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Diffstat (limited to 'users/wpcarro/scratch/data_structures_and_algorithms/optimal-stopping.py')
-rw-r--r-- | users/wpcarro/scratch/data_structures_and_algorithms/optimal-stopping.py | 49 |
1 files changed, 49 insertions, 0 deletions
diff --git a/users/wpcarro/scratch/data_structures_and_algorithms/optimal-stopping.py b/users/wpcarro/scratch/data_structures_and_algorithms/optimal-stopping.py new file mode 100644 index 000000000000..af13239941d0 --- /dev/null +++ b/users/wpcarro/scratch/data_structures_and_algorithms/optimal-stopping.py @@ -0,0 +1,49 @@ +from random import choice +from math import floor + +# Applying Chapter 1 from "Algorithms to Live By", which describes optimal +# stopping problems. Technically this simulation is invalid because the +# `candidates` function takes a lower bound and an upper bound, which allows us +# to know the cardinal number of an individual candidates. The "look then leap" +# algorithm is ideal for no-information games - i.e. games when upper and lower +# bounds aren't known. The `look_then_leap/1` function is ignorant of this +# information, so it behaves as if in a no-information game. Strangely enough, +# this algorithm will pick the best candidate 37% of the time. +# +# Chapter 1 describes two algorithms: +# 1. Look-then-leap: ordinal numbers - i.e. no-information games. Look-then-leap +# finds the best candidate 37% of the time. +# 2. Threshold: cardinal numbers - i.e. where upper and lower bounds are +# known. The Threshold algorithm finds the best candidate ~55% of the time. +# +# All of this and more can be studied as "optimal stopping theory". This applies +# to finding a spouse, parking a car, picking an apartment in a city, and more. + + +# candidates :: Int -> Int -> Int -> [Int] +def candidates(lb, ub, ct): + xs = list(range(lb, ub + 1)) + return [choice(xs) for _ in range(ct)] + + +# look_then_leap :: [Integer] -> Integer +def look_then_leap(candidates): + best = candidates[0] + seen_ct = 1 + ignore_ct = floor(len(candidates) * 0.37) + for x in candidates[1:]: + if ignore_ct > 0: + ignore_ct -= 1 + best = max(best, x) + else: + if x > best: + print('Choosing the {} candidate.'.format(seen_ct)) + return x + seen_ct += 1 + print('You may have waited too long.') + return candidates[-1] + + +candidates = candidates(1, 100, 100) +print(candidates) +print(look_then_leap(candidates)) |