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// SPDX-License-Identifier: Apache-2.0
//
// Copyright 2008-2016 Conrad Sanderson (http://conradsanderson.id.au)
// Copyright 2008-2016 National ICT Australia (NICTA)
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// ------------------------------------------------------------------------
//! \addtogroup op_cov
//! @{
template<typename T1>
inline
void
op_cov::apply(Mat<typename T1::elem_type>& out, const Op<T1,op_cov>& in)
{
arma_extra_debug_sigprint();
typedef typename T1::elem_type eT;
const uword norm_type = in.aux_uword_a;
const unwrap<T1> U(in.m);
const Mat<eT>& A = U.M;
if(A.n_elem == 0)
{
out.reset();
return;
}
const Mat<eT>& AA = (A.n_rows == 1)
? Mat<eT>(const_cast<eT*>(A.memptr()), A.n_cols, A.n_rows, false, false)
: Mat<eT>(const_cast<eT*>(A.memptr()), A.n_rows, A.n_cols, false, false);
const uword N = AA.n_rows;
const eT norm_val = (norm_type == 0) ? ( (N > 1) ? eT(N-1) : eT(1) ) : eT(N);
const Mat<eT> tmp = AA.each_row() - mean(AA,0);
out = tmp.t() * tmp;
out /= norm_val;
}
template<typename T1>
inline
void
op_cov::apply(Mat<typename T1::elem_type>& out, const Op< Op<T1,op_htrans>, op_cov>& in)
{
arma_extra_debug_sigprint();
typedef typename T1::elem_type eT;
const uword norm_type = in.aux_uword_a;
if(is_cx<eT>::yes)
{
const Mat<eT> tmp = in.m; // force the evaluation of Op<T1,op_htrans>
out = cov(tmp, norm_type);
}
else
{
const unwrap<T1> U(in.m.m);
const Mat<eT>& A = U.M;
if(A.n_elem == 0)
{
out.reset();
return;
}
const Mat<eT>& AA = (A.n_cols == 1)
? Mat<eT>(const_cast<eT*>(A.memptr()), A.n_cols, A.n_rows, false, false)
: Mat<eT>(const_cast<eT*>(A.memptr()), A.n_rows, A.n_cols, false, false);
const uword N = AA.n_cols;
const eT norm_val = (norm_type == 0) ? ( (N > 1) ? eT(N-1) : eT(1) ) : eT(N);
const Mat<eT> tmp = AA.each_col() - mean(AA,1);
out = tmp * tmp.t();
out /= norm_val;
}
}
//! @}
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