public virtual void TestBackwardsCompatibility()
{
RVFDataset <string, string> dataset = new WeightedRVFDataset <string, string>();
RVFDatum <string, string> datum1 = NewRVFDatum(null, "a", "b", "a");
dataset.Add(datum1);
RVFDatum <string, string> datum2 = NewRVFDatum(null, "a", "b", "a");
dataset.Add(datum2);
NUnit.Framework.Assert.AreEqual(1.0f, ((WeightedRVFDataset <string, string>)dataset).GetWeights()[0], 1e-10);
NUnit.Framework.Assert.AreEqual(1.0f, ((WeightedRVFDataset <string, string>)dataset).GetWeights()[1], 1e-10);
}
public virtual void TestWeightingWorks()
{
WeightedRVFDataset <string, string> dataset = new WeightedRVFDataset <string, string>();
RVFDatum <string, string> datum1 = NewRVFDatum(null, "a", "b", "a");
dataset.Add(datum1, 42.0f);
RVFDatum <string, string> datum2 = NewRVFDatum(null, "a", "b", "a");
dataset.Add(datum2, 7.3f);
NUnit.Framework.Assert.AreEqual(42.0f, dataset.GetWeights()[0], 1e-10);
NUnit.Framework.Assert.AreEqual(7.3f, dataset.GetWeights()[1], 1e-10);
}
public override Pair <GeneralDataset <L, F>, GeneralDataset <L, F> > Split(int start, int end)
{
int devSize = end - start;
int trainSize = Size() - devSize;
int[][] devData = new int[devSize][];
double[][] devValues = new double[devSize][];
int[] devLabels = new int[devSize];
int[][] trainData = new int[trainSize][];
double[][] trainValues = new double[trainSize][];
int[] trainLabels = new int[trainSize];
lock (typeof(Runtime))
{
System.Array.Copy(data, start, devData, 0, devSize);
System.Array.Copy(values, start, devValues, 0, devSize);
System.Array.Copy(labels, start, devLabels, 0, devSize);
System.Array.Copy(data, 0, trainData, 0, start);
System.Array.Copy(data, end, trainData, start, Size() - end);
System.Array.Copy(values, 0, trainValues, 0, start);
System.Array.Copy(values, end, trainValues, start, Size() - end);
System.Array.Copy(labels, 0, trainLabels, 0, start);
System.Array.Copy(labels, end, trainLabels, start, Size() - end);
}
if (this is WeightedRVFDataset <object, object> )
{
float[] trainWeights = new float[trainSize];
float[] devWeights = new float[devSize];
WeightedRVFDataset <L, F> w = (WeightedRVFDataset <L, F>) this;
lock (typeof(Runtime))
{
System.Array.Copy(w.weights, start, devWeights, 0, devSize);
System.Array.Copy(w.weights, 0, trainWeights, 0, start);
System.Array.Copy(w.weights, end, trainWeights, start, Size() - end);
}
WeightedRVFDataset <L, F> dev = new WeightedRVFDataset <L, F>(labelIndex, devLabels, featureIndex, devData, devValues, devWeights);
WeightedRVFDataset <L, F> train = new WeightedRVFDataset <L, F>(labelIndex, trainLabels, featureIndex, trainData, trainValues, trainWeights);
return(new Pair <GeneralDataset <L, F>, GeneralDataset <L, F> >(train, dev));
}
else
{
GeneralDataset <L, F> dev = new Edu.Stanford.Nlp.Classify.RVFDataset <L, F>(labelIndex, devLabels, featureIndex, devData, devValues);
GeneralDataset <L, F> train = new Edu.Stanford.Nlp.Classify.RVFDataset <L, F>(labelIndex, trainLabels, featureIndex, trainData, trainValues);
return(new Pair <GeneralDataset <L, F>, GeneralDataset <L, F> >(train, dev));
}
}
