//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
}
//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
}