数据处理之聚类分析

# -*- coding: utf-8 -*-
from sklearn.cluster import KMeans
from sklearn.externals import joblib
import numpy ,time,pdb
import matplotlib.pyplot as plt
from sklearn.cluster import MeanShift, estimate_bandwidth
import numpy as np

if __name__ == '__main__':
# step 1: 加载数据
print "step 1: load data..."

dataSet = []
fileIn = open('./MOC_X1000_20170811110600_MHAIL_00.txt')
for line in fileIn.readlines()[1:]:
lineArr = line.strip().split(',')
dataSet.append([float(lineArr[0]), float(lineArr[1])])

numSamples = len(dataSet)
X = np.array(dataSet) #列表类型转换成array数组类型
# bandwidth = estimate_bandwidth(X, quantile=0.2, n_samples=500)
bandwidth = estimate_bandwidth(X, quantile=0.5, n_samples=numSamples)
clf = MeanShift(bandwidth=bandwidth, bin_seeding=True,cluster_all=True).fit(X)

centroids = clf.labels_
print centroids,type(centroids) #显示每一个点的聚类归属
# 计算其自动生成的k，并将聚类数量小于3的排除
arr_flag = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
for i in clf.labels_:
arr_flag[i]+=1
k = 0
# pdb.set_trace()
for i in arr_flag:
if(i > 45):
k +=1
print k

mark = ['or', 'ob', 'og', 'ok', '^r', '+r', 'sr', 'dr', '<r', 'pr']
#画出所有样例点 属于同一分类的绘制同样的颜色
for i in xrange(numSamples):
plt.plot(dataSet[i][0], dataSet[i][1], mark[clf.labels_[i]]) #mark[markIndex])

mark = ['Dr', 'Db', 'Dg', 'Dk', '^b', '+b', 'sb', 'db', '<b', 'pb']
# 画出质点，用特殊图型
centroids =clf.cluster_centers_
for i in range(k):
plt.plot(centroids[i][0], centroids[i][1], mark[i], markersize = 12)
# plt.show()
print centroids #显示中心点坐标
plt.show()