// 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_var //! @{ template inline void op_var::apply(Mat& out, const mtOp& in) { arma_extra_debug_sigprint(); typedef typename T1::pod_type out_eT; const uword norm_type = in.aux_uword_a; const uword dim = in.aux_uword_b; arma_debug_check( (norm_type > 1), "var(): parameter 'norm_type' must be 0 or 1" ); arma_debug_check( (dim > 1), "var(): parameter 'dim' must be 0 or 1" ); const quasi_unwrap U(in.m); if(U.is_alias(out)) { Mat tmp; op_var::apply_noalias(tmp, U.M, norm_type, dim); out.steal_mem(tmp); } else { op_var::apply_noalias(out, U.M, norm_type, dim); } } template inline void op_var::apply_noalias(Mat::result>& out, const Mat& X, const uword norm_type, const uword dim) { arma_extra_debug_sigprint(); typedef typename get_pod_type::result out_eT; const uword X_n_rows = X.n_rows; const uword X_n_cols = X.n_cols; if(dim == 0) { arma_extra_debug_print("op_var::apply_noalias(): dim = 0"); out.set_size((X_n_rows > 0) ? 1 : 0, X_n_cols); if(X_n_rows > 0) { out_eT* out_mem = out.memptr(); for(uword col=0; col 0) ? 1 : 0); if(X_n_cols > 0) { podarray dat(X_n_cols); in_eT* dat_mem = dat.memptr(); out_eT* out_mem = out.memptr(); for(uword row=0; row inline typename T1::pod_type op_var::var_vec(const Base& X, const uword norm_type) { arma_extra_debug_sigprint(); arma_debug_check( (norm_type > 1), "var(): parameter 'norm_type' must be 0 or 1" ); const quasi_unwrap U(X.get_ref()); return op_var::direct_var(U.M.memptr(), U.M.n_elem, norm_type); } template inline typename get_pod_type::result op_var::var_vec(const subview_col& X, const uword norm_type) { arma_extra_debug_sigprint(); arma_debug_check( (norm_type > 1), "var(): parameter 'norm_type' must be 0 or 1" ); return op_var::direct_var(X.colptr(0), X.n_rows, norm_type); } template inline typename get_pod_type::result op_var::var_vec(const subview_row& X, const uword norm_type) { arma_extra_debug_sigprint(); arma_debug_check( (norm_type > 1), "var(): parameter 'norm_type' must be 0 or 1" ); const Mat& A = X.m; const uword start_row = X.aux_row1; const uword start_col = X.aux_col1; const uword end_col_p1 = start_col + X.n_cols; podarray tmp(X.n_elem); eT* tmp_mem = tmp.memptr(); for(uword i=0, col=start_col; col < end_col_p1; ++col, ++i) { tmp_mem[i] = A.at(start_row, col); } return op_var::direct_var(tmp.memptr(), tmp.n_elem, norm_type); } //! find the variance of an array template inline eT op_var::direct_var(const eT* const X, const uword n_elem, const uword norm_type) { arma_extra_debug_sigprint(); if(n_elem >= 2) { const eT acc1 = op_mean::direct_mean(X, n_elem); eT acc2 = eT(0); eT acc3 = eT(0); uword i,j; for(i=0, j=1; j inline eT op_var::direct_var_robust(const eT* const X, const uword n_elem, const uword norm_type) { arma_extra_debug_sigprint(); if(n_elem > 1) { eT r_mean = X[0]; eT r_var = eT(0); for(uword i=1; i inline T op_var::direct_var(const std::complex* const X, const uword n_elem, const uword norm_type) { arma_extra_debug_sigprint(); typedef typename std::complex eT; if(n_elem >= 2) { const eT acc1 = op_mean::direct_mean(X, n_elem); T acc2 = T(0); eT acc3 = eT(0); for(uword i=0; i inline T op_var::direct_var_robust(const std::complex* const X, const uword n_elem, const uword norm_type) { arma_extra_debug_sigprint(); typedef typename std::complex eT; if(n_elem > 1) { eT r_mean = X[0]; T r_var = T(0); for(uword i=1; i