public static MultiClassSvm <TValue, TLabel> Train <TValue, TLabel>(
IEnumerable <Observation <TValue, TLabel> > observations,
IKernel <TValue> k,
IEqualityComparer <TLabel> equalityComparer = null)
{
var classToObservations =
new Dictionary <TLabel, List <TValue> >(equalityComparer ?? EqualityComparer <TLabel> .Default);
var idToLabelMap = new Dictionary <int, TLabel>();
var labelId = 0;
foreach (var labelGroup in observations.GroupBy(t => t.Label))
{
idToLabelMap.Add(labelId, labelGroup.Key);
classToObservations.Add(labelGroup.Key, labelGroup.Select(o => o.Value).ToList());
labelId++;
}
var classifiers = new BinarySvm <TValue> [classToObservations.Count, classToObservations.Count];
for (int i = 0; i < classToObservations.Count; i++)
{
for (int j = i + 1; j < classToObservations.Count; j++)
{
var group1 = classToObservations[idToLabelMap[i]];
var group2 = classToObservations[idToLabelMap[j]];
var merged = group1.Select(entry => new BinaryObservation <TValue>(entry, -1))
.Concat(group2.Select(entry => new BinaryObservation <TValue>(entry, 1)));
classifiers[i, j] = BinarySvmTrainer.Train(merged.ToArray(), k);
}
}
return(new MultiClassSvm <TValue, TLabel>(classifiers, idToLabelMap));
}
public void BinarySvmTest()
{
var svm = BinarySvmTrainer.Train(
new[] {
new BinaryObservation <int[]>(new[] { 0, 1 }, -1), new BinaryObservation <int[]>(new[] { 1, 1 }, -1),
new BinaryObservation <int[]>(new[] { 4, 3 }, -1), new BinaryObservation <int[]>(new[] { 3, 1 }, 1),
new BinaryObservation <int[]>(new[] { 4, 2 }, 1)
},
new DotProduct()
);
Assert.That(svm.Classify(new[] { 0, 0 }), Is.EqualTo(-1));
}