import?numpy?as?np
import?matplotlib.pyplot?as?plt

from?sklearn.datasets?import?make_blobs


def?distanceNorm（NormD_value）:
	#?initialization
	

	#?Norm?for?distance
	if?Norm?==?‘1‘:
		counter?=?np.absolute（D_value）;
		counter?=?np.sum（counter）;
	elif?Norm?==?‘2‘:
		counter?=?np.power（D_value2）;
		counter?=?np.sum（counter）;
		counter?=?np.sqrt（counter）;
	elif?Norm?==?‘Infinity‘:
		counter?=?np.absolute（D_value）;
		counter?=?np.max（counter）;
	else:
		raise?Exception（‘We?will?program?this?later......‘）;

	return?counter;


def?chi（x）:
	if?x?		return?1;
	else:
		return?0;


def?fit（featureslabelstdistanceMethod?=?‘2‘）:
	#?initialization
	distance?=?np.zeros（（len（labels）len（labels）））;
	distance_sort?=?list（）;
	density?=?np.zeros（len（labels））;
	distance_higherDensity?=?np.zeros（len（labels））;


	#?compute?distance
	for?index_i?in?xrange（len（labels））:
		for?index_j?in?xrange（index_i+1len（labels））:
			D_value?=?features[index_i]?-?features[index_j];
			distance[index_iindex_j]?=?distanceNorm（distanceMethodD_value）;
			distance_sort.append（distance[index_iindex_j]）;
	distance?+=?distance.T;

	#?compute?optimal?cutoff
	distance_sort?=?np.array（distance_sort）;
	cutoff?=?int（np.round（distance_sort[len（distance_sort）?*?t]））;

	#?computer?density
	for?index_i?in?xrange（len（labels））:
		distance_cutoff_i?=?distance[index_i]?-?cutoff;
		for?index_j?in?xrange（1len（labels））:
			density[index_i]?+=?chi（distance_cutoff_i[index_j]）;

	#?search?for?the?max?density
	Max?=?np.max（density）;
	MaxIndexList?=?list（）;
	for?index_i?in?xrange（len（labels））:
		if?density[index_i]?==?Max:
			MaxIndexList.extend（[index_i]）;

	#?computer?distance_higherDensity
	Min?=?0;
	for?index_i?in?xrange（len（labels））:
		if?index_i?in?MaxIndexList:
			distance_higherDensity[index_i]?=?np.max（distance[index_i]）;
			continue;
		else:
			Min?=?np.max（distance[index_i]）;
		for?index_j?in?xrange（1len（labels））:
			if?density[index_i]?				Min?=?distance[index_iindex_j];
			else:
				continue;
		distance_high