From eda5bc26f44ee9a6f83dcf8c91f17296d7fc509d Mon Sep 17 00:00:00 2001
From: Nao Pross <np@0hm.ch>
Date: Mon, 12 Feb 2024 14:52:43 +0100
Subject: Move into version control

---
 .../include/armadillo_bits/spop_var_bones.hpp      | 64 ++++++++++++++++++++++
 1 file changed, 64 insertions(+)
 create mode 100644 src/armadillo/include/armadillo_bits/spop_var_bones.hpp

(limited to 'src/armadillo/include/armadillo_bits/spop_var_bones.hpp')

diff --git a/src/armadillo/include/armadillo_bits/spop_var_bones.hpp b/src/armadillo/include/armadillo_bits/spop_var_bones.hpp
new file mode 100644
index 0000000..09f0e24
--- /dev/null
+++ b/src/armadillo/include/armadillo_bits/spop_var_bones.hpp
@@ -0,0 +1,64 @@
+// 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 spop_var
+//! @{
+
+
+
+//! Class for finding variance values of a sparse matrix
+class spop_var
+  : public traits_op_xvec
+  {
+  public:
+
+  template<typename T1>
+  inline static void apply(SpMat<typename T1::pod_type>& out, const mtSpOp<typename T1::pod_type, T1, spop_var>& in);
+
+  template<typename T1>
+  inline static void apply_noalias(SpMat<typename T1::pod_type>& out, const SpProxy<T1>& p, const uword norm_type, const uword dim);
+  
+  // Calculate variance of a sparse vector, where we can directly use the memory.
+  template<typename T1>
+  inline static typename T1::pod_type var_vec(const T1& X, const uword norm_type = 0);
+  
+  // Calculate the variance directly.  Because this is for sparse matrices, we
+  // specify both the number of elements in the array (the length of the array)
+  // as well as the actual number of elements when zeros are included.
+  template<typename eT>
+  inline static eT direct_var(const eT* const X, const uword length, const uword N, const uword norm_type = 0);
+
+  // For complex numbers.
+
+  template<typename T>
+  inline static T direct_var(const std::complex<T>* const X, const uword length, const uword N, const uword norm_type = 0);
+
+  // Calculate the variance using iterators, for non-complex numbers.
+  template<typename T1, typename eT>
+  inline static eT iterator_var(T1& it, const T1& end, const uword n_zero, const uword norm_type, const eT junk1, const typename arma_not_cx<eT>::result* junk2 = nullptr);
+
+  // Calculate the variance using iterators, for complex numbers.
+  template<typename T1, typename eT>
+  inline static typename get_pod_type<eT>::result iterator_var(T1& it, const T1& end, const uword n_zero, const uword norm_type, const eT junk1, const typename arma_cx_only<eT>::result* junk2 = nullptr);
+
+  };
+
+
+
+//! @}
+  
-- 
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