public void LogForwardTest()
{
double[][][] observations;
int[] labels;
var hmm = IndependentMarkovFunctionTest.CreateModel2(out observations, out labels);
MarkovMultivariateFunction function = new MarkovMultivariateFunction(hmm, includePriors: false);
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 LogForwardGesturesDeoptimizedTest()
{
int[] labels;
double[][][] words;
var classifier = IndependentMarkovFunctionTest.CreateModel4(out words, out labels, false);
var function = new MarkovMultivariateFunction(classifier);
function.Deoptimize();
var target = new HiddenConditionalRandomField <double[]>(function);
foreach (var word in words)
{
for (int c = 0; c < 3; c++)
{
var actual = Accord.Statistics.Models.Fields.ForwardBackwardAlgorithm.LogForward(
target.Function.Factors[c], word, c);
var expected = Accord.Statistics.Models.Markov.ForwardBackwardAlgorithm.LogForward(
classifier[c], word);
for (int i = 0; i < actual.GetLength(0); i++)
{
for (int j = 0; j < actual.GetLength(1); j++)
{
double a = actual[i, j];
double e = expected[i, j];
Assert.IsTrue(e.IsRelativelyEqual(a, 0.1));
}
}
}
}
}
public void GradientTest_MarkovIndependentNormal_NoPriors()
{
double[][][] observations;
int[] labels;
HiddenMarkovClassifier <Independent <NormalDistribution> > hmm =
IndependentMarkovFunctionTest.CreateModel4(out observations, out labels, usePriors: false);
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,
observations,
labels);
double[] expected = diff.Compute(function.Weights);
double[] actual = target.Gradient(function.Weights, observations, 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()
{
double[][][] observations;
int[] labels;
var hmm = IndependentMarkovFunctionTest.CreateModel3(out observations, out labels);
var function = new MarkovMultivariateFunction(hmm, includePriors: false);
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.IsTrue(scaling1[i].IsRelativelyEqual(scaling2[i], 1e-10));
Assert.IsFalse(Double.IsNaN(scaling1[i]));
Assert.IsFalse(Double.IsNaN(scaling2[i]));
}
}
}
}
