Class TabularAugmenterBase<T>

Namespace

AiDotNet.Augmentation.Tabular

Assembly

AiDotNet.dll

Base class for tabular data augmentations.

public abstract class TabularAugmenterBase<T> : AugmentationBase<T, Matrix<T>>, IAugmentation<T, Matrix<T>>

Type Parameters

T

The numeric type for calculations.

Inheritance

object

AugmentationBase<T, Matrix<T>>

TabularAugmenterBase<T>

Implements

IAugmentation<T, Matrix<T>>

Derived

FeatureDropout<T>

FeatureNoise<T>

RowShuffle<T>

SmoteAugmenter<T>

Inherited Members

AugmentationBase<T, Matrix<T>>.NumOps

AugmentationBase<T, Matrix<T>>.OnAugmentationApplied

AugmentationBase<T, Matrix<T>>.Name

AugmentationBase<T, Matrix<T>>.Probability

AugmentationBase<T, Matrix<T>>.IsTrainingOnly

AugmentationBase<T, Matrix<T>>.IsEnabled

AugmentationBase<T, Matrix<T>>.Apply(Matrix<T>, AugmentationContext<T>)

AugmentationBase<T, Matrix<T>>.ApplyAugmentation(Matrix<T>, AugmentationContext<T>)

AugmentationBase<T, Matrix<T>>.GetParameters()

AugmentationBase<T, Matrix<T>>.RaiseAugmentationApplied(AugmentationContext<T>, bool)

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Remarks

For Beginners: Tabular augmentation transforms structured data (like spreadsheets) to improve model generalization. Unlike image augmentation which uses geometric transforms, tabular augmentation focuses on:

• Adding noise to numerical features
• Mixing samples together (MixUp)
• Dropping features for regularization
• Synthetic sample generation (SMOTE)

Constructors

TabularAugmenterBase(double)

Initializes a new tabular augmentation.

protected TabularAugmenterBase(double probability = 1)

Parameters

probability double

The probability of applying this augmentation (0.0 to 1.0).

Methods

GetFeatureCount(Matrix<T>)

Gets the number of features in the input data.

protected int GetFeatureCount(Matrix<T> data)

Parameters

data Matrix<T>

The input matrix (rows=samples, columns=features).

Returns

int

The number of features.

GetSampleCount(Matrix<T>)

Gets the number of samples in the input data.

protected int GetSampleCount(Matrix<T> data)

Parameters

data Matrix<T>

The input matrix (rows=samples, columns=features).

Returns

int

The number of samples.