|
static private convertNoCheck ( |
||
| array | ||
| dimension | int | |
| return | double[][] | |
/// <summary>
/// Predicts the next observation occurring after a given observation sequence.
/// </summary>
///
public double[] Predict(double[] observations, int next, out double logLikelihood)
{
if (multivariate)
{
throw new ArgumentException("Model is multivariate.", "observations");
}
if (observations == null)
{
throw new ArgumentNullException("observations");
}
// Convert to multivariate observations
double[][] obs = MarkovHelperMethods.convertNoCheck(observations, dimension);
// Matrix to store the probabilities in assuming the next
// observations (prediction) will belong to each state.
double[][] weights;
// Compute the next observations
double[][] prediction = predict(obs, next, out logLikelihood, out weights);
// Return the first (single) dimension of the next observations.
return(Accord.Math.Matrix.Concatenate(prediction));
}
/// <summary>
/// Predicts the next observation occurring after a given observation sequence.
/// </summary>
///
public double Predict(double[] observations, out double logLikelihood)
{
if (multivariate)
{
throw new ArgumentException("Model is multivariate.", "observations");
}
if (observations == null)
{
throw new ArgumentNullException("observations");
}
// Convert to multivariate observations
double[][] obs = MarkovHelperMethods.convertNoCheck(observations, dimension);
// Matrix to store the probabilities in assuming the next
// observations (prediction) will belong to each state.
double[][] weights;
// Compute the next observation (currently only one ahead is supported).
double[][] prediction = predict(obs, 1, out logLikelihood, out weights);
return(prediction[0][0]);
}
/// <summary>
/// Predicts the next observation occurring after a given observation sequence.
/// </summary>
///
private double[] predict <TUnivariate>(double[] observations,
out double logLikelihood, out Mixture <TUnivariate> probabilities)
where TUnivariate : DistributionBase, TDistribution, IUnivariateDistribution
{
// Convert to multivariate observations
double[][] obs = MarkovHelperMethods.convertNoCheck(observations, dimension);
// Matrix to store the probabilities in assuming the next
// observations (prediction) will belong to each state.
double[][] weights;
// Compute the next observation (currently only one ahead is supported).
double[][] prediction = predict(obs, 1, out logLikelihood, out weights);
// Create the mixture distribution defining the model likelihood in
// assuming the next observation belongs will belong to each state.
TUnivariate[] b = Array.ConvertAll(B, x => (TUnivariate)x);
probabilities = new Mixture <TUnivariate>(weights[1].Exp(), b);
return(prediction[0]);
}
