在numpy中创建有效的数组(Efficient array creation in numpy)

在numpy中创建有效的数组(Efficient array creation in numpy)

我正在尝试创建一个三维数组，Tusing numpy定义如下：

T_ {i，j，k} = \ delta_ {i，k} - \ delta {j，k}

其中\ delta_ {i，j}是Kronecker delta函数（当i = j时为1，否则为0）。 我想知道用numpy做这个最有效的方法。 我可以使用for循环创建两个三维数组并将其减去。 但我怀疑有一种更快更自然的方法。 非常感激任何的帮助。

I am trying to create a three dimensional array, Tusing numpy defined as follows:

T_{i, j, k} = \delta_{i, k} - \delta{j, k}

where \delta_{i, j} is the Kronecker delta function (1 when i=j and 0 otherwise). I am wondering what the most efficient way to do this using numpy. I can create two three dimensional arrays using for loops and subtract them. But I suspect there is a quicker and more idiomatic method. Any help would be most appreciated.

最满意答案

相当于三角洲的eye是裸eye ：

delta = numpy.eye(5) T = delta[:,None,:] - delta[None,:,:]

None创建了一个用于numpy广播的<virtual>维度（不需要额外的内存）。

The equivalent to delta is eye in numpy:

delta = numpy.eye(5) T = delta[:,None,:] - delta[None,:,:]

The None creates a ‹virtual› dimension (doesn't take any additional memory) used for broadcasting in numpy.