func main() {
//Parses the infamous Iris data.
cols, rows, _, labels, data := data.ParseCsv("datasets/iris.csv", 4, []int{0, 1, 2})
//Initialises a new KNN classifier
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)
//Calculates the Euclidean distance and returns the most popular label
labels := cls.Predict(randArray, 3)
fmt.Println(labels)
}
}
func main() {
//Parses the infamous Iris data.
cols, rows, _, labels, data := data.ParseCsv("datasets/randomdata.csv", 2, []int{0, 1})
newlabels := util.ConvertLabelsToFloat(labels)
//Initialises a new KNN classifier
cls := knn.NewKnnRegressor("euclidean")
cls.Fit(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 := mat64.NewDense(1, 2, randArray)
//Calculates the Euclidean distance and returns the most popular label
outcome := cls.Predict(random, 3)
fmt.Println(outcome)
}
}