Q:

Numpy Uniform Distribution With Decay

I'm trying to construct a matrix of uniform distributions decaying to 0 at the same rate in each row. The distributions should be between -1 and 1. What I'm looking at is to construct something that resembles:
[[0.454/exp(0) -0.032/exp(1) 0.641/exp(2)...]
 [-0.234/exp(0) 0.921/exp(1) 0.049/exp(2)...]
 ...
 [0.910/exp(0) 0.003/exp(1) -0.908/exp(2)...]]

I can build a matrix of uniform distributions using:
w = np.array([np.random.uniform(-1, 1, 10) for i in range(10)])

and can achieve the desired result using a for loop with:
for k in range(len(w)):
    for l in range(len(w[0])):
        w[k][l] = w[k][l]/np.exp(l)

but wanted to know if there was a better way of accomplishing this. 

A:

You can use numpy's broadcasting feature to do this:
w = np.random.uniform(-1, 1, size=(10, 10))
weights = np.exp(np.arange(10))
w /= weights

