def bi_kmeans(data, k):
q = K_means.kmeans(data, 2)
index = 2
# 初始化centroids节点
centroid = K_means.init_centroids(data, 2)
index=2
while index!=k:
datasep = []
a = max_key(get_sse(data, centroid, q))
print a
for i in range(len(data)):
if q[i] == a:
datasep.append(data[i])
sep_centroid = K_means.init_centroids(datasep, 2)
index += 1
print centroid
centroid=copy.deepcopy(numpy.delete(centroid, a,0))
print centroid
centroid = numpy.concatenate((centroid, sep_centroid))
print "======================================"
print centroid
print "======================================"
q = K_means.cost_funct(data, centroid)
centroid = K_means.update_centroid(data, centroid, q)
pre_centroid = centroid
q = K_means.cost_funct(data_process.data, centroid)
now_centroid = copy.deepcopy(pre_centroid)
pre_centroid = K_means.update_centroid(data, pre_centroid, q)
print now_centroid - pre_centroid
while (now_centroid != pre_centroid).any():
print pre_centroid
print now_centroid
q = K_means.cost_funct(data, pre_centroid)
print q
now_centroid = copy.deepcopy(pre_centroid)
pre_centroid = K_means.update_centroid(data, pre_centroid, q)
return q
