GaussianClusterCollection Class

Gaussian Mixture Model Cluster Collection.

Inheritance Hierarchy

SystemObject
  Accord.MachineLearningTransformBaseDouble, Int32
    Accord.MachineLearningClassifierBaseDouble, Int32
      Accord.MachineLearningMulticlassClassifierBaseDouble
        Accord.MachineLearningMulticlassScoreClassifierBaseDouble
          Accord.MachineLearningMulticlassLikelihoodClassifierBaseDouble
            Accord.MachineLearningGaussianClusterCollection

Namespace: Accord.MachineLearning
Assembly: Accord.MachineLearning (in Accord.MachineLearning.dll) Version: 3.8.0

Syntax

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[SerializableAttribute]
public class GaussianClusterCollection : MulticlassLikelihoodClassifierBase<double[]>, 
	ICentroidClusterCollection<double[], IMixtureComponent<MultivariateNormalDistribution>, GaussianClusterCollectionGaussianCluster>, 
	IClusterCollectionEx<double[], GaussianClusterCollectionGaussianCluster>, IEnumerable, IClusterCollection<double[]>, 
	IMulticlassClassifier<double[], int>, IClassifier<double[], int>, 
	IClassifier, ITransform<double[], int>, ICovariantTransform<double[], int>, 
	ITransform

<SerializableAttribute>
Public Class GaussianClusterCollection
	Inherits MulticlassLikelihoodClassifierBase(Of Double())
	Implements ICentroidClusterCollection(Of Double(), IMixtureComponent(Of MultivariateNormalDistribution), GaussianClusterCollectionGaussianCluster), 
	IClusterCollectionEx(Of Double(), GaussianClusterCollectionGaussianCluster), IEnumerable, IClusterCollection(Of Double()), 
	IMulticlassClassifier(Of Double(), Integer), IClassifier(Of Double(), Integer), 
	IClassifier, ITransform(Of Double(), Integer), ICovariantTransform(Of Double(), Integer), 
	ITransform

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The GaussianClusterCollection type exposes the following members.

Constructors

	Name	Description
	GaussianClusterCollection	Initializes a new instance of the GaussianClusterCollection class.

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Properties

	Name	Description
	Centroids	Gets or sets the clusters' centroids.
	Clusters	Gets the collection of clusters currently modeled by the clustering algorithm.
	Count	Gets the number of clusters in the collection.
	Covariance	Gets the covariance matrices for each of the clusters.
	Distance	Gets or sets the distance function used to measure the distance between a point and the cluster centroid in this clustering definition.
	Item	Gets the GaussianClusterCollectionGaussianCluster at the specified index.
	Means	Gets the mean vectors for the clusters.
	Model	Gets the mixture model represented by this clustering.
	NumberOfClasses	Gets the number of classes expected and recognized by the classifier. (Inherited from ClassifierBaseTInput, TClasses.)
	NumberOfInputs	Gets the number of inputs accepted by the model. (Inherited from TransformBaseTInput, TOutput.)
	NumberOfOutputs	Gets the number of outputs generated by the model. (Inherited from TransformBaseTInput, TOutput.)
	Proportions	Gets the proportion of samples in each cluster.
	Variance	Gets the variance for each of the clusters.

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Methods

	Name	Description
	Decide(TInput)	Computes class-label decisions for a given set of input vectors. (Inherited from ClassifierBaseTInput, TClasses.)
	Decide(TInput)	Computes a class-label decision for a given input. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Decide(TInput, TClasses)	Computes a class-label decision for a given input. (Inherited from ClassifierBaseTInput, TClasses.)
	Decide(TInput, Boolean)	Computes class-label decisions for the given input. (Inherited from MulticlassClassifierBaseTInput.)
	Decide(TInput, Double)	Computes class-label decisions for the given input. (Inherited from MulticlassClassifierBaseTInput.)
	Decide(TInput, Int32)	Computes class-label decisions for the given input. (Inherited from MulticlassClassifierBaseTInput.)
	Decide(TInput, Double)	Computes a class-label decision for a given input. (Inherited from MulticlassClassifierBaseTInput.)
	Deviance	Gets the deviance of the points in relation to the model.
	Distortion	Calculates the average square distance from the data points to the nearest clusters' centroids.
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)
	GetEnumerator	Returns an enumerator that iterates through the collection.
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	Initialize(KMeans)	Initializes the model with initial values obtained through a run of the K-Means clustering algorithm.
	Initialize(MixtureNormalDistribution)	Initializes the model with initial values.
	Initialize(MultivariateMixtureMultivariateNormalDistribution)	Initializes the model with initial values.
	Initialize(MultivariateNormalDistribution)	Initializes the model with initial values.
	Initialize(NormalDistribution)	Initializes the model with initial values.
	Initialize(Double, MultivariateNormalDistribution)	Initializes the model with initial values.
	Initialize(Double, NormalDistribution)	Initializes the model with initial values.
	LogLikelihood(TInput)	Computes the log-likelihood that the given input vector belongs to its most plausible class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihood(TInput)	Computes the log-likelihood that the given input vectors belongs to each of the possible classes. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihood(Double, Int32)	Computes the log-likelihood that the given input vector belongs to the specified classIndex. (Overrides MulticlassLikelihoodClassifierBaseTInputLogLikelihood(TInput, Int32).)
	LogLikelihood(TInput, Int32)	Predicts a class label vector for the given input vector, returning the log-likelihood that the input vector belongs to its predicted class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihood(TInput, Double)	Computes the log-likelihood that the given input vectors belongs to each of the possible classes. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihood(TInput, Int32)	Computes the log-likelihood that the given input vector belongs to the specified classIndex. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihood(TInput, Int32)	Computes the log-likelihood that the given input vector belongs to the specified classIndex. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihood(TInput, Int32)	Predicts a class label for each input vector, returning the log-likelihood that each vector belongs to its predicted class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihood(TInput, Int32, Double)	Computes the log-likelihood that the given input vector belongs to the specified classIndex. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihood(TInput, Int32, Double)	Computes the log-likelihood that the given input vector belongs to the specified classIndex. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihood(TInput, Int32, Double)	Predicts a class label for each input vector, returning the log-likelihood that each vector belongs to its predicted class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihoods(TInput)	Computes the log-likelihood that the given input vector belongs to each of the possible classes. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihoods(TInput)	Computes the log-likelihood that the given input vectors belongs to each of the possible classes. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihoods(TInput, Double)	Computes the log-likelihood that the given input vector belongs to each of the possible classes. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihoods(TInput, Int32)	Predicts a class label vector for the given input vector, returning the log-likelihoods of the input vector belonging to each possible class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihoods(TInput, Double)	Computes the log-likelihood that the given input vector belongs to each of the possible classes. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihoods(TInput, Int32)	Computes the log-likelihood that the given input vector belongs to the specified classIndex. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihoods(TInput, Int32)	Predicts a class label vector for each input vector, returning the log-likelihoods of the input vector belonging to each possible class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihoods(TInput, Int32, Double)	Predicts a class label vector for the given input vector, returning the log-likelihoods of the input vector belonging to each possible class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihoods(TInput, Int32, Double)	Computes the log-likelihood that the given input vector belongs to the specified classIndex. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihoods(TInput, Int32, Double)	Predicts a class label vector for each input vector, returning the log-likelihoods of the input vector belonging to each possible class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	Probabilities(TInput)	Computes the probabilities that the given input vector belongs to each of the possible classes. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probabilities(TInput)	Computes the probabilities that the given input vector belongs to each of the possible classes. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probabilities(TInput, Double)	Computes the probabilities that the given input vector belongs to each of the possible classes. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probabilities(TInput, Int32)	Predicts a class label vector for the given input vector, returning the probabilities of the input vector belonging to each possible class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probabilities(TInput, Double)	Computes the probabilities that the given input vector belongs to each of the possible classes. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probabilities(TInput, Int32)	Predicts a class label vector for each input vector, returning the probabilities of the input vector belonging to each possible class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probabilities(TInput, Int32, Double)	Predicts a class label vector for the given input vector, returning the probabilities of the input vector belonging to each possible class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probabilities(TInput, Int32, Double)	Predicts a class label vector for each input vector, returning the probabilities of the input vector belonging to each possible class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probability(TInput)	Predicts a class label for the given input vector, returning the probability that the input vector belongs to its predicted class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probability(TInput)	Predicts a class label for the given input vector, returning the probability that the input vector belongs to its predicted class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probability(TInput, Int32)	Computes the probability that the given input vector belongs to the specified classIndex. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probability(TInput, Int32)	Predicts a class label for the given input vector, returning the probability that the input vector belongs to its predicted class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probability(TInput, Double)	Predicts a class label for the given input vector, returning the probability that the input vector belongs to its predicted class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probability(TInput, Int32)	Computes the probability that the given input vector belongs to the specified classIndex. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probability(TInput, Int32)	Computes the probability that the given input vector belongs to the specified classIndex. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probability(TInput, Int32)	Predicts a class label for each input vector, returning the probability that each vector belongs to its predicted class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probability(TInput, Int32, Double)	Computes the probability that the given input vector belongs to the specified classIndex. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probability(TInput, Int32, Double)	Computes the probability that the given input vector belongs to the specified classIndex. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probability(TInput, Int32, Double)	Predicts a class label for each input vector, returning the probability that each vector belongs to its predicted class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Score(TInput)	Computes a numerical score measuring the association between the given input vector and its most strongly associated class (as predicted by the classifier). (Inherited from MulticlassScoreClassifierBaseTInput.)
	Score(TInput)	Computes a numerical score measuring the association between the given input vector and its most strongly associated class (as predicted by the classifier). (Inherited from MulticlassScoreClassifierBaseTInput.)
	Score(TInput, Int32)	Computes a numerical score measuring the association between the given input vector and a given classIndex. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Score(TInput, Int32)	Predicts a class label for the input vector, returning a numerical score measuring the strength of association of the input vector to its most strongly related class. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Score(TInput, Double)	Computes a numerical score measuring the association between the given input vector and its most strongly associated class (as predicted by the classifier). (Inherited from MulticlassScoreClassifierBaseTInput.)
	Score(TInput, Int32)	Computes a numerical score measuring the association between the given input vector and a given classIndex. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Score(TInput, Int32)	Computes a numerical score measuring the association between the given input vector and a given classIndex. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Score(TInput, Int32)	Predicts a class label for each input vector, returning a numerical score measuring the strength of association of the input vector to the most strongly related class. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Score(TInput, Int32, Double)	Computes a numerical score measuring the association between the given input vector and a given classIndex. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Score(TInput, Int32, Double)	Computes a numerical score measuring the association between the given input vector and a given classIndex. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Score(TInput, Int32, Double)	Predicts a class label for each input vector, returning a numerical score measuring the strength of association of the input vector to the most strongly related class. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Scores(TInput)	Computes a numerical score measuring the association between the given input vector and each class. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Scores(TInput)	Computes a numerical score measuring the association between the given input vector and each class. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Scores(TInput, Double)	Computes a numerical score measuring the association between the given input vector and each class. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Scores(TInput, Int32)	Predicts a class label vector for the given input vector, returning a numerical score measuring the strength of association of the input vector to each of the possible classes. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Scores(TInput, Double)	Computes a numerical score measuring the association between the given input vector and each class. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Scores(TInput, Int32)	Predicts a class label vector for each input vector, returning a numerical score measuring the strength of association of the input vector to each of the possible classes. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Scores(TInput, Int32, Double)	Predicts a class label vector for the given input vector, returning a numerical score measuring the strength of association of the input vector to each of the possible classes. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Scores(TInput, Int32, Double)	Predicts a class label vector for each input vector, returning a numerical score measuring the strength of association of the input vector to each of the possible classes. (Inherited from MulticlassScoreClassifierBaseTInput.)
	ToMixtureDistribution	Gets a copy of the mixture distribution modeled by this Gaussian Mixture Model.
	ToMulticlass	Views this instance as a multi-class generative classifier. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	ToMultilabel	Views this instance as a multi-label generative classifier, giving access to more advanced methods, such as the prediction of one-hot vectors. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	ToString	Returns a string that represents the current object. (Inherited from Object.)
	Transform(TInput)	Applies the transformation to an input, producing an associated output. (Inherited from ClassifierBaseTInput, TClasses.)
	Transform(TInput)	Applies the transformation to a set of input vectors, producing an associated set of output vectors. (Inherited from TransformBaseTInput, TOutput.)
	Transform(TInput, TClasses)	Applies the transformation to an input, producing an associated output. (Inherited from ClassifierBaseTInput, TClasses.)
	Transform(TInput, Boolean)	Applies the transformation to an input, producing an associated output. (Inherited from MulticlassClassifierBaseTInput.)
	Transform(TInput, Int32)	Applies the transformation to an input, producing an associated output. (Inherited from MulticlassClassifierBaseTInput.)
	Transform(TInput, Boolean)	Applies the transformation to an input, producing an associated output. (Inherited from MulticlassClassifierBaseTInput.)
	Transform(TInput, Double)	Applies the transformation to an input, producing an associated output. (Inherited from MulticlassClassifierBaseTInput.)
	Transform(TInput, Int32)	Applies the transformation to an input, producing an associated output. (Inherited from MulticlassClassifierBaseTInput.)
	Transform(TInput, Double)	Applies the transformation to an input, producing an associated output. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Transform(TInput, Double)	Applies the transformation to an input, producing an associated output. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Transform(Double, Double, Double)	Transform data points into feature vectors containing the distance between each point and each of the clusters.
	Transform(Double, Int32, Double, Double)	Transform data points into feature vectors containing the distance between each point and each of the clusters.

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Extension Methods

	Name	Description
	HasMethod	Checks whether an object implements a method with the given name. (Defined by ExtensionMethods.)
	IsEqual	Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices. (Defined by Matrix.)
	SetEqualsGaussianClusterCollectionGaussianCluster	Compares two enumerables for set equality. Two enumerables are set equal if they contain the same elements, but not necessarily in the same order. (Defined by Matrix.)
	To(Type)	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)
	ToT	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)

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Remarks

This class contains information about all Gaussian clusters found during a GaussianMixtureModel estimation.

Given a new sample, this class can be used to find the nearest cluster related to this sample through the Nearest method.

Reference

Accord.MachineLearning Namespace

Accord.MachineLearningGaussianMixtureModel

Accord.MachineLearningGaussianClusterCollectionGaussianCluster