/// <summary>
/// Predicts the next observations occurring after a given observation sequence (using Viterbi algorithm)
/// </summary>
public int[] Predict(int[] observations, int next, out double logLikelihood, out double[][] logLikelihoods)
{
int T = next;
double[,] logA = LogTransitionMatrix;
double[,] logB = LogEmissionMatrix;
double[] logPi = LogProbabilityVector;
int[] prediction = new int[next];
logLikelihoods = new double[next][];
// Compute forward probabilities for the given observation sequence.
double[,] lnFw0 = ForwardBackwardAlgorithm.LogForward(logA, logB, logPi, observations, out logLikelihood);
// Create a matrix to store the future probabilities for the prediction
// sequence and copy the latest forward probabilities on its first row.
double[,] lnFwd = new double[T + 1, mStateCount];
// 1. Initialization
for (int i = 0; i < mStateCount; i++)
{
lnFwd[0, i] = lnFw0[observations.Length - 1, i];
}
// 2. Induction
for (int t = 0; t < T; t++)
{
double[] weights = new double[mSymbolCount];
for (int s = 0; s < mSymbolCount; s++)
{
weights[s] = Double.NegativeInfinity;
for (int i = 0; i < mStateCount; i++)
{
double sum = Double.NegativeInfinity;
for (int j = 0; j < mStateCount; j++)
{
sum = LogHelper.LogSum(sum, lnFwd[t, j] + logA[j, i]);
}
lnFwd[t + 1, i] = sum + logB[i, s];
weights[s] = LogHelper.LogSum(weights[s], lnFwd[t + 1, i]);
}
}
double sumWeight = Double.NegativeInfinity;
for (int i = 0; i < weights.Length; i++)
{
sumWeight = LogHelper.LogSum(sumWeight, weights[i]);
}
for (int i = 0; i < weights.Length; i++)
{
weights[i] -= sumWeight;
}
// Select most probable symbol
double maxWeight = weights[0];
prediction[t] = 0;
for (int i = 1; i < weights.Length; i++)
{
if (weights[i] > maxWeight)
{
maxWeight = weights[i];
prediction[t] = i;
}
}
// Recompute log-likelihood
logLikelihoods[t] = weights;
logLikelihood = maxWeight;
}
return(prediction);
}
