AnalysisSystemForRadionucli.../include/armadillo_bits/op_mean_meat.hpp

518 lines
9.6 KiB
C++
Raw Normal View History

2024-06-04 15:25:02 +08:00
// Copyright (C) 2009-2015 National ICT Australia (NICTA)
//
// This Source Code Form is subject to the terms of the Mozilla Public
// License, v. 2.0. If a copy of the MPL was not distributed with this
// file, You can obtain one at http://mozilla.org/MPL/2.0/.
// -------------------------------------------------------------------
//
// Written by Conrad Sanderson - http://conradsanderson.id.au
//! \addtogroup op_mean
//! @{
template<typename T1>
inline
void
op_mean::apply(Mat<typename T1::elem_type>& out, const Op<T1,op_mean>& in)
{
arma_extra_debug_sigprint();
typedef typename T1::elem_type eT;
const uword dim = in.aux_uword_a;
arma_debug_check( (dim > 1), "mean(): parameter 'dim' must be 0 or 1" );
const Proxy<T1> P(in.m);
if(P.is_alias(out) == false)
{
op_mean::apply_noalias(out, P, dim);
}
else
{
Mat<eT> tmp;
op_mean::apply_noalias(tmp, P, dim);
out.steal_mem(tmp);
}
}
template<typename T1>
inline
void
op_mean::apply_noalias(Mat<typename T1::elem_type>& out, const Proxy<T1>& P, const uword dim)
{
arma_extra_debug_sigprint();
if(is_Mat<typename Proxy<T1>::stored_type>::value)
{
op_mean::apply_noalias_unwrap(out, P, dim);
}
else
{
op_mean::apply_noalias_proxy(out, P, dim);
}
}
template<typename T1>
inline
void
op_mean::apply_noalias_unwrap(Mat<typename T1::elem_type>& out, const Proxy<T1>& P, const uword dim)
{
arma_extra_debug_sigprint();
typedef typename T1::elem_type eT;
typedef typename get_pod_type<eT>::result T;
typedef typename Proxy<T1>::stored_type P_stored_type;
const unwrap<P_stored_type> tmp(P.Q);
const typename unwrap<P_stored_type>::stored_type& X = tmp.M;
const uword X_n_rows = X.n_rows;
const uword X_n_cols = X.n_cols;
if(dim == 0)
{
out.set_size((X_n_rows > 0) ? 1 : 0, X_n_cols);
if(X_n_rows == 0) { return; }
eT* out_mem = out.memptr();
for(uword col=0; col < X_n_cols; ++col)
{
out_mem[col] = op_mean::direct_mean( X.colptr(col), X_n_rows );
}
}
else
if(dim == 1)
{
out.zeros(X_n_rows, (X_n_cols > 0) ? 1 : 0);
if(X_n_cols == 0) { return; }
eT* out_mem = out.memptr();
for(uword col=0; col < X_n_cols; ++col)
{
const eT* col_mem = X.colptr(col);
for(uword row=0; row < X_n_rows; ++row)
{
out_mem[row] += col_mem[row];
}
}
out /= T(X_n_cols);
for(uword row=0; row < X_n_rows; ++row)
{
if(arma_isfinite(out_mem[row]) == false)
{
out_mem[row] = op_mean::direct_mean_robust( X, row );
}
}
}
}
template<typename T1>
arma_hot
inline
void
op_mean::apply_noalias_proxy(Mat<typename T1::elem_type>& out, const Proxy<T1>& P, const uword dim)
{
arma_extra_debug_sigprint();
typedef typename T1::elem_type eT;
typedef typename get_pod_type<eT>::result T;
const uword P_n_rows = P.get_n_rows();
const uword P_n_cols = P.get_n_cols();
if(dim == 0)
{
out.set_size((P_n_rows > 0) ? 1 : 0, P_n_cols);
if(P_n_rows == 0) { return; }
eT* out_mem = out.memptr();
for(uword col=0; col < P_n_cols; ++col)
{
eT val1 = eT(0);
eT val2 = eT(0);
uword i,j;
for(i=0, j=1; j < P_n_rows; i+=2, j+=2)
{
val1 += P.at(i,col);
val2 += P.at(j,col);
}
if(i < P_n_rows)
{
val1 += P.at(i,col);
}
out_mem[col] = (val1 + val2) / T(P_n_rows);
}
}
else
if(dim == 1)
{
out.zeros(P_n_rows, (P_n_cols > 0) ? 1 : 0);
if(P_n_cols == 0) { return; }
eT* out_mem = out.memptr();
for(uword col=0; col < P_n_cols; ++col)
for(uword row=0; row < P_n_rows; ++row)
{
out_mem[row] += P.at(row,col);
}
out /= T(P_n_cols);
}
if(out.is_finite() == false)
{
// TODO: replace with dedicated handling to avoid unwrapping
op_mean::apply_noalias_unwrap(out, P, dim);
}
}
template<typename eT>
arma_pure
inline
eT
op_mean::direct_mean(const eT* const X, const uword n_elem)
{
arma_extra_debug_sigprint();
typedef typename get_pod_type<eT>::result T;
const eT result = arrayops::accumulate(X, n_elem) / T(n_elem);
return arma_isfinite(result) ? result : op_mean::direct_mean_robust(X, n_elem);
}
template<typename eT>
arma_pure
inline
eT
op_mean::direct_mean_robust(const eT* const X, const uword n_elem)
{
arma_extra_debug_sigprint();
// use an adapted form of the mean finding algorithm from the running_stat class
typedef typename get_pod_type<eT>::result T;
uword i,j;
eT r_mean = eT(0);
for(i=0, j=1; j<n_elem; i+=2, j+=2)
{
const eT Xi = X[i];
const eT Xj = X[j];
r_mean = r_mean + (Xi - r_mean)/T(j); // we need i+1, and j is equivalent to i+1 here
r_mean = r_mean + (Xj - r_mean)/T(j+1);
}
if(i < n_elem)
{
const eT Xi = X[i];
r_mean = r_mean + (Xi - r_mean)/T(i+1);
}
return r_mean;
}
template<typename eT>
inline
eT
op_mean::direct_mean(const Mat<eT>& X, const uword row)
{
arma_extra_debug_sigprint();
typedef typename get_pod_type<eT>::result T;
const uword X_n_cols = X.n_cols;
eT val = eT(0);
uword i,j;
for(i=0, j=1; j < X_n_cols; i+=2, j+=2)
{
val += X.at(row,i);
val += X.at(row,j);
}
if(i < X_n_cols)
{
val += X.at(row,i);
}
const eT result = val / T(X_n_cols);
return arma_isfinite(result) ? result : op_mean::direct_mean_robust(X, row);
}
template<typename eT>
inline
eT
op_mean::direct_mean_robust(const Mat<eT>& X, const uword row)
{
arma_extra_debug_sigprint();
typedef typename get_pod_type<eT>::result T;
const uword X_n_cols = X.n_cols;
eT r_mean = eT(0);
for(uword col=0; col < X_n_cols; ++col)
{
r_mean = r_mean + (X.at(row,col) - r_mean)/T(col+1);
}
return r_mean;
}
template<typename eT>
inline
eT
op_mean::mean_all(const subview<eT>& X)
{
arma_extra_debug_sigprint();
typedef typename get_pod_type<eT>::result T;
const uword X_n_rows = X.n_rows;
const uword X_n_cols = X.n_cols;
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);
if(X_n_rows == 1)
{
const Mat<eT>& 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;
uword i,j;
for(i=start_col, j=start_col+1; j < end_col_p1; i+=2, j+=2)
{
val += A.at(start_row, i);
val += A.at(start_row, j);
}
if(i < end_col_p1)
{
val += A.at(start_row, i);
}
}
else
{
for(uword col=0; col < X_n_cols; ++col)
{
val += arrayops::accumulate(X.colptr(col), X_n_rows);
}
}
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 subview<eT>& X)
{
arma_extra_debug_sigprint();
typedef typename get_pod_type<eT>::result T;
const uword X_n_rows = X.n_rows;
const uword X_n_cols = X.n_cols;
const uword start_row = X.aux_row1;
const uword start_col = X.aux_col1;
const uword end_row_p1 = start_row + X_n_rows;
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 Base<typename T1::elem_type, T1>& X)
{
arma_extra_debug_sigprint();
typedef typename T1::elem_type eT;
const 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);
}
//! @}