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 (negative and zero values can be removed) and the highest weight is
1500. Weight is determined by intuition by people (how somebody thinks the item is important to community). Because it's not easy to determine the most important item, I'd like to use some random factor, so that items with lower weight will have less chance to be chosen and their weight will be adjusted in the future (some mix of common sense and randomness).
Do you know how to code a function
def getItem(dict): # this function should return random item from # the dictionary of item-weight pairs (or list of tuples) # Normally I would return only random item from the dictionary, # but now I'd like to have this: The item with weight 1500 should # have much more chance to be returned than the item with weight 10. # What's my idea is to sum up the weights of all items and then compute # some ratios. But maybe you have better idea. return randomItem
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.