public IPredictionResult Predict(PredictorType predictorType, double[][] observations, double[] weights)
{
var model = (HiddenMarkovModelMultivariateGaussianDistribution)HiddenMarkovModelFactory.GetModel(new ModelCreationParameters <IMultivariateDistribution> {
Pi = _pi, TransitionProbabilityMatrix = _transitionProbabilityMatrix, Emissions = _emission
}); //new HiddenMarkovModelState<IMultivariateDistribution>(_pi, _transitionProbabilityMatrix, _emission);
model.Normalized = Normalized;
var request = new PredictionRequest();
request.TrainingSet = observations;
request.NumberOfDays = 1;
request.Tolerance = PREDICTION_LIKELIHOOD_TOLERANCE;
var predictor = HiddenMarkovModelPredictorFactory.GetPredictor(predictorType);
return(predictor.Predict(model, request));
}
public IPredictionResult Predict(PredictorType predictorType, double[][] observations, int numberOfIterations, double likelihoodTolerance)
{
var model = (HiddenMarkovModelWeightedMixtureDistribution)HiddenMarkovModelFactory.GetModel <HiddenMarkovModelWeightedMixtureDistribution, Mixture <IMultivariateDistribution> >(new ModelCreationParameters <Mixture <IMultivariateDistribution> > {
Pi = _pi, TransitionProbabilityMatrix = _transitionProbabilityMatrix, Emissions = _emission
}); //HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters<Mixture<IMultivariateDistribution>> { Pi = _pi, TransitionProbabilityMatrix = _transitionProbabilityMatrix, Emissions = _emission });
model.Normalized = Normalized;
var request = new PredictionRequest();
request.TrainingSet = observations;
request.NumberOfDays = 1;
request.Tolerance = PREDICTION_LIKELIHOOD_TOLERANCE;
request.TrainingLikelihoodTolerance = likelihoodTolerance;
request.NumberOfTrainingIterations = numberOfIterations;
var predictor = HiddenMarkovModelPredictorFactory.GetPredictor(predictorType);
return(predictor.Predict(model, request));
}
