comparison armadillo-2.4.4/include/armadillo_bits/op_mean_meat.hpp @ 0:8b6102e2a9b0

Armadillo Library

+// Copyright (C) 2009-2011 NICTA (www.nicta.com.au)
+// Copyright (C) 2009-2011 Conrad Sanderson
+// 
+// This file is part of the Armadillo C++ library.
+// It is provided without any warranty of fitness
+// for any purpose. You can redistribute this file
+// and/or modify it under the terms of the GNU
+// Lesser General Public License (LGPL) as published
+// by the Free Software Foundation, either version 3
+// of the License or (at your option) any later version.
+// (see http://www.opensource.org/licenses for more info)
+
+
+//! \addtogroup op_mean
+//! @{
+
+
+
+template<typename eT>
+arma_pure
+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(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);
+  
+  for(uword col=0; col<X_n_cols; ++col)
+    {
+    val += X.at(row,col);
+    }
+  
+  const eT result = val / T(X_n_cols);
+  
+  return arma_isfinite(result) ? result : direct_mean_robust(X, row);
+  }
+
+
+
+template<typename eT>
+inline 
+eT
+op_mean::direct_mean(const subview<eT>& X)
+  {
+  arma_extra_debug_sigprint();
+  
+  typedef typename get_pod_type<eT>::result T;
+  
+  const uword X_n_elem = X.n_elem;
+  
+  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 : direct_mean_robust(X);
+  }
+
+
+
+template<typename eT>
+inline 
+eT
+op_mean::direct_mean(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 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 : direct_mean_robust(X);
+  }
+
+
+
+//! \brief
+//! For each row or for each column, find the mean value.
+//! The result is stored in a dense matrix that has either one column or one row.
+//! The dimension, for which the means are found, is set via the mean() function.
+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;
+  typedef typename get_pod_type<eT>::result  T;
+  
+  const unwrap_check<T1> tmp(in.m, out);
+  const Mat<eT>& X = tmp.M;
+  
+  const uword dim = in.aux_uword_a;
+  arma_debug_check( (dim > 1), "mean(): incorrect usage. dim must be 0 or 1");
+  
+  const uword X_n_rows = X.n_rows;
+  const uword X_n_cols = X.n_cols;
+  
+  if(dim == 0)
+    {
+    arma_extra_debug_print("op_mean::apply(), dim = 0");
+    
+    out.set_size( (X_n_rows > 0) ? 1 : 0, X_n_cols );
+    
+    if(X_n_rows > 0)
+      {
+      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)
+    {
+    arma_extra_debug_print("op_mean::apply(), dim = 1");
+    
+    out.set_size(X_n_rows, (X_n_cols > 0) ? 1 : 0);
+    
+    if(X_n_cols > 0)
+      {
+      eT* out_mem = out.memptr();
+      
+      for(uword row=0; row<X_n_rows; ++row)
+        {
+        out_mem[row] = op_mean::direct_mean( X, row );
+        }
+      }
+    }
+  }
+
+
+
+template<typename eT>
+arma_pure
+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_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::direct_mean_robust(const subview<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 eT>
+inline 
+eT
+op_mean::direct_mean_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;
+  }
+
+
+
+//! @}
+