716 lines
14 KiB
C++
716 lines
14 KiB
C++
// SPDX-License-Identifier: Apache-2.0
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//
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// Copyright 2008-2016 Conrad Sanderson (http://conradsanderson.id.au)
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// Copyright 2008-2016 National ICT Australia (NICTA)
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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// ------------------------------------------------------------------------
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//! \addtogroup op_mean
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//! @{
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template<typename T1>
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inline
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void
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op_mean::apply(Mat<typename T1::elem_type>& out, const Op<T1,op_mean>& in)
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{
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arma_extra_debug_sigprint();
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typedef typename T1::elem_type eT;
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const uword dim = in.aux_uword_a;
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arma_debug_check( (dim > 1), "mean(): parameter 'dim' must be 0 or 1" );
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const Proxy<T1> P(in.m);
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if(P.is_alias(out) == false)
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{
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op_mean::apply_noalias(out, P, dim);
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}
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else
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{
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Mat<eT> tmp;
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op_mean::apply_noalias(tmp, P, dim);
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out.steal_mem(tmp);
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}
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}
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template<typename T1>
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inline
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void
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op_mean::apply_noalias(Mat<typename T1::elem_type>& out, const Proxy<T1>& P, const uword dim)
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{
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arma_extra_debug_sigprint();
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if(is_Mat<typename Proxy<T1>::stored_type>::value)
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{
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op_mean::apply_noalias_unwrap(out, P, dim);
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}
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else
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{
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op_mean::apply_noalias_proxy(out, P, dim);
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}
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}
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template<typename T1>
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inline
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void
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op_mean::apply_noalias_unwrap(Mat<typename T1::elem_type>& out, const Proxy<T1>& P, const uword dim)
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{
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arma_extra_debug_sigprint();
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typedef typename T1::elem_type eT;
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typedef typename get_pod_type<eT>::result T;
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typedef typename Proxy<T1>::stored_type P_stored_type;
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const unwrap<P_stored_type> tmp(P.Q);
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const typename unwrap<P_stored_type>::stored_type& X = tmp.M;
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const uword X_n_rows = X.n_rows;
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const uword X_n_cols = X.n_cols;
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if(dim == 0)
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{
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out.set_size((X_n_rows > 0) ? 1 : 0, X_n_cols);
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if(X_n_rows == 0) { return; }
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eT* out_mem = out.memptr();
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for(uword col=0; col < X_n_cols; ++col)
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{
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out_mem[col] = op_mean::direct_mean( X.colptr(col), X_n_rows );
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}
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}
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else
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if(dim == 1)
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{
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out.zeros(X_n_rows, (X_n_cols > 0) ? 1 : 0);
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if(X_n_cols == 0) { return; }
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eT* out_mem = out.memptr();
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for(uword col=0; col < X_n_cols; ++col)
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{
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const eT* col_mem = X.colptr(col);
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for(uword row=0; row < X_n_rows; ++row)
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{
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out_mem[row] += col_mem[row];
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}
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}
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out /= T(X_n_cols);
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for(uword row=0; row < X_n_rows; ++row)
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{
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if(arma_isfinite(out_mem[row]) == false)
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{
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out_mem[row] = op_mean::direct_mean_robust( X, row );
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}
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}
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}
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}
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template<typename T1>
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arma_hot
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inline
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void
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op_mean::apply_noalias_proxy(Mat<typename T1::elem_type>& out, const Proxy<T1>& P, const uword dim)
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{
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arma_extra_debug_sigprint();
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typedef typename T1::elem_type eT;
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typedef typename get_pod_type<eT>::result T;
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const uword P_n_rows = P.get_n_rows();
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const uword P_n_cols = P.get_n_cols();
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if(dim == 0)
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{
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out.set_size((P_n_rows > 0) ? 1 : 0, P_n_cols);
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if(P_n_rows == 0) { return; }
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eT* out_mem = out.memptr();
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for(uword col=0; col < P_n_cols; ++col)
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{
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eT val1 = eT(0);
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eT val2 = eT(0);
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uword i,j;
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for(i=0, j=1; j < P_n_rows; i+=2, j+=2)
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{
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val1 += P.at(i,col);
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val2 += P.at(j,col);
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}
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if(i < P_n_rows)
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{
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val1 += P.at(i,col);
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}
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out_mem[col] = (val1 + val2) / T(P_n_rows);
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}
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}
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else
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if(dim == 1)
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{
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out.zeros(P_n_rows, (P_n_cols > 0) ? 1 : 0);
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if(P_n_cols == 0) { return; }
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eT* out_mem = out.memptr();
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for(uword col=0; col < P_n_cols; ++col)
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for(uword row=0; row < P_n_rows; ++row)
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{
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out_mem[row] += P.at(row,col);
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}
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out /= T(P_n_cols);
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}
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if(out.is_finite() == false)
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{
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// TODO: replace with dedicated handling to avoid unwrapping
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op_mean::apply_noalias_unwrap(out, P, dim);
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}
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}
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//
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// cubes
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template<typename T1>
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inline
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void
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op_mean::apply(Cube<typename T1::elem_type>& out, const OpCube<T1,op_mean>& in)
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{
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arma_extra_debug_sigprint();
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typedef typename T1::elem_type eT;
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const uword dim = in.aux_uword_a;
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arma_debug_check( (dim > 2), "mean(): parameter 'dim' must be 0 or 1 or 2" );
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const ProxyCube<T1> P(in.m);
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if(P.is_alias(out) == false)
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{
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op_mean::apply_noalias(out, P, dim);
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}
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else
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{
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Cube<eT> tmp;
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op_mean::apply_noalias(tmp, P, dim);
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out.steal_mem(tmp);
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}
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}
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template<typename T1>
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inline
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void
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op_mean::apply_noalias(Cube<typename T1::elem_type>& out, const ProxyCube<T1>& P, const uword dim)
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{
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arma_extra_debug_sigprint();
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if(is_Cube<typename ProxyCube<T1>::stored_type>::value)
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{
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op_mean::apply_noalias_unwrap(out, P, dim);
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}
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else
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{
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op_mean::apply_noalias_proxy(out, P, dim);
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}
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}
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template<typename T1>
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inline
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void
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op_mean::apply_noalias_unwrap(Cube<typename T1::elem_type>& out, const ProxyCube<T1>& P, const uword dim)
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{
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arma_extra_debug_sigprint();
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typedef typename T1::elem_type eT;
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typedef typename get_pod_type<eT>::result T;
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typedef typename ProxyCube<T1>::stored_type P_stored_type;
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const unwrap_cube<P_stored_type> U(P.Q);
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const Cube<eT>& X = U.M;
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const uword X_n_rows = X.n_rows;
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const uword X_n_cols = X.n_cols;
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const uword X_n_slices = X.n_slices;
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if(dim == 0)
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{
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out.set_size((X_n_rows > 0) ? 1 : 0, X_n_cols, X_n_slices);
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if(X_n_rows == 0) { return; }
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for(uword slice=0; slice < X_n_slices; ++slice)
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{
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eT* out_mem = out.slice_memptr(slice);
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for(uword col=0; col < X_n_cols; ++col)
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{
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out_mem[col] = op_mean::direct_mean( X.slice_colptr(slice,col), X_n_rows );
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}
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}
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}
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else
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if(dim == 1)
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{
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out.zeros(X_n_rows, (X_n_cols > 0) ? 1 : 0, X_n_slices);
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if(X_n_cols == 0) { return; }
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for(uword slice=0; slice < X_n_slices; ++slice)
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{
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eT* out_mem = out.slice_memptr(slice);
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for(uword col=0; col < X_n_cols; ++col)
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{
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const eT* col_mem = X.slice_colptr(slice,col);
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for(uword row=0; row < X_n_rows; ++row)
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{
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out_mem[row] += col_mem[row];
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}
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}
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const Mat<eT> tmp('j', X.slice_memptr(slice), X_n_rows, X_n_cols);
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for(uword row=0; row < X_n_rows; ++row)
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{
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out_mem[row] /= T(X_n_cols);
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if(arma_isfinite(out_mem[row]) == false)
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{
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out_mem[row] = op_mean::direct_mean_robust( tmp, row );
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}
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}
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}
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}
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else
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if(dim == 2)
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{
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out.zeros(X_n_rows, X_n_cols, (X_n_slices > 0) ? 1 : 0);
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if(X_n_slices == 0) { return; }
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eT* out_mem = out.memptr();
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for(uword slice=0; slice < X_n_slices; ++slice)
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{
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arrayops::inplace_plus(out_mem, X.slice_memptr(slice), X.n_elem_slice );
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}
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out /= T(X_n_slices);
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podarray<eT> tmp(X_n_slices);
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for(uword col=0; col < X_n_cols; ++col)
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for(uword row=0; row < X_n_rows; ++row)
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{
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if(arma_isfinite(out.at(row,col,0)) == false)
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{
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for(uword slice=0; slice < X_n_slices; ++slice)
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{
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tmp[slice] = X.at(row,col,slice);
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}
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out.at(row,col,0) = op_mean::direct_mean_robust(tmp.memptr(), X_n_slices);
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}
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}
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}
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}
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template<typename T1>
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arma_hot
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inline
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void
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op_mean::apply_noalias_proxy(Cube<typename T1::elem_type>& out, const ProxyCube<T1>& P, const uword dim)
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{
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arma_extra_debug_sigprint();
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op_mean::apply_noalias_unwrap(out, P, dim);
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// TODO: implement specialised handling
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}
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//
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template<typename eT>
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inline
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eT
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op_mean::direct_mean(const eT* const X, const uword n_elem)
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{
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arma_extra_debug_sigprint();
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typedef typename get_pod_type<eT>::result T;
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const eT result = arrayops::accumulate(X, n_elem) / T(n_elem);
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return arma_isfinite(result) ? result : op_mean::direct_mean_robust(X, n_elem);
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}
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template<typename eT>
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inline
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eT
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op_mean::direct_mean_robust(const eT* const X, const uword n_elem)
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{
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arma_extra_debug_sigprint();
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// use an adapted form of the mean finding algorithm from the running_stat class
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typedef typename get_pod_type<eT>::result T;
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uword i,j;
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eT r_mean = eT(0);
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for(i=0, j=1; j<n_elem; i+=2, j+=2)
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{
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const eT Xi = X[i];
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const eT Xj = X[j];
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r_mean = r_mean + (Xi - r_mean)/T(j); // we need i+1, and j is equivalent to i+1 here
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r_mean = r_mean + (Xj - r_mean)/T(j+1);
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}
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if(i < n_elem)
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{
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const eT Xi = X[i];
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r_mean = r_mean + (Xi - r_mean)/T(i+1);
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}
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return r_mean;
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}
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template<typename eT>
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inline
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eT
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op_mean::direct_mean(const Mat<eT>& X, const uword row)
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{
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arma_extra_debug_sigprint();
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typedef typename get_pod_type<eT>::result T;
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const uword X_n_cols = X.n_cols;
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eT val = eT(0);
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uword i,j;
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for(i=0, j=1; j < X_n_cols; i+=2, j+=2)
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{
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val += X.at(row,i);
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val += X.at(row,j);
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}
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if(i < X_n_cols)
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{
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val += X.at(row,i);
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}
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const eT result = val / T(X_n_cols);
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return arma_isfinite(result) ? result : op_mean::direct_mean_robust(X, row);
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}
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template<typename eT>
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inline
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eT
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op_mean::direct_mean_robust(const Mat<eT>& X, const uword row)
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{
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arma_extra_debug_sigprint();
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typedef typename get_pod_type<eT>::result T;
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const uword X_n_cols = X.n_cols;
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eT r_mean = eT(0);
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for(uword col=0; col < X_n_cols; ++col)
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{
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r_mean = r_mean + (X.at(row,col) - r_mean)/T(col+1);
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}
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return r_mean;
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}
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template<typename eT>
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inline
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eT
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op_mean::mean_all(const subview<eT>& X)
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{
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arma_extra_debug_sigprint();
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typedef typename get_pod_type<eT>::result T;
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const uword X_n_rows = X.n_rows;
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const uword X_n_cols = X.n_cols;
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const uword X_n_elem = X.n_elem;
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if(X_n_elem == 0)
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{
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arma_debug_check(true, "mean(): object has no elements");
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return Datum<eT>::nan;
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}
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eT val = eT(0);
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if(X_n_rows == 1)
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{
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const Mat<eT>& A = X.m;
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const uword start_row = X.aux_row1;
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const uword start_col = X.aux_col1;
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const uword end_col_p1 = start_col + X_n_cols;
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uword i,j;
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for(i=start_col, j=start_col+1; j < end_col_p1; i+=2, j+=2)
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{
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val += A.at(start_row, i);
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val += A.at(start_row, j);
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}
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if(i < end_col_p1)
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{
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val += A.at(start_row, i);
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}
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}
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else
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{
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for(uword col=0; col < X_n_cols; ++col)
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{
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val += arrayops::accumulate(X.colptr(col), X_n_rows);
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}
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}
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const eT result = val / T(X_n_elem);
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return arma_isfinite(result) ? result : op_mean::mean_all_robust(X);
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}
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template<typename eT>
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inline
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eT
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op_mean::mean_all_robust(const subview<eT>& X)
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{
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arma_extra_debug_sigprint();
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typedef typename get_pod_type<eT>::result T;
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const uword X_n_rows = X.n_rows;
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const uword X_n_cols = X.n_cols;
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const uword start_row = X.aux_row1;
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const uword start_col = X.aux_col1;
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const uword end_row_p1 = start_row + X_n_rows;
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|
const uword end_col_p1 = start_col + X_n_cols;
|
|
|
|
const Mat<eT>& A = X.m;
|
|
|
|
|
|
eT r_mean = eT(0);
|
|
|
|
if(X_n_rows == 1)
|
|
{
|
|
uword i=0;
|
|
|
|
for(uword col = start_col; col < end_col_p1; ++col, ++i)
|
|
{
|
|
r_mean = r_mean + (A.at(start_row,col) - r_mean)/T(i+1);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
uword i=0;
|
|
|
|
for(uword col = start_col; col < end_col_p1; ++col)
|
|
for(uword row = start_row; row < end_row_p1; ++row, ++i)
|
|
{
|
|
r_mean = r_mean + (A.at(row,col) - r_mean)/T(i+1);
|
|
}
|
|
}
|
|
|
|
return r_mean;
|
|
}
|
|
|
|
|
|
|
|
template<typename eT>
|
|
inline
|
|
eT
|
|
op_mean::mean_all(const diagview<eT>& X)
|
|
{
|
|
arma_extra_debug_sigprint();
|
|
|
|
typedef typename get_pod_type<eT>::result T;
|
|
|
|
const uword X_n_elem = X.n_elem;
|
|
|
|
if(X_n_elem == 0)
|
|
{
|
|
arma_debug_check(true, "mean(): object has no elements");
|
|
|
|
return Datum<eT>::nan;
|
|
}
|
|
|
|
eT val = eT(0);
|
|
|
|
for(uword i=0; i<X_n_elem; ++i)
|
|
{
|
|
val += X[i];
|
|
}
|
|
|
|
const eT result = val / T(X_n_elem);
|
|
|
|
return arma_isfinite(result) ? result : op_mean::mean_all_robust(X);
|
|
}
|
|
|
|
|
|
|
|
template<typename eT>
|
|
inline
|
|
eT
|
|
op_mean::mean_all_robust(const diagview<eT>& X)
|
|
{
|
|
arma_extra_debug_sigprint();
|
|
|
|
typedef typename get_pod_type<eT>::result T;
|
|
|
|
const uword X_n_elem = X.n_elem;
|
|
|
|
eT r_mean = eT(0);
|
|
|
|
for(uword i=0; i<X_n_elem; ++i)
|
|
{
|
|
r_mean = r_mean + (X[i] - r_mean)/T(i+1);
|
|
}
|
|
|
|
return r_mean;
|
|
}
|
|
|
|
|
|
|
|
template<typename T1>
|
|
inline
|
|
typename T1::elem_type
|
|
op_mean::mean_all(const Op<T1,op_vectorise_col>& X)
|
|
{
|
|
arma_extra_debug_sigprint();
|
|
|
|
return op_mean::mean_all(X.m);
|
|
}
|
|
|
|
|
|
|
|
template<typename T1>
|
|
inline
|
|
typename T1::elem_type
|
|
op_mean::mean_all(const Base<typename T1::elem_type, T1>& X)
|
|
{
|
|
arma_extra_debug_sigprint();
|
|
|
|
typedef typename T1::elem_type eT;
|
|
|
|
const quasi_unwrap<T1> tmp(X.get_ref());
|
|
const Mat<eT>& A = tmp.M;
|
|
|
|
const uword A_n_elem = A.n_elem;
|
|
|
|
if(A_n_elem == 0)
|
|
{
|
|
arma_debug_check(true, "mean(): object has no elements");
|
|
|
|
return Datum<eT>::nan;
|
|
}
|
|
|
|
return op_mean::direct_mean(A.memptr(), A_n_elem);
|
|
}
|
|
|
|
|
|
|
|
template<typename eT>
|
|
arma_inline
|
|
eT
|
|
op_mean::robust_mean(const eT A, const eT B)
|
|
{
|
|
return A + (B - A)/eT(2);
|
|
}
|
|
|
|
|
|
|
|
template<typename T>
|
|
arma_inline
|
|
std::complex<T>
|
|
op_mean::robust_mean(const std::complex<T>& A, const std::complex<T>& B)
|
|
{
|
|
return A + (B - A)/T(2);
|
|
}
|
|
|
|
|
|
|
|
//! @}
|
|
|