private KNNClassifier InitClassifier(Parameters p)
{
KNNClassifier.Builder builder = KNNClassifier.GetBuilder();
KNNClassifier knn = builder.Build();
p.Apply(knn);
return(knn);
}
public void TestBuilder()
{
KNNClassifier.Builder builder = KNNClassifier.GetBuilder();
builder.K(42)
.Exact(true)
.DistanceNorm(12.5)
.DistanceMethod(DistanceMethod.PctInputOverlap)
.DistanceThreshold(2.3)
.DoBinarization(true)
.BinarizationThreshold(3.0)
.UseSparseMemory(true)
.SparseThreshold(349.0)
.RelativeThreshold(true)
.NumWinners(100)
.NumSVDSamples(4)
.NumSVDDims((int)KnnMode.ADAPTIVE)
.FractionOfMax(0.84)
.MaxStoredPatterns(30)
.ReplaceDuplicates(true)
.CellsPerCol(32);
KNNClassifier knn = builder.Build();
Assert.AreEqual(42, knn.GetK());
Assert.IsTrue(knn.IsExact());
Assert.AreEqual(12.5, knn.GetDistanceNorm(), 0.0);
Assert.AreEqual(DistanceMethod.PctInputOverlap, knn.GetDistanceMethod());
Assert.AreEqual(2.3, knn.GetDistanceThreshold(), 0.0);
Assert.IsTrue(knn.IsDoBinarization());
Assert.AreEqual(3.0, knn.GetBinarizationThreshold(), 0.0);
Assert.IsTrue(knn.IsRelativeThreshold());
Assert.IsTrue(knn.IsUseSparseMemory());
Assert.AreEqual(349.0, knn.GetSparseThreshold(), 0.0);
Assert.AreEqual(100, knn.GetNumWinners());
Assert.AreEqual(4, knn.GetNumSVDSamples());
Assert.AreEqual((int)KnnMode.ADAPTIVE, knn.GetNumSVDDims());
Assert.AreEqual(0.84, knn.GetFractionOfMax().GetValueOrDefault(), 0.0);
Assert.AreEqual(30, knn.GetMaxStoredPatterns());
Assert.IsTrue(knn.IsReplaceDuplicates());
Assert.AreEqual(32, knn.GetCellsPerCol());
}
