Class StandardScaler<T>

Namespace

AiDotNet.Preprocessing.Scalers

Assembly

AiDotNet.dll

Standardizes features by removing the mean and scaling to unit variance.

public class StandardScaler<T> : TransformerBase<T, Matrix<T>, Matrix<T>>, IDataTransformer<T, Matrix<T>, Matrix<T>>

Type Parameters

T

The numeric type for calculations (e.g., float, double).

Inheritance

object

TransformerBase<T, Matrix<T>, Matrix<T>>

StandardScaler<T>

Implements

IDataTransformer<T, Matrix<T>, Matrix<T>>

Inherited Members

TransformerBase<T, Matrix<T>, Matrix<T>>.NumOps

TransformerBase<T, Matrix<T>, Matrix<T>>.Engine

TransformerBase<T, Matrix<T>, Matrix<T>>.IsFitted

TransformerBase<T, Matrix<T>, Matrix<T>>.ColumnIndices

TransformerBase<T, Matrix<T>, Matrix<T>>.SupportsInverseTransform

TransformerBase<T, Matrix<T>, Matrix<T>>.Fit(Matrix<T>)

TransformerBase<T, Matrix<T>, Matrix<T>>.Transform(Matrix<T>)

TransformerBase<T, Matrix<T>, Matrix<T>>.FitTransform(Matrix<T>)

TransformerBase<T, Matrix<T>, Matrix<T>>.InverseTransform(Matrix<T>)

TransformerBase<T, Matrix<T>, Matrix<T>>.GetFeatureNamesOut(string[])

TransformerBase<T, Matrix<T>, Matrix<T>>.FitCore(Matrix<T>)

TransformerBase<T, Matrix<T>, Matrix<T>>.TransformCore(Matrix<T>)

TransformerBase<T, Matrix<T>, Matrix<T>>.InverseTransformCore(Matrix<T>)

TransformerBase<T, Matrix<T>, Matrix<T>>.ValidateInputData(Matrix<T>)

TransformerBase<T, Matrix<T>, Matrix<T>>.EnsureFitted()

TransformerBase<T, Matrix<T>, Matrix<T>>.GetColumnsToProcess(int)

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Remarks

Standard scaling (Z-score normalization) transforms data to have a mean of 0 and a standard deviation of 1. This is important for many machine learning algorithms as it puts different features on comparable scales.

For Beginners: This scaler converts your data to a standard scale: - The center of your data (mean) becomes 0 - The spread of your data (standard deviation) becomes 1

This is like converting different currencies to a common one - it makes different features comparable and helps many ML algorithms work better.

Constructors

StandardScaler(bool, bool, int[]?)

Creates a new instance of StandardScaler<T>.

public StandardScaler(bool withMean = true, bool withStd = true, int[]? columnIndices = null)

Parameters

withMean bool

If true, center the data before scaling (subtract mean). Default is true.

withStd bool

If true, scale the data to unit variance. Default is true.

columnIndices int[]

The column indices to scale, or null for all columns.

Properties

Mean

Gets the mean of each feature computed during fitting.

public Vector<T>? Mean { get; }

Property Value

Vector<T>

StandardDeviation

Gets the standard deviation of each feature computed during fitting.

public Vector<T>? StandardDeviation { get; }

Property Value

Vector<T>

SupportsInverseTransform

Gets whether this transformer supports inverse transformation.

public override bool SupportsInverseTransform { get; }

Property Value

bool

WithMean

Gets whether this scaler centers the data (subtracts mean).

public bool WithMean { get; }

Property Value

bool

WithStd

Gets whether this scaler scales the data (divides by std).

public bool WithStd { get; }

Property Value

bool

Methods

FitCore(Matrix<T>)

Computes the mean and standard deviation of each feature from the training data.

protected override void FitCore(Matrix<T> data)

Parameters

data Matrix<T>

The training data matrix where each column is a feature.

GetFeatureNamesOut(string[]?)

Gets the output feature names after transformation.

public override string[] GetFeatureNamesOut(string[]? inputFeatureNames = null)

Parameters

inputFeatureNames string[]

The input feature names.

Returns

string[]

The same feature names (StandardScaler doesn't change number of features).

InverseTransformCore(Matrix<T>)

Reverses the standardization transformation.

protected override Matrix<T> InverseTransformCore(Matrix<T> data)

Parameters

data Matrix<T>

The standardized data.

Returns

Matrix<T>

The original-scale data.

TransformCore(Matrix<T>)

Transforms the data by applying the computed mean and standard deviation.

protected override Matrix<T> TransformCore(Matrix<T> data)

Parameters

data Matrix<T>

The data to transform.

Returns

Matrix<T>

The standardized data.