/// <summary>
/// Trains a single model based on a generator a predefined number of times with the provided
/// examples and data split and selects the best (or most accurate) model.
/// </summary>
/// <param name="examples">Source data.</param>
/// <param name="trainingPercentage">Data split percentage.</param>
/// <param name="repeat">Number of repetitions per generator.</param>
/// <param name="generator">Model generator used.</param>
/// <returns>Best model for provided generator.</returns>
public static LearningModel Learn(IEnumerable<object> examples, double trainingPercentage, int repeat, IGenerator generator)
{
// count only once
var total = examples.Count();
var descriptor = generator.Descriptor;
var data = descriptor.Convert(examples).ToExamples();
Matrix x = data.Item1;
Vector y = data.Item2;
var models = new IModel[repeat];
//var accuracy = Vector.Zeros(repeat);
var scores = new Score[repeat];
if (trainingPercentage > 1.0) trainingPercentage /= 100.0;
// safe for parallisation
// read-only references to the data model
// and update indices independently
for (int i = 0; i < models.Length; i++)
{
var t = GenerateModel(generator, x, y, examples, trainingPercentage, total);
models[i] = t.Model;
scores[i] = t.Score;
}
int idx = scores.Select(s => s.RMSE).MinIndex();
// sanity check, for convergence failures
if (idx < 0 && trainingPercentage < 1d) throw new Exception("All models failed to initialize properly");
else if (idx < 0) idx = 0;
return new LearningModel { Generator = generator, Model = models[idx], Score = scores[idx] };
}
