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authorNao Pross <np@0hm.ch>2024-02-12 14:52:43 +0100
committerNao Pross <np@0hm.ch>2024-02-12 14:52:43 +0100
commiteda5bc26f44ee9a6f83dcf8c91f17296d7fc509d (patch)
treebc2efa38ff4e350f9a111ac87065cd7ae9a911c7 /src/armadillo/include/armadillo_bits/op_mean_bones.hpp
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Move into version control
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+// 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
+//! @{
+
+
+//! Class for finding mean values of a matrix
+class op_mean
+ : public traits_op_xvec
+ {
+ public:
+
+ // dense matrices
+
+ template<typename T1>
+ inline static void apply(Mat<typename T1::elem_type>& out, const Op<T1,op_mean>& in);
+
+ template<typename T1>
+ inline static void apply_noalias(Mat<typename T1::elem_type>& out, const Proxy<T1>& P, const uword dim);
+
+ template<typename T1>
+ inline static void apply_noalias_unwrap(Mat<typename T1::elem_type>& out, const Proxy<T1>& P, const uword dim);
+
+ template<typename T1>
+ inline static void apply_noalias_proxy(Mat<typename T1::elem_type>& out, const Proxy<T1>& P, const uword dim);
+
+
+ // cubes
+
+ template<typename T1>
+ inline static void apply(Cube<typename T1::elem_type>& out, const OpCube<T1,op_mean>& in);
+
+ template<typename T1>
+ inline static void apply_noalias(Cube<typename T1::elem_type>& out, const ProxyCube<T1>& P, const uword dim);
+
+ template<typename T1>
+ inline static void apply_noalias_unwrap(Cube<typename T1::elem_type>& out, const ProxyCube<T1>& P, const uword dim);
+
+ template<typename T1>
+ inline static void apply_noalias_proxy(Cube<typename T1::elem_type>& out, const ProxyCube<T1>& P, const uword dim);
+
+
+ //
+
+ template<typename eT>
+ inline static eT direct_mean(const eT* const X, const uword N);
+
+ template<typename eT>
+ inline static eT direct_mean_robust(const eT* const X, const uword N);
+
+
+ //
+
+ template<typename eT>
+ inline static eT direct_mean(const Mat<eT>& X, const uword row);
+
+ template<typename eT>
+ inline static eT direct_mean_robust(const Mat<eT>& X, const uword row);
+
+
+ //
+
+ template<typename eT>
+ inline static eT mean_all(const subview<eT>& X);
+
+ template<typename eT>
+ inline static eT mean_all_robust(const subview<eT>& X);
+
+
+ //
+
+ template<typename eT>
+ inline static eT mean_all(const diagview<eT>& X);
+
+ template<typename eT>
+ inline static eT mean_all_robust(const diagview<eT>& X);
+
+
+ //
+
+ template<typename T1>
+ inline static typename T1::elem_type mean_all(const Op<T1,op_vectorise_col>& X);
+
+ template<typename T1>
+ inline static typename T1::elem_type mean_all(const Base<typename T1::elem_type, T1>& X);
+
+
+ //
+
+ template<typename eT>
+ arma_inline static eT robust_mean(const eT A, const eT B);
+
+ template<typename T>
+ arma_inline static std::complex<T> robust_mean(const std::complex<T>& A, const std::complex<T>& B);
+ };
+
+
+
+//! @}