public IPredictionResult Predict(IGaussianMixtureModelState model, IPredictionRequest request)
{
var trainingSet = (double[][])request.TrainingSet.Clone();
var predictions = new PredictionResult {
Predicted = new double[request.NumberOfDays][]
};
for (int i = 0; i < request.NumberOfDays; i++)
{
var prediction = PredictNextValue(model, request, trainingSet);
if (request.NumberOfDays > 1)
{
trainingSet = new double[request.TrainingSet.Length + i + 1][];
request.TrainingSet.CopyTo(trainingSet, 0);
for (var j = 0; j < i + 1; j++)
{
trainingSet[request.TrainingSet.Length + j] = request.TestSet[j];
}
}
predictions.Predicted[i] = prediction;
}
return(predictions);
}
private IList <ObservationWithLikelihood <double[]> > FindMostSimilarObservations(IGaussianMixtureModelState model, double[][] trainingSet, double yesterdayLikelihood, double tolerance)
{
var N = trainingSet.Length;
var guessess = new List <ObservationWithLikelihood <double[]> >();
for (var n = N - 2; n > 0; n--)
{
var x = new[] { trainingSet[n] };
var likelihood = LogLikelihood.Calculate(x, model.Mixture.Coefficients, GetCovariances(model.Mixture.Components), GetMeans(model.Mixture.Components));
//Debug.Write((new Vector(observations[n])).ToString() + " : " + likelihood + " " + Environment.NewLine);
if (Math.Abs(yesterdayLikelihood) - tolerance < Math.Abs(likelihood) && Math.Abs(yesterdayLikelihood) + tolerance > Math.Abs(likelihood))
{
guessess.Add(new ObservationWithLikelihood <double[]>()
{
LogLikelihood = likelihood, Observation = trainingSet[n], PlaceInSequence = n - 1
});
}
}
return(guessess);
}
