public void LogForwardTest()
{
HiddenMarkovClassifier <Independent> hmm = IndependentMarkovClassifierPotentialFunctionTest
.CreateModel2();
MarkovMultivariateFunction function = new MarkovMultivariateFunction(hmm, includePriors: false);
double[][][] observations = IndependentMarkovClassifierPotentialFunctionTest.sequences;
int[] labels = IndependentMarkovClassifierPotentialFunctionTest.labels;
foreach (double[][] x in observations)
{
foreach (int y in labels)
{
double[,] actual = new double[5, 2];
Accord.Statistics.Models.Fields.
ForwardBackwardAlgorithm.LogForward(function.Factors[y], x, y, actual);
double[,] expected = new double[5, 2];
Accord.Statistics.Models.Markov.
ForwardBackwardAlgorithm.LogForward(hmm.Models[y], x, expected);
for (int i = 0; i < actual.GetLength(0); i++)
{
for (int j = 0; j < actual.GetLength(1); j++)
{
Assert.AreEqual(expected[i, j], actual[i, j], 1e-10);
}
}
}
}
}
public void GradientTest4()
{
var hmm = IndependentMarkovClassifierPotentialFunctionTest.CreateModel2();
var function = new MarkovMultivariateFunction(hmm);
var model = new HiddenConditionalRandomField <double[]>(function);
var target = new ForwardBackwardGradient <double[]>(model);
target.Regularization = 0;
FiniteDifferences diff = new FiniteDifferences(function.Weights.Length);
diff.Function = parameters => func(model, parameters,
IndependentMarkovClassifierPotentialFunctionTest.sequences,
IndependentMarkovClassifierPotentialFunctionTest.labels);
double[] expected = diff.Compute(function.Weights);
double[] actual = target.Gradient(function.Weights,
IndependentMarkovClassifierPotentialFunctionTest.sequences,
IndependentMarkovClassifierPotentialFunctionTest.labels);
for (int i = 0; i < actual.Length; i++)
{
if (double.IsNaN(expected[i]))
{
continue;
}
Assert.AreEqual(expected[i], actual[i], 1e-5);
Assert.IsFalse(double.IsNaN(actual[i]));
}
}
public void ForwardTest2()
{
HiddenMarkovClassifier <Independent> hmm = IndependentMarkovClassifierPotentialFunctionTest
.CreateModel3();
var function = new MarkovMultivariateFunction(hmm, includePriors: false);
double[][][] observations = IndependentMarkovClassifierPotentialFunctionTest.sequences2;
int[] labels = IndependentMarkovClassifierPotentialFunctionTest.labels2;
foreach (double[][] x in observations)
{
foreach (int y in labels)
{
double[] scaling1;
double logLikelihood1;
double[,] actual = Accord.Statistics.Models.Fields.
ForwardBackwardAlgorithm.Forward(function.Factors[y], x, y, out scaling1, out logLikelihood1);
double[] scaling2;
double logLikelihood2;
double[,] expected = Accord.Statistics.Models.Markov.
ForwardBackwardAlgorithm.Forward(hmm.Models[y], x, out scaling2, out logLikelihood2);
for (int i = 0; i < actual.GetLength(0); i++)
{
for (int j = 0; j < actual.GetLength(1); j++)
{
Assert.AreEqual(expected[i, j], actual[i, j], 1e-10);
Assert.IsFalse(Double.IsNaN(actual[i, j]));
}
}
Assert.AreEqual(logLikelihood1, logLikelihood2, 1e-10);
for (int i = 0; i < scaling1.Length; i++)
{
Assert.AreEqual(scaling1[i], scaling2[i], 1e-8);
Assert.IsFalse(Double.IsNaN(scaling1[i]));
Assert.IsFalse(Double.IsNaN(scaling2[i]));
}
}
}
}
