Knn refactor

knn/knn.go

@@ -44,27 +44,21 @@ func (KNN *KNNClassifier) PredictOne(vector []float64) string {
 	convertedVector := util.FloatsToMatrix(vector)
 
 	// Check what distance function we are using
+	var distanceFunc pairwiseMetrics.PairwiseDistanceFunc
 	switch KNN.DistanceFunc {
 	case "euclidean":
-		{
-			euclidean := pairwiseMetrics.NewEuclidean()
-			for i := 0; i < rows; i++ {
-				row := KNN.TrainingData.GetRowVectorWithoutClass(i)
-				rowMat := util.FloatsToMatrix(row)
-				distance := euclidean.Distance(rowMat, convertedVector)
-				rownumbers[i] = distance
-			}
-		}
+		distanceFunc = pairwiseMetrics.NewEuclidean()
 	case "manhattan":
-		{
-			manhattan := pairwiseMetrics.NewEuclidean()
-			for i := 0; i < rows; i++ {
-				row := KNN.TrainingData.GetRowVectorWithoutClass(i)
-				rowMat := util.FloatsToMatrix(row)
-				distance := manhattan.Distance(rowMat, convertedVector)
-				rownumbers[i] = distance
-			}
-		}
+		distanceFunc = pairwiseMetrics.NewManhattan()
+	default:
+		panic("unsupported distance function")
+	}
+
+	for i := 0; i < rows; i++ {
+		row := KNN.TrainingData.GetRowVectorWithoutClass(i)
+		rowMat := util.FloatsToMatrix(row)
+		distance := distanceFunc.Distance(rowMat, convertedVector)
+		rownumbers[i] = distance
 	}
 
 	sorted := util.SortIntMap(rownumbers)
@@ -125,27 +119,21 @@ func (KNN *KNNRegressor) Predict(vector *mat64.Dense, K int) float64 {
 	labels := make([]float64, 0)
 
 	// Check what distance function we are using
+	var distanceFunc pairwiseMetrics.PairwiseDistanceFunc
 	switch KNN.DistanceFunc {
 	case "euclidean":
-		{
-			euclidean := pairwiseMetrics.NewEuclidean()
-			for i := 0; i < rows; i++ {
-				row := KNN.Data.RowView(i)
-				rowMat := util.FloatsToMatrix(row)
-				distance := euclidean.Distance(rowMat, vector)
-				rownumbers[i] = distance
-			}
-		}
+		distanceFunc = pairwiseMetrics.NewEuclidean()
 	case "manhattan":
-		{
-			manhattan := pairwiseMetrics.NewEuclidean()
-			for i := 0; i < rows; i++ {
-				row := KNN.Data.RowView(i)
-				rowMat := util.FloatsToMatrix(row)
-				distance := manhattan.Distance(rowMat, vector)
-				rownumbers[i] = distance
-			}
-		}
+		distanceFunc = pairwiseMetrics.NewManhattan()
+	default:
+		panic("unsupported distance function")
+	}
+
+	for i := 0; i < rows; i++ {
+		row := KNN.Data.RowView(i)
+		rowMat := util.FloatsToMatrix(row)
+		distance := distanceFunc.Distance(rowMat, vector)
+		rownumbers[i] = distance
 	}
 
 	sorted := util.SortIntMap(rownumbers)

metrics/pairwise/pairwise.go

@@ -1,2 +1,10 @@
 // Package pairwise implements utilities to evaluate pairwise distances or inner product (via kernel).
 package pairwise
+
+import (
+	"github.com/gonum/matrix/mat64"
+)
+
+type PairwiseDistanceFunc interface {
+	Distance(vectorX *mat64.Dense, vectorY *mat64.Dense) float64
+}