I have a list of approx. 10000 items. The current situation is that every item has an associated weight (priority or importance). Now the smallest weight is -100
If you're storing your data in a database, you can use SQL:
SELECT * FROM table ORDER BY weight*random() DESC LIMIT 1
Have a look at this, i think it's what you need with some nice comparision between different methods Weighted random generation in Python
The simplest approach suggested is:
import random
def weighted_choice(weights):
totals = []
running_total = 0
for w in weights:
running_total += w
totals.append(running_total)
rnd = random.random() * running_total
for i, total in enumerate(totals):
if rnd < total:
return i
You can find more details and possible improvements as well as some different approaches in the link above.
Python 3.6 introduced random.choices()
def get_item(items, items_weights):
return random.choices(items, weights=items_weights)[0]
You should extract a random number between 0 and the sum of weights (positive by definition). Then you get the item from a list by using bisect: http://docs.python.org/library/bisect.html (the bisect standard moduke).
import random
import bisect
weight = {'a':0.3,'b':3.2,'c':2.4}
items = weight.keys()
mysum = 0
breakpoints = []
for i in items:
mysum += weight[i]
breakpoints.append(mysum)
def getitem(breakpoints,items):
score = random.random() * breakpoints[-1]
i = bisect.bisect(breakpoints, score)
return items[i]
print getitem(breakpoints,items)
It's easier to do if the weights are not negative. If you have to have negative weights, you'll have to offset the weights by the lowest possible weight. In your case, offsetted_weight = itemweight + 100
In pseudocode, it goes like this:
Calculate the sum of all the weights.
Do a random from 0 to the sum of the weights
Set i to 0
While the random number > 0
Subtract the weight of the item at index i from random
If the random number is < 0 return item[i]
Add 1 to i