private static int PredictBagLabel(IClassifier<SparseBag> classifier, BagTestData[] bags, SparseBag bagToTest)
{
var sparseBags = BagTestData.ToSparseBags(bags);
classifier.Train(sparseBags);
return classifier.PredictWinner(bagToTest);
}
static internal IEnumerable<SparseBag> ToSparseBags(BagTestData[] bags)
{
var sparseBags = new SparseBag[bags.Length];
for (var bagId = 0; bagId < sparseBags.Length; bagId++)
{
var istances = new IDictionary<int, float>[bags[bagId].Points.Length];
for (var pointId = 0; pointId < bags[bagId].Points.Length; pointId++)
{
var j = 1;
var features = (from f in bags[bagId].Points[pointId] select f).ToDictionary(i => (j++), i => (float)i);
istances[pointId] = features;
}
sparseBags[bagId] = new SparseBag(bags[bagId].Label, bags[bagId].Name.ToString(), istances);
}
return sparseBags;
}
private static int PredictBagLabel(BagTestData[] bags, SparseBag bagToTest, int M)
{
var sparseBags = BagTestData.ToSparseBags(bags);
var citationNearestNeighbor = ClassifierFactory.GetMultiInstanceClassifier("rbfn:distance=hausdorff,M=" + M+",sigma=1");
citationNearestNeighbor.Train(sparseBags);
return citationNearestNeighbor.PredictWinner(bagToTest);
}
private static int PredictBagLabel(BagTestData[] bags, SparseBag bagToTest, int K)
{
var sparseBags = BagTestData.ToSparseBags(bags);
// When
var citationNearestNeighbor = ClassifierFactory.GetMultiInstanceClassifier("nn:distance=hausdorff,K=" + K);
citationNearestNeighbor.Train(sparseBags);
return citationNearestNeighbor.PredictWinner(bagToTest);
}
public void PredictWinner_NegativeWins_EmptyKnowledge()
{
// Given
var bags = new BagTestData[0];
var bagToTest = new SparseBag(1, "1", new[]
{
new Dictionary<int, float> {{1, 22.8529f}, {2, 22.6664f}},
new Dictionary<int, float> {{1, 59.1986f}, {2, 34.5393f}}
});
int predictedLabelForOne = PredictBagLabel(bags, bagToTest, 2);
// Then
Assert.That(predictedLabelForOne, Is.EqualTo(-1));
}
