/*
* @Theory
* public void testOptimizeLogLikelihoodWithConstraints(AbstractBatchOptimizer optimizer,
* @ForAll(sampleSize = 5) @From(LogLikelihoodFunctionTest.GraphicalModelDatasetGenerator.class) GraphicalModel[] dataset,
* @ForAll(sampleSize = 2) @From(LogLikelihoodFunctionTest.WeightsGenerator.class) ConcatVector initialWeights,
* @ForAll(sampleSize = 2) @InRange(minDouble = 0.0, maxDouble = 5.0) double l2regularization) throws Exception {
* Random r = new Random(42);
*
* int constraintComponent = r.nextInt(initialWeights.getNumberOfComponents());
* double constraintValue = r.nextDouble();
*
* if (r.nextBoolean()) {
* optimizer.addSparseConstraint(constraintComponent, 0, constraintValue);
* } else {
* optimizer.addDenseConstraint(constraintComponent, new double[]{constraintValue});
* }
*
* // Put in some constraints
*
* AbstractDifferentiableFunction<GraphicalModel> ll = new LogLikelihoodDifferentiableFunction();
* ConcatVector finalWeights = optimizer.optimize(dataset, ll, initialWeights, l2regularization, 1.0e-9, false);
* System.err.println("Finished optimizing");
*
* assertEquals(constraintValue, finalWeights.getValueAt(constraintComponent, 0), 1.0e-9);
*
* double logLikelihood = getValueSum(dataset, finalWeights, ll, l2regularization);
*
* // Check in a whole bunch of random directions really nearby that there is no nearby point with a higher log
* // likelihood
* for (int i = 0; i < 1000; i++) {
* int size = finalWeights.getNumberOfComponents();
* ConcatVector randomDirection = new ConcatVector(size);
* for (int j = 0; j < size; j++) {
* if (j == constraintComponent) continue;
* double[] dense = new double[finalWeights.isComponentSparse(j) ? finalWeights.getSparseIndex(j) + 1 : finalWeights.getDenseComponent(j).length];
* for (int k = 0; k < dense.length; k++) {
* dense[k] = (r.nextDouble() - 0.5) * 1.0e-3;
* }
* randomDirection.setDenseComponent(j, dense);
* }
*
* ConcatVector randomPerturbation = finalWeights.deepClone();
* randomPerturbation.addVectorInPlace(randomDirection, 1.0);
*
* double randomPerturbedLogLikelihood = getValueSum(dataset, randomPerturbation, ll, l2regularization);
*
* // Check that we're within a very small margin of error (around 3 decimal places) of the randomly
* // discovered value
*
* if (logLikelihood < randomPerturbedLogLikelihood - (1.0e-3 * Math.max(1.0, Math.abs(logLikelihood)))) {
* System.err.println("Thought optimal point was: " + logLikelihood);
* System.err.println("Discovered better point: " + randomPerturbedLogLikelihood);
* }
*
* assertTrue(logLikelihood >= randomPerturbedLogLikelihood - (1.0e-3 * Math.max(1.0, Math.abs(logLikelihood))));
* }
* }
*/
private double GetValueSum <T>(T[] dataset, ConcatVector weights, AbstractDifferentiableFunction <T> fn, double l2regularization)
{
double value = 0.0;
foreach (T t in dataset)
{
value += fn.GetSummaryForInstance(t, weights, new ConcatVector(0));
}
return((value / dataset.Length) - (weights.DotProduct(weights) * l2regularization));
}
public virtual void Run()
{
long startTime = ManagementFactory.GetThreadMXBean().GetThreadCpuTime(jvmThreadId);
foreach (T datum in queue)
{
localLogLikelihood += fn.GetSummaryForInstance(datum, weights, localDerivative);
// Check for user interrupt
if (mainWorker.isFinished)
{
return;
}
}
finishedAtTime = Runtime.CurrentTimeMillis();
long endTime = ManagementFactory.GetThreadMXBean().GetThreadCpuTime(jvmThreadId);
cpuTimeRequired = endTime - startTime;
}