Standardizes the data so the each feature has zero mean and unit variance. More...

Inheritance diagram for StandardizationScaler:

Public Member Functions
	StandardizationScaler (const MatrixXd &features, double norm_factor=1.0)
	Main constructor for StandardizationScaler. More...

Public Member Functions inherited from DataScaler
void	scale_samples (const MatrixXd &unscaled_samples, MatrixXd &scaled_samples)
	Apply scaling to a set of unscaled samples. More...

MatrixXd	scale_samples (const MatrixXd &unscaled_samples)
	Apply scaling to a set of unscaled samples. More...

const VectorXd &	get_scaler_features_offsets () const
	Get the vector of offsets. More...

const VectorXd &	get_scaler_features_scale_factors () const
	Get the vector of scaling factors. More...

bool	check_for_zero_scaler_factor (int index)
	Checks an individual scaler feature scale factor for being close to zero; If it is near zero, we can potentially run into a divide-by-zero error if not handled appropriately. More...

Additional Inherited Members
Public Types inherited from DataScaler
enum	SCALER_TYPE { NONE, STANDARDIZATION, MEAN_NORMALIZATION, MINMAX_NORMALIZATION }
	Enumeration for supported types of DataScalers.

Static Public Member Functions inherited from DataScaler
static SCALER_TYPE	scaler_type (const std::string &scaler_name)
	Convert scaler name to enum type. More...

Protected Attributes inherited from DataScaler
bool	hasScaling
	Bool for whether or not the the scaling coefficients have been computed.

RowVectorXd	scaledSample
	Vector for a single scaled sample - (num_features); avoids resize memory allocs.

VectorXd	scalerFeaturesOffsets
	Vector of offsets - (num_features)

VectorXd	scalerFeaturesScaleFactors
	Vector of scaling factors - (num_features)

Detailed Description

Standardizes the data so the each feature has zero mean and unit variance.

scaler_offsets = mean

scale_factors = standard_deviation/norm_factor

Constructor & Destructor Documentation

Main constructor for StandardizationScaler.

Parameters

[in]	features	Unscaled data matrix - (num_samples by num_features)
[in]	norm_factor	Optional scaling factor applied to each feature Has a default value of 1.0

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