arma_hot
inline
void
running_stat_vec<eT>::operator() (const Base<typename get_pod_type<eT>::result, T1>& X)
{
arma_extra_debug_sigprint();
//typedef typename get_pod_type<eT>::result T;
const unwrap<T1> tmp(X.get_ref());
const Mat<eT>& sample = tmp.M;
if( sample.is_empty() )
{
return;
}
if( sample.is_finite() == false )
{
arma_print("running_stat_vec: sample ignored as it has non-finite elements");
return;
}
running_stat_vec_aux::update_stats(*this, sample);
}
inline
void
op_princomp::direct_princomp
(
Mat< std::complex<T> >& coeff_out,
const Mat< std::complex<T> >& in
)
{
arma_extra_debug_sigprint();
typedef typename std::complex<T> eT;
if(in.n_elem != 0)
{
// singular value decomposition
Mat<eT> U;
Col< T> s;
const Mat<eT> tmp = in - repmat(mean(in), in.n_rows, 1);
const bool svd_ok = svd(U,s,coeff_out, tmp);
if(svd_ok == false)
{
arma_print("princomp(): singular value decomposition failed");
coeff_out.reset();
}
}
else
{
coeff_out.reset();
}
}
inline
void
arma_hot
arma_warn(const bool state, const arma_boost::basic_format<T1,T2>& x)
{
if(state==true)
arma_print(x);
}
inline
void
arma_hot
arma_warn(const bool state, const T1& x, const T2& y)
{
if(state==true)
{
arma_print(x,y);
}
}
inline
void
op_princomp::direct_princomp
(
Mat< std::complex<T> >& coeff_out,
Mat< std::complex<T> >& score_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();
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();
}
}
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();
}
else
{
coeff_out.reset();
score_out.reset();
}
}
}
inline
void
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)
{
arma_print("princomp(): singular value decomposition failed");
coeff_out.reset();
score_out.reset();
latent_out.reset();
tsquared_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 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 // 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();
}
}
}
