Working on Euclidean distance

knn.go

@@ -8,18 +8,37 @@ import (
 type KNNClassifier struct {
 	Data mat.DenseMatrix
 	Name string
+	Labels []string
 }
 
-func (Class *KNNClassifier) New(name string, numbers []float64, x int, y int){
-	Class.Data = *mat.MakeDenseMatrix(numbers, x, y)
-	Class.Name = name
+//Initialises a new classifier
+func (KNN *KNNClassifier) New(name string, labels []string, numbers []float64, x int, y int){
+	KNN.Data = *mat.MakeDenseMatrix(numbers, x, y)
+	KNN.Name = name
+	KNN.Labels = labels
 }
 
+func (KNN *KNNClassifier) ComputeDistance(vector mat.DenseMatrix) mat.DenseMatrix {
+	//Add switches for different distance metrics
+	result, _ := KNN.Data.TimesDense(&vector)
+	return *result
+}
+
+func (KNN *KNNClassifier) Predict(vector mat.DenseMatrix) mat.DenseMatrix {
+	blah := KNN.ComputeDistance(vector)
+	//return *mat.Difference(&KNN.Data, &vector)
+	return blah
+}
+
+func (KNN *KNNClassifier) GetLabel(index int) string {
+	return KNN.Labels[index]
+}
 
 func main(){
 	knn := KNNClassifier{}
-	knn.New("Testing", []float64{1,2,3,4,5,6},2,3)
-	another := KNNClassifier{}
-	another.New("Blah", []float64{2,4,5,3,4,6},2,3)
-	fmt.Println(mat.Difference(&knn.Data, &another.Data))
+	dense := *mat.MakeDenseMatrix([]float64{4,5,1,3,4,2},2,3)
+	knn.New("Testing", []string{"this sucks", "hiya"}, []float64{1,2,3,4,5,6},2,3)
+	//hey := knn.ComputeDistance(dense)
+	blof := knn.Predict(dense)
+	fmt.Println(blof)
 }