Subscript bug using Matft.arange()

[nnethery]

Numpy version

a = np.zeros([88, 2, 2])
b = np.zeros([32, 88, 2])
print(a[np.arange(88), b[0, :, 0].astype(int), 0].shape) # Outputs to (88,)

Matft version

let a = Matft.nums(0, shape: [88, 2, 2])
let b = Matft.nums(0, shape: [32, 88, 2]).astype(.Int)
print(a[Matft.arange(start: 0, to: 88, by: 1), b[0, Matft.all, 0], 0].shape) // Outputs to [88, 88]

According to Numpy the shape of the MfArray should be [88], not [88, 88].

[jjjkkkjjj]

@nnethery
Thank you for reporting the bug. I misunderstood the fancy indexing rule...
Do you know why this operation returned the shape as (88,)?
I misunderstood the returned array's shape will be same as the shape combined by all input's shape. For the example you mentioned, input indexing array's shapes are (88,)=np.arange(88), (88,)=b[0, :, 0].astype(int), (0=scalar,). So, returned shape will be (88, 88, 0) = (88,88) by combining the input indexing array's shapes.

[jjjkkkjjj]

Ah, I may understand.
The input indexing shape will be broadcasted.

>>> np.arange(8).reshape(4,2)[[2,3], [0,2,1]]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
IndexError: shape mismatch: indexing arrays could not be broadcast together with shapes (2,) (3,) 

[jjjkkkjjj]

@nnethery
I've just fixed this bug!
Please check it again.

[nnethery]

@nnethery I've just fixed this bug! Please check it again.

It works great. You rock @jjjkkkjjj 🎉!