/// <summary>
/// Generates a random vector of observations from the model.
/// </summary>
///
/// <param name="samples">The number of samples to generate.</param>
/// <param name="logLikelihood">The log-likelihood of the generated observation sequence.</param>
/// <param name="path">The Viterbi path of the generated observation sequence.</param>
///
/// <example>
/// An usage example is available at the <see cref="Generate(int)"/> documentation page.
/// </example>
///
/// <returns>A random vector of observations drawn from the model.</returns>
///
public int[] Generate(int samples, out int[] path, out double logLikelihood)
{
double[] transitions = Probabilities;
double[] emissions;
int[] observations = new int[samples];
logLikelihood = Double.NegativeInfinity;
path = new int[samples];
// For each observation to be generated
for (int t = 0; t < observations.Length; t++)
{
// Navigate randomly on one of the state transitions
int state = GeneralDiscreteDistribution.Random(Matrix.Exp(transitions));
// Generate a sample for the state
emissions = Emissions.GetRow(state);
int symbol = GeneralDiscreteDistribution.Random(Matrix.Exp(emissions));
// Store the sample
observations[t] = symbol;
path[t] = state;
// Compute log-likelihood up to this point
logLikelihood = Accord.Math.Special.LogSum(logLikelihood,
transitions[state] + emissions[symbol]);
// Continue sampling
transitions = Transitions.GetRow(state);
}
return(observations);
}
/// <summary>
/// Generates a random vector of observations from the model.
/// </summary>
///
/// <param name="samples">The number of samples to generate.</param>
/// <param name="logLikelihood">The log-likelihood of the generated observation sequence.</param>
/// <param name="path">The Viterbi path of the generated observation sequence.</param>
///
/// <returns>A random vector of observations drawn from the model.</returns>
///
public Array Generate(int samples, out int[] path, out double logLikelihood)
{
double[] transitions = Probabilities;
logLikelihood = Double.NegativeInfinity;
path = new int[samples];
var multivariate = Emissions as ISampleableDistribution <double[]>[];
if (multivariate != null)
{
double[][] observations = new double[samples][];
// For each observation to be generated
for (int t = 0; t < observations.Length; t++)
{
// Navigate randomly on one of the state transitions
int state = GeneralDiscreteDistribution.Random(Matrix.Exp(transitions));
// Generate a sample for the state
double[] symbol = multivariate[state].Generate();
// Store the sample
observations[t] = symbol;
path[t] = state;
// Compute log-likelihood up to this point
logLikelihood = Accord.Math.Special.LogSum(logLikelihood,
transitions[state] + Emissions[state].LogProbabilityFunction(symbol));
// Continue sampling
transitions = Transitions.GetRow(state);
}
return(observations);
}
var univariate = Emissions as ISampleableDistribution <double>[];
if (univariate != null)
{
double[] observations = new double[samples];
// For each observation to be generated
for (int t = 0; t < observations.Length; t++)
{
// Navigate randomly on one of the state transitions
int state = GeneralDiscreteDistribution.Random(Matrix.Exp(transitions));
// Generate a sample for the state
double symbol = univariate[state].Generate();
// Store the sample
observations[t] = symbol;
path[t] = state;
// Compute log-likelihood up to this point
logLikelihood = Accord.Math.Special.LogSum(logLikelihood,
transitions[state] + Emissions[state].LogProbabilityFunction(symbol));
// Continue sampling
transitions = Transitions.GetRow(state);
}
return(observations);
}
throw new ArgumentException("The model's emission distributions do not support sampling.");
}