func (classifier *kNNClassifier) Classify(testRow row.Row) (slice.Slice, error) {
trainingData := classifier.trainingData
if trainingData == nil {
return nil, knnerrors.NewUntrainedClassifierError()
}
numTestRowFeatures := testRow.NumFeatures()
numTrainingDataFeatures := trainingData.NumFeatures()
if numTestRowFeatures != numTrainingDataFeatures {
return nil, knnerrors.NewRowLengthMismatchError(numTestRowFeatures, numTrainingDataFeatures)
}
testFeatures, ok := testRow.Features().(slice.FloatSlice)
if !ok {
return nil, knnerrors.NewNonFloatFeaturesTestRowError()
}
testFeatureValues := testFeatures.Values()
nearestNeighbours := knnutilities.NewKNNTargetCollection(classifier.k)
for i := 0; i < trainingData.NumRows(); i++ {
trainingRow, _ := trainingData.Row(i)
trainingFeatures, _ := trainingRow.Features().(slice.FloatSlice)
trainingFeatureValues := trainingFeatures.Values()
distance := knnutilities.Euclidean(testFeatureValues, trainingFeatureValues, nearestNeighbours.MaxDistance())
if distance < nearestNeighbours.MaxDistance() {
nearestNeighbours.Insert(trainingRow.Target(), distance)
}
}
return nearestNeighbours.Vote(), nil
}
"github.com/amitkgupta/goodlearn/data/slice"
"math"
. "github.com/onsi/ginkgo"
. "github.com/onsi/gomega"
)
var _ = Describe("SortedTargetCollection", func() {
Describe("Insert and MaxDistance", func() {
var stc knnutilities.SortedTargetCollection
var target slice.Slice
var err error
BeforeEach(func() {
stc = knnutilities.NewKNNTargetCollection(2)
target, err = slice.SliceFromRawValues(true, []int{}, []columntype.ColumnType{}, []float64{})
Ω(err).ShouldNot(HaveOccurred())
})
Context("Before the collection is full", func() {
It("The MaxDistance should be +Inf", func() {
Ω(stc.MaxDistance()).Should(Equal(math.MaxFloat64))
stc.Insert(target, 1.0)
Ω(stc.MaxDistance()).Should(Equal(math.MaxFloat64))
})
})
Context("When the collection is full", func() {
initialMax := 3.0