CAMLprim value ml_gsl_stats_covariance_m(value mean1, value data1,
value mean2, value data2)
{
size_t len = Double_array_length(data1);
double result;
check_array_size(data1, data2);
result = gsl_stats_covariance_m(Double_array_val(data1), 1,
Double_array_val(data2), 1, len,
Double_val(mean1), Double_val(mean2));
return copy_double(result);
}
void update_chi2_t(chi2_t *chichi, hist_t *obsHist, double *dataMat)
{
//-- data should be n*d matrix
int N = chichi->N;
int d = chichi->d;
gsl_vector *X_model = chichi->X_model;
gsl_matrix *cov = chichi->cov;
gsl_matrix *cov2 = chichi->cov2;
double value;
//-- Update mean
int i;
for (i=0; i<d; i++) {
value = gsl_stats_mean(dataMat+i*N, 1, N);
gsl_vector_set(X_model, i, value);
gsl_vector_set(chichi->X_obs, i, (double)(obsHist->n[i]));
}
//-- Update covariance
int j;
for (j=0; j<d; j++) {
for (i=0; i<d; i++) {
if (i < j) {
value = gsl_matrix_get(cov, j, i);
gsl_matrix_set(cov, i, j, value);
continue;
}
value = gsl_stats_covariance_m(dataMat+i*N, 1, dataMat+j*N, 1, N, gsl_vector_get(X_model, i), gsl_vector_get(X_model, j));
gsl_matrix_set(cov, i, j, value);
}
}
//-- Make a copy, because the invertion will destroy cov
gsl_matrix_memcpy(cov2, cov); //-- Copy cov to cov2
//-- Make LU decomposition and invert
int s;
gsl_linalg_LU_decomp(cov2, chichi->perm, &s);
gsl_linalg_LU_invert(cov2, chichi->perm, chichi->invCov);
//-- Debias the C_{ij}^{-1}
//-- C^-1_nonbias = (N - d - 2) / (N - 1) * C^-1_bias
double factor = (N - d - 2) / (double)(N - 1);
gsl_matrix_scale(chichi->invCov, factor); //-- invCov *= factor
return;
}
void GbA::compute_likelihood(double *data, int nstats, int nbands, int nsim,
double mean[2], double cov[2][2]){
int i, j, k, l, idx;
gsl_vector *lsq = gsl_vector_alloc (_td->get_noftraces());
gsl_vector *input = gsl_vector_alloc (nbands);
gsl_vector *trace = gsl_vector_alloc (nbands);
double *mags, *dists;
double misfit_l2;
size_t *indices = new size_t[nsim];
mags = new double[nsim];
dists = new double[nsim];
for (i=0; i<nstats; i++){
for (j=0; j<nbands; j++){
gsl_vector_set(input, j, std::log10(data[i*nbands+j]));
}
for (k=0; k<_td->get_noftraces(); k++){
gsl_matrix_get_row(trace, _td->_tdata_gsl, k);
gsl_vector_sub(trace,input);
misfit_l2 = gsl_blas_dnrm2(trace);
gsl_vector_set(lsq, k, misfit_l2*misfit_l2);
}
gsl_sort_vector_smallest_index(indices, nsim,lsq);
mean[0] = 0;
mean[1] = 0;
for(l=0; l<nsim; l++){
dists[l] = std::log10(gsl_vector_get(_td->_r_gsl, indices[l]));
mags[l] = gsl_vector_get(_td->_m_gsl, indices[l]);
//std::cout << gsl_vector_get(_td->_m_gsl,indices[l]) << std::endl;
}
mean[0] = gsl_stats_mean(dists,1,nsim);
mean[1] = gsl_stats_mean(mags,1,nsim);
cov[0][0] = gsl_stats_variance(dists, 1, nsim);
cov[1][1] = gsl_stats_variance(mags, 1, nsim);
cov[0][1] = gsl_stats_covariance_m(dists,1,mags,1,nsim,mean[0],mean[1]);
cov[1][0] = cov[0][1];
std::cout << "Distance: " << mean[0] << "; Magnitude: " << mean[1] << std::endl;
std::cout << "Covariance matrix:" << std::endl;
for(i=0; i<2; i++){
for(j=0;j<2;j++){
printf("%d, %d, %g\n",i,j,cov[i][j]);
}
}
}
}
void GbA::process(double *data, int nbands, float time, int cmpnt){
assert(nbands == _nbands);
pthread_mutex_lock(&_process_lock);
int i, j, k, timeidx=0;
double timemin=std::numeric_limits<double>::max();
gsl_vector *lsq = gsl_vector_alloc (_td->get_noftraces());
gsl_vector *input = gsl_vector_alloc (nbands);
gsl_vector *trace = gsl_vector_alloc (nbands);
gsl_matrix *tdata;
double misfit_l2, terror;
size_t *indices = new size_t[_nsim];
TData::Component _c = static_cast<TData::Component>(cmpnt);
_status[_c] = true;
// Find time index
for(i=0;i<_td->get_noftimes();i++){
terror = gsl_vector_get(_td->get_times(),i) - time;
if(abs(terror)< timemin){
timeidx = i;
timemin = abs(terror);
}
}
tdata = _td->get_amps(timeidx,_c);
for (i=0; i<nbands; i++){
gsl_vector_set(input, i, std::log10(data[i]));
}
for (j=0; j<_td->get_noftraces(); j++){
gsl_matrix_get_row(trace, tdata, j);
gsl_vector_sub(trace,input);
misfit_l2 = gsl_blas_dnrm2(trace);
gsl_vector_set(lsq, j, misfit_l2*misfit_l2);
}
gsl_sort_vector_smallest_index(indices, _nsim,lsq);
for(k=0; k<_nsim; k++){
gsl_vector_set(&_r[_c].vector,k, std::log10(gsl_vector_get(_td->get_dist(), indices[k])));
gsl_vector_set(&_m[_c].vector,k, gsl_vector_get(_td->get_mag(), indices[k]));
}
if (_status[TData::vertical] && _status[TData::horizontal]){
_mv = gsl_vector_subvector(_mags,0,_mags->size);
_rv = gsl_vector_subvector(_dists,0,_dists->size);
} else if(_status[TData::vertical]){
_mv = gsl_vector_subvector(_mags,0,_nsim);
_rv = gsl_vector_subvector(_dists,0,_nsim);
} else if(_status[TData::horizontal]){
_mv = gsl_vector_subvector(_mags,_nsim,_nsim);
_rv = gsl_vector_subvector(_dists,_nsim,_nsim);
}
_mn[0] = gsl_stats_mean(_rv.vector.data,1,_rv.vector.size);
_mn[1] = gsl_stats_mean(_mv.vector.data,1,_mv.vector.size);
_cov[0][0] = gsl_stats_variance(_rv.vector.data, 1, _rv.vector.size);
_cov[1][1] = gsl_stats_variance(_mv.vector.data, 1, _mv.vector.size);
_cov[0][1] = gsl_stats_covariance_m(_rv.vector.data,1,_mv.vector.data,1,
_rv.vector.size,_mn[0],_mn[1]);
_cov[1][0] = _cov[0][1];
delete[] indices;
pthread_mutex_unlock(&_process_lock);
}
