func main() {
//Parses the infamous Iris data.
cols, rows, _, labels, data := base.ParseCsv("datasets/randomdata.csv", 2, []int{0, 1})
newlabels := util.ConvertLabelsToFloat(labels)
//Initialises a new KNN classifier
knn := knnclass.KNNRegressor{}
knn.New("Testing", newlabels, data, rows, cols)
for {
//Creates a random array of N float64s between 0 and Y
randArray := util.RandomArray(2, 100)
//Initialises a vector with this array
random := mat.MakeDenseMatrix(randArray, 1, 2)
//Calculates the Euclidean distance and returns the most popular label
outcome, _ := knn.Predict(random, 3)
fmt.Println(outcome)
}
}
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.C
knn.New("Testing", 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 := mat.MakeDenseMatrix(randArray, 1, 3)
//Calculates the Euclidean distance and returns the most popular label
labels, _ := knn.Predict(random, 3)
fmt.Println(labels)
}
}