Algorithm 1. KNN.
Input:	Samples that need to be categorized: ${\mathrm{X}}_j$; the known sample pairs: (${\mathrm{X}}_i$, ${\mathrm{y}}_i$)
Output:	Prediction classification: ${\mathrm{y}}_j$
1:	for every sample in the dataset to be predicted do
2:	calculate the distance between (${\mathrm{X}}_i$, ${\mathrm{y}}_i$) and the current sample ${\mathrm{X}}_j$
3:	sort the distances in increasing order
4:	select the k samples with the smallest distances to ${\mathrm{X}}_j$
5:	find the majority class of the k samples
6:	return the majority class as the prediction classification ${\mathrm{y}}_j$
7:	end For