numpy-broadcasting

I recently posted a question here which was answered exactly as I asked. However, I think I overestimated my ability to manipulate the answer further. I read the broadcasting doc, and followed a few links that led me way back to 2002 about numpy broadcasting. I've used the second method of array creation using broadcasting : N = 10 out = np.zeros((N**3,4),dtype=int) out[:,:3] = (np.arange(N**3)[:,None]/[N**2,N,1])%N which outputs: [[0,0,0,0] [0,0,1,0] ... [0,1,0,0] [0,1,1,0] ... [9,9,8,0] [9,9,9,0]] but I do not understand via the docs how to manipulate that. I would ideally like to be able to

torch.add(torch.ones(4,1), torch.randn(4)) produces a Tensor with size: torch.Size([4,4]) . Can someone provide a logic behind this? PyTorch broadcasting is based on numpy broadcasting semantics which can be understood by reading numpy broadcasting rules or PyTorch broadcasting guide . Expounding the concept with an example would be intuitive to understand it better. So, please see the example below: In [27]: t_rand Out[27]: tensor([ 0.23451, 0.34562, 0.45673]) In [28]: t_ones Out[28]: tensor([[ 1.], [ 1.], [ 1.], [ 1.]]) Now for torch.add(t_rand, t_ones) , visualize it like: # shape of (3,)

Related to this question , I came across an indexing behaviour via Boolean arrays and broadcasting I do not understand. We know it's possible to index a NumPy array in 2 dimensions using integer indices and broadcasting. This is specified in the docs : a = np.array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) b1 = np.array([False, True, True]) b2 = np.array([True, False, True, False]) c1 = np.where(b1)[0] # i.e. [1, 2] c2 = np.where(b2)[0] # i.e. [0, 2] a[c1[:, np.newaxis], c2] # or a[c1[:, None], c2] array([[ 4, 6], [ 8, 10]]) However, the same does not work for Boolean arrays. a[b1[:,

This question is close to what is asked in Overriding other __rmul__ with your class's __mul__ but I am under the impression that this is a more general problem then only numerical data. Also that is not answered and I really don't want to use the matrix multiplication @ for this operation. Hence, the question. I do have an object which accepts multiplication with scalars and numerical arrays. As usual, the left multiplication works fine since it is the myobj() methods are used but in the right multiplication, NumPy uses broadcasting rules and gives elementwise results with dtype=object . This