Added example documentation

examples/knn_iris.go

@@ -0,0 +1,30 @@
+package main
+
+import (
+		mat "github.com/skelterjohn/go.matrix"
+		base "golearn/base"
+		util "golearn/utilities"
+		knnclass "golearn/knn"
+		"fmt"
+		)
+
+func main(){
+	//Parses the infamous Iris data.
+	cols, rows, _, labels, data := base.ParseCsv("datasets/iris.csv", 4, []int{0,1,2})
+
+	//Initialises a new KNN classifier
+	knn := knnclass.KNNClassifier{}
+	knn.New("Testing", labels, data, rows, cols)
+	
+	for {
+		//Creates a random array of N float64s between 0 and 7
+		randArray := util.RandomArray(3)
+
+		//Initialises a vector with this array
+		random := mat.MakeDenseMatrix(randArray,1,3)
+
+		//Calculates the Euclidean distance and returns the most popular label
+		labels, _ := knn.Predict(random, 3)
+		fmt.Println(labels)
+	}
+}

knn/knn.go

@@ -1,4 +1,4 @@
-package main
+package knn
 
 import (
 		mat "github.com/skelterjohn/go.matrix"
@@ -6,7 +6,6 @@ import (
 		"fmt"
 		util "golearn/utilities"
 		base "golearn/base"
-		// "errors""
 		)
 
 //A KNN Classifier. Consists of a data matrix, associated labels in the same order as the matrix, and a name.
@@ -78,17 +77,4 @@ func (KNN *KNNClassifier) Predict(vector *mat.DenseMatrix, K int) (string, []int
 	label := sortedlabels[0]
 
 	return label, values
-}
-
-func main(){
-	cols, rows, _, labels, data := base.ParseCsv("../datasets/iris.csv", 4, []int{0,1,2})
-	knn := KNNClassifier{}
-	knn.New("Testing", labels, data, rows, cols)
-	
-	for {
-		randArray := util.RandomArray(3)
-		random := mat.MakeDenseMatrix(randArray,1,3)
-		labels, _ := knn.Predict(random, 3)
-		fmt.Println(labels)
-	}
 }

knn/testknn.go

@@ -1,5 +1 @@
-package knn
-
-import (
-		"testing"
-		)
+package knn

utilities/utilities.go

@@ -3,7 +3,6 @@ package utilities
 import (
 		"sort"
 		rand "math/rand"
-		"fmt"
 		)
 
 type sortedIntMap struct {
@@ -71,6 +70,5 @@ func RandomArray(n int) []float64 {
 	for i := 0; i < n; i++ {
 		ReturnedArray[i] = rand.Float64() * float64(rand.Intn(7))
 	}
-	fmt.Println(ReturnedArray)
 	return ReturnedArray
 }