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author | Nao Pross <np@0hm.ch> | 2024-02-12 14:52:43 +0100 |
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committer | Nao Pross <np@0hm.ch> | 2024-02-12 14:52:43 +0100 |
commit | eda5bc26f44ee9a6f83dcf8c91f17296d7fc509d (patch) | |
tree | bc2efa38ff4e350f9a111ac87065cd7ae9a911c7 /src/armadillo/include/armadillo_bits/op_mean_meat.hpp | |
download | fsisotool-eda5bc26f44ee9a6f83dcf8c91f17296d7fc509d.tar.gz fsisotool-eda5bc26f44ee9a6f83dcf8c91f17296d7fc509d.zip |
Move into version control
Diffstat (limited to 'src/armadillo/include/armadillo_bits/op_mean_meat.hpp')
-rw-r--r-- | src/armadillo/include/armadillo_bits/op_mean_meat.hpp | 713 |
1 files changed, 713 insertions, 0 deletions
diff --git a/src/armadillo/include/armadillo_bits/op_mean_meat.hpp b/src/armadillo/include/armadillo_bits/op_mean_meat.hpp new file mode 100644 index 0000000..7e7a49d --- /dev/null +++ b/src/armadillo/include/armadillo_bits/op_mean_meat.hpp @@ -0,0 +1,713 @@ +// 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_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> +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.internal_has_nonfinite()) + { + // TODO: replace with dedicated handling to avoid unwrapping + op_mean::apply_noalias_unwrap(out, P, dim); + } + } + + + +// +// cubes + + + +template<typename T1> +inline +void +op_mean::apply(Cube<typename T1::elem_type>& out, const OpCube<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 > 2), "mean(): parameter 'dim' must be 0 or 1 or 2" ); + + const ProxyCube<T1> P(in.m); + + if(P.is_alias(out) == false) + { + op_mean::apply_noalias(out, P, dim); + } + else + { + Cube<eT> tmp; + + op_mean::apply_noalias(tmp, P, dim); + + out.steal_mem(tmp); + } + } + + + +template<typename T1> +inline +void +op_mean::apply_noalias(Cube<typename T1::elem_type>& out, const ProxyCube<T1>& P, const uword dim) + { + arma_extra_debug_sigprint(); + + if(is_Cube<typename ProxyCube<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(Cube<typename T1::elem_type>& out, const ProxyCube<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 ProxyCube<T1>::stored_type P_stored_type; + + const unwrap_cube<P_stored_type> U(P.Q); + + const Cube<eT>& X = U.M; + + const uword X_n_rows = X.n_rows; + const uword X_n_cols = X.n_cols; + const uword X_n_slices = X.n_slices; + + if(dim == 0) + { + out.set_size((X_n_rows > 0) ? 1 : 0, X_n_cols, X_n_slices); + + if(X_n_rows == 0) { return; } + + for(uword slice=0; slice < X_n_slices; ++slice) + { + eT* out_mem = out.slice_memptr(slice); + + for(uword col=0; col < X_n_cols; ++col) + { + out_mem[col] = op_mean::direct_mean( X.slice_colptr(slice,col), X_n_rows ); + } + } + } + else + if(dim == 1) + { + out.zeros(X_n_rows, (X_n_cols > 0) ? 1 : 0, X_n_slices); + + if(X_n_cols == 0) { return; } + + for(uword slice=0; slice < X_n_slices; ++slice) + { + eT* out_mem = out.slice_memptr(slice); + + for(uword col=0; col < X_n_cols; ++col) + { + const eT* col_mem = X.slice_colptr(slice,col); + + for(uword row=0; row < X_n_rows; ++row) + { + out_mem[row] += col_mem[row]; + } + } + + const Mat<eT> tmp('j', X.slice_memptr(slice), X_n_rows, X_n_cols); + + for(uword row=0; row < X_n_rows; ++row) + { + out_mem[row] /= T(X_n_cols); + + if(arma_isfinite(out_mem[row]) == false) + { + out_mem[row] = op_mean::direct_mean_robust( tmp, row ); + } + } + } + } + else + if(dim == 2) + { + out.zeros(X_n_rows, X_n_cols, (X_n_slices > 0) ? 1 : 0); + + if(X_n_slices == 0) { return; } + + eT* out_mem = out.memptr(); + + for(uword slice=0; slice < X_n_slices; ++slice) + { + arrayops::inplace_plus(out_mem, X.slice_memptr(slice), X.n_elem_slice ); + } + + out /= T(X_n_slices); + + podarray<eT> tmp(X_n_slices); + + for(uword col=0; col < X_n_cols; ++col) + for(uword row=0; row < X_n_rows; ++row) + { + if(arma_isfinite(out.at(row,col,0)) == false) + { + for(uword slice=0; slice < X_n_slices; ++slice) + { + tmp[slice] = X.at(row,col,slice); + } + + out.at(row,col,0) = op_mean::direct_mean_robust(tmp.memptr(), X_n_slices); + } + } + } + } + + + +template<typename T1> +inline +void +op_mean::apply_noalias_proxy(Cube<typename T1::elem_type>& out, const ProxyCube<T1>& P, const uword dim) + { + arma_extra_debug_sigprint(); + + op_mean::apply_noalias_unwrap(out, P, dim); + + // TODO: implement specialised handling + } + + + + +// + + + +template<typename eT> +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> +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 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); + } + + + +//! @} + |