I have been asked to use the following set of column indices:
y = np.array([3, 0, 4, 1])
to turn into 1 all the elements in the following matrix:
x = np.zeros(shape = (4, 5))
that have y as starting column and rows given by the position of y. Just to be clear.
The final result has to be the following:
[[0. 0. 0. 1. 1.]
[1. 1. 1. 1. 1.]
[0. 0. 0. 0. 1.]
[0. 1. 1. 1. 1.]]
For example:
y[0] = 3,
then row 0, columns 3 and 4 need to be equal to 1.
I did it like this:
for (idx, num) in enumerate(y): x[idx, num:] = 1
Can this result be written differently and/or improved by using other Numpy functions (for example, using vectorization)?
Lots of ways of doing this. For example, since x is basically a boolean mask, you can compute a mask and turn it into whatever type you want:
x = (np.arange(5) < y[:, None]).astype(float)
You might also use np.where to avoid the conversion:
x = np.where(np.arange(5) < y[:, None], 1.0, 0.0)