public IPredictionResult Predict(double[][] observations, double[] weights)
{
var model = HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters <DiscreteDistribution> {
Pi = _pi, TransitionProbabilityMatrix = _transitionProbabilityMatrix, Emissions = _emission
}); //new HiddenMarkovModelState<IMultivariateDistribution>(_pi, _transitionProbabilityMatrix, _emission);
var request = new PredictionRequest();
request.TrainingSet = observations;
request.NumberOfDays = 1;
request.Tolerance = 0.01;
var predictor = new LikelihoodBasedPredictor();
return(predictor.Predict((IHiddenMarkovModel <IDistribution>)model, request));
}
public IEvaluationResult EvaluatePrediction(IPredictionResult results, double[][] observations)
{
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 EvaluationRequest();
request.PredictionParameters = new PredictionRequest();
request.PredictionToEvaluate = new PredictionResult();
request.EstimatorType = ErrorEstimatorType.Value;
request.PredictionParameters.Tolerance = PREDICTION_LIKELIHOOD_TOLERANCE;
request.PredictionParameters.TestSet = observations;
request.PredictionToEvaluate.Predicted = results.Predicted;
var predictor = new LikelihoodBasedPredictor();
return(predictor.Evaluate(request));
}
