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
}
func GradientDescent(
initialGuess []float64,
learningRate, precision float64,
maxIterations int,
gradient func([]float64) ([]float64, error),
) ([]float64, error) {
if len(initialGuess) == 0 {
return nil, errors.New("initialGuess cannot be empty")
}
oldResult := make([]float64, len(initialGuess))
newResult := make([]float64, len(initialGuess))
copy(oldResult, initialGuess)
for i := 0; i < maxIterations; i++ {
gradientAtOldResult, err := gradient(oldResult)
if err != nil {
return nil, err
}
newResult = vectorutilities.Add(oldResult, vectorutilities.Scale(-learningRate, gradientAtOldResult))
if (knnutilities.Euclidean(newResult, oldResult, precision)) < precision*precision {
return newResult, nil
} else {
oldResult = newResult
}
}
return newResult, nil
}
package knnutilities_test
import (
"github.com/amitkgupta/goodlearn/classifier/knn/knnutilities"
. "github.com/onsi/ginkgo"
. "github.com/onsi/gomega"
)
var _ = Describe("Euclidean", func() {
x := []float64{1, 2, -3.14}
y := []float64{-5, 6, 2.718}
squareEuclideanDistance := 86.316164
Context("When the square of the Euclidean distance is less than the bailout", func() {
var bailout float64 = 90
It("Returns the square distance", func() {
Ω(knnutilities.Euclidean(x, y, bailout)).Should(BeNumerically("~", squareEuclideanDistance, 0.001))
})
})
Context("When the square of the Euclidean distance is greater than or equal to the bailout", func() {
var bailout float64 = 80
It("Returns the bailout", func() {
Ω(knnutilities.Euclidean(x, y, bailout)).Should(Equal(bailout))
})
})
})