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ExpectationMaximizationTObservation Class |
Expectation Maximization algorithm for mixture model fitting.
Inheritance HierarchySystemObject
Accord.MachineLearningParallelLearningBase
Accord.Statistics.Distributions.FittingExpectationMaximizationTObservation
Accord.MachineLearningParallelLearningBase
Accord.Statistics.Distributions.FittingExpectationMaximizationTObservation
Namespace: Accord.Statistics.Distributions.Fitting
Assembly: Accord.Statistics (in Accord.Statistics.dll) Version: 3.8.0
SyntaxThe ExpectationMaximizationTObservation type exposes the following members.
Constructors| Name | Description | |
|---|---|---|
 | ExpectationMaximizationTObservation |
Creates a new ExpectationMaximizationTObservation algorithm.
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Properties| Name | Description | |
|---|---|---|
 | Coefficients |
Gets the current coefficient values.
|
| Convergence |
Gets or sets convergence properties for
the expectation-maximization algorithm.
|
| Distributions |
Gets the current component distribution values.
|
| Gamma |
Gets the responsibility of each input vector when estimating
each of the component distributions, in the last iteration.
|
| InnerOptions |
Gets or sets the fitting options to be used
when any of the component distributions need
to be estimated from the data.
|
| 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.) |
Methods| Name | Description | |
|---|---|---|
| Compute(TObservation) |
Estimates a mixture distribution for the given observations
using the Expectation-Maximization algorithm.
|
| Compute(TObservation, Double) |
Estimates a mixture distribution for the given observations
using the Expectation-Maximization algorithm, assuming different
weights for each observation.
|
| 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.) |
 | LogLikelihood(Double, IDistributionTObservation, TObservation) |
Computes the log-likelihood of the distribution
for a given set of observations.
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| LogLikelihood(Double, IDistributionTObservation, TObservation, ParallelOptions) |
Computes the log-likelihood of the distribution
for a given set of observations.
|
| LogLikelihood(Double, IDistributionTObservation, TObservation, Double, Double) |
Computes the log-likelihood of the distribution
for a given set of observations.
|
| LogLikelihood(Double, IDistributionTObservation, TObservation, Double, Double, ParallelOptions) |
Computes the log-likelihood of the distribution
for a given set of observations.
|
| MemberwiseClone | Creates a shallow copy of the current Object. (Inherited from Object.) |
| ToString | Returns a string that represents the current object. (Inherited from Object.) |
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.) |
RemarksThis class implements a generic version of the Expectation-Maximization algorithm which can be used with both univariate or multivariate distribution types.
See Also