// TODO: Configurable Cache!
public DataFeatureMatrixProvider(DataFeatureSelectionStrategy selectionStrategy, IDataFeatureProvider dataFeatureProvider, Strings featureIDs = null)
{
Contract.Requires(selectionStrategy != null);
Contract.Requires(dataFeatureProvider != null);
this.selectionStrategy = selectionStrategy;
DataFeatureProvider = dataFeatureProvider;
vectorizer = new FeatureSetVectorizer(featureIDs == null ? dataFeatureProvider.FeatureDescriptions : dataFeatureProvider.FeatureDescriptions.GetSubset(featureIDs));
MatrixWidth = vectorizer.FeatureDescriptions.Sum(d => d.FeatureValueCount);
}
private void Initialize()
{
var set = new FeatureDescriptionSet(desc);
FeatureDescriptionSet inputSet = null;
FeatureDescriptionSet outputSet = null;
try
{
inputSet = set.GetSubset(InputFeatureIDs);
outputSet = set.GetSubset(OutputFeatureIDs);
}
catch (Exception ex)
{
throw new InvalidOperationException("Cannot generate feature subset. See inner exception for details.", ex);
}
inputVectorizer = new FeatureSetVectorizer(inputSet);
outputVectorizer = new FeatureSetVectorizer(outputSet);
if (inputVectorizer.FeatureDescriptions.Sum(d => d.FeatureValueCount) != Unit.InputInterface.Length)
{
throw new InvalidOperationException("Invalid number of input features.");
}
if (outputVectorizer.FeatureDescriptions.Sum(d => d.FeatureValueCount) != Unit.OutputInterface.Length)
{
throw new InvalidOperationException("Invalid number of output features.");
}
inputIDLookup = CreateLookup(InputFeatureIDs);
if (InputFeatureIDs.Count + OutputFeatureIDs.Count < set.Count)
{
desc = Cut(set, InputFeatureIDs.Concat(OutputFeatureIDs));
}
computation = new Learningutation<double>(NumberOfIterations);
}
