Op_stddev

//! More...

Classes
class	op_stddev
	Class for finding the standard deviation. More...
Functions
template<typename eT >
static void	op_stddev::apply (Mat< typename get_pod_type< eT >::result > &out, const Mat< eT > &X, const u32 norm_type, const u32 dim)
	//! For each row or for each column, find the standard deviation. //! The result is stored in a dense matrix that has either one column or one row. //! The dimension for which the standard deviations are found is set via the stddev() function.

---

Detailed Description

//!

---

Function Documentation

template<typename eT >

void op_stddev::apply	(	Mat< typename get_pod_type< eT >::result > &	out,
		const Mat< eT > &	X,
		const u32	norm_type,
		const u32	dim
	)			[inline, static, inherited]

//! For each row or for each column, find the standard deviation. //! The result is stored in a dense matrix that has either one column or one row. //! The dimension for which the standard deviations are found is set via the stddev() function.

Definition at line 28 of file op_stddev_meat.hpp.

References Mat< eT >::at(), Mat< eT >::colptr(), op_var::direct_var(), podarray< eT >::memptr(), Mat< eT >::n_cols, Mat< eT >::n_elem, Mat< eT >::n_rows, and sqrt().

Referenced by stddev().

  {
  arma_extra_debug_sigprint();
  
  arma_debug_check( (X.n_elem == 0), "stddev(): given matrix has no elements" );
  
  arma_debug_check( (norm_type > 1), "stddev(): incorrect usage. norm_type must be 0 or 1");
  arma_debug_check( (dim > 1),       "stddev(): incorrect usage. dim must be 0 or 1"      );
  
  if(dim == 0)
    {
    arma_extra_debug_print("op_stddev::apply(), dim = 0");
    
    out.set_size(1, X.n_cols);
    
    for(u32 col=0; col<X.n_cols; ++col)
      {
      out[col] = std::sqrt( op_var::direct_var( X.colptr(col), X.n_rows, norm_type ) );
      }
    }
  else
  if(dim == 1)
    {
    arma_extra_debug_print("op_stddev::apply(), dim = 1");
    
    const u32 n_rows = X.n_rows;
    const u32 n_cols = X.n_cols;
    
    out.set_size(n_rows, 1);
    
    podarray<eT> tmp(n_cols);
    
    eT* tmp_mem = tmp.memptr();
    
    for(u32 row=0; row<n_rows; ++row)
      {
      for(u32 col=0; col<n_cols; ++col)
        {
        tmp_mem[col] = X.at(row,col);
        }
      
      out[row] = std::sqrt( op_var::direct_var(tmp_mem, n_cols, norm_type) );
      }
    
    }
  
  }