Generate random numbers with a given (numerical) distribution

Question

I have a file with some probabilities for different values e.g.:

1 0.1
2 0.05
3 0.05
4 0.2
5 0.4
6 0.2

I would like to generate random numb

Answer 1

based on other solutions, you generate accumulative distribution (as integer or float whatever you like), then you can use bisect to make it fast

this is a simple example (I used integers here)

l=[(20, 'foo'), (60, 'banana'), (10, 'monkey'), (10, 'monkey2')]
def get_cdf(l):
    ret=[]
    c=0
    for i in l: c+=i[0]; ret.append((c, i[1]))
    return ret

def get_random_item(cdf):
    return cdf[bisect.bisect_left(cdf, (random.randint(0, cdf[-1][0]),))][1]

cdf=get_cdf(l)
for i in range(100): print get_random_item(cdf),

the get_cdf function would convert it from 20, 60, 10, 10 into 20, 20+60, 20+60+10, 20+60+10+10

now we pick a random number up to 20+60+10+10 using random.randint then we use bisect to get the actual value in a fast way