public void LearnTest10_Independent()
{
// Let's say we have 2 meteorological sensors gathering data
// from different time periods of the day. Those periods are
// represented below:
double[][][] data =
{
new double[][] // first sequence (we just repeated the measurements
{ // once, so there is only one observation sequence)
new double[] { 1, 2 }, // Day 1, 15:00 pm
new double[] { 6, 7 }, // Day 1, 16:00 pm
new double[] { 2, 3 }, // Day 1, 17:00 pm
new double[] { 2, 2 }, // Day 1, 18:00 pm
new double[] { 9, 8 }, // Day 1, 19:00 pm
new double[] { 1, 0 }, // Day 1, 20:00 pm
new double[] { 1, 3 }, // Day 1, 21:00 pm
new double[] { 8, 9 }, // Day 1, 22:00 pm
new double[] { 3, 3 }, // Day 1, 23:00 pm
new double[] { 1, 3 }, // Day 2, 00:00 am
new double[] { 1, 1 }, // Day 2, 01:00 am
}
};
// Let's assume those sensors are unrelated (for simplicity). As
// such, let's assume the data gathered from the sensors may reside
// into circular centroids denoting each state the underlying system
// might be in.
NormalDistribution[] initial_components =
{
new NormalDistribution(), // initial value for the first variable's distribution
new NormalDistribution() // initial value for the second variable's distribution
};
// Specify a initial independent normal distribution for the samples.
var density = new Independent<NormalDistribution, double>(initial_components);
// Creates a continuous hidden Markov Model with two states organized in an Ergodic
// topology and an underlying independent Normal distribution as probability density.
var model = new HiddenMarkovModel<Independent<NormalDistribution>, double[]>(new Ergodic(2), density);
// Configure the learning algorithms to train the sequence classifier until the
// difference in the average log-likelihood changes only by as little as 0.0001
var teacher = new BaumWelchLearning<Independent<NormalDistribution>, double[]>(model)
{
Tolerance = 0.0001,
Iterations = 0,
};
// Fit the model
teacher.Learn(data);
double error = teacher.LogLikelihood;
// Get the hidden state associated with each observation
//
double logLikelihood; // log-likelihood of the Viterbi path
int[] hidden_states = model.Decode(data[0], out logLikelihood);
Assert.AreEqual(-33.978800850637882, error);
Assert.AreEqual(-33.9788008509802, logLikelihood);
Assert.AreEqual(11, hidden_states.Length);
}
