Normalizes the data using max and min feature values. More...

Inheritance diagram for NormalizationScaler:

Public Member Functions
	NormalizationScaler (const MatrixXd &features, bool mean_normalization, double norm_factor=1.0)
	Main constructor for NormalizationScaler. 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

Normalizes the data using max and min feature values.

if (mean_normalization): scaler_offsets = mean else: scaler_offsets = min

scale_factors = (max - min)/norm_factor

Setting mean_normalization = false scales each feature to [0,1]

Constructor & Destructor Documentation

Main constructor for NormalizationScaler.

Parameters

[in]	features	Unscaled data matrix - (num_samples by num_features)
[in]	mean_normalization	Flag for whether to use mean or min value as the offset
[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: