Python 中的“@=”符号是什么？

What is the '@=' symbol for in Python?

提问by Octavia Togami

I know @is for decorators, but what is @=for in Python? Is it just reservation for some future idea?

我知道@是为了装饰器，但是@=在 Python 中是为了什么？是否只是为一些未来的想法保留？

This is just one of my many questions while reading tokenizer.py.

这只是我阅读时的众多问题之一tokenizer.py。

采纳答案by rightfold

From thedocumentation:

从该文档：

The @(at) operator is intended to be used for matrix multiplication. No builtin Python types implement this operator.

的@（在）操作者意图被用于矩阵乘法。没有内置的 Python 类型实现此运算符。

The @operator was introduced in Python 3.5. @=is matrix multiplication followed by assignment, as you would expect. They map to __matmul__, __rmatmul__or __imatmul__similar to how +and +=map to __add__, __radd__or __iadd__.

该@运算符是在 Python 3.5 中引入的。@=正如您所期望的那样，是矩阵乘法，然后是赋值。它们映射到__matmul__，__rmatmul__或__imatmul__类似于 how+和+=映射到__add__，__radd__或__iadd__。

The operator and the rationale behind it are discussed in detail in PEP 465.

PEP 465中详细讨论了操作符及其背后的原理。

回答by Andrzej Pronobis

@=and @are new operators introduced in Python 3.5performing matrix multiplication. They are meant to clarify the confusion which existed so far with the operator *which was used either for element-wise multiplication or matrix multiplication depending on the convention employed in that particular library/code. As a result, in the future, the operator *is meant to be used for element-wise multiplication only.

@=和@是 Python 3.5 中引入的执行矩阵乘法的新运算符。它们旨在澄清迄今为止​​与*用于元素乘法或矩阵乘法的运算符存在的混淆，具体取决于该特定库/代码中采用的约定。因此，将来，该运算符*仅用于元素乘法。

As explained in PEP0465, two operators were introduced:

如PEP0465 中所述，引入了两个运算符：

• A new binary operator A @ B, used similarly as A * B
• An in-place version A @= B, used similarly as A *= B

• 一个新的二元运算符A @ B，用法类似A * B
• 就地版本A @= B，使用方式类似A *= B

Matrix Multiplication vs Element-wise Multiplication

矩阵乘法与元素乘法

To quickly highlight the difference, for two matrices:

为了快速突出差异，对于两个矩阵：

A = [[1, 2],    B = [[11, 12],
     [3, 4]]         [13, 14]]

• Element-wise multiplication will yield:

  A * B = [[1 * 11,   2 * 12], 
           [3 * 13,   4 * 14]]
  

• Matrix multiplication will yield:

  A @ B  =  [[1 * 11 + 2 * 13,   1 * 12 + 2 * 14],
             [3 * 11 + 4 * 13,   3 * 12 + 4 * 14]]
  

• 按元素乘法将产生：

  A * B = [[1 * 11,   2 * 12], 
           [3 * 13,   4 * 14]]
  

• 矩阵乘法将产生：

  A @ B  =  [[1 * 11 + 2 * 13,   1 * 12 + 2 * 14],
             [3 * 11 + 4 * 13,   3 * 12 + 4 * 14]]
  

Usage in Numpy

在 Numpy 中的使用

So far, Numpy used the following convention:

到目前为止，Numpy 使用了以下约定：

• the *operator (and arithmetic operatorsin general) were defined as element-wise operations on ndarraysand as matrix-multiplication on numpy.matrixtype.

• method/functiondotwas used for matrix multiplication of ndarrays

• 的*操作者（和算术运算符在普通）被定义为在元件为单位的运算ndarrays并作为矩阵乘法numpy.matrix类型。

• 方法/函数dot用于 ndarrays 的矩阵乘法

Introduction of the @operator makes the code involving matrix multiplications much easier to read. PEP0465 gives us an example:

@运算符的引入使涉及矩阵乘法的代码更易于阅读。PEP0465 给了我们一个例子：

# Current implementation of matrix multiplications using dot function
S = np.dot((np.dot(H, beta) - r).T,
            np.dot(inv(np.dot(np.dot(H, V), H.T)), np.dot(H, beta) - r))

# Current implementation of matrix multiplications using dot method
S = (H.dot(beta) - r).T.dot(inv(H.dot(V).dot(H.T))).dot(H.dot(beta) - r)

# Using the @ operator instead
S = (H @ beta - r).T @ inv(H @ V @ H.T) @ (H @ beta - r)

Clearly, the last implementation is much easier to read and interpret as an equation.

显然，最后一个实现更容易阅读和解释为等式。

回答by amehta

@ is the new operator for Matrix Multiplication added in Python3.5

@ 是 Python3.5 中新增的矩阵乘法运算符

Reference: https://docs.python.org/3/whatsnew/3.5.html#whatsnew-pep-465

参考：https: //docs.python.org/3/whatsnew/3.5.html#whatsnew-pep-465

Example

例子

C = A @ B