/// <summary>
/// Trains the model to recognise the specified input as the specified symbol
/// </summary>
public void Train(WrappedBitmap inputData, TSymbol representedValue, uint rounds)
{
var initialGeneration = Enumerable.Range(0, (int)rounds)
.Select(i => new Scorer(new TuningParams.Scoring())) // TODO nested constructors is smelly
.Select(s => new ScorerScorePair(s, s.Score(inputData)))
.AsParallel()
.OrderByDescending(s => s.Score);
// Having acquired the initial set of sampling pixels via sheer randomness, we'll work with
// the best we've got and try and massage them into something better.
var mutParams = new TuningParams.Mutation();
var initialBestCandidates = initialGeneration.Take(100); // TODO parameterise magic value (which was decided totally arbitrarily anyway)
var initialBestCandidatesWithMutatedVariants = from p in initialBestCandidates
let mutatedOffspring = p.Scorer.MutateMany(mutParams)
let mutatedScores = from m in mutatedOffspring
let mutatedScore = m.Score(inputData)
select new ScorerScorePair(m, mutatedScore)
select new ParentChildScoreCollection(p, mutatedScores);
var bestOftheMutantStrains = initialBestCandidatesWithMutatedVariants
.SelectMany(x => x.Children)
.OrderByDescending(s => s.Score)
.First();
// Compute some stats for these mutations. These stats are for interest only, they don't affect any computation.
Benchmarking.Mutation.AddDataPoints(initialBestCandidatesWithMutatedVariants);
var rec = new Recogniser <TSymbol>(representedValue, bestOftheMutantStrains.Scorer);
RegisterRecogniser(rec);
}
/// <summary>
/// Adds a recogniser to the collection, replacing it if it already exists
/// </summary>
private void RegisterRecogniser(Recogniser <TSymbol> recogniser)
{
// TODO could change to Dictionary<TSymbol, List<Recogniser<TSymbol>>> for training multiple inputs for the same symbol??
_recognisers[recogniser.Symbol] = recogniser;
}
