Refactored KNN to implement the estimator interface

base/domain.go

@@ -15,7 +15,6 @@ import (
 // An object that can ingest some data and train on it.
 type Estimator interface {
 	Fit()
-	Summarise()
 }
 
 // An object that provides predictions.

examples/knnclassifier_iris.go

@@ -3,7 +3,6 @@ package main
 import (
 	"fmt"
 
-	mat64 "github.com/gonum/matrix/mat64"
 	data "github.com/sjwhitworth/golearn/data"
 	knn "github.com/sjwhitworth/golearn/knn"
 	util "github.com/sjwhitworth/golearn/utilities"
@@ -14,17 +13,15 @@ func main() {
 	cols, rows, _, labels, data := data.ParseCsv("datasets/iris.csv", 4, []int{0, 1, 2})
 
 	//Initialises a new KNN classifier
-	cls := knn.NewKnnClassifier(labels, data, rows, cols, "euclidean")
+	cls := knn.NewKnnClassifier("euclidean")
+	cls.Fit(labels, data, rows, cols)
 
 	for {
 		//Creates a random array of N float64s between 0 and 7
 		randArray := util.RandomArray(3, 7)
 
-		//Initialises a vector with this array
-		random := mat64.NewDense(1, 3, randArray)
-
 		//Calculates the Euclidean distance and returns the most popular label
-		labels := cls.Predict(random, 3)
+		labels := cls.Predict(randArray, 3)
 		fmt.Println(labels)
 	}
 }

examples/knnregressor_random.go

@@ -15,7 +15,8 @@ func main() {
 	newlabels := util.ConvertLabelsToFloat(labels)
 
 	//Initialises a new KNN classifier
-	cls := knn.NewKnnRegressor(newlabels, data, rows, cols, "euclidean")
+	cls := knn.NewKnnRegressor("euclidean")
+	cls.Fit(newlabels, data, rows, cols)
 
 	for {
 		//Creates a random array of N float64s between 0 and Y

knn/knn.go

@@ -19,16 +19,19 @@ type KNNClassifier struct {
 }
 
 // Returns a new classifier
-func NewKnnClassifier(labels []string, numbers []float64, rows int, cols int, distfunc string) *KNNClassifier {
+func NewKnnClassifier(distfunc string) *KNNClassifier {
+	KNN := KNNClassifier{}
+	KNN.DistanceFunc = distfunc
+	return &KNN
+}
+
+func (KNN *KNNClassifier) Fit(labels []string, numbers []float64, rows int, cols int) {
 	if rows != len(labels) {
 		panic(mat64.ErrShape)
 	}
 
-	KNN := KNNClassifier{}
 	KNN.Data = mat64.NewDense(rows, cols, numbers)
 	KNN.Labels = labels
-	KNN.DistanceFunc = distfunc
-	return &KNN
 }
 
 // Returns a classification for the vector, based on a vector input, using the KNN algorithm.
@@ -94,14 +97,21 @@ type KNNRegressor struct {
 }
 
 // Mints a new classifier.
-func NewKnnRegressor(values []float64, numbers []float64, x int, y int, distfunc string) *KNNRegressor {
+func NewKnnRegressor(distfunc string) *KNNRegressor {
 	KNN := KNNRegressor{}
-	KNN.Data = mat64.NewDense(x, y, numbers)
-	KNN.Values = values
 	KNN.DistanceFunc = distfunc
 	return &KNN
 }
 
+func (KNN *KNNRegressor) Fit(values []float64, numbers []float64, rows int, cols int) {
+	if rows != len(values) {
+		panic(mat64.ErrShape)
+	}
+
+	KNN.Data = mat64.NewDense(rows, cols, numbers)
+	KNN.Values = values
+}
+
 //Returns an average of the K nearest labels/variables, based on a vector input.
 func (KNN *KNNRegressor) Predict(vector *mat64.Dense, K int) float64 {
 

trees/decision_trees.go

@@ -0,0 +1,12 @@
+package trees
+
+import base "github.com/sjwhitworth/golearn/base"
+
+type DecisionTree struct {
+	base.BaseEstimator
+}
+
+type Branch struct {
+	LeftBranch  Branch
+	RightBranch Branch
+}

trees/trees.go

@@ -0,0 +1,2 @@
+// Package trees provides a number of tree based ensemble learners.
+package trees