密度峰值聚类算法总结及其python实现

先上代码：

#-*-coding:utf-8 -*-
from __future__ import division
import numpy as np
from sklearn import preprocessing
from scipy.special import comb
from sklearn.metrics import adjusted_rand_score,f1_score,accuracy_score,precision_score,precision_recall_fscore_support
from sklearn.datasets import *
import sys
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import csv
import random
from time import time
from matplotlib.font_manager import FontProperties
defaultencoding = 'utf-8'
font = FontProperties(fname='/Library/Fonts/Songti.ttc')
if sys.getdefaultencoding() != defaultencoding:
    reload(sys)
    sys.setdefaultencoding(defaultencoding)

def calculate(data,percent):
    #计算距离
    distlist = []
    dist = np.zeros((len(data), len(data)));
    for i in range(len(data)-1):
        for j in range(i+1,len(data)):
            distance = np.sqrt(np.sum(np.square(data[i] - data[j])))
            dist[i, j] = distance
            dist[j, i] = distance
            if(i != j):
                distlist.append(distance)
    # dc
    # for i in range(len(dist) - 1):
    # 	for j in range(len(dist) - 1):
    # 		if(dist[i,j] != 0):
    sortdist = sorted(distlist)
    position = round(len(distlist) * percent / 100)
    dc = sortdist[position]
    print('dc',dc)
    #计算局部密度
    # 计算局部密度 rho (利用 Gaussian 核)
    rho = np.zeros(len(dist))
    # Gaussian kernel
    for i in range(len(dist) - 1):
        for j in range(i + 1, len(dist)):
            rho[i] = rho[i] + np.exp(-(dist[i, j] / dc) * (dist[i, j] / dc))
            rho[j] = rho[j] + np.exp(-(dist[i, j] / dc) * (dist[i, j] / dc))
    rho = [item / max(rho) for item in rho]
    # 生成 delta 和 nneigh 数组
    # delta 距离数组
    # nneigh 比本身密度大的最近的点
    # 记录 rho 值更大的数据点中与 ordrho(ii) 距离最近的点的编号 ordrho(jj)
    # 将 rho 按降序排列，ordrho 保持序
    ordrho = np.flipud(np.argsort(rho))
    delta = np.zeros(len(dist))
    nneigh = np.zeros(len(dist),dtype=int)
    delta[ordrho[0]] = -1.
    nneigh[ordrho[0]] = 0
    # 求最大距离
    maxd = max(dist.flatten())
    for ii in range(1,len(dist)):
        delta[ordrho[ii]] = maxd
        for jj in range(ii):
            if dist[ordrho[ii], ordrho[jj]] < delta[ordrho[ii]]:
                delta[ordrho[ii]] = dist[ordrho[ii], ordrho[jj]]
                nneigh[ordrho[ii]] = ordrho[jj]
        delta[ordrho[0]] = max(delta)
        # delta = [item/max(delta) for item in delta]
    gamma = [rho[i] * delta[i] for i in range(len(dist))]
    plt.figure(1);
    for index in range(len(dist)):
        plt.plot(rho[index], delta[index], 'ko');
    pos = plt.ginput(1)
    rhomin = pos[0][0]
    deltamin = pos[0][1]
    cl = -1*np.ones(len(dist),dtype=int)
    icl = []
    NCLUST = 1
    for i in range(len(dist)):
        if rho[i]>rhomin and delta[i]>deltamin:
            cl[i] = NCLUST #第 i 号数据点属于第 NCLUST 个 cluster
            icl.append(i) # 逆映射,第 NCLUST 个 cluster 的中心为第 i 号数据点
            NCLUST+=1
    for i in range(len(dist)):
        if cl[ordrho[i]] == -1:
            cl[ordrho[i]] = cl[nneigh[ordrho[i]]]
    print(cl)
    color = ['bo', 'co', 'go', 'ko', 'mo', 'ro', 'wo', 'yo']
    s = random.sample(color, NCLUST-1)
    plt.close()
    for i in range(len(data)):
        plt.plot(data[i][0],data[i][1],s[cl[i]-1])
    plt.show();
def loadData():
    dataSet = []
    # fileIn = open('C:/data/jain.txt')
    fileIn = open('C:/data/jain.dat')
    for line in fileIn.readlines():
        lineArr = line.strip().split('\t')
        dataSet.append([float(lineArr[0]), float(lineArr[1])])
    data = np.array(dataSet);
    return data;

#data,label = load_breast_cancer(True)
#min_max_scaler = preprocessing.MinMaxScaler()
#data = min_max_scaler.fit_transform(data)
def test():
    data = loadData()
    dataSet = np.array(data);
    dist = calculate(dataSet,2)
    # local_density(dist,2)
if __name__ == '__main__': test();
# len = len(data)
# # start = time()
# fsdp = FSDP(len,7.8605,data)
# fsdp.clustering(0.4703,0.7108)
# print (time() - start)/60.0
# print fsdp.cl
# print label
# print "f1_score"
# print f1_score(fsdp.cl,label,average="weighted")
# print "accary"
# print accuracy_score(fsdp.cl,label)
# print adjusted_rand_score(fsdp.cl,label)
# gamma = np.flipud(np.sort(fsdp.gamma))
# plt.figure("标准CS")
# plt.subplot(311)
# x = [t for t in range(0, len(gamma))]
# plt.plot(x, gamma, 'r*')
# plt.subplot(312)
# plt.plot(fsdp.rho, fsdp.delta, 'r*')
# plt.legend(loc='upper right', prop=font)
#
# plt.subplot(313)
# color = []
# for i in fsdp.cl:
#     color.append(colors.cnames.values()[i])
# plt.scatter(data[:,0], data[:,1],marker=".",c=color)
# plt.legend(loc='upper right', prop=font)
# plt.show()