public virtual void TestOptimizeLogLikelihood(AbstractBatchOptimizer optimizer, GraphicalModel[] dataset, ConcatVector initialWeights, double l2regularization)
{
AbstractDifferentiableFunction <GraphicalModel> ll = new LogLikelihoodDifferentiableFunction();
ConcatVector finalWeights = optimizer.Optimize((GraphicalModel[])dataset, ll, (ConcatVector)initialWeights, (double)l2regularization, 1.0e-9, true);
System.Console.Error.WriteLine("Finished optimizing");
double logLikelihood = GetValueSum((GraphicalModel[])dataset, finalWeights, ll, (double)l2regularization);
// Check in a whole bunch of random directions really nearby that there is no nearby point with a higher log
// likelihood
Random r = new Random(42);
for (int i = 0; i < 1000; i++)
{
int size = finalWeights.GetNumberOfComponents();
ConcatVector randomDirection = new ConcatVector(size);
for (int j = 0; j < size; j++)
{
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((GraphicalModel[])dataset, randomPerturbation, ll, (double)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.Console.Error.WriteLine("Thought optimal point was: " + logLikelihood);
System.Console.Error.WriteLine("Discovered better point: " + randomPerturbedLogLikelihood);
}
NUnit.Framework.Assert.IsTrue(logLikelihood >= randomPerturbedLogLikelihood - (1.0e-3 * Math.Max(1.0, Math.Abs(logLikelihood))));
}
}