Package org.hipparchus.stat.fitting
Class MultivariateNormalMixtureExpectationMaximization
java.lang.Object
org.hipparchus.stat.fitting.MultivariateNormalMixtureExpectationMaximization
Expectation-Maximization algorithm for fitting the parameters of
multivariate normal mixture model distributions.
This implementation is pure original code based on
EM Demystified: An Expectation-Maximization Tutorial by Yihua Chen and Maya R. Gupta,
Department of Electrical Engineering, University of Washington, Seattle, WA 98195.
It was verified using external tools like CRAN Mixtools
(see the JUnit test cases) but it is not based on Mixtools code at all.
The discussion of the origin of this class can be seen in the comments of the MATH-817 JIRA issue.
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Constructor Summary
ConstructorDescriptionMultivariateNormalMixtureExpectationMaximization
(double[][] data) Creates an object to fit a multivariate normal mixture model to data. -
Method Summary
Modifier and TypeMethodDescriptionestimate
(double[][] data, int numComponents) Helper method to create a multivariate normal mixture model which can be used to initializefit(MixtureMultivariateNormalDistribution)
.void
fit
(MixtureMultivariateNormalDistribution initialMixture) Fit a mixture model to the data supplied to the constructor.void
fit
(MixtureMultivariateNormalDistribution initialMixture, int maxIterations, double threshold) Fit a mixture model to the data supplied to the constructor.Gets the fitted model.double
Gets the log likelihood of the data under the fitted model.
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Constructor Details
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MultivariateNormalMixtureExpectationMaximization
public MultivariateNormalMixtureExpectationMaximization(double[][] data) throws MathIllegalArgumentException Creates an object to fit a multivariate normal mixture model to data.- Parameters:
data
- Data to use in fitting procedure- Throws:
MathIllegalArgumentException
- if data has no rowsMathIllegalArgumentException
- if rows of data have different numbers of columnsMathIllegalArgumentException
- if the number of columns in the data is less than 2
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Method Details
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fit
public void fit(MixtureMultivariateNormalDistribution initialMixture, int maxIterations, double threshold) throws MathIllegalArgumentException Fit a mixture model to the data supplied to the constructor. The quality of the fit depends on the concavity of the data provided to the constructor and the initial mixture provided to this function. If the data has many local optima, multiple runs of the fitting function with different initial mixtures may be required to find the optimal solution. If a MathIllegalArgumentException is encountered, it is possible that another initialization would work.- Parameters:
initialMixture
- Model containing initial values of weights and multivariate normalsmaxIterations
- Maximum iterations allowed for fitthreshold
- Convergence threshold computed as difference in logLikelihoods between successive iterations- Throws:
MathIllegalArgumentException
- if any component's covariance matrix is singular during fittingMathIllegalArgumentException
- if numComponents is less than one or threshold is less than Double.MIN_VALUEMathIllegalArgumentException
- if initialMixture mean vector and data number of columns are not equal
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fit
public void fit(MixtureMultivariateNormalDistribution initialMixture) throws MathIllegalArgumentException Fit a mixture model to the data supplied to the constructor. The quality of the fit depends on the concavity of the data provided to the constructor and the initial mixture provided to this function. If the data has many local optima, multiple runs of the fitting function with different initial mixtures may be required to find the optimal solution. If a MathIllegalArgumentException is encountered, it is possible that another initialization would work.- Parameters:
initialMixture
- Model containing initial values of weights and multivariate normals- Throws:
MathIllegalArgumentException
- if any component's covariance matrix is singular during fittingMathIllegalArgumentException
- if numComponents is less than one or threshold is less than Double.MIN_VALUE
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estimate
public static MixtureMultivariateNormalDistribution estimate(double[][] data, int numComponents) throws MathIllegalArgumentException Helper method to create a multivariate normal mixture model which can be used to initializefit(MixtureMultivariateNormalDistribution)
. This method uses the data supplied to the constructor to try to determine a good mixture model at which to start the fit, but it is not guaranteed to supply a model which will find the optimal solution or even converge.- Parameters:
data
- Data to estimate distributionnumComponents
- Number of components for estimated mixture- Returns:
- Multivariate normal mixture model estimated from the data
- Throws:
MathIllegalArgumentException
- ifnumComponents
is greater than the number of data rows.MathIllegalArgumentException
- ifnumComponents < 2
.MathIllegalArgumentException
- if data has less than 2 rowsMathIllegalArgumentException
- if rows of data have different numbers of columns
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getLogLikelihood
public double getLogLikelihood()Gets the log likelihood of the data under the fitted model.- Returns:
- Log likelihood of data or zero of no data has been fit
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getFittedModel
Gets the fitted model.- Returns:
- fitted model or
null
if no fit has been performed yet.
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