// Returns a classification for the vector, based on a vector input, using the KNN algorithm.
// See http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm.
func (KNN *KNNClassifier) Predict(vector []float64, K int) string {
convertedVector := util.FloatsToMatrix(vector)
// Get the number of rows
rows, _ := KNN.Data.Dims()
rownumbers := make(map[int]float64)
labels := make([]string, 0)
maxmap := make(map[string]int)
// Check what distance function we are using
switch KNN.DistanceFunc {
case "euclidean":
{
euclidean := pairwiseMetrics.NewEuclidean()
for i := 0; i < rows; i++ {
row := KNN.Data.RowView(i)
rowMat := util.FloatsToMatrix(row)
distance := euclidean.Distance(rowMat, convertedVector)
rownumbers[i] = distance
}
}
case "manhattan":
{
manhattan := pairwiseMetrics.NewEuclidean()
for i := 0; i < rows; i++ {
row := KNN.Data.RowView(i)
rowMat := util.FloatsToMatrix(row)
distance := manhattan.Distance(rowMat, convertedVector)
rownumbers[i] = distance
}
}
}
sorted := util.SortIntMap(rownumbers)
values := sorted[:K]
for _, elem := range values {
// It's when we access this map
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
}
// Returns a classification for the vector, based on a vector input, using the KNN algorithm.
// See http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm.
func (KNN *KNNClassifier) PredictOne(vector []float64) string {
rows := KNN.TrainingData.Rows
rownumbers := make(map[int]float64)
labels := make([]string, 0)
maxmap := make(map[string]int)
convertedVector := util.FloatsToMatrix(vector)
// Check what distance function we are using
switch KNN.DistanceFunc {
case "euclidean":
{
euclidean := pairwiseMetrics.NewEuclidean()
for i := 0; i < rows; i++ {
row := KNN.TrainingData.GetRowVectorWithoutClass(i)
rowMat := util.FloatsToMatrix(row)
distance := euclidean.Distance(rowMat, convertedVector)
rownumbers[i] = distance
}
}
case "manhattan":
{
manhattan := pairwiseMetrics.NewEuclidean()
for i := 0; i < rows; i++ {
row := KNN.TrainingData.GetRowVectorWithoutClass(i)
rowMat := util.FloatsToMatrix(row)
distance := manhattan.Distance(rowMat, convertedVector)
rownumbers[i] = distance
}
}
}
sorted := util.SortIntMap(rownumbers)
values := sorted[:KNN.NearestNeighbours]
for _, elem := range values {
label := KNN.TrainingData.GetClass(elem)
labels = append(labels, label)
if _, ok := maxmap[label]; ok {
maxmap[label] += 1
} else {
maxmap[label] = 1
}
}
sortedlabels := util.SortStringMap(maxmap)
label := sortedlabels[0]
return label
}