#include "utils.h"
// Compute pseudo-inverse of a square matrix
-static double* pinv(const double* matrix, int dim)
+static float* pinv(const float* 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);
- double EPS = 1e-10; //threshold for singular value "== 0"
+ float EPS = 1e-10; //threshold for singular value "== 0"
//copy matrix into U
copyArray(matrix, U->data, dim*dim);
gsl_vector_free(work);
// Obtain pseudo-inverse by V*S^{-1}*t(U)
- double* inverse = (double*)malloc(dim*dim*sizeof(double));
+ float* inverse = (float*)malloc(dim*dim*sizeof(float));
for (int i=0; i<dim; i++)
{
for (int ii=0; ii<dim; ii++)
{
- double dotProduct = 0.0;
+ float 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;
// TODO: comment EMGrank purpose
void EMGrank_core(
// IN parameters
- const double* Pi, // parametre de proportion
- const double* Rho, // parametre initial de variance renormalisé
+ const float* Pi, // parametre de proportion
+ const float* 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 double* X, // régresseurs
- const double* Y, // réponse
- double tau, // seuil pour accepter la convergence
+ const float* X, // régresseurs
+ const float* Y, // réponse
+ float tau, // seuil pour accepter la convergence
const int* rank, // vecteur des rangs possibles
// OUT parameters
- double* phi, // parametre de moyenne renormalisé, calculé par l'EM
- double* LLF, // log vraisemblance associé à cet échantillon, pour les valeurs estimées des paramètres
+ float* phi, // parametre de moyenne renormalisé, calculé par l'EM
+ float* 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 k) // nombre de composantes
{
// Allocations, initializations
- double* Phi = (double*)calloc(p*m*k,sizeof(double));
- double* hatBetaR = (double*)malloc(p*m*sizeof(double));
+ float* Phi = (float*)calloc(p*m*k,sizeof(float));
+ float* hatBetaR = (float*)malloc(p*m*sizeof(float));
int signum;
- double invN = 1.0/n;
+ float invN = 1.0/n;
int deltaPhiBufferSize = 20;
- double* deltaPhi = (double*)malloc(deltaPhiBufferSize*sizeof(double));
+ float* deltaPhi = (float*)malloc(deltaPhiBufferSize*sizeof(float));
int ite = 0;
- double sumDeltaPhi = 0.0;
- double* YiRhoR = (double*)malloc(m*sizeof(double));
- double* XiPhiR = (double*)malloc(m*sizeof(double));
- double* Xr = (double*)malloc(n*p*sizeof(double));
- double* Yr = (double*)malloc(n*m*sizeof(double));
- double* tXrXr = (double*)malloc(p*p*sizeof(double));
- double* tXrYr = (double*)malloc(p*m*sizeof(double));
+ 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));
gsl_matrix* matrixM = gsl_matrix_alloc(p, m);
gsl_matrix* matrixE = gsl_matrix_alloc(m, m);
gsl_permutation* permutation = gsl_permutation_alloc(m);
{
for (int jj=0; jj<p; jj++)
{
- double dotProduct = 0.0;
+ float 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}
- double* invTXrXr = pinv(tXrXr, p);
+ float* invTXrXr = pinv(tXrXr, p);
// Compute tXrYr = t(Xr) * Yr
for (int j=0; j<p; j++)
{
for (int jj=0; jj<m; jj++)
{
- double dotProduct = 0.0;
+ float 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;
{
for (int jj=0; jj<m; jj++)
{
- double dotProduct = 0.0;
+ float 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;
{
for (int jj=0; jj<m; jj++)
{
- double dotProduct = 0.0;
+ float 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;
{
for (int jj=0; jj<m; jj++)
{
- double dotProduct=0.0;
+ float 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 //
/////////////
- double sumLogLLF2 = 0.0;
+ float sumLogLLF2 = 0.0;
for (int i=0; i<n; i++)
{
- double sumLLF1 = 0.0;
- double maxLogGamIR = -INFINITY;
+ float sumLLF1 = 0.0;
+ float maxLogGamIR = -INFINITY;
for (int r=0; r<k; r++)
{
//Compute
matrixE->data[j*m+jj] = Rho[ai(j,jj,r,m,m,k)];
}
gsl_linalg_LU_decomp(matrixE, permutation, &signum);
- double detRhoR = gsl_linalg_LU_det(matrixE, signum);
+ float detRhoR = gsl_linalg_LU_det(matrixE, signum);
//compute Y(i,:)*Rho(:,:,r)
for (int j=0; j<m; j++)
}
//compute dotProduct < Y(:,i)*rho(:,:,r)-X(i,:)*phi(:,:,r) . Y(:,i)*rho(:,:,r)-X(i,:)*phi(:,:,r) >
- double dotProduct = 0.0;
+ float dotProduct = 0.0;
for (int u=0; u<m; u++)
dotProduct += (YiRhoR[u]-XiPhiR[u]) * (YiRhoR[u]-XiPhiR[u]);
- double logGamIR = log(Pi[r]) + log(detRhoR) - 0.5*dotProduct;
+ float logGamIR = log(Pi[r]) + log(detRhoR) - 0.5*dotProduct;
//Z(i) = index of max (gam(i,:))
if (logGamIR > maxLogGamIR)
*LLF = -invN * sumLogLLF2;
//newDeltaPhi = max(max((abs(phi-Phi))./(1+abs(phi))));
- double newDeltaPhi = 0.0;
+ float newDeltaPhi = 0.0;
for (int j=0; j<p; j++)
{
for (int jj=0; jj<m; jj++)
{
for (int r=0; r<k; r++)
{
- double tmpDist = fabs(phi[ai(j,jj,r,p,m,k)]-Phi[ai(j,jj,r,p,m,k)])
+ float 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;
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