public PatternGeneratorResult <T> Process(IEnumerable <T> source)
{
List <T> elements = source.ToList();
var hash = new Dictionary <T, PatternItem <T> >();
foreach (var i in elements)
{
hash.Add(i, new PatternItem <T>(i));
}
foreach (var pattern in Patterns)
{
var avaiable = pattern.GetSelector().Select(new WhereSelector <T>(x => !hash[x].IsProcessed).Select(elements));
foreach (var range in avaiable)
{
var processed = pattern.Apply(range);
hash.AddNewKeys(processed, x => new PatternItem <T>(x));
if (pattern.IsUnique)
{
foreach (var i in processed)
{
hash[i].IsProcessed = true;
}
}
OnBatch?.Invoke(range, processed);
int index = elements.IndexOf(range.First());
elements.RemoveRange(index, range.Count());
elements.InsertRange(index, processed); // TODO probably replace with linked list
}
}
var result = new PatternGeneratorResult <T>();
result.Processed = elements.Except(source).ToList();
result.Unprocessed = source.Except(elements).ToList();
return(result);
}
public void Train(Dataset dataset, ILoss loss, IOptimizer optimizer, int batchSize, int nEpoches, double minError, Dataset valDataset, IMetric metric, bool shuffle = true)
{
if (dataset.InputShape != InputShape)
{
throw new ShapeMismatchException($"{nameof(dataset)} {nameof(dataset.InputShape)}");
}
if (dataset.TargetShape != OutputShape)
{
throw new ShapeMismatchException($"{nameof(dataset)} {nameof(dataset.TargetShape)}");
}
if (valDataset.InputShape != InputShape)
{
throw new ShapeMismatchException($"{nameof(valDataset)} {nameof(dataset.InputShape)}");
}
if (valDataset.TargetShape != OutputShape)
{
throw new ShapeMismatchException($"{nameof(valDataset)} {nameof(dataset.TargetShape)}");
}
ClearCache();
double valError = Validate(valDataset, metric);
int epoch;
OnStart?.Invoke(valError);
for (epoch = 0; epoch < nEpoches; epoch++)
{
if (valError <= minError)
{
break;
}
if (shuffle)
{
dataset.Shuffle();
}
dataset.ForEach((D, index) =>
{
Forward(D.Inputs, true);
CalcGrads(loss, D.Targets);
optimizer.UpdateEpoch(epoch);
Optimize(optimizer);
if (index % batchSize == 0)
{
Update();
OnBatch?.Invoke(index / batchSize);
}
});
valError = Validate(valDataset, metric);
OnEpoch?.Invoke(epoch, valError);
}
OnFinish?.Invoke(epoch, valError);
}