Difference between revisions of "Memoization"
m (→Memoization with recursion: slight clarification for newer Haskellers)
(→Memoization with recursion: the eta-expanded version doesn't actually memoize properly)
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memoized_fib :: Int -> Integer
memoized_fib :: Int -> Integer
memoized_fib = map fib [0 ..] !!
where fib 0 = 0
where fib 0 = 0
fib 1 = 1
fib 1 = 1
Revision as of 06:16, 19 January 2012
Memoization is a technique for storing values of a function instead of recomputing them each time the function is called.
Memoization without recursion
You can just write a memoization function using a data structure that is suitable for your application.
We don't go into the details of this case.
If you want a general solution for several types,
you need a type class, say
memoize :: Memoizable a => (a->b) -> (a->b)
Now, how to implement something like this? Of course, one needs a finite
map that stores values
b for keys of type
It turns out that such a map can be constructed recursively based on the structure of
Map () b := b Map (Either a a') b := (Map a b, Map a' b) Map (a,a') b := Map a (Map a' b)
Map a b is the type of a finite map from keys
a to values
Its construction is based on the following laws for functions
() -> b =~= b (a + a') -> b =~= (a -> b) x (a' -> b) -- = case analysis (a x a') -> b =~= a -> (a' -> b) -- = currying
For further and detailed explanations, see
- Ralf Hinze: Memo functions, polytypically !
- Ralf Hinze: Generalizing generalized tries
- Conal Elliott: Elegant memoization with functional memo tries and other posts on memoization.
- Conal Elliott Denotational design with type class morphisms, section 9 (Memo tries).
Memoization with recursion
Things become more complicated if the function is recursively defined and it should use memoized calls to itself. A classic example is the recursive computation of Fibonacci numbers.
The naive implementation of Fibonacci numbers without memoization is horribly slow.
slow_fib 30, not too much higher than that and it hangs.
slow_fib :: Int -> Integer slow_fib 0 = 0 slow_fib 1 = 1 slow_fib n = slow_fib (n-2) + slow_fib (n-1)
The memoized version is much faster.
memoized_fib :: Int -> Integer memoized_fib = (map fib [0 ..] !!) where fib 0 = 0 fib 1 = 1 fib n = memoized_fib (n-2) + memoized_fib (n-1)
Memoizing fix point operator
You can factor out the memoizing trick to a function, the memoizing fix point operator,
which we will call
fib :: (Int -> Integer) -> Int -> Integer fib f 0 = 1 fib f 1 = 1 fib f n = f (n-1) + f (n-2) fibonacci :: Int -> Integer fibonacci = memoFix fib
I suppose if you want to "put it in a library",
you should just put
and allow the user to call
memoFix fib to make a new version when necessary.
This allows the user e.g. to define the data structure used for memoization.
The memoising fixpoint operator works by putting the result of the first call of the function for each natural number into a data structure and using that value for subsequent calls ;-)
In general it is
memoFix :: ((a -> b) -> (a -> b)) -> a -> b memoFix f = let mf = memoize (f mf) in mf
Efficient tree data structure for maps from Int to somewhere
Here we present a special tree data type (data-inttrie) which is useful as memoizing data structure e.g. for the Fibonacci function.
memoizeInt :: (Int -> a) -> (Int -> a) memoizeInt f = (fmap f (naturals 1 0) !!!)
A data structure with a node corresponding to each natural number to use as a memo.
data NaturalTree a = Node a (NaturalTree a) (NaturalTree a)
Map the nodes to the naturals in this order:
0 1 2 3 5 4 6 7 ...
Look up the node for a particular number
Node a tl tr !!! 0 = a Node a tl tr !!! n = if odd n then tl !!! top else tr !!! (top-1) where top = n `div` 2
We surely want to be able to map on these things...
instance Functor NaturalTree where fmap f (Node a tl tr) = Node (f a) (fmap f tl) (fmap f tr)
If only so that we can write cute,
but inefficient things like the below,
which is just a
naturals!!!n == n:
naturals = Node 0 (fmap ((+1).(*2)) naturals) (fmap ((*2).(+1)) naturals)
The following is probably more efficient
(and, having arguments won't hang around at top level, I think)
-- have I put more
$!s than necessary?
naturals r n = Node n ((naturals $! r2) $! (n+r)) ((naturals $! r2) $! (n+r2)) where r2 = 2*r
Note: This is migrated from the old wiki.
Memoising constructor functions gives you HashConsing, and you can sometimes use MemoisingCafs to implement that.
The MemoisingCafs idiom also supports recursion.
Consider, for example:
wonderous :: Integer -> Integer wonderous 1 = 0 wonderous x | even x = 1 + wonderous (x `div` 2) | otherwise = 1 + wonderous (3*x+1)
This function is not at all understood by mathematicians and has a surprisingly complex recursion pattern, so if you need to call it many times with different values, optimising it would not be easy.
However, we can memoise some of the domain using an array CAF:
wonderous2 :: Integer -> Integer wonderous2 x | x <= maxMemo = memoArray ! x | otherwise = wonderous2' x where maxMemo = 100 memoArray = array (1,maxMemo) [ (x, wonderous2' x) | x <- [1..maxMemo] ] wonderous2' 1 = 0 wonderous2' x | even x = 1 + wonderous2 (x `div` 2) | otherwise = 1 + wonderous2' (3*x+1)
When using this pattern in your own code, note carefully when to call the memoised version (wonderous2 in the above example) and when not to. In general, the partially memoised version (wonderous2' in the above example) should call the memoised version if it needs to perform a recursive call. However, in this instance, we only memoize for small values of x, so the branch of the recursion that passes a larger argument need not bother checking the memo table. (This does slow the array initialization, however.) Thanks to lazy evaluation, we can even memoise an infinite domain, though we lose constant time lookup. This data structure is O(log N):
type MemoTable a = [(Integer, BinTree a)] data BinTree a = Leaf a | Node Integer (BinTree a) (BinTree a) wonderous3 :: Integer -> Integer wonderous3 x = searchMemoTable x memoTable where memoTable :: MemoTable Integer memoTable = buildMemoTable 1 5 buildMemoTable n i = (nextn, buildMemoTable' n i) : buildMemoTable nextn (i+1) where nextn = n + 2^i buildMemoTable' base 0 = Leaf (wonderous3' base) buildMemoTable' base i = Node (base + midSize) (buildMemoTable' base (i-1)) (buildMemoTable' (base + midSize) (i-1)) where midSize = 2 ^ (i-1) searchMemoTable x ((x',tree):ms) | x < x' = searchMemoTree x tree | otherwise = searchMemoTable x ms searchMemoTree x (Leaf y) = y searchMemoTree x (Node mid l r) | x < mid = searchMemoTree x l | otherwise = searchMemoTree x r wonderous3' 1 = 0 wonderous3' x | even x = 1 + wonderous3 (x `div` 2) | otherwise = 1 + wonderous3 (3*x+1)
Naturally, these techniques can be combined, say, by using a fast CAF data structure for the most common part of the domain and an infinite CAF data structure for the rest.
Memoizing polymorphic functions
What about memoizing polymorphic functions defined with polymorphic recursion? How can such functions be memoized? The caching data structures used in memoization typically handle only one type of argument at a time. For instance, one can have finite maps of differing types, but each concrete finite map holds just one type of key and one type of value.
- Haskell-Cafe "speeding up fibonacci with memoizing"
- Haskell-Cafe about memoization utility function
- Haskell-Cafe "memoisation"
- Haskell-Cafe about Memoization and Data.Map
- Monadic Memoization Mixins by Daniel Brown and William R. Cook
- data-memocombinators: Combinators for building memo tables.
- MemoTrie: Trie-based memo functions