HiddenMarkovModelPredict Method (Int32, Double)

public int Predict(
	int[] observations,
	out double[] logLikelihoods
)

Public Function Predict ( 
	observations As Integer(),
	<OutAttribute> ByRef logLikelihoods As Double()
) As Integer

// We will try to create a Hidden Markov Model which
// can recognize (and predict) the following sequences:
int[][] sequences =
{
    new[] { 1, 3, 5, 7, 9, 11, 13 },
    new[] { 1, 3, 5, 7, 9, 11 },
    new[] { 1, 3, 5, 7, 9, 11, 13 },
    new[] { 1, 3, 3, 7, 7, 9, 11, 11, 13, 13 },
    new[] { 1, 3, 7, 9, 11, 13 },
};

// Create a Baum-Welch HMM algorithm:
var teacher = new BaumWelchLearning()
{
    // Let's creates a left-to-right (forward)
    // Hidden Markov Model with 7 hidden states
    Topology = new Forward(7),

    // We'll try to fit the model to the data until the difference in
    // the average log-likelihood changes only by as little as 0.0001
    Tolerance = 0.0001,
    Iterations = 0 // do not impose a limit on the number of iterations
};

// Use the algorithm to learn a new Markov model:
HiddenMarkovModel hmm = teacher.Learn(sequences);

// Now, we will try to predict the next 1 observation in a base symbol sequence
int[] prediction = hmm.Predict(observations: new[] { 1, 3, 5, 7, 9 }, next: 1);

// At this point, prediction should be int[] { 11 }
int nextSymbol = prediction[0]; // should be 11.

// We can try to predict further, but this might not work very 
// well due the Markov assumption between the transition states:
int[] nextSymbols = hmm.Predict(observations: new[] { 1, 3, 5, 7 }, next: 2);

// At this point, nextSymbols should be int[] { 9, 11 }
int nextSymbol1 = nextSymbols[0]; // 9
int nextSymbol2 = nextSymbols[1]; // 11