cmp::scaler::MinMaxScaler Class Reference

Linearly scales features to a target bounding box range. More...

#include <scaler.h>

Inheritance diagram for cmp::scaler::MinMaxScaler:

Collaboration diagram for cmp::scaler::MinMaxScaler:

Public Member Functions
	MinMaxScaler ()=default
	Default constructor.
	MinMaxScaler (const Eigen::Ref< const Eigen::VectorXd > &min, const Eigen::Ref< const Eigen::VectorXd > &max)
	Constructs a MinMaxScaler bounded to a specific target range.
	MinMaxScaler (const MinMaxScaler &other)=default
	MinMaxScaler (MinMaxScaler &&other)=default
	~MinMaxScaler ()=default
MinMaxScaler &	operator= (const MinMaxScaler &other)=default
MinMaxScaler &	operator= (MinMaxScaler &&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	getDataMin () const
	Retrieves the empirical minimum vector \( \mathbf{x}_{\text{min}} \) found during fitting.
Eigen::VectorXd	getDataMax () const
	Retrieves the empirical maximum vector \( \mathbf{x}_{\text{max}} \) found during fitting.
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.
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.

Private Attributes
Eigen::VectorXd	min_
	Target minimum value bounding vector \( \mathbf{a} \).
Eigen::VectorXd	max_
	Target maximum value bounding vector \( \mathbf{b} \).
Eigen::VectorXd	data_min_
	Empirical minimum value vector \( \mathbf{x}_{\text{min}} \) observed in the training dataset.
Eigen::VectorXd	data_max_
	Empirical maximum value vector \( \mathbf{x}_{\text{max}} \) observed in the training dataset.

Detailed Description

Linearly scales features to a target bounding box range.

Mathematical Formulation

Scales each feature vector component to live within a target range \([a_j, b_j]\) (typically \([0, 1]\)):

\[ y_j = a_j + \frac{x_j - x_{\text{min}, j}}{x_{\text{max}, j} - x_{\text{min}, j}} (b_j - a_j) \]

where \(x_{\text{min}, j}\) and \(x_{\text{max}, j}\) are the training data's empirical minimum and maximum values for feature \(j\).

• ### Implementation Algorithm

1. fit() computes column-wise minima and maxima vectors of the training matrix.
2. transform() and inverseTransform() apply element-wise division and scaling multiplication.

Constructor & Destructor Documentation

MinMaxScaler() [1/4]

cmp::scaler::MinMaxScaler::MinMaxScaler ( )

default

Default constructor.

MinMaxScaler() [2/4]

cmp::scaler::MinMaxScaler::MinMaxScaler ( const Eigen::Ref< const Eigen::VectorXd > &  min, const Eigen::Ref< const Eigen::VectorXd > &  max  )

inline

Constructs a MinMaxScaler bounded to a specific target range.

Parameters

min	The target minimum bounding vector \( \mathbf{a} \).
max	The target maximum bounding vector \( \mathbf{b} \).

MinMaxScaler() [3/4]

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

default

MinMaxScaler() [4/4]

cmp::scaler::MinMaxScaler::MinMaxScaler ( MinMaxScaler &&  other )

default

~MinMaxScaler()

cmp::scaler::MinMaxScaler::~MinMaxScaler ( )

default

Member Function Documentation

fit()

void cmp::scaler::MinMaxScaler::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::MinMaxScaler::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.

getDataMax()

Eigen::VectorXd cmp::scaler::MinMaxScaler::getDataMax ( ) const

inline

Retrieves the empirical maximum vector \( \mathbf{x}_{\text{max}} \) found during fitting.

Returns

The maximum data vector.

getDataMin()

Eigen::VectorXd cmp::scaler::MinMaxScaler::getDataMin ( ) const

inline

Retrieves the empirical minimum vector \( \mathbf{x}_{\text{min}} \) found during fitting.

Returns

The minimum data vector.

getIntercept()

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

overridevirtual

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

Returns

The intercept vector.

Implements cmp::scaler::Scaler.

getScale()

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

overridevirtual

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

Returns

The scaling matrix.

Implements cmp::scaler::Scaler.

inverseTransform()

Eigen::VectorXd cmp::scaler::MinMaxScaler::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]

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

default

operator=() [2/2]

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

default

transform()

Eigen::VectorXd cmp::scaler::MinMaxScaler::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

data_max_

Eigen::VectorXd cmp::scaler::MinMaxScaler::data_max_

private

Empirical maximum value vector \( \mathbf{x}_{\text{max}} \) observed in the training dataset.

data_min_

Eigen::VectorXd cmp::scaler::MinMaxScaler::data_min_

private

Empirical minimum value vector \( \mathbf{x}_{\text{min}} \) observed in the training dataset.

max_

Eigen::VectorXd cmp::scaler::MinMaxScaler::max_

private

Target maximum value bounding vector \( \mathbf{b} \).

min_

Eigen::VectorXd cmp::scaler::MinMaxScaler::min_

private

Target minimum value bounding vector \( \mathbf{a} \).

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

• include/scaler.h
• src/scaler.cpp