FST::Data_Scaler_white< DATATYPE, IDXTYPE > Class Template Reference

Implements data whitening (as result, each feature mean value is 0 with normalized stddev). More...

#include <data_scaler_white.hpp>

Inheritance diagram for FST::Data_Scaler_white< DATATYPE, IDXTYPE >:

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Collaboration diagram for FST::Data_Scaler_white< DATATYPE, IDXTYPE >:

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List of all members.

Public Member Functions
	Data_Scaler_white (const int dims=1)
	Data_Scaler_white (const DATATYPE missing_val_code, const int dims)
	Data_Scaler_white (const Data_Scaler_white &ds)
virtual int	learn_loops () const
virtual bool	startFirstLoop ()
virtual bool	startNextLoop ()
virtual void	learn (const DATATYPE &value)
virtual DATATYPE	scale (const DATATYPE &value)
	return the scaled value
virtual void	scale_inplace (DATATYPE &value)
	scale the value in place
virtual std::ostream &	print (std::ostream &os) const
Protected Attributes
int	learn_loop
IDXTYPE	count1
DATATYPE	mean
IDXTYPE	count2
DATATYPE	stddev
bool	missing_values
const DATATYPE	_missing_val_code

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Detailed Description

template<typename DATATYPE, typename IDXTYPE>
class FST::Data_Scaler_white< DATATYPE, IDXTYPE >

Implements data whitening (as result, each feature mean value is 0 with normalized stddev).

Note:

this implementation supports only scaling of one-dimensional data, thus is applicable only to scale feature values individually and independently for each other feature

Optionally substitutes missing values by the mean of those values that are available (separately per feature). Missing values are assumed to be coded by dedicated numerical value 'missing_val_code'.

better implementation may be needed in case of large sample sizes to prevent mean computation bias

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The documentation for this class was generated from the following file:

• data_scaler_white.hpp