def nearest(point, cluster_centers):
min_dist = FLOAT_MAX
m = np.shape(cluster_centers)[0]
for i in range(m):
d = distance(point, cluster_centers[i, ])
if min_dist > d:
min_dist = d
return min_dist
def nearest(point, cluster_centers):
'''计算point和cluster_centers之间的最小距离
input: point(mat):当前的样本点
cluster_centers(mat):当前已经初始化的聚类中心
output: min_dist(float):点point和当前的聚类中心之间的最短距离
'''
min_dist = FLOAT_MAX
m = np.shape(cluster_centers)[0] # 当前已经初始化的聚类中心的个数
for i in xrange(m):
# 计算point与每个聚类中心之间的距离
d = distance(point, cluster_centers[i, ])
# 选择最短距离
if min_dist > d:
min_dist = d
return min_dist
def nearest(point, cluster_centers):
'''计算point和cluster_centers之间的最小距离
input: point(mat):当前的样本点
cluster_centers(mat):当前已经初始化的聚类中心
output: min_dist(float):点point和当前的聚类中心之间的最短距离
'''
min_dist = FLOAT_MAX
m = np.shape(cluster_centers)[0] # 当前已经初始化的聚类中心的个数
for i in range(m):
# 计算point与每个聚类中心之间的距离
d = distance(point, cluster_centers[i, ])
# 选择最短距离
if min_dist > d:
min_dist = d
return min_dist