The multiplication maybe is not the best example. Actually, I wanted to say "addition".
I wanted that my operator `+` works for: 1) np.ndarray + np.ndarray 2) np.ndarray + LRmatrix 3) LRmatrix + np.ndarray 4) LRmatrix + LRmatrix 1) is a part of Numpy. 3) and 4) can be implemented by `LRmatrix.__add__`. However, 3) does not work because `np.ndarray.__add__` has higher priority than `LRmatrix.__radd__`. In this case, it will be awkward to use an `add` function to wrap them. On Saturday, October 8, 2016 at 8:35:19 AM UTC+2, Sisyphuss wrote: > > You mean: > def mydot(A,B): > if isinstance(A, np.ndarray) and isinstance(B, np.ndarray): > return np.dot(A, B) > else: > # mycode > ? > Maybe you are right. Since I can't overload `np.dot`, maybe the neater way > is to write a higher level function and then delegate the work by > inspecting the type. > > > On Friday, October 7, 2016 at 7:56:59 PM UTC+2, Gabriel Gellner wrote: >> >> Any reason to not just use a function? (like np.dot etc) >> >> My understanding is that in python '*' means elementwise multiplication, >> so even if you could monkeypatch numpys __mul__ method to do the right >> thing wouldn't you be changing the semantics? >> >> Gabriel >> >> On Friday, October 7, 2016 at 3:51:11 AM UTC-6, Sisyphuss wrote: >> >>> In Julia, we can do multiple dispatch for operators, that is the >>> interpreter can identify: >>> float + integer >>> integer + integer >>> integer + float >>> float + float >>> as well as *user-defined* data structure. >>> >>> Recently, I am working on Python (I have no choice because Spark hasn't >>> yet a Julia binding). I intended to do the same thing -- multiplication -- >>> between a Numpy matrix and self-defined Low-rank matrix. Of course, I >>> defined the `__rmul__ ` method for Low-rank matrix. However, it seems to me >>> that the Numpy matrix intercepts the `*` operator as its `__mul__` method, >>> which expects the argument on the right side of `*` to be a scalar. >>> >>> I would like to know if there is anyway around? >>> >>>
