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/op_var_meat.hpp         | 330 +++++++++++++++++++++
 1 file changed, 330 insertions(+)
 create mode 100644 src/armadillo/include/armadillo_bits/op_var_meat.hpp

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

diff --git a/src/armadillo/include/armadillo_bits/op_var_meat.hpp b/src/armadillo/include/armadillo_bits/op_var_meat.hpp
new file mode 100644
index 0000000..49a252d
--- /dev/null
+++ b/src/armadillo/include/armadillo_bits/op_var_meat.hpp
@@ -0,0 +1,330 @@
+// 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_var
+//! @{
+
+
+
+template<typename T1>
+inline
+void
+op_var::apply(Mat<typename T1::pod_type>& out, const mtOp<typename T1::pod_type, T1, op_var>& in)
+  {
+  arma_extra_debug_sigprint();
+  
+  typedef typename T1::pod_type out_eT;
+  
+  const uword norm_type = in.aux_uword_a;
+  const uword dim       = in.aux_uword_b;
+  
+  arma_debug_check( (norm_type > 1), "var(): parameter 'norm_type' must be 0 or 1" );
+  arma_debug_check( (dim > 1),       "var(): parameter 'dim' must be 0 or 1"       );
+  
+  const quasi_unwrap<T1> U(in.m);
+  
+  if(U.is_alias(out))
+    {
+    Mat<out_eT> tmp;
+    
+    op_var::apply_noalias(tmp, U.M, norm_type, dim);
+    
+    out.steal_mem(tmp);
+    }
+  else
+    {
+    op_var::apply_noalias(out, U.M, norm_type, dim);
+    }
+  }
+
+
+
+template<typename in_eT>
+inline
+void
+op_var::apply_noalias(Mat<typename get_pod_type<in_eT>::result>& out, const Mat<in_eT>& X, const uword norm_type, const uword dim)
+  {
+  arma_extra_debug_sigprint();
+  
+  typedef typename get_pod_type<in_eT>::result out_eT;
+  
+  const uword X_n_rows = X.n_rows;
+  const uword X_n_cols = X.n_cols;
+  
+  if(dim == 0)
+    {
+    arma_extra_debug_print("op_var::apply_noalias(): dim = 0");
+    
+    out.set_size((X_n_rows > 0) ? 1 : 0, X_n_cols);
+    
+    if(X_n_rows > 0)
+      {
+      out_eT* out_mem = out.memptr();
+      
+      for(uword col=0; col<X_n_cols; ++col)
+        {
+        out_mem[col] = op_var::direct_var( X.colptr(col), X_n_rows, norm_type );
+        }
+      }
+    }
+  else
+  if(dim == 1)
+    {
+    arma_extra_debug_print("op_var::apply_noalias(): dim = 1");
+    
+    out.set_size(X_n_rows, (X_n_cols > 0) ? 1 : 0);
+    
+    if(X_n_cols > 0)
+      {
+      podarray<in_eT> dat(X_n_cols);
+      
+      in_eT*  dat_mem = dat.memptr();
+      out_eT* out_mem = out.memptr();
+      
+      for(uword row=0; row<X_n_rows; ++row)
+        {
+        dat.copy_row(X, row);
+        
+        out_mem[row] = op_var::direct_var( dat_mem, X_n_cols, norm_type );
+        }
+      }
+    }
+  }
+
+
+
+template<typename T1>
+inline
+typename T1::pod_type
+op_var::var_vec(const Base<typename T1::elem_type, T1>& X, const uword norm_type)
+  {
+  arma_extra_debug_sigprint();
+  
+  arma_debug_check( (norm_type > 1), "var(): parameter 'norm_type' must be 0 or 1" );
+  
+  const quasi_unwrap<T1> U(X.get_ref());
+  
+  return op_var::direct_var(U.M.memptr(), U.M.n_elem, norm_type);
+  }
+
+
+
+template<typename eT>
+inline
+typename get_pod_type<eT>::result
+op_var::var_vec(const subview_col<eT>& X, const uword norm_type)
+  {
+  arma_extra_debug_sigprint();
+  
+  arma_debug_check( (norm_type > 1), "var(): parameter 'norm_type' must be 0 or 1" );
+  
+  return op_var::direct_var(X.colptr(0), X.n_rows, norm_type);
+  }
+
+
+
+
+template<typename eT>
+inline
+typename get_pod_type<eT>::result
+op_var::var_vec(const subview_row<eT>& X, const uword norm_type)
+  {
+  arma_extra_debug_sigprint();
+  
+  arma_debug_check( (norm_type > 1), "var(): parameter 'norm_type' must be 0 or 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;
+  
+  podarray<eT> tmp(X.n_elem);
+  eT* tmp_mem = tmp.memptr();
+  
+  for(uword i=0, col=start_col; col < end_col_p1; ++col, ++i)
+    {
+    tmp_mem[i] = A.at(start_row, col);
+    }
+  
+  return op_var::direct_var(tmp.memptr(), tmp.n_elem, norm_type);
+  }
+
+
+
+//! find the variance of an array
+template<typename eT>
+inline
+eT
+op_var::direct_var(const eT* const X, const uword n_elem, const uword norm_type)
+  {
+  arma_extra_debug_sigprint();
+  
+  if(n_elem >= 2)
+    {
+    const eT acc1 = op_mean::direct_mean(X, n_elem);
+    
+    eT acc2 = eT(0);
+    eT acc3 = eT(0);
+    
+    uword i,j;
+    
+    for(i=0, j=1; j<n_elem; i+=2, j+=2)
+      {
+      const eT Xi = X[i];
+      const eT Xj = X[j];
+      
+      const eT tmpi = acc1 - Xi;
+      const eT tmpj = acc1 - Xj;
+      
+      acc2 += tmpi*tmpi + tmpj*tmpj;
+      acc3 += tmpi      + tmpj;
+      }
+    
+    if(i < n_elem)
+      {
+      const eT Xi = X[i];
+      
+      const eT tmpi = acc1 - Xi;
+      
+      acc2 += tmpi*tmpi;
+      acc3 += tmpi;
+      }
+    
+    const eT norm_val = (norm_type == 0) ? eT(n_elem-1) : eT(n_elem);
+    const eT var_val  = (acc2 - acc3*acc3/eT(n_elem)) / norm_val;
+    
+    return arma_isfinite(var_val) ? var_val : op_var::direct_var_robust(X, n_elem, norm_type);
+    }
+  else
+    {
+    return eT(0);
+    }
+  }
+
+
+
+//! find the variance of an array (robust but slow)
+template<typename eT>
+inline
+eT
+op_var::direct_var_robust(const eT* const X, const uword n_elem, const uword norm_type)
+  {
+  arma_extra_debug_sigprint();
+  
+  if(n_elem > 1)
+    {
+    eT r_mean = X[0];
+    eT r_var  = eT(0);
+    
+    for(uword i=1; i<n_elem; ++i)
+      {
+      const eT tmp      = X[i] - r_mean;
+      const eT i_plus_1 = eT(i+1);
+      
+      r_var  = eT(i-1)/eT(i) * r_var + (tmp*tmp)/i_plus_1;
+      
+      r_mean = r_mean + tmp/i_plus_1;
+      }
+    
+    return (norm_type == 0) ? r_var : (eT(n_elem-1)/eT(n_elem)) * r_var;
+    }
+  else
+    {
+    return eT(0);
+    }
+  }
+
+
+
+//! find the variance of an array (version for complex numbers)
+template<typename T>
+inline
+T
+op_var::direct_var(const std::complex<T>* const X, const uword n_elem, const uword norm_type)
+  {
+  arma_extra_debug_sigprint();
+  
+  typedef typename std::complex<T> eT;
+  
+  if(n_elem >= 2)
+    {
+    const eT acc1 = op_mean::direct_mean(X, n_elem);
+    
+    T  acc2 =  T(0);
+    eT acc3 = eT(0);
+    
+    for(uword i=0; i<n_elem; ++i)
+      {
+      const eT tmp = acc1 - X[i];
+      
+      acc2 += std::norm(tmp);
+      acc3 += tmp;
+      }
+    
+    const T norm_val = (norm_type == 0) ? T(n_elem-1) : T(n_elem);
+    const T var_val  = (acc2 - std::norm(acc3)/T(n_elem)) / norm_val;
+    
+    return arma_isfinite(var_val) ? var_val : op_var::direct_var_robust(X, n_elem, norm_type);
+    }
+  else
+    {
+    return T(0);
+    }
+  }
+
+
+
+//! find the variance of an array (version for complex numbers) (robust but slow)
+template<typename T>
+inline
+T
+op_var::direct_var_robust(const std::complex<T>* const X, const uword n_elem, const uword norm_type)
+  {
+  arma_extra_debug_sigprint();
+  
+  typedef typename std::complex<T> eT;
+  
+  if(n_elem > 1)
+    {
+    eT r_mean = X[0];
+     T r_var  = T(0);
+    
+    for(uword i=1; i<n_elem; ++i)
+      {
+      const eT tmp      = X[i] - r_mean;
+      const  T i_plus_1 = T(i+1);
+      
+      r_var  = T(i-1)/T(i) * r_var + std::norm(tmp)/i_plus_1;
+      
+      r_mean = r_mean + tmp/i_plus_1;
+      }
+    
+    return (norm_type == 0) ? r_var : (T(n_elem-1)/T(n_elem)) * r_var;
+    }
+  else
+    {
+    return T(0);
+    }
+  }
+
+
+
+//! @}
+
-- 
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