我的朋友写了一个程序,它比较骰子面的随机排列,以找到分布最均匀的面——尤其是当面不仅仅是一个序列时。
我将他的程序翻译成haskell,因为我一直在寻找一个理由来谈论haskell 有多酷。然而,我对 haskell 不是很精通(我花了很长时间才写这个,并且经历了几次巨大的重构),所以我有两个问题。
- 他一直在优化他的版本,这不是很快,也不是线性扩展。我搞砸了一些尾递归还是某种更大的问题?
- 由此产生的代码实际上并不像我预期的那样优雅。我知道这不是一个讨论板,但是如果您对如何简化它有任何想法,我会全力以赴
这是最相关的代码:
-- _CENTERS :: [{ x :: Float, y :: Float, z :: Float}]
-- _VALUES :: [Num]
-- Basically just (repeat $ map rand [0.._SIDES]), but never using a seed twice
randstates from = (take _SIDES (infrand from)) : randstates newseed
where infrand seed = seed : infrand (shuffle seed)
newseed = (infrand from) !! (_SIDES + 1)
-- yates shuffle
yates _ (last:[]) = [last]
yates (rand:pass) (swap:order) = choice:yates pass rorder
where choice = order !! index
index = (randfrom rand) `mod` (length order)
rorder = take (index) order ++ swap : drop (index + 1) order
arrangements seed = map arrange $ randstates seed
where arrange rands = yates rands [0.._SIDES - 2]
-- fns comparing arrangements --
arcLength i j = 1 / (1 + _WEIGHT * acos(dot3D / _VEC_LEN_SQUARED))
where dot3D = apply x + apply y + apply z
apply fn = (fn i) * (fn j)
matrix arr = map crosscmp arr
where crosscmp s1 = [ value s1 * (distance s1 s2) | s2 <- arr ]
distance a b = arcLength (_CENTERS !! a) (_CENTERS !! b)
value s = fromInteger $ _VALUES !! s
variance arr = sum $ map perside (matrix arr)
where perside s = (sum s - mean) ^ 2
mean = (sum (concat $ matrix arr)) / (sides + 1)
sides = fromInteger $ toInteger _SIDES
maxDistr = maximumBy (\a b -> variance a `compare` variance b)
主要基本上只是
print $ maxDistr $ take _TRIALS $ arrangements seed