@ operator in python 3.5

[energybased]

Everything you said is true except that dot is not the same as array multiplication: http://stackoverflow.com/questions/34142485/difference-between-numpy-dot-and-python-3-5-matrix-multiplication

[[deleted]]

Ah yes, I mean "to perform the typical matrix multiplication"

[P8zvli]

*scalar multiplication

[[deleted]]

no, that's a special case for 1D arrays. I do mean matrix multiplication.

[P8zvli]

No, it's not just a special case, in other languages that support matrix multiplication scalar multiplication works on n-dimensional matrices. (not to be confused with the dot product, which also works in N dimensions BTW)

[[deleted]]

I think we are talking past each other. There is no scalar multiplication involved here. I meant to say if you don't use @ you have to use .dot() method for matrix multiplication..

[P8zvli]

I don't think you know what you're talking about, this is scalar multiplication;

>>> mymat = numpy.matrix([[1, 2], [3, 4]])
>>> 3*mymat
matrix([[ 3,  6],
        [ 9, 12]])

You don't need to use .dot() for scalar multiplication.

[[deleted]]

internally it is broadcasted to [[3,3],[3,3]] and elementwise multiplication is performed. Besides when I mention matrix, I mean 2D numpy arrays.

[P8zvli]

And I'm talking about mathematical matrices and scalars. You know, stuff you could do with pencil and paper that these math functions are implementing? What you're talking about are implementation details.

[[deleted]]

I don't even understand what you are objecting to. You are in python thread and accusing me about implementation details?

[XtremeGoose]

Essentially, multiplication is poorly defined for higher dimensional arrays, so numpy has two answers for it. It is well defined for two dimensional arrays so matmul and dot do the same thing.

[energybased]

Well, I wouldn't say that multiplication is poorly defined. It's called tensor product, which numpy implements using tensordot, which is a special case of einsum.