#-*- coding: utf-8 -*-

importnumpy as npclassKMeansClassifier():def __init__(self, k=3, initCent='random', max_iter=500):

self._k=k

self._initCent=initCent

self._max_iter=max_iter

self._clusterAssment=None

self._labels=None

self._sse=Nonedef_calEDist(self, arrA, arrB):"""功能：欧拉距离距离计算

输入：两个一维数组"""

return np.math.sqrt(sum(np.power(arrA-arrB, 2)))def_calMDist(self, arrA, arrB):"""功能：曼哈顿距离距离计算

输入：两个一维数组"""

return sum(np.abs(arrA-arrB))def_randCent(self, data_X, k):"""功能：随机选取k个质心

输出：centroids #返回一个m*n的质心矩阵"""n= data_X.shape[1] #获取特征的维数

centroids = np.empty((k,n)) #使用numpy生成一个k*n的矩阵，用于存储质心

for j inrange(n):

minJ=min(data_X[:, j])

rangeJ= float(max(data_X[:, j] -minJ))#使用flatten拉平嵌套列表(nested list)

centroids[:, j] = (minJ + rangeJ * np.random.rand(k, 1)).flatten()returncentroidsdeffit(self, data_X):"""输入：一个m*n维的矩阵"""

if not isinstance(data_X, np.ndarray) or\

isinstance(data_X, np.matrixlib.defmatrix.matrix):try:

data_X=np.asarray(data_X)except:raise TypeError("numpy.ndarray resuired for data_X")

m= data_X.shape[0] #获取样本的个数

#一个m*2的二维矩阵，矩阵第一列存储样本点所属的族的索引值，

#第二列存储该点与所属族的质心的平方误差

self._clusterAssment = np.zeros((m,2))if self._initCent == 'random':

self._centroids=self._randCent(data_X, self._k)

clusterChanged=Truefor _ in range(self._max_iter): #使用"_"主要是因为后面没有用到这个值

clusterChanged =Falsefor i in range(m): #将每个样本点分配到离它最近的质心所属的族

minDist = np.inf #首先将minDist置为一个无穷大的数

minIndex = -1 #将最近质心的下标置为-1

for j in range(self._k): #次迭代用于寻找最近的质心

arrA =self._centroids[j,:]

arrB=data_X[i,:]

distJI= self._calEDist(arrA, arrB) #计算误差值

if distJI

minDist=distJI

minIndex=jif self._clusterAssment[i, 0] != minIndex or self._clusterAssment[i, 1] > minDist**2:

clusterChanged=True

self._clusterAssment[i,:]= minIndex, minDist**2

if not clusterChanged:#若所有样本点所属的族都不改变,则已收敛,结束迭代

break

for i in range(self._k):#更新质心，将每个族中的点的均值作为质心

index_all = self._clusterAssment[:,0] #取出样本所属簇的索引值

value = np.nonzero(index_all==i) #取出所有属于第i个簇的索引值

ptsInClust = data_X[value[0]] #取出属于第i个簇的所有样本点

self._centroids[i,:] = np.mean(ptsInClust, axis=0) #计算均值

self._labels=self._clusterAssment[:,0]

self._sse= sum(self._clusterAssment[:,1])def predict(self, X):#根据聚类结果，预测新输入数据所属的族

#类型检查

if notisinstance(X,np.ndarray):try:

X=np.asarray(X)except:raise TypeError("numpy.ndarray required for X")

m= X.shape[0]#m代表样本数量

preds =np.empty((m,))for i in range(m):#将每个样本点分配到离它最近的质心所属的族

minDist =np.inffor j inrange(self._k):

distJI=self._calEDist(self._centroids[j,:], X[i,:])if distJI

minDist=distJI

preds[i]=jreturnpredsclassbiKMeansClassifier():"this is a binary k-means classifier"

def __init__(self, k=3):

self._k=k

self._centroids=None

self._clusterAssment=None

self._labels=None

self._sse=Nonedef_calEDist(self, arrA, arrB):"""功能：欧拉距离距离计算

输入：两个一维数组"""

return np.math.sqrt(sum(np.power(arrA-arrB, 2)))deffit(self, X):

m=X.shape[0]

self._clusterAssment= np.zeros((m,2))

centroid0= np.mean(X, axis=0).tolist()

centList=[centroid0]for j in range(m):#计算每个样本点与质心之间初始的平方误差

self._clusterAssment[j,1] =self._calEDist(np.asarray(centroid0), \

X[j,:])**2

while (len(centList)

lowestSSE=np.inf#尝试划分每一族,选取使得误差最小的那个族进行划分

for i inrange(len(centList)):

index_all= self._clusterAssment[:,0] #取出样本所属簇的索引值

value = np.nonzero(index_all==i) #取出所有属于第i个簇的索引值

ptsInCurrCluster = X[value[0],:] #取出属于第i个簇的所有样本点

clf = KMeansClassifier(k=2)

clf.fit(ptsInCurrCluster)#划分该族后，所得到的质心、分配结果及误差矩阵

centroidMat, splitClustAss =clf._centroids, clf._clusterAssment

sseSplit= sum(splitClustAss[:,1])

index_all=self._clusterAssment[:,0]

value= np.nonzero(index_all==i)

sseNotSplit= sum(self._clusterAssment[value[0],1])if (sseSplit + sseNotSplit)

bestCentToSplit=i

bestNewCents=centroidMat

bestClustAss=splitClustAss.copy()

lowestSSE= sseSplit +sseNotSplit#该族被划分成两个子族后,其中一个子族的索引变为原族的索引

#另一个子族的索引变为len(centList),然后存入centList

bestClustAss[np.nonzero(bestClustAss[:,0]==1)[0],0]=len(centList)

bestClustAss[np.nonzero(bestClustAss[:,0]==0)[0],0]=bestCentToSplit

centList[bestCentToSplit]=bestNewCents[0,:].tolist()

centList.append(bestNewCents[1,:].tolist())

self._clusterAssment[np.nonzero(self._clusterAssment[:,0]==\

bestCentToSplit)[0],:]=bestClustAss

self._labels=self._clusterAssment[:,0]

self._sse= sum(self._clusterAssment[:,1])

self._centroids=np.asarray(centList)def predict(self, X):#根据聚类结果，预测新输入数据所属的族

#类型检查

if notisinstance(X,np.ndarray):try:

X=np.asarray(X)except:raise TypeError("numpy.ndarray required for X")

m= X.shape[0]#m代表样本数量

preds =np.empty((m,))for i in range(m):#将每个样本点分配到离它最近的质心所属的族

minDist =np.inffor j inrange(self._k):

distJI=self._calEDist(self._centroids[j,:],X[i,:])if distJI

minDist=distJI

preds[i]=jreturn preds