[
https://issues.apache.org/jira/browse/MATH-817?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13495728#comment-13495728
]
Gilles commented on MATH-817:
-----------------------------
bq. [...] You aren't required to specify full mixture components during
initialization.
Isn't this syntactic sugar? The parameters _are_ needed for the computation to
take place; IIUC, you provide (in method
"createMultivariateNormalForInitialization") a way to create the missing data.
IMHO, it would be better, design-wise, to make that method "public", with a
descriptive name like:
{code}
public static MultivariateNormalDistribution
estimateMultivariateNormalDistribution(double[][] data) {
// ...
}
{code}
The API would then require the initial parameters to be specified, either
independently or through the provided estimator (at the user's choice).
Similarly, if the weights are not specified, you assign random values under the
hood ("initRandomComponentWeights"). I think that it is better to make it
explicit that initial values are needed, but that random values can be used.
You could then provide another syntactic sugar method such as:
{code}
public static List<Pair<Double, MultivariateNormalDistribution>>
makeRandomMultivariateNormalDistributionMixture(List<MultivariateNormalDistribution>
components) {
// Assign a random weight to each distribution in "components".
}
{code}
Or even combine both methods:
{code}
public static List<Pair<Double, MultivariateNormalDistribution>>
estimateRandomMultivariateNormalDistributionMixture(double[][] data,
int numComponents) {
// Estimate and assign random weights.
}
{code}
IIUC, this approach would avoid the many constructors with disparate parameters
than can be null and, if not null, must be checked for consistency (number of
dimensions), and the various code paths that depend on whether some argument is
null or not.
> Multivariate Normal Mixture Model Fitting by Expectation Maximization
> ---------------------------------------------------------------------
>
> Key: MATH-817
> URL: https://issues.apache.org/jira/browse/MATH-817
> Project: Commons Math
> Issue Type: New Feature
> Reporter: Jared Becksfort
> Priority: Minor
> Attachments: AbstractMultivariateRealDistribution.java.patch,
> MixtureMultivariateRealDistribution.java.patch,
> MultivariateNormalDistribution.java.patch,
> MultivariateNormalMixtureExpectationMaximizationFitter.java,
> MultivariateNormalMixtureExpectationMaximizationFitterTest.java
>
> Original Estimate: 1m
> Remaining Estimate: 1m
>
> I will submit a class for fitting Multivariate Normal Mixture Models using
> Expectation Maximization.
> > Hello,
> >
> > I have implemented some classes for multivariate Normal distributions,
> > multivariate normal mixture models, and an expectation maximization fitting
> > class for the mixture model. I would like to submit it to Apache Commons
> > Math. I still have some touching up to do so that they fit the style
> > guidelines and implement the correct interfaces. Before I do so, I thought
> > I would at least ask if the developers of the project are interested in me
> > submitting them.
> >
> > Thanks,
> > Jared Becksfort
> Dear Jared,
> Yes, that would be very nice to have such an addition! Remember to also
> include unit tests (refer to the current ones for examples). The best would
> be to split a submission up into multiple minor ones, each covering a natural
> submission (e.g. multivariate Normal distribution in one submission), and
> create an issue as described at
> http://commons.apache.org/math/issue-tracking.html .
> If you run into any problems, please do not hesitate to ask on this mailing
> list.
> Cheers, Mikkel.
--
This message is automatically generated by JIRA.
If you think it was sent incorrectly, please contact your JIRA administrators
For more information on JIRA, see: http://www.atlassian.com/software/jira
