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kMeans.py
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kMeans.py
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import numpy as np
import urllib
import json
from time import sleep
import matplotlib
import matplotlib.pyplot as plt
def loadClsDataSet(file, delim=','):
"""
加载分类数据集
:param file: 文件名
:param delim: 数值分隔符
:return: 数据集
"""
fp = open(file)
strArr = fp.readlines()
data, labels = [], []
for line in strArr[1:]:
words = line.strip().split(delim)
data.append(np.array(words[1:], dtype=float))
labels.append(np.int(words[0]))
data = np.array(data)
labels = np.array(labels)
return data, labels
def distEuclid(X, Y):
"""
Calculate Euclid distance between X and Y
:param X: array type X
:param Y: array type Y
:return: The Euclid distance between X and Y
"""
return np.sqrt(np.sum(np.power(np.subtract(X, Y), 2)))
def randomCenter(data_set, k):
"""
程序清单10-1 K-均值聚类支持函数
:param data_set: 数据集
:param k: 随机质心数目
:return: k个随机生成的质心
"""
m, n = np.shape(data_set)
center = np.zeros((k, n))
for j in range(n):
minJ = np.min(data_set[:, j])
rangeJ = np.float(np.max(data_set[:, j]) - minJ)
center[:, j] = minJ + rangeJ * np.random.rand(1, k)
return center
def kMeans(dataSet, k, measure=distEuclid, createCenter=randomCenter):
"""
程序清单10-2 K-均值聚类算法
:param dataSet: 数据集
:param k: 簇数
:param measure: 测度
:param createCenter: 初始聚类中心
:return: 聚类中心,簇划分
"""
m, n = np.shape(dataSet)
clusterAssign = np.zeros((m, 2))
centroids = createCenter(dataSet, k)
clusterChanged = True
while clusterChanged:
clusterChanged = False
# 对于每一个样例
for i in range(m):
minDist, minIndex = np.inf, -1
# 寻找最近的质心
for j in range(k):
distJI = measure(centroids[j, :], dataSet[i, :])
if distJI < minDist:
minDist, minIndex = distJI, j
# 簇划分发生变化
if clusterAssign[i][0] != minIndex:
clusterChanged = True
clusterAssign[i, :] = minIndex, minDist ** 2
# 更新质心的位置
for i in range(k):
pointInCluster = dataSet[np.nonzero(clusterAssign[:, 0] == i)[0]]
centroids[i, :] = np.mean(pointInCluster, axis=0)
return centroids, clusterAssign
def biKMeans(dataSet, k, measure=distEuclid):
"""
程序清单10-3 二分K-均值聚类算法
:param dataSet: 数据集
:param k: 簇数
:param measure: 度量
:return: 聚类中心,簇划分列表
"""
m, n = np.shape(dataSet)
clusterAssign = np.mat(np.zeros((m, 2)))
# 1-创建一个初始簇
center = np.mean(dataSet, axis=0).tolist()[0]
centers = [center]
for j in range(m):
clusterAssign[j, 1] = measure(np.mat(center), dataSet[j, :]) ** 2
while len(centers) < k:
lowestSSE = np.inf
for i in range(len(centers)):
# 2-尝试划分每一簇
ptInCurrCluster = dataSet[np.nonzero(clusterAssign[:, 0] == i)[0], :]
centerMat, splitClusterAss = kMeans(ptInCurrCluster, 2, measure)
sseSplit = sum(splitClusterAss[:, 1])
sseNotSplit = sum(clusterAssign[np.nonzero(clusterAssign[:, 0] != i)[0], i])
print('split and not split: ', sseSplit, sseNotSplit)
if sseSplit + sseNotSplit < lowestSSE:
bestCentToSplit = i
bestNewCenters = centerMat
bestClusterAss = splitClusterAss.copy()
lowestSSE = sseSplit + sseNotSplit
# 3-更新簇的分配结果
bestClusterAss[np.nonzero(bestClusterAss[:, 0] == 1)[0], 0] = len(centers)
bestClusterAss[np.nonzero(bestClusterAss[:, 0] == 0)[0], 0] = bestCentToSplit
print('the best center to split is : ', bestCentToSplit)
print('the len of best cluster assign is : ', len(bestClusterAss))
centers[bestCentToSplit] = bestNewCenters[0, :]
centers.append(bestNewCenters[1, :])
clusterAssign[np.nonzero(clusterAssign[:, 0] == bestCentToSplit)[0], :] = bestClusterAss
return np.mat(centers), clusterAssign
def geoGrab(address, city):
"""
程序清单10-4 Yahoo! PlaceFinder API
:param address: 地址
:param city: 城市
:return: JSON数据
"""
apiStem = 'http://where.yahooapis.com/geocode?'
params = {
'flags': 'J',
'appid': 'ppp68N8t',
'location': '%s %s' % (address, city)
}
url_params = urllib.urlencode(params)
yahooApi = apiStem + url_params
print(yahooApi)
c = urllib.open(yahooApi)
return json.load(c.read())
def massPlaceFind(filename):
"""
程序清单10-4 Yahoo! PlaceFinder API
:param filename: 文件名
:return: 无
"""
fr = open(filename)
fw = open('places.txt', 'w')
for line in fr.readlines():
line = line.strip()
other, address, city = line.split('\t')
retDict = geoGrab(address, city)
ResultSet = retDict['ResultSet']
if ResultSet['Error'] == 0:
Results = ResultSet['Results']
lat = np.float(Results[0]['latitude'])
long = np.float(Results[0]['longitude'])
print(other, lat, long)
fw.write('%s\t%f\t%f\n' % (line, lat, long))
else:
print('error fetching')
sleep(1)
fw.close()
fr.close()
def distSLC(vecA, vecB):
"""
程序清单10-5 球面距离计算
:param vecA: 球面坐标A
:param vecB: 球面坐标B
:return: 球面距离
"""
a = np.sin(vecA[0, 1] * np.pi / 180) * np.sin(vecB[0, 1] * np.pi / 180)
b = np.cos(vecA[0, 1] * np.pi / 180) * np.cos(vecB[0, 1] * np.pi / 180)
return np.arccos(a + b) / 6371.0
def clusterClubs(numCluster=5):
"""
程序清单10-5 聚类测试
:param numCluster: 簇数
:return: 无
"""
# 读取文件数据
fr = open('places.txt')
data = []
for line in fr.readlines():
strArr = line.split('\t')
data.append([float(strArr[4]), float(strArr[3])])
dataMat = np.mat(data)
# 二分K-均值聚类
centers, clusterAssign = biKMeans(dataMat, numCluster, measure=distSLC)
# 图表绘制
fig = plt.figure()
# 设置区域、标记
rect = [0.1, 0.1, 0.8, 0.8]
scatterMarkers = ['s', 'o', '^', '8', 'p', 'd', 'v', 'h', '>', '<']
axprops = dict(xticks=[], yticks=[])
# 绘制背景
ax0 = fig.add_axes(rect, label='ax0', **axprops)
imgP = plt.imread('Portland.png')
ax0.imshow(imgP)
# 绘制数据点
ax1 = fig.add_axes(rect, label='ax1', frameon=False)
for i in range(numCluster):
pstInCurrCluster = dataMat[np.nonzero(clusterAssign[:, 0] == i), :]
markerStyle = scatterMarkers[i % len(scatterMarkers)]
ax1.scatter(pstInCurrCluster[:, 0].flatten().A[0], pstInCurrCluster[:, 1].flatten().A[0], marker=markerStyle, s=90)
# 绘制聚类中心
ax1.scatter(centers[:, 0].flatten().A[0], centers[:, 1].flatten().A[0], marker='+', s=300)
plt.show()
# 在wine数据集上做聚类测试
data, labels = loadClsDataSet('./dataset/wine/wine.data')
centroids, clusterAssign = kMeans(data, 3)
print(centroids)
print(clusterAssign)
centroids, clusterAssign = biKMeans(data, 3)
print(centroids)
print(clusterAssign)