cupynumeric.dot#

cupynumeric.dot( a: ndarray, b: ndarray, out: ndarray | None = None, ) → ndarray#

Dot product of two arrays. Specifically,

If both a and b are 1-D arrays, it is inner product of vectors (without complex conjugation).
If both a and b are 2-D arrays, it is matrix multiplication, but using a @ b is preferred.
If either a or b is 0-D (scalar), it is equivalent to multiply() and using cupynumeric.multiply(a, b) or a * b is preferred.
If a is an N-D array and b is a 1-D array, it is a sum product over the last axis of a and b.
If a is an N-D array and b is an M-D array (where M>=2), it is a sum product over the last axis of a and the second-to-last axis of b:
```
dot(a: ndarray, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])
```

Parameters:

a (array_like) – First argument.
b (array_like) – Second argument.
out (ndarray, optional) – Output argument. This must have the exact shape and dtype that would be returned if it was not present.

Returns:

output – Returns the dot product of a and b. If out is given, then it is returned.

Return type:

ndarray

Notes

The cuPyNumeric implementation is a little more liberal than NumPy in terms of allowed broadcasting, e.g. dot(ones((3,1)), ones((4,5))) is allowed.

Except for the inner-product case, only floating-point types are supported.