inline bool op_princomp::direct_princomp ( Mat<typename T1::elem_type>& coeff_out, Mat<typename T1::elem_type>& score_out, const Base<typename T1::elem_type, T1>& X, const typename arma_not_cx<typename T1::elem_type>::result* junk ) { arma_extra_debug_sigprint(); arma_ignore(junk); typedef typename T1::elem_type eT; const unwrap_check<T1> Y( X.get_ref(), score_out ); const Mat<eT>& in = Y.M; const uword n_rows = in.n_rows; const uword n_cols = in.n_cols; if(n_rows > 1) // more than one sample { // subtract the mean - use score_out as temporary matrix score_out = in; score_out.each_row() -= mean(in); // singular value decomposition Mat<eT> U; Col<eT> s; const bool svd_ok = svd(U, s, coeff_out, score_out); if(svd_ok == false) { return false; } // normalize the eigenvalues s /= std::sqrt( double(n_rows - 1) ); // project the samples to the principals score_out *= coeff_out; if(n_rows <= n_cols) // number of samples is less than their dimensionality { score_out.cols(n_rows-1,n_cols-1).zeros(); Col<eT> s_tmp = zeros< Col<eT> >(n_cols); s_tmp.rows(0,n_rows-2) = s.rows(0,n_rows-2); s = s_tmp; } } else // 0 or 1 samples { coeff_out.eye(n_cols, n_cols); score_out.copy_size(in); score_out.zeros(); } return true; }
inline bool op_princomp::direct_princomp ( Mat<eT>& coeff_out, Mat<eT>& score_out, const Mat<eT>& in ) { arma_extra_debug_sigprint(); const u32 n_rows = in.n_rows; const u32 n_cols = in.n_cols; if(n_rows > 1) // more than one sample { // subtract the mean - use score_out as temporary matrix score_out = in - repmat(mean(in), n_rows, 1); // singular value decomposition Mat<eT> U; Col<eT> s; const bool svd_ok = svd(U,s,coeff_out,score_out); if(svd_ok == false) { return false; } // U.reset(); // normalize the eigenvalues s /= std::sqrt( double(n_rows - 1) ); // project the samples to the principals score_out *= coeff_out; if(n_rows <= n_cols) // number of samples is less than their dimensionality { score_out.cols(n_rows-1,n_cols-1).zeros(); Col<eT> s_tmp = zeros< Col<eT> >(n_cols); s_tmp.rows(0,n_rows-2) = s.rows(0,n_rows-2); s = s_tmp; } } else // 0 or 1 samples { coeff_out.eye(n_cols, n_cols); score_out.copy_size(in); score_out.zeros(); } return true; }
void expectedRowsElemDivide() { for(int n = 0; n < _elemInds.n_elem; n++) { if(!_genColVec.in_range(_elemInds.at(n))) { return; } } if(_genRowVec.n_elem != _elemInds.n_elem) { return; } cout << "- Compute expectedElemElemDivide() ... "; _genColVec.rows(_elemInds) /= _genRowVec; save<double>("Col.rowsElemDivide", _genColVec); cout << "done." << endl; }
void expectedColVecRowsElemDivide() { if(_elemIndRange.whole) { return; } if (!_genColVec.in_range(_elemIndRange)) { return; } if(_genRowVec.n_cols != _genColVec.n_cols || _genRowVec.n_rows != _elemIndRange.b - _elemIndRange.a + 1) { return; } cout << "- Compute expectedColVecRowsElemDivide() ... "; _genColVec.rows(_elemIndRange.a, _elemIndRange.b) /= _genRowVec; save<double>("Col.rowsElemDivide", _genColVec); cout << "done." << endl; }
inline bool op_princomp::direct_princomp ( Mat< std::complex<typename T1::pod_type> >& coeff_out, Mat< std::complex<typename T1::pod_type> >& score_out, Col< typename T1::pod_type >& latent_out, Col< std::complex<typename T1::pod_type> >& tsquared_out, const Base< std::complex<typename T1::pod_type>, T1 >& X, const typename arma_cx_only<typename T1::elem_type>::result* junk ) { arma_extra_debug_sigprint(); arma_ignore(junk); typedef typename T1::pod_type T; typedef std::complex<T> eT; const unwrap_check<T1> Y( X.get_ref(), score_out ); const Mat<eT>& in = Y.M; const uword n_rows = in.n_rows; const uword n_cols = in.n_cols; if(n_rows > 1) // more than one sample { // subtract the mean - use score_out as temporary matrix score_out = in; score_out.each_row() -= mean(in); // singular value decomposition Mat<eT> U; Col< T> s; const bool svd_ok = svd(U, s, coeff_out, score_out); if(svd_ok == false) { return false; } // normalize the eigenvalues s /= std::sqrt( double(n_rows - 1) ); // project the samples to the principals score_out *= coeff_out; if(n_rows <= n_cols) // number of samples is less than their dimensionality { score_out.cols(n_rows-1,n_cols-1).zeros(); Col<T> s_tmp = zeros< Col<T> >(n_cols); s_tmp.rows(0,n_rows-2) = s.rows(0,n_rows-2); s = s_tmp; // compute the Hotelling's T-squared s_tmp.rows(0,n_rows-2) = 1.0 / s_tmp.rows(0,n_rows-2); const Mat<eT> S = score_out * diagmat(Col<T>(s_tmp)); tsquared_out = sum(S%S,1); } else { // compute the Hotelling's T-squared const Mat<eT> S = score_out * diagmat(Col<T>(T(1) / s)); tsquared_out = sum(S%S,1); } // compute the eigenvalues of the principal vectors latent_out = s%s; } else // 0 or 1 samples { coeff_out.eye(n_cols, n_cols); score_out.copy_size(in); score_out.zeros(); latent_out.set_size(n_cols); latent_out.zeros(); tsquared_out.set_size(n_rows); tsquared_out.zeros(); } return true; }
inline void op_princomp::direct_princomp ( Mat< std::complex<T> >& coeff_out, Mat< std::complex<T> >& score_out, Col<T>& latent_out, const Mat< std::complex<T> >& in ) { arma_extra_debug_sigprint(); typedef std::complex<T> eT; const u32 n_rows = in.n_rows; const u32 n_cols = in.n_cols; if(n_rows > 1) // more than one sample { // subtract the mean - use score_out as temporary matrix score_out = in - repmat(mean(in), n_rows, 1); // singular value decomposition Mat<eT> U; Col< T> s; const bool svd_ok = svd(U,s,coeff_out,score_out); if(svd_ok == false) { arma_print("princomp(): singular value decomposition failed"); coeff_out.reset(); score_out.reset(); latent_out.reset(); return; } // U.reset(); // normalize the eigenvalues s /= std::sqrt(n_rows - 1); // project the samples to the principals score_out *= coeff_out; if(n_rows <= n_cols) // number of samples is less than their dimensionality { score_out.cols(n_rows-1,n_cols-1).zeros(); Col<T> s_tmp = zeros< Col<T> >(n_cols); s_tmp.rows(0,n_rows-2) = s.rows(0,n_rows-2); s = s_tmp; } // compute the eigenvalues of the principal vectors latent_out = s%s; } else // single sample - row { if(n_rows == 1) { coeff_out = eye< Mat<eT> >(n_cols, n_cols); score_out.copy_size(in); score_out.zeros(); latent_out.set_size(n_cols); latent_out.zeros(); } else { coeff_out.reset(); score_out.reset(); latent_out.reset(); } } }
inline void op_princomp::direct_princomp ( Mat<eT>& coeff_out, Mat<eT>& score_out, Col<eT>& latent_out, Col<eT>& tsquared_out, const Mat<eT>& in ) { arma_extra_debug_sigprint(); const u32 n_rows = in.n_rows; const u32 n_cols = in.n_cols; if(n_rows > 1) // more than one sample { // subtract the mean - use score_out as temporary matrix score_out = in - repmat(mean(in), n_rows, 1); // singular value decomposition Mat<eT> U; Col<eT> s; const bool svd_ok = svd(U,s,coeff_out,score_out); if(svd_ok == false) { arma_print("princomp(): singular value decomposition failed"); coeff_out.reset(); score_out.reset(); latent_out.reset(); tsquared_out.reset(); return; } //U.reset(); // TODO: do we need this ? U will get automatically deleted anyway // normalize the eigenvalues s /= std::sqrt(n_rows - 1); // project the samples to the principals score_out *= coeff_out; if(n_rows <= n_cols) // number of samples is less than their dimensionality { score_out.cols(n_rows-1,n_cols-1).zeros(); //Col<eT> s_tmp = zeros< Col<eT> >(n_cols); Col<eT> s_tmp(n_cols); s_tmp.zeros(); s_tmp.rows(0,n_rows-2) = s.rows(0,n_rows-2); s = s_tmp; // compute the Hotelling's T-squared s_tmp.rows(0,n_rows-2) = eT(1) / s_tmp.rows(0,n_rows-2); const Mat<eT> S = score_out * diagmat(Col<eT>(s_tmp)); tsquared_out = sum(S%S,1); } else { // compute the Hotelling's T-squared const Mat<eT> S = score_out * diagmat(Col<eT>( eT(1) / s)); tsquared_out = sum(S%S,1); } // compute the eigenvalues of the principal vectors latent_out = s%s; } else // single sample - row { if(n_rows == 1) { coeff_out = eye< Mat<eT> >(n_cols, n_cols); score_out.copy_size(in); score_out.zeros(); latent_out.set_size(n_cols); latent_out.zeros(); tsquared_out.set_size(1); tsquared_out.zeros(); } else { coeff_out.reset(); score_out.reset(); latent_out.reset(); tsquared_out.reset(); } } }
inline bool op_princomp::direct_princomp ( Mat< std::complex<T> >& coeff_out, Mat< std::complex<T> >& score_out, Col<T>& latent_out, Col< std::complex<T> >& tsquared_out, const Mat< std::complex<T> >& in ) { arma_extra_debug_sigprint(); typedef std::complex<T> eT; const u32 n_rows = in.n_rows; const u32 n_cols = in.n_cols; if(n_rows > 1) // more than one sample { // subtract the mean - use score_out as temporary matrix score_out = in - repmat(mean(in), n_rows, 1); // singular value decomposition Mat<eT> U; Col<T> s; const bool svd_ok = svd(U,s,coeff_out,score_out); if(svd_ok == false) { return false; } //U.reset(); // normalize the eigenvalues s /= std::sqrt( double(n_rows - 1) ); // project the samples to the principals score_out *= coeff_out; if(n_rows <= n_cols) // number of samples is less than their dimensionality { score_out.cols(n_rows-1,n_cols-1).zeros(); Col<T> s_tmp = zeros< Col<T> >(n_cols); s_tmp.rows(0,n_rows-2) = s.rows(0,n_rows-2); s = s_tmp; // compute the Hotelling's T-squared s_tmp.rows(0,n_rows-2) = 1.0 / s_tmp.rows(0,n_rows-2); const Mat<eT> S = score_out * diagmat(Col<T>(s_tmp)); tsquared_out = sum(S%S,1); } else { // compute the Hotelling's T-squared const Mat<eT> S = score_out * diagmat(Col<T>(T(1) / s)); tsquared_out = sum(S%S,1); } // compute the eigenvalues of the principal vectors latent_out = s%s; } else // 0 or 1 samples { coeff_out.eye(n_cols, n_cols); score_out.copy_size(in); score_out.zeros(); latent_out.set_size(n_cols); latent_out.zeros(); tsquared_out.set_size(n_rows); tsquared_out.zeros(); } return true; }