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?xrange（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??=?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‘?‘????if?k?>?len（mark）:??
????????print?“Sorry!?Your?k?is?too?large!“??
????????return?1??
??
????#?draw?all?samples??
????for?i?in?xrange（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‘?‘????#?draw?the?centroids??
????for?i?in?range（k）:??
????????plt.plot（centroids[i?0]?centroids[i?1]?mark[i]?markersize?=?12）??
??
????plt.show（）?

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????文件??????????80??2014-05-26?14:26??kmeans\data.txt
?????文件??????????80??2014-05-26?14:25??kmeans\data.txt~
?????文件????????2749??2014-05-26?14:29??kmeans\kmeans.py
?????文件????????2649??2014-05-26?14:29??kmeans\kmeans.pyc
?????文件????????2749??2014-05-26?14:29??kmeans\kmeans.py~
?????文件?????????599??2014-05-26?14:28??kmeans\test.py
?????文件?????????599??2014-05-26?14:28??kmeans\test.py~
?????目錄???????????0??2014-05-26?21:32??kmeans\