372 lines
8.4 KiB
C++
372 lines
8.4 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_sp_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_sp_mean::apply(Mat<typename T1::elem_type>& out, const mtSpReduceOp<typename T1::elem_type, T1, op_sp_mean>& in)
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{
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arma_debug_sigprint();
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const uword dim = in.aux_uword_a;
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arma_conform_check( (dim > 1), "mean(): parameter 'dim' must be 0 or 1" );
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const SpProxy<T1> p(in.m);
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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( (p_n_rows == 0) || (p_n_cols == 0) || (p.get_n_nonzero() == 0) )
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{
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if(dim == 0) { out.zeros((p_n_rows > 0) ? 1 : 0, p_n_cols); }
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if(dim == 1) { out.zeros(p_n_rows, (p_n_cols > 0) ? 1 : 0); }
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return;
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}
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op_sp_mean::apply_fast(out, p, dim);
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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_sp_mean::apply_fast
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(
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Mat<typename T1::elem_type>& out,
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const SpProxy<T1>& p,
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const uword dim
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)
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{
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arma_debug_sigprint();
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typedef typename T1::elem_type eT;
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typedef typename T1::pod_type 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) // find the mean in each column
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{
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arma_debug_print("op_sp_mean::apply_fast(): dim = 0");
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out.zeros(1, p_n_cols);
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eT* out_mem = out.memptr();
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if(SpProxy<T1>::use_iterator)
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{
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typename SpProxy<T1>::const_iterator_type it = p.begin();
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const uword N = p.get_n_nonzero();
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for(uword i=0; i < N; ++i) { out_mem[it.col()] += (*it); ++it; }
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out /= T(p_n_rows);
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}
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else
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{
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for(uword col = 0; col < p_n_cols; ++col)
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{
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out_mem[col] = arrayops::accumulate
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(
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&p.get_values()[p.get_col_ptrs()[col]],
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p.get_col_ptrs()[col + 1] - p.get_col_ptrs()[col]
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) / T(p_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) // find the mean in each row
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{
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arma_debug_print("op_sp_mean::apply_fast(): dim = 1");
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out.zeros(p_n_rows, 1);
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eT* out_mem = out.memptr();
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typename SpProxy<T1>::const_iterator_type it = p.begin();
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const uword N = p.get_n_nonzero();
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for(uword i=0; i < N; ++i) { out_mem[it.row()] += (*it); ++it; }
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out /= T(p_n_cols);
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}
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if(out.internal_has_nonfinite())
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{
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op_sp_mean::apply_slow(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_sp_mean::apply_slow
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(
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Mat<typename T1::elem_type>& out,
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const SpProxy<T1>& p,
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const uword dim
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)
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{
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arma_debug_sigprint();
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typedef typename T1::elem_type eT;
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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) // find the mean in each column
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{
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arma_debug_print("op_sp_mean::apply_slow(): dim = 0");
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out.zeros(1, p_n_cols);
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for(uword col = 0; col < p_n_cols; ++col)
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{
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// Do we have to use an iterator or can we use memory directly?
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if(SpProxy<T1>::use_iterator)
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{
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typename SpProxy<T1>::const_iterator_type it = p.begin_col(col);
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typename SpProxy<T1>::const_iterator_type end = p.begin_col(col + 1);
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const uword n_zero = p_n_rows - (end.pos() - it.pos());
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out.at(0,col) = op_sp_mean::iterator_mean(it, end, n_zero, eT(0));
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}
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else
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{
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out.at(0,col) = op_sp_mean::direct_mean
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(
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&p.get_values()[p.get_col_ptrs()[col]],
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p.get_col_ptrs()[col + 1] - p.get_col_ptrs()[col],
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p_n_rows
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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 == 1) // find the mean in each row
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{
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arma_debug_print("op_sp_mean::apply_slow(): dim = 1");
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out.zeros(p_n_rows, 1);
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for(uword row = 0; row < p_n_rows; ++row)
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{
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// We must use an iterator regardless of how it is stored.
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typename SpProxy<T1>::const_row_iterator_type it = p.begin_row(row);
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typename SpProxy<T1>::const_row_iterator_type end = p.end_row(row);
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const uword n_zero = p_n_cols - (end.pos() - it.pos());
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out.at(row,0) = op_sp_mean::iterator_mean(it, end, n_zero, eT(0));
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}
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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_sp_mean::direct_mean
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(
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const eT* const X,
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const uword length,
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const uword N
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)
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{
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arma_debug_sigprint();
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typedef typename get_pod_type<eT>::result T;
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const eT result = ((length > 0) && (N > 0)) ? eT(arrayops::accumulate(X, length) / T(N)) : eT(0);
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return arma_isfinite(result) ? result : op_sp_mean::direct_mean_robust(X, length, N);
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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_sp_mean::direct_mean_robust
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(
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const eT* const X,
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const uword length,
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const uword N
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)
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{
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arma_debug_sigprint();
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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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const uword diff = (N - length); // number of zeros
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for(i = 0, j = 1; j < length; 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 += (Xi - r_mean) / T(diff + j);
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r_mean += (Xj - r_mean) / T(diff + j + 1);
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}
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if(i < length)
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{
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const eT Xi = X[i];
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r_mean += (Xi - r_mean) / T(diff + i + 1);
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}
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return r_mean;
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}
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template<typename T1>
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inline
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typename T1::elem_type
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op_sp_mean::mean_all(const SpBase<typename T1::elem_type, T1>& X)
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{
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arma_debug_sigprint();
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typedef typename T1::elem_type eT;
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SpProxy<T1> p(X.get_ref());
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if(p.get_n_elem() == 0)
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{
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arma_conform_check(true, "mean(): object has no elements");
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return Datum<eT>::nan;
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}
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if(SpProxy<T1>::use_iterator)
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{
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typename SpProxy<T1>::const_iterator_type it = p.begin();
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typename SpProxy<T1>::const_iterator_type end = p.end();
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return op_sp_mean::iterator_mean(it, end, p.get_n_elem() - p.get_n_nonzero(), typename T1::elem_type(0));
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}
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else // use_iterator == false; that is, we can directly access the values array
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{
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return op_sp_mean::direct_mean(p.get_values(), p.get_n_nonzero(), p.get_n_elem());
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}
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}
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template<typename T1, typename spop_type>
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inline
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typename T1::elem_type
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op_sp_mean::mean_all(const SpOp<T1, spop_type>& expr)
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{
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arma_debug_sigprint();
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typedef typename T1::elem_type eT;
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constexpr bool is_vectorise = \
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(is_same_type<spop_type, spop_vectorise_row>::yes)
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|| (is_same_type<spop_type, spop_vectorise_col>::yes)
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|| (is_same_type<spop_type, spop_vectorise_all>::yes);
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if(is_vectorise)
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{
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return op_sp_mean::mean_all(expr.m);
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}
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const SpMat<eT> tmp = expr;
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return op_sp_mean::mean_all(tmp);
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}
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template<typename T1, typename eT>
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inline
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eT
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op_sp_mean::iterator_mean(T1& it, const T1& end, const uword n_zero, const eT junk)
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{
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arma_debug_sigprint();
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arma_ignore(junk);
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typedef typename get_pod_type<eT>::result T;
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eT acc = eT(0);
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T1 backup_it(it); // in case we have to use robust iterator_mean
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const uword it_begin_pos = it.pos();
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while(it != end)
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{
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acc += (*it);
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++it;
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}
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const uword count = n_zero + (it.pos() - it_begin_pos);
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const eT result = (count > 0) ? eT(acc / T(count)) : eT(0);
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return arma_isfinite(result) ? result : op_sp_mean::iterator_mean_robust(backup_it, end, n_zero, eT(0));
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}
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template<typename T1, typename eT>
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inline
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eT
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op_sp_mean::iterator_mean_robust(T1& it, const T1& end, const uword n_zero, const eT junk)
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{
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arma_debug_sigprint();
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arma_ignore(junk);
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typedef typename get_pod_type<eT>::result T;
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eT r_mean = eT(0);
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const uword it_begin_pos = it.pos();
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while(it != end)
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{
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r_mean += ((*it - r_mean) / T(n_zero + (it.pos() - it_begin_pos) + 1));
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++it;
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}
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return r_mean;
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}
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//! @}
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