Lukasz Lukasz - 1 month ago 12
Python Question

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
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.


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