cmp::scaler::EllipticScaler Class Reference

Scaler that standardizes features independently using diagonal variance scaling. More...

#include <scaler.h>

Inheritance diagram for cmp::scaler::EllipticScaler:

Collaboration diagram for cmp::scaler::EllipticScaler:

Public Member Functions
	EllipticScaler ()=default
	Default constructor.
	EllipticScaler (const Eigen::Ref< const Eigen::VectorXd > &mean, const Eigen::Ref< const Eigen::VectorXd > &std)
	Constructs an EllipticScaler with specified mean and standard deviations.
	EllipticScaler (const EllipticScaler &other)=default
	EllipticScaler (EllipticScaler &&other)=default
	~EllipticScaler ()=default
EllipticScaler &	operator= (const EllipticScaler &other)=default
EllipticScaler &	operator= (EllipticScaler &&other)=default
Eigen::VectorXd	transform (const Eigen::Ref< const Eigen::VectorXd > &data) const override
	Transforms the input data from physical space to the scaled latent space.
Eigen::VectorXd	inverseTransform (const Eigen::Ref< const Eigen::VectorXd > &data) const override
	Reconstructs the data from the scaled latent space back to physical space.
Eigen::VectorXd	getIntercept () const override
	Retrieves the intercept vector \( \mu \) used in the transformation.
Eigen::MatrixXd	getScale () const override
	Retrieves the scaling matrix \( S \) used in the transformation.
void	fit (const Eigen::Ref< const Eigen::MatrixXd > &data) override
	Learns the scaling parameters (e.g., mean, variance) from the training data.
Eigen::MatrixXd	fit_transform (const Eigen::Ref< const Eigen::MatrixXd > &data) override
	Fits the scaling parameters to the data and subsequently transforms the data.
void	setMean (const Eigen::Ref< const Eigen::VectorXd > &mean)
	Explicitly overrides the mean parameters \( \boldsymbol{\mu} \).
void	setStd (const Eigen::Ref< const Eigen::VectorXd > &std)
	Explicitly overrides the standard deviation parameters \( \boldsymbol{\sigma} \).

Private Attributes
Eigen::VectorXd	mean_
	The empirical mean vector \( \boldsymbol{\mu} \) of the training dataset.
Eigen::VectorXd	std_
	The standard deviation vector \( \boldsymbol{\sigma} \) for each feature.

Detailed Description

Scaler that standardizes features independently using diagonal variance scaling.

Mathematical Formulation

Standardizes each feature coordinate \( j \) independently:

\[ y_j = \frac{x_j - \mu_j}{\sigma_j} \]

where \( \mu_j \) is the mean and \( \sigma_j \) is the standard deviation of feature \( j \). The inverse transformation is:

\[ x_j = y_j \sigma_j + \mu_j \]

Constructor & Destructor Documentation

EllipticScaler() [1/4]

cmp::scaler::EllipticScaler::EllipticScaler ( )

default

Default constructor.

EllipticScaler() [2/4]

cmp::scaler::EllipticScaler::EllipticScaler ( const Eigen::Ref< const Eigen::VectorXd > &  mean, const Eigen::Ref< const Eigen::VectorXd > &  std  )

inline

Constructs an EllipticScaler with specified mean and standard deviations.

Parameters

mean	Pre-computed mean vector \( \boldsymbol{\mu} \).
std	Pre-computed standard deviation vector \( \boldsymbol{\sigma} \).

EllipticScaler() [3/4]

cmp::scaler::EllipticScaler::EllipticScaler ( const EllipticScaler &  other )

default

EllipticScaler() [4/4]

cmp::scaler::EllipticScaler::EllipticScaler ( EllipticScaler &&  other )

default

~EllipticScaler()

cmp::scaler::EllipticScaler::~EllipticScaler ( )

default

Member Function Documentation

fit()

void cmp::scaler::EllipticScaler::fit ( const Eigen::Ref< const Eigen::MatrixXd > &  data )

overridevirtual

Learns the scaling parameters (e.g., mean, variance) from the training data.

Parameters

data	A matrix \( X \in \mathbb{R}^{n \times d} \) of training samples.

Implements cmp::scaler::Scaler.

fit_transform()

Eigen::MatrixXd cmp::scaler::EllipticScaler::fit_transform ( const Eigen::Ref< const Eigen::MatrixXd > &  data )

overridevirtual

Fits the scaling parameters to the data and subsequently transforms the data.

Parameters

data	A matrix \( X \in \mathbb{R}^{n \times d} \) of training samples.

Returns

The transformed dataset matrix \( Y \).

Implements cmp::scaler::Scaler.

getIntercept()

Eigen::VectorXd cmp::scaler::EllipticScaler::getIntercept ( ) const

inlineoverridevirtual

Retrieves the intercept vector \( \mu \) used in the transformation.

Returns

The intercept vector.

Implements cmp::scaler::Scaler.

getScale()

Eigen::MatrixXd cmp::scaler::EllipticScaler::getScale ( ) const

inlineoverridevirtual

Retrieves the scaling matrix \( S \) used in the transformation.

Returns

The scaling matrix.

Implements cmp::scaler::Scaler.

inverseTransform()

Eigen::VectorXd cmp::scaler::EllipticScaler::inverseTransform ( const Eigen::Ref< const Eigen::VectorXd > &  data ) const

overridevirtual

Reconstructs the data from the scaled latent space back to physical space.

Parameters

data	A column vector \( y \) in the scaled space.

Returns

The unscaled physical data vector \( x \).

Implements cmp::scaler::Scaler.

operator=() [1/2]

EllipticScaler & cmp::scaler::EllipticScaler::operator= ( const EllipticScaler &  other )

default

operator=() [2/2]

EllipticScaler & cmp::scaler::EllipticScaler::operator= ( EllipticScaler &&  other )

default

setMean()

void cmp::scaler::EllipticScaler::setMean ( const Eigen::Ref< const Eigen::VectorXd > &  mean )

inline

Explicitly overrides the mean parameters \( \boldsymbol{\mu} \).

Parameters

mean	The new mean vector.

setStd()

void cmp::scaler::EllipticScaler::setStd ( const Eigen::Ref< const Eigen::VectorXd > &  std )

inline

Explicitly overrides the standard deviation parameters \( \boldsymbol{\sigma} \).

Parameters

std	The new standard deviation vector.

transform()

Eigen::VectorXd cmp::scaler::EllipticScaler::transform ( const Eigen::Ref< const Eigen::VectorXd > &  data ) const

overridevirtual

Transforms the input data from physical space to the scaled latent space.

Parameters

data	A column vector \( x \) representing a single data point in physical space.

Returns

The scaled data vector \( y \).

Implements cmp::scaler::Scaler.

Member Data Documentation

mean_

Eigen::VectorXd cmp::scaler::EllipticScaler::mean_

private

The empirical mean vector \( \boldsymbol{\mu} \) of the training dataset.

std_

Eigen::VectorXd cmp::scaler::EllipticScaler::std_

private

The standard deviation vector \( \boldsymbol{\sigma} \) for each feature.

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

• include/scaler.h
• src/scaler.cpp