k均值聚类（k-means）

from numpy import *
import time
import matplotlib.pyplot as plt

# calculate Euclidean distance
def euclDistance(vector1, vector2):
return sqrt(sum(power(vector2 - vector1, 2)))

# init centroids with random samples
def initCentroids(dataSet, k):
numSamples, dim = dataSet.shape
centroids = zeros((k, dim))
for i in range(k):
index = int(random.uniform(0, numSamples))
centroids[i, :] = dataSet[index, :]
return centroids

# k-means cluster
def kmeans(dataSet, k):
numSamples = dataSet.shape[0]
# first column stores which cluster this sample belongs to,
# second column stores the error between this sample and its centroid
clusterAssment = mat(zeros((numSamples, 2)))
clusterChanged = True

## step 1: init centroids
centroids = initCentroids(dataSet, k)

while clusterChanged:
clusterChanged = False
## for each sample
for i in range(numSamples):
minDist = 100000.0
minIndex = 0
## for each centroid
## step 2: find the centroid who is closest
for j in range(k):
distance = euclDistance(centroids[j, :], dataSet[i, :])
if distance < minDist:
minDist = distance
minIndex = j

## step 3: update its cluster
if clusterAssment[i, 0] != minIndex:
clusterChanged = True
clusterAssment[i, :] = minIndex, minDist ** 2

## step 4: update centroids
for j in range(k):
pointsInCluster = dataSet[nonzero(clusterAssment[:, 0].A == j)[0]]
centroids[j, :] = mean(pointsInCluster, axis=0)

print('Congratulations, cluster complete!')
return centroids, clusterAssment

# show your cluster only available with 2-D data
def showCluster(dataSet, k, centroids, clusterAssment):
numSamples, dim = dataSet.shape
if dim != 2:
print("Sorry! I can not draw because the dimension of your data is not 2!")
return 1

mark = ['or', 'ob', 'og', 'ok', '^r', '+r', 'sr', 'dr', '<r', 'pr']
if k > len(mark):
print("Sorry! Your k is too large! please contact Zouxy")
return 1

# draw all samples
for i in range(numSamples):
markIndex = int(clusterAssment[i, 0])
plt.plot(dataSet[i, 0], dataSet[i, 1], mark[markIndex])

mark = ['Dr', 'Db', 'Dg', 'Dk', '^b', '+b', 'sb', 'db', '<b', 'pb']
# draw the centroids
for i in range(k):
plt.plot(centroids[i, 0], centroids[i, 1], mark[i], markersize=12)

plt.show()

## step 1: load data
print("step 1: load data...")
dataSet = []
fileIn = open('/home/zengxl/PycharmProjects/test5.py/aaa/机器学习实战代码/Ch10/testSet.txt')
for line in fileIn.readlines():
lineArr = line.strip().split('\t')
dataSet.append([float(lineArr[0]), float(lineArr[1])])

## step 2: clustering...
print("step 2: clustering...")
dataSet = mat(dataSet)
k = 4
centroids, clusterAssment = kmeans(dataSet, k)

## step 3: show the result
print("step 3: show the result...")
showCluster(dataSet, k, centroids, clusterAssment)