func (c *KNN) PredictMultiClass(sample *core.Sample) *core.ArrayVector {
x := sample.GetFeatureVector()
predictions := []*eval.LabelPrediction{}
for i, s := range c.sv {
predictions = append(predictions, &(eval.LabelPrediction{Label: c.labels[i], Prediction: c.Kernel(s, x)}))
}
compare := func(p1, p2 *eval.LabelPrediction) bool {
return p1.Prediction > p2.Prediction
}
eval.By(compare).Sort(predictions)
ret := core.NewArrayVector()
for i, pred := range predictions {
if i > c.k {
break
}
ret.AddValue(pred.Label, 1.0)
}
return ret
}
func (c *SVM) Predict(sample *core.Sample) float64 {
x := sample.GetFeatureVector()
return c.PredictVector(x)
}