MCMCMultivariateNormalSummary extends AbstractObservable implements MCMCResultsListener, Summary, MultivariateNormalSummary { public Parameter[] getParameters() { return d_parameters; } MCMCMultivariateNormalSummary(MCMCResults results, Parameter[] parameters); Parameter[] getParameters(); boolean getDefined(); void resultsEvent(MCMCResultsEvent event); double[] getMeanVector(); double[][] getCovarianceMatrix();  }
@Test public void testCalculationsAfterEvent() { MultivariateNormalSummary summary = new MCMCMultivariateNormalSummary(d_results, d_results.getParameters()); d_results.makeSamplesAvailable(); verifyResults(summary); }
@Test public void testCalculationsOnConstruction() { d_results.makeSamplesAvailable(); MultivariateNormalSummary summary = new MCMCMultivariateNormalSummary(d_results, d_results.getParameters()); verifyResults(summary); }
@Test public void testResultsPreservedOnClear() { MultivariateNormalSummary summary = new MCMCMultivariateNormalSummary(d_results, d_results.getParameters()); d_results.makeSamplesAvailable(); d_results.clear(); verifyResults(summary); }
@Test public void testPropertyChangeOnAvailable() { MCMCMultivariateNormalSummary x = new MCMCMultivariateNormalSummary(d_results, d_results.getParameters()); final List<String> properties = new ArrayList<String>(); x.addPropertyChangeListener(new PropertyChangeListener() { public void propertyChange(PropertyChangeEvent evt) { properties.add(evt.getPropertyName()); } }); d_results.makeSamplesAvailable(); assertAllAndOnly( Arrays.asList(new String[] { Summary.PROPERTY_DEFINED, MultivariateNormalSummary.PROPERTY_MEAN_VECTOR, MultivariateNormalSummary.PROPERTY_COVARIANCE_MATRIX}), properties); }