Class LogMeanVarianceScaler<T>

Namespace

AiDotNet.Preprocessing.Scalers

Assembly

AiDotNet.dll

Applies logarithmic transformation followed by mean-variance standardization.

public class LogMeanVarianceScaler<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>>

LogMeanVarianceScaler<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

Log-mean-variance scaling combines logarithmic transformation with z-score standardization. It first applies log transformation (with shift for negative values), then standardizes using mean and standard deviation. This is ideal for data spanning multiple orders of magnitude.

For Beginners: This scaler is perfect for highly skewed or exponentially distributed data: - First, it takes the log of values (compressing large differences) - Then, it standardizes to zero mean and unit variance

Example: [1000, 10000, 100000, 1000000] → after log: [6.9, 9.2, 11.5, 13.8] → standardized This makes patterns in exponential data much easier to detect.

Constructors

LogMeanVarianceScaler(int[]?)

Creates a new instance of LogMeanVarianceScaler<T>.

public LogMeanVarianceScaler(int[]? columnIndices = null)

Parameters

columnIndices int[]

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

Properties

LogMean

Gets the mean of log-transformed values for each feature.

public Vector<T>? LogMean { get; }

Property Value

Vector<T>

LogStdDev

Gets the standard deviation of log-transformed values for each feature.

public Vector<T>? LogStdDev { get; }

Property Value

Vector<T>

Shift

Gets the shift applied to each feature to ensure positive values.

public Vector<T>? Shift { get; }

Property Value

Vector<T>

SupportsInverseTransform

Gets whether this transformer supports inverse transformation.

public override bool SupportsInverseTransform { get; }

Property Value

bool

Methods

FitCore(Matrix<T>)

Computes the shift, log mean, and log standard deviation for each feature.

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 (LogMeanVarianceScaler doesn't change number of features).

InverseTransformCore(Matrix<T>)

Reverses the log-mean-variance scaling transformation.

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

Parameters

data Matrix<T>

The scaled data.

Returns

Matrix<T>

The original-scale data.

TransformCore(Matrix<T>)

Transforms the data by applying log transformation and standardization.

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

Parameters

data Matrix<T>

The data to transform.

Returns

Matrix<T>

The log-standardized data.