我正在尝试为聚类实现自定义距离度量。代码片段如下所示：
import numpy as np
from sklearn.cluster import KMeans, DBSCAN, MeanShift
def distance(x, y):
# print(x, y) -> This x and y aren't one-hot vectors and is the source of this question
match_count = 0.
for xi, yi in zip(x, y):
if float(xi) == 1. and xi == yi:
match_count += 1
return match_count
def custom_metric(x, y):
# x, y are two vectors
# distance(.,.) calculates count of elements when both xi and yi are True
return distance(x, y)
vectorized_text …我想用值0更新2D张量中的索引。所以数据是2D张量，其第二行第二列索引值将被0代替。但是，我遇到类型错误。有人可以帮我吗？
TypeError：“ ScatterUpdate”操作的输入“ ref”需要输入左值
data = tf.Variable([[1,2,3,4,5], [6,7,8,9,0], [1,2,3,4,5]])
data2 = tf.reshape(data, [-1])
sparse_update = tf.scatter_update(data2, tf.constant([7]), tf.constant([0]))
#data = tf.reshape(data, [N,S])
init_op = tf.initialize_all_variables()
sess = tf.Session()
sess.run([init_op])
print "Values before:", sess.run([data])
#sess.run([updated_data_subset])
print "Values after:", sess.run([sparse_update])