Refactored knn

golearn.go

@@ -1,2 +1,2 @@
-//golearn is a machine learning library for Go.
+//Package golearn is a machine learning library for Go.
 package golearn

knn/knn.go

@@ -86,3 +86,65 @@ func (KNN *KNNClassifier) Predict(vector []float64, K int) string {
 
 	return label
 }
+
+//A KNN Regressor. Consists of a data matrix, associated result variables in the same order as the matrix, and a name.
+type KNNRegressor struct {
+	base.BaseEstimator
+	Values       []float64
+	DistanceFunc string
+}
+
+// Mints a new classifier.
+func NewKnnRegressor(values []float64, numbers []float64, x int, y int, distfunc string) *KNNRegressor {
+	KNN := KNNRegressor{}
+	KNN.Data = mat64.NewDense(x, y, numbers)
+	KNN.Values = values
+	KNN.DistanceFunc = distfunc
+	return &KNN
+}
+
+//Returns an average of the K nearest labels/variables, based on a vector input.
+func (KNN *KNNRegressor) Predict(vector *mat64.Dense, K int) float64 {
+
+	// Get the number of rows
+	rows, _ := KNN.Data.Dims()
+	rownumbers := make(map[int]float64)
+	labels := make([]float64, 0)
+
+	// Check what distance function we are using
+	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
+			}
+		}
+	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
+			}
+		}
+	}
+
+	sorted := util.SortIntMap(rownumbers)
+	values := sorted[:K]
+
+	var sum float64
+	for _, elem := range values {
+		value := KNN.Values[elem]
+		labels = append(labels, value)
+		sum += value
+	}
+
+	average := sum / float64(K)
+	return average
+}