public void ComputeTest2()
{
double[][][] sequences;
int[] labels;
var model = CreateModel2(out sequences, out labels);
var target = new MarkovMultivariateFunction(model);
var hcrf = new HiddenConditionalRandomField<double[]>(target);
double actual;
double expected;
double[][] x = { new double[] { 0, 1.7 }, new double[] { 2, 2.1 } };
for (int c = 0; c < model.Classes; c++)
{
for (int i = 0; i < model[c].States; i++)
{
// Check initial state transitions
expected = model.Priors[c] * Math.Exp(model[c].Probabilities[i]) * model[c].Emissions[i].ProbabilityDensityFunction(x[0]);
actual = Math.Exp(target.Factors[c].Compute(-1, i, x, 0, c));
Assert.AreEqual(expected, actual, 1e-6);
Assert.IsFalse(double.IsNaN(actual));
}
for (int t = 1; t < x.Length; t++)
{
// Check normal state transitions
for (int i = 0; i < model[c].States; i++)
{
for (int j = 0; j < model[c].States; j++)
{
double xb = Math.Exp(model[c].Transitions[i, j]);
double xc = model[c].Emissions[j].ProbabilityDensityFunction(x[t]);
expected = xb * xc;
actual = Math.Exp(target.Factors[c].Compute(i, j, x, t, c));
Assert.AreEqual(expected, actual, 1e-6);
Assert.IsFalse(double.IsNaN(actual));
}
}
}
actual = model.LogLikelihood(x, c);
expected = hcrf.LogLikelihood(x, c);
Assert.AreEqual(expected, actual);
Assert.IsFalse(double.IsNaN(actual));
}
}