X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2Fsrc%2Fsources%2FEMGrank.c;fp=pkg%2Fsrc%2Fsources%2FEMGrank.c;h=0000000000000000000000000000000000000000;hp=3a9bf94b23a05c443d7cb6f32dd835d19667274f;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=ea5860f1b4fc91f06e371a0b26915198474a849d

diff --git a/pkg/src/sources/EMGrank.c b/pkg/src/sources/EMGrank.c
deleted file mode 100644
index 3a9bf94..0000000
--- a/pkg/src/sources/EMGrank.c
+++ /dev/null
@@ -1,307 +0,0 @@
-#include <stdlib.h>
-#include <gsl/gsl_linalg.h>
-#include "utils.h"
-
-// Compute pseudo-inverse of a square matrix
-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);
-	Real EPS = 1e-10; //threshold for singular value "== 0"
-	
-	//copy matrix into U
-	copyArray(matrix, U->data, dim*dim);
-
-	//U,S,V = SVD of matrix
-	gsl_linalg_SV_decomp(U, V, S, work);
-	gsl_vector_free(work);
-
-	// Obtain pseudo-inverse by V*S^{-1}*t(U)
-	Real* inverse = (Real*)malloc(dim*dim*sizeof(Real));
-	for (int i=0; i<dim; i++)
-	{
-		for (int ii=0; ii<dim; ii++)
-		{
-			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;
-		}
-	}
-	
-	gsl_matrix_free(U);
-	gsl_matrix_free(V);
-	gsl_vector_free(S);
-	return inverse;
-}
-
-// TODO: comment EMGrank purpose
-void EMGrank_core(
-	// IN parameters
-	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 Real* X, // régresseurs
-	const Real* Y, // réponse
-	Real tau, // seuil pour accepter la convergence
-	const int* rank, // vecteur des rangs possibles
-	// OUT parameters
-	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
-	int m, // taille de Y (multivarié)
-	int k) // nombre de composantes
-{
-	// Allocations, initializations
-	Real* Phi = (Real*)calloc(p*m*k,sizeof(Real));
-	Real* hatBetaR = (Real*)malloc(p*m*sizeof(Real));
-	int signum;
-	Real invN = 1.0/n;
-	int deltaPhiBufferSize = 20;
-	Real* deltaPhi = (Real*)malloc(deltaPhiBufferSize*sizeof(Real));
-	int ite = 0;
-	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);
-	gsl_matrix* V = gsl_matrix_alloc(m,m);
-	gsl_vector* S = gsl_vector_alloc(m);
-	gsl_vector* work = gsl_vector_alloc(m);
-
-	//Initialize class memberships (all elements in class 0; TODO: randomize ?)
-	int* Z = (int*)calloc(n, sizeof(int));
-
-	//Initialize phi to zero, because some M loops might exit before phi affectation
-	zeroArray(phi, p*m*k);
-
-	while (ite<mini || (ite<maxi && sumDeltaPhi>tau))
-	{
-		/////////////
-		// Etape M //
-		/////////////
-		
-		//M step: Mise à jour de Beta (et donc phi)
-		for (int r=0; r<k; r++)
-		{
-			//Compute Xr = X(Z==r,:) and Yr = Y(Z==r,:)
-			int cardClustR=0;
-			for (int i=0; i<n; i++)
-			{
-				if (Z[i] == r)
-				{
-					for (int j=0; j<p; j++)
-						Xr[mi(cardClustR,j,n,p)] = X[mi(i,j,n,p)];
-					for (int j=0; j<m; j++)
-						Yr[mi(cardClustR,j,n,m)] = Y[mi(i,j,n,m)];
-					cardClustR++;
-				}
-			}
-			if (cardClustR == 0)
-				continue;
-
-			//Compute tXrXr = t(Xr) * Xr
-			for (int j=0; j<p; j++)
-			{
-				for (int jj=0; jj<p; jj++)
-				{
-					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;
-				}
-			}
-
-			//Get pseudo inverse = (t(Xr)*Xr)^{-1}
-			Real* invTXrXr = pinv(tXrXr, p);
-			
-			// Compute tXrYr = t(Xr) * Yr
-			for (int j=0; j<p; j++)
-			{
-				for (int jj=0; jj<m; jj++)
-				{
-					Real dotProduct = 0.0;
-					for (int u=0; u<cardClustR; u++)
-						dotProduct += Xr[mi(u,j,n,p)] * Yr[mi(u,jj,n,m)];
-					tXrYr[mi(j,jj,p,m)] = dotProduct;
-				}
-			}
-
-			//Fill matrixM with inverse * tXrYr = (t(Xr)*Xr)^{-1} * t(Xr) * Yr
-			for (int j=0; j<p; j++)
-			{
-				for (int jj=0; jj<m; jj++)
-				{
-					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;
-				}
-			}
-			free(invTXrXr);
-
-			//U,S,V = SVD of (t(Xr)Xr)^{-1} * t(Xr) * Yr
-			gsl_linalg_SV_decomp(matrixM, V, S, work);
-
-			//Set m-rank(r) singular values to zero, and recompose
-			//best rank(r) approximation of the initial product
-			for (int j=rank[r]; j<m; j++)
-				S->data[j] = 0.0;
-			
-			//[intermediate step] Compute hatBetaR = U * S * t(V)
-			double* U = matrixM->data; //GSL require double precision
-			for (int j=0; j<p; j++)
-			{
-				for (int jj=0; jj<m; jj++)
-				{
-					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;
-				}
-			}
-
-			//Compute phi(:,:,r) = hatBetaR * Rho(:,:,r)
-			for (int j=0; j<p; j++)
-			{
-				for (int jj=0; jj<m; jj++)
-				{
-					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;
-				}
-		}
-		}
-
-		/////////////
-		// Etape E //
-		/////////////
-		
-		Real sumLogLLF2 = 0.0;
-		for (int i=0; i<n; i++)
-		{
-			Real sumLLF1 = 0.0;
-			Real maxLogGamIR = -INFINITY;
-			for (int r=0; r<k; r++)
-			{
-				//Compute
-				//Gam(i,r) = Pi(r) * det(Rho(:,:,r)) * exp( -1/2 * (Y(i,:)*Rho(:,:,r) - X(i,:)...
-				//*phi(:,:,r)) * transpose( Y(i,:)*Rho(:,:,r) - X(i,:)*phi(:,:,r) ) );
-				//split in several sub-steps
-				
-				//compute det(Rho(:,:,r)) [TODO: avoid re-computations]
-				for (int j=0; j<m; j++)
-				{
-					for (int jj=0; jj<m; jj++)
-						matrixE->data[j*m+jj] = Rho[ai(j,jj,r,m,m,k)];
-				}
-				gsl_linalg_LU_decomp(matrixE, permutation, &signum);
-				Real detRhoR = gsl_linalg_LU_det(matrixE, signum);
-
-				//compute Y(i,:)*Rho(:,:,r)
-				for (int j=0; j<m; j++)
-				{
-					YiRhoR[j] = 0.0;
-					for (int u=0; u<m; u++)
-						YiRhoR[j] += Y[mi(i,u,n,m)] * Rho[ai(u,j,r,m,m,k)];
-				}
-
-				//compute X(i,:)*phi(:,:,r)
-				for (int j=0; j<m; j++)
-				{
-					XiPhiR[j] = 0.0;
-					for (int u=0; u<p; u++)
-						XiPhiR[j] += X[mi(i,u,n,p)] * phi[ai(u,j,r,p,m,k)];
-				}
-
-				//compute dotProduct < Y(:,i)*rho(:,:,r)-X(i,:)*phi(:,:,r) . Y(:,i)*rho(:,:,r)-X(i,:)*phi(:,:,r) >
-				Real dotProduct = 0.0;
-				for (int u=0; u<m; u++)
-					dotProduct += (YiRhoR[u]-XiPhiR[u]) * (YiRhoR[u]-XiPhiR[u]);
-				Real logGamIR = log(Pi[r]) + log(detRhoR) - 0.5*dotProduct;
-
-				//Z(i) = index of max (gam(i,:))
-				if (logGamIR > maxLogGamIR)
-				{
-					Z[i] = r;
-					maxLogGamIR = logGamIR;
-				}
-				sumLLF1 += exp(logGamIR) / pow(2*M_PI,m/2.0);
-			}
-
-			sumLogLLF2 += log(sumLLF1);
-		}
-
-		// Assign output variable LLF
-		*LLF = -invN * sumLogLLF2;
-
-		//newDeltaPhi = max(max((abs(phi-Phi))./(1+abs(phi))));
-		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++)
-				{
-					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;
-				}
-			}
-		}
-
-		//update distance parameter to check algorithm convergence (delta(phi, Phi))
-		//TODO: deltaPhi should be a linked list for perf.
-		if (ite < deltaPhiBufferSize)
-			deltaPhi[ite] = newDeltaPhi;
-		else
-		{
-			sumDeltaPhi -= deltaPhi[0];
-			for (int u=0; u<deltaPhiBufferSize-1; u++)
-				deltaPhi[u] = deltaPhi[u+1];
-			deltaPhi[deltaPhiBufferSize-1] = newDeltaPhi;
-		}
-		sumDeltaPhi += newDeltaPhi;
-
-		// update other local variables
-		for (int j=0; j<m; j++)
-		{
-			for (int jj=0; jj<p; jj++)
-			{
-				for (int r=0; r<k; r++)
-					Phi[ai(jj,j,r,p,m,k)] = phi[ai(jj,j,r,p,m,k)];
-			}
-		}
-		ite++;
-	}
-
-	//free memory
-	free(hatBetaR);
-	free(deltaPhi);
-	free(Phi);
-	gsl_matrix_free(matrixE);
-	gsl_matrix_free(matrixM);
-	gsl_permutation_free(permutation);
-	gsl_vector_free(work);
-	gsl_matrix_free(V);
-	gsl_vector_free(S);
-	free(XiPhiR);
-	free(YiRhoR);
-	free(Xr);
-	free(Yr);
-	free(tXrXr);
-	free(tXrYr);
-	free(Z);
-}