p);
 			
 			// Compute tXrYr = t(Xr) * Yr
 			for (int j=0; j<p; j++)
 			{
 				for (int jj=0; jj<m; jj++)
 				{
-					float dotProduct = 0.0;
+					Real dotProduct = 0.0;
 					for (int u=0; u<cardClustR; u++)
 						dotProduct += Xr[mi(u,j,n,p)] * Yr[mi(u,j,n,m)];
 					tXrYr[mi(j,jj,p,m)] = dotProduct;
@@ -142,7 +142,7 @@ void EMGrank_core(
 			{
 				for (int jj=0; jj<m; jj++)
 				{
-					float dotProduct = 0.0;
+					Real dotProduct = 0.0;
 					for (int u=0; u<p; u++)
 						dotProduct += invTXrXr[mi(j,u,p,p)] * tXrYr[mi(u,jj,p,m)];
 					matrixM->data[j*m+jj] = dotProduct;
@@ -159,12 +159,12 @@ void EMGrank_core(
 				S->data[j] = 0.0;
 			
 			//[intermediate step] Compute hatBetaR = U * S * t(V)
-			double* U = matrixM->data;
+			double* U = matrixM->data; //GSL require double precision
 			for (int j=0; j<p; j++)
 			{
 				for (int jj=0; jj<m; jj++)
 				{
-					float dotProduct = 0.0;
+					Real dotProduct = 0.0;
 					for (int u=0; u<m; u++)
 						dotProduct += U[j*m+u] * S->data[u] * V->data[jj*m+u];
 					hatBetaR[mi(j,jj,p,m)] = dotProduct;
@@ -176,7 +176,7 @@ void EMGrank_core(
 			{
 				for (int jj=0; jj<m; jj++)
 				{
-					float dotProduct=0.0;
+					Real dotProduct=0.0;
 					for (int u=0; u<m; u++)
 						dotProduct += hatBetaR[mi(j,u,p,m)] * Rho[ai(u,jj,r,m,m,k)];
 					phi[ai(j,jj,r,p,m,k)] = dotProduct;
@@ -188,11 +188,11 @@ void EMGrank_core(
 		// Etape E //
 		/////////////
 		
-		float sumLogLLF2 = 0.0;
+		Real sumLogLLF2 = 0.0;
 		for (int i=0; i<n; i++)
 		{
-			float sumLLF1 = 0.0;
-			float maxLogGamIR = -INFINITY;
+			Real sumLLF1 = 0.0;
+			Real maxLogGamIR = -INFINITY;
 			for (int r=0; r<k; r++)
 			{
 				//Compute
@@ -207,7 +207,7 @@ void EMGrank_core(
 						matrixE->data[j*m+jj] = Rho[ai(j,jj,r,m,m,k)];
 				}
 				gsl_linalg_LU_decomp(matrixE, permutation, &signum);
-				float detRhoR = gsl_linalg_LU_det(matrixE, signum);
+				Real detRhoR = gsl_linalg_LU_det(matrixE, signum);
 
 				//compute Y(i,:)*Rho(:,:,r)
 				for (int j=0; j<m; j++)
@@ -226,10 +226,10 @@ void EMGrank_core(
 				}
 
 				//compute dotProduct < Y(:,i)*rho(:,:,r)-X(i,:)*phi(:,:,r) . Y(:,i)*rho(:,:,r)-X(i,:)*phi(:,:,r) >
-				float dotProduct = 0.0;
+				Real dotProduct = 0.0;
 				for (int u=0; u<m; u++)
 					dotProduct += (YiRhoR[u]-XiPhiR[u]) * (YiRhoR[u]-XiPhiR[u]);
-				float logGamIR = log(Pi[r]) + log(detRhoR) - 0.5*dotProduct;
+				Real logGamIR = log(Pi[r]) + log(detRhoR) - 0.5*dotProduct;
 
 				//Z(i) = index of max (gam(i,:))
 				if (logGamIR > maxLogGamIR)
@@ -247,14 +247,14 @@ void EMGrank_core(
 		*LLF = -invN * sumLogLLF2;
 
 		//newDeltaPhi = max(max((abs(phi-Phi))./(1+abs(phi))));
-		float newDeltaPhi = 0.0;
+		Real newDeltaPhi = 0.0;
 		for (int j=0; j<p; j++)
 		{
 			for (int jj=0; jj<m; jj++)
 			{
 				for (int r=0; r<k; r++)
 				{
-					float tmpDist = fabs(phi[ai(j,jj,r,p,m,k)]-Phi[ai(j,jj,r,p,m,k)])
+					Real tmpDist = fabs(phi[ai(j,jj,r,p,m,k)]-Phi[ai(j,jj,r,p,m,k)])
 						/ (1.0+fabs(phi[ai(j,jj,r,p,m,k)]));
 					if (tmpDist > newDeltaPhi)
 						newDeltaPhi = tmpDist;