def test1():
datMat = mat(kMeans.loadDataSet('testSet.txt'))
print(min(datMat[:, 0]))
print(kMeans.randCent(datMat, 2))
print(kMeans.distEclud(datMat[0], datMat[1]))
#myCentroids, clustAssing = kMeans.kMeans(datMat,3)
myCentroids, clustAssing = kMeans.biKmeans(datMat, 4)
print(myCentroids)
kMeans.plot1(datMat, myCentroids)
#!/usr/bin/env python
__coding__ = "utf-8"
__author__ = "Ng WaiMing"
from kMeans import kMeans
from kMeans import loadDataSet
from kMeans import randCent
from kMeans import distEclud
from kMeans import biKmeans
from numpy import *
if __name__ == '__main__':
dataMat = mat(loadDataSet('testSet.txt'))
print('min(dataMat[:, 0])', min(dataMat[:, 0]), '\n')
print('min(dataMat[:, 1])', min(dataMat[:, 1]), '\n')
print('max(dataMat[:, 0])', max(dataMat[:, 0]), '\n')
print('max(dataMat[:, 1])', max(dataMat[:, 1]), '\n')
print(randCent(dataMat, 2), '\n')
print(distEclud(dataMat[0], dataMat[1]))
centroids, clusterAssment = kMeans(dataMat, 4)
print('centroids:\n', centroids, '\n')
print('clusterAssment:\n', clusterAssment, '\n')
dataMat3 = mat(loadDataSet('testSet2.txt'))
centList, myNewAssments = biKmeans(dataMat3, 3)
print('centList: \n', centList, '\n')
# fileName = '../../../../data/k-means/places.txt'
# imgName = '../../../../data/k-means/Portland.png'
# kMeans.clusterClubs(fileName=fileName, imgName=imgName, numClust=5)
import kMeans
from numpy import *
datMat = mat(kMeans.loadDataSet('testSet.txt'))
print min(datMat[:, 0])
print min(datMat[:, 1])
print max(datMat[:, 0])
print max(datMat[:, 1])
print kMeans.randCent(datMat, 2)
print kMeans.distEclud(datMat[0], datMat[1])
myCentroids, clustAssing = kMeans.kMeans(datMat, 4)
#print myCentroids, clustAssing
datMat3 = mat(kMeans.loadDataSet('testSet2.txt'))
centList, myNewAssments = kMeans.biKmeans(datMat3, 3)
print centList
import time
st = time.time()
k = 3
dataMat = mat(kMeans.loadDataSet('testSet2.txt'))
oldClassLabel = zeros(len(dataMat), int)
newClassLabel = ones(len(dataMat), int)
center = kMeans.randCent(dataMat, k)
dist = []
m = 0
while newClassLabel.tolist().__eq__(
oldClassLabel.tolist()) != True: # 所有的点的新分类的标签和旧分类的标签不一致时就继续进行划分
m += 1 # 迭代次数
for di in range(len(dataMat)): # 对数据集中的每个数据点
dist = []
for ci in range(len(center)): # 对每个质心，计算某个点到质心的距离
dist.append(kMeans.distEclud(dataMat[di],
center[ci])) # dist 用于记录一个点到所有簇点的距离
distsort = array(dist).argsort() # 对距离排序，返回从小到大的索引
oldClassLabel = newClassLabel.copy() # ！！！！注意这里是引用不能直接用等号，否则将会使得两个值一起变
newClassLabel[di] = distsort[0] # 取出索引的最小值，就是距离最近的点
for j in range(k):
x = mean(array(dataMat)[kMeans.find_all_index(newClassLabel, j),
0]) # 重新计算簇心的坐标
y = mean(array(dataMat)[kMeans.find_all_index(newClassLabel, j), 1])
center[j] = [x, y]
fig = plt.figure()
ax = fig.add_subplot(111)
center = array(center)
ax.scatter(center[:, 0], center[:, 1], marker='+', c='r')
print center
print m
dataMat = array(dataMat)
fr = open(fileName)
for line in fr.readlines():
lineArr = []
curLine = line.strip().split('\t')
for i in range(num):
lineArr.append(float(curLine[i]))
dataMat.append(lineArr)
dataSet = mat(dataMat)
k = 3
m = shape(dataSet)[0]
clusterAssment = mat(zeros((m, 2)))
centroid0 = mean(dataSet, axis=0).tolist()[0]
centList = [centroid0] #create a list with one centroid
for j in range(m): #calc initial Error
clusterAssment[j, 1] = distEclud(mat(centroid0), dataSet[j, :])**2
while (len(centList) < k):
lowestSSE = inf
for i in range(len(centList)):
ptsInCurrCluster = dataSet[nonzero(
clusterAssment[:, 0].A ==
i)[0], :] #get the data points currently in cluster i
centroidMat, splitClustAss = kMeans(ptsInCurrCluster, 2, distEclud)
sseSplit = sum(
splitClustAss[:, 1]) #compare the SSE to the currrent minimum
sseNotSplit = sum(
clusterAssment[nonzero(clusterAssment[:, 0].A != i)[0], 1])
print("sseSplit, and notSplit: ", sseSplit, sseNotSplit)
if (sseSplit + sseNotSplit) < lowestSSE:
bestCentToSplit = i
bestNewCents = centroidMat
@author: 凯风
"""
import kMeans
import numpy as np
from imp import reload
reload(kMeans)
datMat = np.mat(kMeans.loadDataSet('testSet.txt'))
min(datMat[:,0])
max(datMat[:,0])
min(datMat[:,1])
max(datMat[:,1])
kMeans.randCent(datMat,2) # 看一下初始化的质心是否在取值范围内
kMeans.distEclud(datMat[0],datMat[1])
# 在实际数据上看下K-means
reload(kMeans)
datMat = np.mat(kMeans.loadDataSet('testSet.txt'))
myCentroids,clustAssing = kMeans.kMeans(datMat,4) # 不一定是全局最优解
# 二分k-means
reload(kMeans)
datMat3 = np.mat(kMeans.loadDataSet('testSet2.txt'))
centList,myNewAssments = kMeans.biKmeans(datMat3,3) # 其实依然无法保证全局最优解，只能是局部最优解
centList
myNewAssments
# 利用二分k-means在图上画出簇
import kMeans
from numpy import *
datMat = mat(kMeans.loadDataSet('testSet.txt'))
print min(datMat[:,0])
print min(datMat[:,1])
print max(datMat[:,0])
print max(datMat[:,1])
print kMeans.randCent(datMat, 2)
print kMeans.distEclud(datMat[0], datMat[1])
myCentroids, clustAssing = kMeans.kMeans(datMat, 4)
#print myCentroids, clustAssing
datMat3 = mat(kMeans.loadDataSet('testSet2.txt'))
centList, myNewAssments = kMeans.biKmeans(datMat3, 3)
print centList
import urllib
import json
# homedir= os.getcwd()+'/machinelearninginaction/ch10/' #绝对路径
homedir = '' #相对路径
#10.1 k均值聚类算法
datMat = mat(kMeans.loadDataSet(homedir + 'testSet.txt'))
myCentroids, clustAssing = kMeans.kMeans(datMat, 4)
print "datMat:", datMat
print "min(datMat[:,0]):", min(datMat[:, 0])
print "min(datMat[:,1]):", min(datMat[:, 1])
print "max(datMat[:,0]):", max(datMat[:, 0])
print "max(datMat[:,1]):", max(datMat[:, 1])
print "randCent(datMat,2):", kMeans.randCent(datMat, 2)
print "distEclud( datMat[ 0], datMat[ 1]):", kMeans.distEclud(
datMat[0], datMat[1])
print "myCentroids:", myCentroids
print "clustAssing:", clustAssing
print ":",
print ":",
#10.3 二分k均值算法
datMat3 = mat(kMeans.loadDataSet(homedir + 'testSet2.txt'))
centList, myNewAssments = kMeans.biKmeans(datMat3, 3)
print "datMat3:", datMat3
print "centList:", centList
print "myNewAssments:", myNewAssments
#10.4.1 Yahoo!PlaceFinder API
# geoResults=kMeans.geoGrab('1 VA Center', 'Augusta, ME')
# print "geoResults:",geoResults
def test1():
dataMat = np.mat(kMeans.loadDataSet('testSet.txt'))
print kMeans.randCent(dataMat, 2)
print kMeans.distEclud(dataMat[0], dataMat[1])
