public void PosteriorTest1()
{
// Example from http://ai.stanford.edu/~serafim/CS262_2007/notes/lecture5.pdf
double[,] A =
{
{ 0.95, 0.05 }, // fair dice state
{ 0.05, 0.95 }, // loaded dice state
};
double[,] B =
{
{ 1 / 6.0, 1 / 6.0, 1 / 6.0, 1 / 6.0, 1 / 6.0, 1 / 6.0 }, // fair dice probabilities
{ 1 / 10.0, 1 / 10.0, 1 / 10.0, 1 / 10.0, 1 / 10.0, 1 / 2.0 }, // loaded probabilities
};
double[] pi = { 0.5, 0.5 };
HiddenMarkovModel hmm = new HiddenMarkovModel(A, B, pi);
int[] x = new int[] { 1, 2, 1, 5, 6, 2, 1, 5, 2, 4 }.Subtract(1);
int[] y = new int[] { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 };
double py = Math.Exp(hmm.Evaluate(x, y));
Assert.AreEqual(0.00000000521158647211, py, 1e-16);
x = new int[] { 1, 2, 1, 5, 6, 2, 1, 5, 2, 4 }.Subtract(1);
y = new int[] { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 };
py = Math.Exp(hmm.Evaluate(x, y));
Assert.AreEqual(0.00000000015756235243, py, 1e-16);
Accord.Math.Tools.SetupGenerator(0);
var u = new UniformDiscreteDistribution(0, 6);
int[] sequence = u.Generate(1000);
int start = 120;
int end = 150;
for (int i = start; i < end; i += 2)
sequence[i] = 5;
// Predict the next observation in sequence
int[] path;
double[][] p = hmm.Posterior(sequence, out path);
for (int i = 0; i < path.Length; i++)
Assert.AreEqual(1, p[i][0] + p[i][1], 1e-10);
int loaded = 0;
for (int i = 0; i < start; i++)
if (p[i][1] > 0.95)
loaded++;
Assert.AreEqual(0, loaded);
loaded = 0;
for (int i = start; i < end; i++)
if (p[i][1] > 0.95)
loaded++;
Assert.IsTrue(loaded > 15);
loaded = 0;
for (int i = end; i < p.Length; i++)
if (p[i][1] > 0.95)
loaded++;
Assert.AreEqual(0, loaded);
}
