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diff --git a/src/armadillo/include/armadillo_bits/spop_var_meat.hpp b/src/armadillo/include/armadillo_bits/spop_var_meat.hpp
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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 spop_var
+//! @{
+
+
+
+template<typename T1>
+inline
+void
+spop_var::apply(SpMat<typename T1::pod_type>& out, const mtSpOp<typename T1::pod_type, T1, spop_var>& in)
+  {
+  arma_extra_debug_sigprint();
+  
+  //typedef typename T1::elem_type  in_eT;
+  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 SpProxy<T1> p(in.m);
+  
+  if(p.is_alias(out) == false)
+    {
+    spop_var::apply_noalias(out, p, norm_type, dim);
+    }
+  else
+    {
+    SpMat<out_eT> tmp;
+    
+    spop_var::apply_noalias(tmp, p, norm_type, dim);
+    
+    out.steal_mem(tmp);
+    }
+  }
+
+
+
+template<typename T1>
+inline
+void
+spop_var::apply_noalias
+  (
+        SpMat<typename T1::pod_type>& out,
+  const SpProxy<T1>&                  p,
+  const uword                         norm_type,
+  const uword                         dim
+  )
+  {
+  arma_extra_debug_sigprint();
+  
+  typedef typename T1::elem_type  in_eT;
+  //typedef typename T1::pod_type  out_eT;
+  
+  const uword p_n_rows = p.get_n_rows();
+  const uword p_n_cols = p.get_n_cols();
+  
+  // TODO: this is slow; rewrite based on the approach used by sparse mean()
+  
+  if(dim == 0)  // find variance in each column
+    {
+    arma_extra_debug_print("spop_var::apply_noalias(): dim = 0");
+    
+    out.set_size((p_n_rows > 0) ? 1 : 0, p_n_cols);
+    
+    if( (p_n_rows == 0) || (p.get_n_nonzero() == 0) )  { return; }
+    
+    for(uword col = 0; col < p_n_cols; ++col)
+      {
+      if(SpProxy<T1>::use_iterator)
+        {
+        // We must use an iterator; we can't access memory directly.
+        typename SpProxy<T1>::const_iterator_type it  = p.begin_col(col);
+        typename SpProxy<T1>::const_iterator_type end = p.begin_col(col + 1);
+        
+        const uword n_zero = p_n_rows - (end.pos() - it.pos());
+        
+        // in_eT is used just to get the specialization right (complex / noncomplex)
+        out.at(0, col) = spop_var::iterator_var(it, end, n_zero, norm_type, in_eT(0));
+        }
+      else
+        {
+        // We can use direct memory access to calculate the variance.
+        out.at(0, col) = spop_var::direct_var
+          (
+          &p.get_values()[p.get_col_ptrs()[col]],
+          p.get_col_ptrs()[col + 1] - p.get_col_ptrs()[col],
+          p_n_rows,
+          norm_type
+          );
+        }
+      }
+    }
+  else
+  if(dim == 1)  // find variance in each row
+    {
+    arma_extra_debug_print("spop_var::apply_noalias(): dim = 1");
+    
+    out.set_size(p_n_rows, (p_n_cols > 0) ? 1 : 0);
+    
+    if( (p_n_cols == 0) || (p.get_n_nonzero() == 0) )  { return; }
+    
+    for(uword row = 0; row < p_n_rows; ++row)
+      {
+      // We have to use an iterator here regardless of whether or not we can
+      // directly access memory.
+      typename SpProxy<T1>::const_row_iterator_type it  = p.begin_row(row);
+      typename SpProxy<T1>::const_row_iterator_type end = p.end_row(row);
+      
+      const uword n_zero = p_n_cols - (end.pos() - it.pos());
+      
+      out.at(row, 0) = spop_var::iterator_var(it, end, n_zero, norm_type, in_eT(0));
+      }
+    }
+  }
+
+
+
+template<typename T1>
+inline
+typename T1::pod_type
+spop_var::var_vec
+  (
+  const 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" );
+  
+  // conditionally unwrap it into a temporary and then directly operate.
+  
+  const unwrap_spmat<T1> tmp(X);
+  
+  return direct_var(tmp.M.values, tmp.M.n_nonzero, tmp.M.n_elem, norm_type);
+  }
+
+
+
+template<typename eT>
+inline
+eT
+spop_var::direct_var
+  (
+  const eT* const X,
+  const uword length,
+  const uword N,
+  const uword norm_type
+  )
+  {
+  arma_extra_debug_sigprint();
+
+  if(length >= 2 && N >= 2)
+    {
+    const eT acc1 = spop_mean::direct_mean(X, length, N);
+
+    eT acc2 = eT(0);
+    eT acc3 = eT(0);
+
+    uword i, j;
+
+    for(i = 0, j = 1; j < length; 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 < length)
+      {
+      const eT Xi = X[i];
+
+      const eT tmpi = acc1 - Xi;
+
+      acc2 += tmpi * tmpi;
+      acc3 += tmpi;
+      }
+
+    // Now add in all zero elements.
+    acc2 += (N - length) * (acc1 * acc1);
+    acc3 += (N - length) * acc1;
+
+    const eT norm_val = (norm_type == 0) ? eT(N - 1) : eT(N);
+    const eT var_val  = (acc2 - (acc3 * acc3) / eT(N)) / norm_val;
+
+    return var_val;
+    }
+  else if(length == 1 && N > 1) // if N == 1, then variance is zero.
+    {
+    const eT mean = X[0] / eT(N);
+    const eT val = mean - X[0];
+
+    const eT acc2 = (val * val) + (N - length) * (mean * mean);
+    const eT acc3 = val + (N - length) * mean;
+
+    const eT norm_val = (norm_type == 0) ? eT(N - 1) : eT(N);
+    const eT var_val  = (acc2 - (acc3 * acc3) / eT(N)) / norm_val;
+
+    return var_val;
+    }
+  else
+    {
+    return eT(0);
+    }
+  }
+
+
+
+template<typename T>
+inline
+T
+spop_var::direct_var
+  (
+  const std::complex<T>* const X,
+  const uword length,
+  const uword N,
+  const uword norm_type
+  )
+  {
+  arma_extra_debug_sigprint();
+
+  typedef typename std::complex<T> eT;
+
+  if(length >= 2 && N >= 2)
+    {
+    const eT acc1 = spop_mean::direct_mean(X, length, N);
+
+     T acc2 =  T(0);
+    eT acc3 = eT(0);
+
+    for(uword i = 0; i < length; ++i)
+      {
+      const eT tmp = acc1 - X[i];
+
+      acc2 += std::norm(tmp);
+      acc3 += tmp;
+      }
+
+    // Add zero elements to sums
+    acc2 += std::norm(acc1) * T(N - length);
+    acc3 += acc1 * T(N - length);
+
+    const T norm_val = (norm_type == 0) ? T(N - 1) : T(N);
+    const T var_val  = (acc2 - std::norm(acc3) / T(N)) / norm_val;
+
+    return var_val;
+    }
+  else if(length == 1 && N > 1) // if N == 1, then variance is zero.
+    {
+    const eT mean = X[0] / T(N);
+    const eT val = mean - X[0];
+
+    const T acc2 = std::norm(val) + (N - length) * std::norm(mean);
+    const eT acc3 = val + T(N - length) * mean;
+
+    const T norm_val = (norm_type == 0) ? T(N - 1) : T(N);
+    const T var_val  = (acc2 - std::norm(acc3) / T(N)) / norm_val;
+
+    return var_val;
+    }
+  else
+    {
+    return T(0); // All elements are zero
+    }
+  }
+
+
+
+template<typename T1, typename eT>
+inline
+eT
+spop_var::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
+  )
+  {
+  arma_extra_debug_sigprint();
+  arma_ignore(junk1);
+  arma_ignore(junk2);
+
+  T1 new_it(it); // for mean
+  // T1 backup_it(it); // in case we have to call robust iterator_var
+  eT mean = spop_mean::iterator_mean(new_it, end, n_zero, eT(0));
+
+  eT acc2 = eT(0);
+  eT acc3 = eT(0);
+
+  const uword it_begin_pos = it.pos();
+
+  while(it != end)
+    {
+    const eT tmp = mean - (*it);
+
+    acc2 += (tmp * tmp);
+    acc3 += (tmp);
+
+    ++it;
+    }
+
+  const uword n_nonzero = (it.pos() - it_begin_pos);
+  if(n_nonzero == 0)
+    {
+    return eT(0);
+    }
+
+  if(n_nonzero + n_zero == 1)
+    {
+    return eT(0); // only one element
+    }
+
+  // Add in entries for zeros.
+  acc2 += eT(n_zero) * (mean * mean);
+  acc3 += eT(n_zero) * mean;
+
+  const eT norm_val = (norm_type == 0) ? eT(n_zero + n_nonzero - 1) : eT(n_zero + n_nonzero);
+  const eT var_val  = (acc2 - (acc3 * acc3) / eT(n_nonzero + n_zero)) / norm_val;
+
+  return var_val;
+  }
+
+
+
+template<typename T1, typename eT>
+inline
+typename get_pod_type<eT>::result
+spop_var::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
+  )
+  {
+  arma_extra_debug_sigprint();
+  arma_ignore(junk1);
+  arma_ignore(junk2);
+
+  typedef typename get_pod_type<eT>::result T;
+
+  T1 new_it(it); // for mean
+  // T1 backup_it(it); // in case we have to call robust iterator_var
+  eT mean = spop_mean::iterator_mean(new_it, end, n_zero, eT(0));
+
+   T acc2 =  T(0);
+  eT acc3 = eT(0);
+
+  const uword it_begin_pos = it.pos();
+
+  while(it != end)
+    {
+    eT tmp = mean - (*it);
+
+    acc2 += std::norm(tmp);
+    acc3 += (tmp);
+
+    ++it;
+    }
+
+  const uword n_nonzero = (it.pos() - it_begin_pos);
+  if(n_nonzero == 0)
+    {
+    return T(0);
+    }
+
+  if(n_nonzero + n_zero == 1)
+    {
+    return T(0); // only one element
+    }
+
+  // Add in entries for zero elements.
+  acc2 += T(n_zero) * std::norm(mean);
+  acc3 += T(n_zero) * mean;
+
+  const T norm_val = (norm_type == 0) ? T(n_zero + n_nonzero - 1) : T(n_zero + n_nonzero);
+  const T var_val  = (acc2 - std::norm(acc3) / T(n_nonzero + n_zero)) / norm_val;
+
+  return var_val;
+  }
+
+
+
+//! @}