/// <summary>
/// Computes the log-likelihood of the observations given this model.
/// </summary>
///
public double[] LogLikelihood(T[] observations)
{
double[] logLikelihoods = new double[NumberOfOutputs];
#if SERIAL || DEBUG // For all possible outputs for the model,
for (int y = 0; y < logLikelihoods.Length; y++)
#else
Parallel.For(0, logLikelihoods.Length, y =>
#endif
{
double logLikelihood;
// Compute the factor log-likelihood for the output
ForwardBackwardAlgorithm.LogForward(Function.Factors[y],
observations, y, out logLikelihood);
// Accumulate output's likelihood
logLikelihoods[y] = logLikelihood;
Accord.Diagnostics.Debug.Assert(!Double.IsNaN(logLikelihood));
}
#if !(SERIAL || DEBUG)
);
#endif
return logLikelihoods;
}
/// <summary>
/// Computes the Log of the partition function.
/// </summary>
///
public double LogPartition(T[] observations)
{
double logLikelihood;
double[,] fwd = ForwardBackwardAlgorithm.LogForward(Function.Factors[0], observations, 0, out logLikelihood);
return(logLikelihood);
}
private double[] computeLogLikelihood(T[] observations)
{
double[] logLikelihoods = new double[Outputs];
// For all possible outputs for the model,
Parallel.For(0, logLikelihoods.Length, y =>
{
double logLikelihood;
// Compute the factor log-likelihood for the output
ForwardBackwardAlgorithm.LogForward(Function.Factors[y],
observations, y, out logLikelihood);
// Accumulate output's likelihood
logLikelihoods[y] = logLikelihood;
#if DEBUG
if (Double.IsNaN(logLikelihood))
{
throw new Exception();
}
#endif
});
return(logLikelihoods);
}
private double[] computeLogLikelihood(T[] observations)
{
double[] logLikelihoods = new double[Outputs];
#if SERIAL // For all possible outputs for the model,
for (int y = 0; y < logLikelihoods.Length; y++)
#else
global::Accord.Threading.Tasks.Parallel.For(0, logLikelihoods.Length, y =>
#endif
{
double logLikelihood;
// Compute the factor log-likelihood for the output
ForwardBackwardAlgorithm.LogForward(Function.Factors[y],
observations, y, out logLikelihood);
// Accumulate output's likelihood
logLikelihoods[y] = logLikelihood;
System.Diagnostics.Debug.Assert(!Double.IsNaN(logLikelihood));
}
#if !SERIAL
);
#endif
return logLikelihoods;
}
/// <summary>
/// Computes Forward probabilities for a given potential function and a set of observations.
/// </summary>
///
public static double[,] LogForward <TObservation>(FactorPotential <TObservation> function,
TObservation[] observations, int output)
{
int states = function.States;
double[,] lnFwd = new double[observations.Length, states];
ForwardBackwardAlgorithm.LogForward(function, observations, output, lnFwd);
return(lnFwd);
}
/// <summary>
/// Computes Forward probabilities for a given potential function and a set of observations.
/// </summary>
///
public static double[,] LogForward <TObservation>(FactorPotential <TObservation> function,
TObservation[] observations, int output, out double logLikelihood)
{
int states = function.States;
double[,] lnFwd = new double[observations.Length, states];
int T = observations.Length;
ForwardBackwardAlgorithm.LogForward(function, observations, output, lnFwd);
logLikelihood = Double.NegativeInfinity;
for (int j = 0; j < states; j++)
{
logLikelihood = Special.LogSum(logLikelihood, lnFwd[T - 1, j]);
}
return(lnFwd);
}
