public class LogExpectationMaximization<TObservation> : ParallelLearningBase
Public Class LogExpectationMaximization(Of TObservation) Inherits ParallelLearningBase
Thetype exposes the following members.
Gets the current coefficient values.
Gets or sets convergence properties for the expectation-maximization algorithm.
Gets the current component distribution values.
Gets or sets the fitting options to be used when any of the component distributions need to be estimated from the data.
Gets the responsibility of each input vector when estimating each of the component distributions, in the last iteration.
Gets or sets the parallelization options for this algorithm.(Inherited from ParallelLearningBase.)
Gets or sets a cancellation token that can be used to cancel the algorithm while it is running.(Inherited from ParallelLearningBase.)
Estimates a mixture distribution for the given observations using the Expectation-Maximization algorithm.
Determines whether the specified object is equal to the current object.(Inherited from .)
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.(Inherited from .)
Serves as the default hash function.(Inherited from .)
Gets the(Inherited from of the current instance. .)
Computes the log-likelihood of the distribution for a given set of observations.
Creates a shallow copy of the current(Inherited from . .)
Returns a string that represents the current object.(Inherited from .)
Checks whether an object implements a method with the given name.(Defined by ExtensionMethods.)
Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.(Defined by Matrix.)
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.)
This class implements a generic version of the Expectation-Maximization algorithm which can be used with both univariate or multivariate distribution types.