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ExpectationMaximizationTObservation Class

Expectation Maximization algorithm for mixture model fitting.
Inheritance Hierarchy
SystemObject
  Accord.MachineLearningParallelLearningBase
    Accord.Statistics.Distributions.FittingExpectationMaximizationTObservation

Namespace:  Accord.Statistics.Distributions.Fitting
Assembly:  Accord.Statistics (in Accord.Statistics.dll) Version: 3.5.0
Syntax
public class ExpectationMaximization<TObservation> : ParallelLearningBase
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Type Parameters

TObservation
The type of the observations being fitted.

The ExpectationMaximizationTObservation type exposes the following members.

Constructors
  NameDescription
Public methodExpectationMaximizationTObservation
Creates a new ExpectationMaximizationTObservation algorithm.
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Properties
  NameDescription
Public propertyCoefficients
Gets the current coefficient values.
Public propertyConvergence
Gets or sets convergence properties for the expectation-maximization algorithm.
Public propertyDistributions
Gets the current component distribution values.
Public propertyGamma
Gets the responsibility of each input vector when estimating each of the component distributions, in the last iteration.
Public propertyInnerOptions
Gets or sets the fitting options to be used when any of the component distributions need to be estimated from the data.
Public propertyParallelOptions
Gets or sets the parallelization options for this algorithm.
(Inherited from ParallelLearningBase.)
Public propertyToken
Gets or sets a cancellation token that can be used to cancel the algorithm while it is running.
(Inherited from ParallelLearningBase.)
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Methods
  NameDescription
Public methodCompute(TObservation)
Estimates a mixture distribution for the given observations using the Expectation-Maximization algorithm.
Public methodCompute(TObservation, Double)
Estimates a mixture distribution for the given observations using the Expectation-Maximization algorithm, assuming different weights for each observation.
Public methodEquals
Determines whether the specified object is equal to the current object.
(Inherited from Object.)
Protected methodFinalize
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object.)
Public methodGetHashCode
Serves as the default hash function.
(Inherited from Object.)
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Public methodStatic memberLogLikelihood(Double, IDistributionTObservation, TObservation)
Computes the log-likelihood of the distribution for a given set of observations.
Public methodStatic memberLogLikelihood(Double, IDistributionTObservation, TObservation, ParallelOptions)
Computes the log-likelihood of the distribution for a given set of observations.
Public methodStatic memberLogLikelihood(Double, IDistributionTObservation, TObservation, Double, Double)
Computes the log-likelihood of the distribution for a given set of observations.
Public methodStatic memberLogLikelihood(Double, IDistributionTObservation, TObservation, Double, Double, ParallelOptions)
Computes the log-likelihood of the distribution for a given set of observations.
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
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Extension Methods
  NameDescription
Public Extension MethodHasMethod
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)
Public Extension MethodIsEqual
Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.
(Defined by Matrix.)
Public Extension MethodToTOverloaded.
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.)
Public Extension MethodToTOverloaded.
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 Matrix.)
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Remarks

This class implements a generic version of the Expectation-Maximization algorithm which can be used with both univariate or multivariate distribution types.

See Also