Class CategoricalNaiveBayes<T>

Namespace

AiDotNet.Classification.NaiveBayes

Assembly

AiDotNet.dll

Categorical Naive Bayes classifier for categorical/discrete features.

public class CategoricalNaiveBayes<T> : NaiveBayesBase<T>, IProbabilisticClassifier<T>, IClassifier<T>, IFullModel<T, Matrix<T>, Vector<T>>, IModel<Matrix<T>, Vector<T>, ModelMetadata<T>>, IModelSerializer, ICheckpointableModel, IParameterizable<T, Matrix<T>, Vector<T>>, IFeatureAware, IFeatureImportance<T>, ICloneable<IFullModel<T, Matrix<T>, Vector<T>>>, IGradientComputable<T, Matrix<T>, Vector<T>>, IJitCompilable<T>

Type Parameters

T

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

Inheritance

object

ClassifierBase<T>

ProbabilisticClassifierBase<T>

NaiveBayesBase<T>

CategoricalNaiveBayes<T>

Implements

IProbabilisticClassifier<T>

IClassifier<T>

IFullModel<T, Matrix<T>, Vector<T>>

IModel<Matrix<T>, Vector<T>, ModelMetadata<T>>

IModelSerializer

ICheckpointableModel

IParameterizable<T, Matrix<T>, Vector<T>>

IFeatureAware

IFeatureImportance<T>

ICloneable<IFullModel<T, Matrix<T>, Vector<T>>>

IGradientComputable<T, Matrix<T>, Vector<T>>

IJitCompilable<T>

Inherited Members

NaiveBayesBase<T>.Options

NaiveBayesBase<T>.LogPriors

NaiveBayesBase<T>.ClassCounts

NaiveBayesBase<T>.Train(Matrix<T>, Vector<T>)

NaiveBayesBase<T>.ComputeLogPriors(int)

NaiveBayesBase<T>.PredictLogProbabilities(Matrix<T>)

NaiveBayesBase<T>.PredictProbabilities(Matrix<T>)

NaiveBayesBase<T>.LogSumExp(Vector<T>)

NaiveBayesBase<T>.GetClassIndex(T)

NaiveBayesBase<T>.GetParameters()

NaiveBayesBase<T>.WithParameters(Vector<T>)

NaiveBayesBase<T>.SetParameters(Vector<T>)

NaiveBayesBase<T>.ComputeGradients(Matrix<T>, Vector<T>, ILossFunction<T>)

NaiveBayesBase<T>.ApplyGradients(Vector<T>, T)

ProbabilisticClassifierBase<T>.Predict(Matrix<T>)

ProbabilisticClassifierBase<T>.ApplySoftmax(Matrix<T>)

ProbabilisticClassifierBase<T>.ApplySigmoid(Vector<T>)

ClassifierBase<T>.NumOps

ClassifierBase<T>.Engine

ClassifierBase<T>.Regularization

ClassifierBase<T>.NumClasses

ClassifierBase<T>.TaskType

ClassifierBase<T>.ClassLabels

ClassifierBase<T>.NumFeatures

ClassifierBase<T>.FeatureNames

ClassifierBase<T>.ExpectedParameterCount

ClassifierBase<T>.InferTaskType(Vector<T>)

ClassifierBase<T>.ExtractClassLabels(Vector<T>)

ClassifierBase<T>.ComputeClassWeights(Vector<T>)

ClassifierBase<T>.GetClassIndexFromLabel(T)

ClassifierBase<T>.Serialize()

ClassifierBase<T>.Deserialize(byte[])

ClassifierBase<T>.GetActiveFeatureIndices()

ClassifierBase<T>.IsFeatureUsed(int)

ClassifierBase<T>.SetActiveFeatureIndices(IEnumerable<int>)

ClassifierBase<T>.GetFeatureImportance()

ClassifierBase<T>.DeepCopy()

ClassifierBase<T>.ParameterCount

ClassifierBase<T>.DefaultLossFunction

ClassifierBase<T>.SaveModel(string)

ClassifierBase<T>.LoadModel(string)

ClassifierBase<T>.SaveState(Stream)

ClassifierBase<T>.LoadState(Stream)

ClassifierBase<T>.SupportsJitCompilation

ClassifierBase<T>.ExportComputationGraph(List<ComputationNode<T>>)

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Extension Methods

DistributedExtensions.AsDistributedForHighBandwidth<T, TInput, TOutput>(IFullModel<T, TInput, TOutput>, ICommunicationBackend<T>)

DistributedExtensions.AsDistributedForLowBandwidth<T, TInput, TOutput>(IFullModel<T, TInput, TOutput>, ICommunicationBackend<T>)

DistributedExtensions.AsDistributed<T, TInput, TOutput>(IFullModel<T, TInput, TOutput>, ICommunicationBackend<T>)

DistributedExtensions.AsDistributed<T, TInput, TOutput>(IFullModel<T, TInput, TOutput>, IShardingConfiguration<T>)

Remarks

Categorical Naive Bayes handles features that take on discrete categorical values. Unlike Multinomial NB (which works with counts) or Bernoulli NB (which works with binary features), Categorical NB handles multi-valued categorical features directly.

For Beginners: Use this when your features are categories, like: - Color: Red, Blue, Green - Size: Small, Medium, Large - Weather: Sunny, Rainy, Cloudy

The classifier computes: P(category_value | class) for each feature.

For example, predicting if someone will buy an umbrella:

• P(Rainy | Will Buy) might be high
• P(Sunny | Will Buy) might be low

Features should be encoded as integers (0, 1, 2, ...) representing categories.

Use Categorical NB when:

• Features are truly categorical (not ordinal)
• Features can have more than 2 values (otherwise use Bernoulli)
• Features are not counts (otherwise use Multinomial)

Constructors

CategoricalNaiveBayes(NaiveBayesOptions<T>?, IRegularization<T, Matrix<T>, Vector<T>>?)

Initializes a new instance of the CategoricalNaiveBayes class.

public CategoricalNaiveBayes(NaiveBayesOptions<T>? options = null, IRegularization<T, Matrix<T>, Vector<T>>? regularization = null)

Parameters

options NaiveBayesOptions<T>

Configuration options for the classifier.

regularization IRegularization<T, Matrix<T>, Vector<T>>

Optional regularization strategy.

Methods

Clone()

Creates a clone of the classifier model.

public override IFullModel<T, Matrix<T>, Vector<T>> Clone()

Returns

IFullModel<T, Matrix<T>, Vector<T>>

A new instance of the model with the same parameters and options.

ComputeClassParameters(Matrix<T>, Vector<T>)

Computes category probabilities for all classes.

protected override void ComputeClassParameters(Matrix<T> x, Vector<T> y)

Parameters

x Matrix<T>

y Vector<T>

ComputeLogLikelihood(Vector<T>, int)

Computes the log-likelihood for a sample given a class.

protected override T ComputeLogLikelihood(Vector<T> sample, int classIndex)

Parameters

sample Vector<T>

classIndex int

Returns

T

CreateNewInstance()

Creates a new instance of the same type as this classifier.

protected override IFullModel<T, Matrix<T>, Vector<T>> CreateNewInstance()

Returns

IFullModel<T, Matrix<T>, Vector<T>>

A new instance of the same classifier type.

GetModelMetadata()

Gets metadata about the model.

public override ModelMetadata<T> GetModelMetadata()

Returns

ModelMetadata<T>

A ModelMetadata object containing information about the model.

Remarks

This method returns metadata about the model, including its type, feature count, complexity, description, and additional information specific to classification.

For Beginners: Model metadata provides information about the model itself, rather than the predictions it makes. This includes details about the model's structure (like how many features it uses) and characteristics (like how many classes it can predict). This information can be useful for understanding and comparing different models.

GetModelType()

Returns the model type identifier for this classifier.

protected override ModelType GetModelType()

Returns

ModelType