GaussianMixtureModel Class

Gaussian mixture model clustering.

Inheritance Hierarchy

SystemObject
Accord.MachineLearningParallelLearningBase
Accord.MachineLearningGaussianMixtureModel

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

Syntax

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[SerializableAttribute]
public class GaussianMixtureModel : ParallelLearningBase, 
	IUnsupervisedLearning<GaussianClusterCollection, double[], int>, IClusteringAlgorithm<double[], double>, 
	IClusteringAlgorithm<double[]>, IUnsupervisedLearning<IClusterCollection<double[]>, double[], int>

<SerializableAttribute>
Public Class GaussianMixtureModel
	Inherits ParallelLearningBase
	Implements IUnsupervisedLearning(Of GaussianClusterCollection, Double(), Integer), 
	IClusteringAlgorithm(Of Double(), Double), IClusteringAlgorithm(Of Double()), 
	IUnsupervisedLearning(Of IClusterCollection(Of Double()), Double(), Integer)

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The GaussianMixtureModel type exposes the following members.

Constructors

	Name	Description
	GaussianMixtureModel(Int32)	Initializes a new instance of the GaussianMixtureModel class.
	GaussianMixtureModel(KMeans)	Initializes a new instance of the GaussianMixtureModel class.
	GaussianMixtureModel(MixtureNormalDistribution)	Initializes a new instance of the GaussianMixtureModel class.
	GaussianMixtureModel(MultivariateMixtureMultivariateNormalDistribution)	Initializes a new instance of the GaussianMixtureModel class.

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Properties

	Name	Description
	Clusters	Obsolete. Gets the collection of clusters currently modeled by the clustering algorithm.
	ComputeLabels	Gets or sets whether cluster labels should be computed at the end of the learning iteration. Setting to False might save a few computations in case they are not necessary.
	ComputeLogLikelihood	Gets or sets whether the log-likelihood should be computed at the end of the learning iteration. Setting to False might save a few computations in case they are not necessary.
	Gaussians	Gets the Gaussian components of the mixture model.
	Initializations	Gets or sets how many random initializations to try. Default is 3.
	Iterations	Gets how many iterations have been performed in the last call to Compute(Double).
	LogLikelihood	Gets the log-likelihood of the model at the last iteration.
	MaxIterations	Gets or sets the maximum number of iterations to be performed by the method. If set to zero, no iteration limit will be imposed. Default is 0.
	Options	Gets or sets the fitting options for the component Gaussian distributions of the mixture model.
	ParallelOptions	Gets or sets the parallelization options for this algorithm. (Inherited from ParallelLearningBase.)
	Token	Gets or sets a cancellation token that can be used to cancel the algorithm while it is running. (Inherited from ParallelLearningBase.)
	Tolerance	Gets or sets the convergence criterion for the Expectation-Maximization algorithm. Default is 1e-3.
	UseLogarithm	Gets or sets whether to make computations using the log -domain. This might improve accuracy on large datasets.

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Methods

	Name	Description
	Compute(Double)	Obsolete. Divides the input data into K clusters modeling each cluster as a multivariate Gaussian distribution.
	Compute(Double, GaussianMixtureModelOptions)	Obsolete. Divides the input data into K clusters modeling each cluster as a multivariate Gaussian distribution.
	Compute(Double, Double)	Obsolete. Divides the input data into K clusters modeling each cluster as a multivariate Gaussian distribution.
	Compute(Double, Double)	Obsolete. Divides the input data into K clusters modeling each cluster as a multivariate Gaussian distribution.
	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.)
	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.
	Initialize(Double, Double)	Initializes the model with initial values obtained through a run of the K-Means clustering algorithm.
	Learn	Learns a model that can map the given inputs to the desired outputs.
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	ToMixtureDistribution	Gets a copy of the mixture distribution modeled by this Gaussian Mixture Model.
	ToString	Returns a string that represents the current object. (Inherited from Object.)

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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.)
	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

Gaussian Mixture Models are one of most widely used model-based clustering methods. This specialized class provides a wrap-up around the Mixture<NormalDistribution> distribution and provides mixture initialization using the K-Means clustering algorithm.

Examples

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Accord.Math.Random.Generator.Seed = 0;

// Test Samples
double[][] samples =
{
    new double[] { 0, 1 },
    new double[] { 1, 2 },
    new double[] { 1, 1 },
    new double[] { 0, 7 },
    new double[] { 1, 1 },
    new double[] { 6, 2 },
    new double[] { 6, 5 },
    new double[] { 5, 1 },
    new double[] { 7, 1 },
    new double[] { 5, 1 }
};

// Create a new Gaussian Mixture Model with 2 components
GaussianMixtureModel gmm = new GaussianMixtureModel(2);

// Estimate the Gaussian Mixture
var clusters = gmm.Learn(samples);

// Predict cluster labels for each sample
int[] predicted = clusters.Decide(samples);

// We can also obtain the log-likelihoods for each sample:
double[] logLikelihoods = clusters.LogLikelihood(samples);

// As well as the probability of belonging to each cluster
double[][] probabilities = clusters.Probabilities(samples);

Reference

Accord.MachineLearning Namespace

Accord.MachineLearningKMeans

Accord.MachineLearningMeanShift