Class FeatureNoise<T>

Namespace

AiDotNet.Augmentation.Tabular

Assembly

AiDotNet.dll

Adds Gaussian noise to numerical features in tabular data.

public class FeatureNoise<T> : TabularAugmenterBase<T>, IAugmentation<T, Matrix<T>>

Type Parameters

T

The numeric type for calculations.

Inheritance

object

AugmentationBase<T, Matrix<T>>

TabularAugmenterBase<T>

FeatureNoise<T>

Implements

IAugmentation<T, Matrix<T>>

Inherited Members

TabularAugmenterBase<T>.GetFeatureCount(Matrix<T>)

TabularAugmenterBase<T>.GetSampleCount(Matrix<T>)

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: This augmentation adds small random variations to your data, similar to measurement noise in real-world data. This helps models become robust to small fluctuations and prevents overfitting to exact values.

When to use:

• Numerical features that have natural variation
• Small datasets where regularization is needed
• When you want to simulate measurement uncertainty

When NOT to use:

• Categorical features (use other augmentations)
• Features with strict constraints (e.g., binary flags)

Constructors

FeatureNoise(double, double, int[]?)

Creates a new feature noise augmentation.

public FeatureNoise(double noiseStdDev = 0.01, double probability = 0.5, int[]? featureIndices = null)

Parameters

noiseStdDev double

Standard deviation of Gaussian noise (default: 0.01).

probability double

Probability of applying this augmentation (default: 0.5).

featureIndices int[]

Optional array of feature indices to apply noise to.

Properties

FeatureIndices

Gets or sets the indices of features to apply noise to.

public int[]? FeatureIndices { get; set; }

Property Value

int[]

Remarks

If null, applies to all features.

NoiseStdDev

Gets the standard deviation of the noise.

public double NoiseStdDev { get; }

Property Value

double

Remarks

Default: 0.01 (1% of unit variance)

Higher values add more noise. Typical range: 0.001 to 0.1

Methods

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

Implement this method to perform the actual augmentation.

protected override Matrix<T> ApplyAugmentation(Matrix<T> data, AugmentationContext<T> context)

Parameters

data Matrix<T>

The input data.

context AugmentationContext<T>

The augmentation context.

Returns

Matrix<T>

The augmented data.

GetParameters()

Gets the parameters of this augmentation.

public override IDictionary<string, object> GetParameters()

Returns

IDictionary<string, object>

A dictionary of parameter names to values.