void point_pca_ep(const YA_BaseT &input, const eltype epsilon,
const ya_type2 &dims, EigenOptions &eigopts, YA_RowD &vmean,
YA_RowD &vmin, YA_RowD &vmax, YA_RowD &vstd,
const int verbose) {
YA_DEBUG_ERROR(epsilon>0,
"Epsilon must be greater than 0 for point_pca");
int me, num_procs;
ya_mpi_info(me,num_procs);
YA_ColI counts, starts;
ya_mpi_counts(input.rows(),num_procs,counts,starts);
eigopts.dim(input.cols());
YA_MatT output;
_point_pca_ep(input,epsilon,eigopts,output,true,verbose,me,num_procs,
counts,starts);
if (input.cols()!=dims.numel() && counts[me]>0)
output=YA_MatT(output(":",dims-1));
#ifdef YA_MPI
if (num_procs>1)
mpi_vstat(output,dims.numel(),vmin,vmax,vmean,vstd);
else
#endif
vstat(output,vmin,vmax,vmean,vstd);
}
void point_pca_ep(const YA_BaseT &input, const eltype epsilon,
const ya_type2 &dims, EigenOptions &eigopts,
ya_type3 &output, const int verbose_out) {
YA_DEBUG_ERROR(epsilon>0,
"Epsilon must be greater than 0 for point_pca");
int me, num_procs;
ya_mpi_info(me,num_procs);
YA_ColI counts, starts;
ya_mpi_counts(input.rows(),num_procs,counts,starts);
eigopts.dim(input.cols());
_point_pca_ep(input,epsilon,eigopts,output,true,verbose_out,me,num_procs,
counts,starts);
if (num_procs>1) {
YA_MatRMT piece_t;
#ifdef YA_MPI
if (counts[me]>0)
piece_t=output(":",dims-1);
ya_mpi_concat_rows(piece_t,output,dims.numel(),me,num_procs,counts,starts);
#endif
} else if (input.cols()!=dims.numel())
output=YA_MatT(output(":",dims-1));
}
int YAPCAReduce<eltype>::find_map(const ya_type1 &input,
ya_type2 &output, ya_sizet dim,
EigenOptions &eigopts) {
// Target dimensionality
YA_DEBUG_ERROR(dim<=input.cols(),
"Not enough dimensions in input matrix");
eigopts.dim(dim);
this->_high_dim=input.cols();
this->_low_dim=dim;
// Column center the matrix
column_means=sum(input)/static_cast<eltype>(input.rows());
YA_MatT input_cen(input.rows(),input.cols());
ya_sizet iC=input.cols();
for (ya_sizet i=0; i<iC; i++)
input_cen(":",i)=input(":",i)-column_means(i);
// Calculate covarience matrix
#ifdef YA_PROGMETER
YA_TimeKeeper tk;
YA_ProgMeter pm;
if (this->_verbose) {
pm.start("Computing Forward and Reverse Maps:", 70, 0, false);
tk.start();
}
#endif
VM_SymMat covmat=input_cen.T()*input_cen;
eigs(covmat,eigen_vectors,this->eigen_values,eigopts);
#ifdef YA_PROGMETER
if (this->_verbose) {
pm.finish();
tk.end();
cerr << "Done. " << tk << ".\n";
}
#endif
// Calculate the reduced output
output=input_cen*eigen_vectors;
return 0;
}
void point_pca(const YA_BaseT &input, const ya_sizet k, const ya_type2 &dims,
EigenOptions &eigopts, ya_type3 &output,const int verbose_out) {
int me, num_procs;
ya_mpi_info(me,num_procs);
eigopts.dim(input.cols());
YA_ColI counts, starts;
ya_mpi_counts(input.rows(),num_procs,counts,starts);
_point_pca(input,k,eigopts,output,true,verbose_out,me,num_procs,counts,
starts);
if (num_procs>1) {
YA_MatRMT piece_t;
#ifdef YA_MPI
if (counts[me]>0)
piece_t=output(":",dims-1);
ya_mpi_concat_rows(piece_t,output,dims.numel(),me,num_procs,counts,starts);
#endif
} else if (input.cols()!=dims.numel())
output=YA_MatT(output(":",dims-1));
}
void point_pca(const YA_BaseT &input, const ya_sizet k, const ya_type2 &dims,
EigenOptions &eigopts, YA_RowD &vmean, YA_RowD &vmin,
YA_RowD &vmax, YA_RowD &vstd, const int verbose) {
int me, num_procs;
ya_mpi_info(me,num_procs);
eigopts.dim(input.cols());
YA_ColI counts, starts;
ya_mpi_counts(input.rows(),num_procs,counts,starts);
YA_MatT output;
_point_pca(input,k,eigopts,output,true,verbose,me,num_procs,counts,starts);
if (input.cols()!=dims.numel() && counts[me]>0)
output=YA_MatT(output(":",dims-1));
#ifdef YA_MPI
if (num_procs>1)
mpi_vstat(output,dims.numel(),vmin,vmax,vmean,vstd);
else
#endif
vstat(output,vmin,vmax,vmean,vstd);
}
