Пример #1
0
//Returns a classification for the vector, based on a vector input, using the KNN algorithm.
func (KNN *KNNClassifier) Predict(vector *mat.DenseMatrix, K int) (string, []int) {

	rows := KNN.Data.Rows()
	rownumbers := make(map[int]float64)
	labels := make([]string, 0)
	maxmap := make(map[string]int)

	for i := 0; i < rows; i++ {
		row := KNN.Data.GetRowVector(i)
		eucdistance := KNN.ComputeDistance(row, vector)
		rownumbers[i] = eucdistance
	}

	sorted := util.SortIntMap(rownumbers)
	values := sorted[:K]

	for _, elem := range values {
		labels = append(labels, KNN.Labels[elem])

		if _, ok := maxmap[KNN.Labels[elem]]; ok {
			maxmap[KNN.Labels[elem]] += 1
		} else {
			maxmap[KNN.Labels[elem]] = 1
		}
	}

	sortedlabels := util.SortStringMap(maxmap)
	label := sortedlabels[0]

	return label, values
}
Пример #2
0
//Returns an average of the K nearest labels/variables, based on a vector input.
func (KNN *KNNRegressor) Predict(vector *mat.DenseMatrix, K int) (float64, []int) {

	rows := KNN.Data.Rows()
	rownumbers := make(map[int]float64)
	labels := make([]float64, 1)
	sum := 0.0

	for i := 0; i < rows; i++ {
		row := KNN.Data.GetRowVector(i)
		eucdistance := KNN.ComputeDistance(row, vector)
		rownumbers[i] = eucdistance
	}

	sorted := util.SortIntMap(rownumbers)
	values := sorted[:K]

	for _, elem := range values {
		value := KNN.Labels[elem]
		labels = append(labels, value)
		sum += value
	}

	average := sum / float64(K)
	return average, values
}