Accord.NET Framework

## HiddenMarkovModelPredict Method (Int32, Int32, Double, Double) |

Predicts the next observations occurring after a given observation sequence.

Syntax

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

- observations
- Type: SystemInt32

A sequence of observations. Predictions will be made regarding the next observations that should be coming after the last observation in this sequence. - next
- Type: SystemInt32

The number of observations to be predicted. Default is 1. - logLikelihood
- Type: SystemDouble

The log-likelihood of the given sequence, plus the predicted next observations. Exponentiate this value (use the System.Math.Exp function) to obtain a likelihood value. - logLikelihoods
- Type: SystemDouble

The log-likelihood of the different symbols for each predicted next observations. In order to convert those values to probabilities, exponentiate the values in the vectors (using the Exp function) and divide each value by their vector's sum.

Examples

// 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

See Also