python numpy ValueError:操作数无法与形状一起广播 [英] python numpy ValueError: operands could not be broadcast together with shapes

问题描述

在 numpy 中，我有两个数组"，X 是 (m,n) 而 y 是一个向量 (n,1)

In numpy, I have two "arrays", X is (m,n) and y is a vector (n,1)

使用

X*y

我收到错误

ValueError: operands could not be broadcast together with shapes (97,2) (2,1) 

当 (97,2)x(2,1) 显然是一个合法的矩阵运算并且应该给我一个 (97,1) 向量

When (97,2)x(2,1) is clearly a legal matrix operation and should give me a (97,1) vector

我已经使用 X.dot(y) 更正了这个问题，但最初的问题仍然存在.

I have corrected this using X.dot(y) but the original question still remains.

推荐答案

dot 是矩阵乘法，但 * 做其他事情.

dot is matrix multiplication, but * does something else.

我们有两个数组:

• X，形状 (97,2)
• y，形状 (2,1)

• X, shape (97,2)
• y, shape (2,1)

使用 Numpy 数组，操作

With Numpy arrays, the operation

X * y

是按元素完成的，但可以在一维或多维中扩展其中一个或两个值以使其兼容.此操作称为广播.尺寸为1或缺失的尺寸可用于广播.

is done element-wise, but one or both of the values can be expanded in one or more dimensions to make them compatible. This operation is called broadcasting. Dimensions, where size is 1 or which are missing, can be used in broadcasting.

在上面的例子中，尺寸是不兼容的，因为:

In the example above the dimensions are incompatible, because:

97   2
 2   1

这里在第一维(97 和 2)中有冲突的数字.这就是上面的 ValueError 所抱怨的.第二个维度没问题，因为数字 1 与任何东西都不冲突.

Here there are conflicting numbers in the first dimension (97 and 2). That is what the ValueError above is complaining about. The second dimension would be ok, as number 1 does not conflict with anything.

关于广播规则的更多信息:http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html

For more information on broadcasting rules: http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html

(请注意，如果X 和y 是numpy.matrix 类型，那么星号可以用作矩阵乘法.我的建议是远离 numpy.matrix，它往往比简化事情更复杂.)

(Please note that if X and y are of type numpy.matrix, then asterisk can be used as matrix multiplication. My recommendation is to keep away from numpy.matrix, it tends to complicate more than simplifying things.)

numpy.dot 你的数组应该没问题；如果您在 numpy.dot 上遇到错误，那么您一定有其他一些错误.如果 numpy.dot 的形状是错误的，你会得到一个不同的例外:

Your arrays should be fine with numpy.dot; if you get an error on numpy.dot, you must have some other bug. If the shapes are wrong for numpy.dot, you get a different exception:

ValueError: matrices are not aligned

如果您仍然遇到此错误，请发布问题的最小示例.与您的形状相似的数组的示例乘法成功:

If you still get this error, please post a minimal example of the problem. An example multiplication with arrays shaped like yours succeeds:

In [1]: import numpy

In [2]: numpy.dot(numpy.ones([97, 2]), numpy.ones([2, 1])).shape
Out[2]: (97, 1)

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