Cleanup existing GA code

This commit is contained in:
David Pätzel 2019-10-17 18:23:19 +02:00
parent 296b2e218c
commit 49b105f42a

109
src/GA.hs
View File

@ -1,17 +1,18 @@
{-# LANGUAGE DeriveFunctor #-}
{-# LANGUAGE DeriveFoldable #-}
{-# LANGUAGE DeriveFunctor #-}
{-# LANGUAGE DeriveTraversable #-}
{-# LANGUAGE GeneralizedNewtypeDeriving #-}
{-# LANGUAGE NoImplicitPrelude #-}
{-# LANGUAGE TupleSections #-}
module GA where
import Protolude
-- NEXT commit everything
-- TODO add factory floor optimizer:
-- [2019-07-15] GA that optimizes factory floor
-- - data: graph of workstations with edge weights being the number of walks between them
-- - desired: optimal configuration that reduces crossings
-- - space: 15 workstations that can be positioned in a 20 x 20 space
import Control.Arrow hiding (first)
import qualified Data.List as L
import Data.List.NonEmpty ((<|))
@ -19,20 +20,25 @@ import qualified Data.List.NonEmpty as NE
import Data.Random
import Data.Random.Distribution.Categorical
import Data.Random.Sample
import Pretty
import Protolude
import Test.QuickCheck hiding (sample, shuffle)
import Test.QuickCheck.Instances
import Test.QuickCheck.Monadic
import Pretty
-- TODO Enforce this being > 0
type N = Int
type R = Float
type R = Double
-- alternative could be
-- data I a
-- = I
-- { mutate :: m (I a),
-- crossover1 :: (MonadRandom m) => I a -> m (Maybe (I a, I a))
-- }
class Eq i => Individual i where
{-|
Generates a completely random individual given an existing individual.
@ -42,16 +48,21 @@ class Eq i => Individual i where
be done nicer!
-}
new :: (MonadRandom m) => i -> m i
{-|
Generates a random population of the given size.
-}
population :: (MonadRandom m) => N -> i -> m (Population i)
population 0 _ = undefined
population n i = Pop . NE.fromList <$> replicateM n (new i)
mutate :: (MonadRandom m) => i -> m i
crossover1 :: (MonadRandom m) => i -> i -> m (Maybe (i, i))
-- TODO Perhaps rather add a 'features' function (and parametrize select1 etc. with fitness function)?
fitness :: (Monad m) => i -> m R
{-|
Performs an n-point crossover.
@ -61,36 +72,33 @@ class Eq i => Individual i where
-}
crossover :: (MonadRandom m) => Int -> i -> i -> m (Maybe (i, i))
crossover n i1 i2
| n <= 0 = return $ Just (i1, i2)
| n <= 0 = return $ Just (i1, i2)
| otherwise = do
isM <- crossover1 i1 i2
maybe (return Nothing) (uncurry (crossover (n - 1))) isM
isM <- crossover1 i1 i2
maybe (return Nothing) (uncurry (crossover (n - 1))) isM
-- TODO Do i want to model the population using Data.Vector.Sized?
-- TODO Perhaps use Data.Vector.Sized for the population?
{-|
It would be nice to model populations as GADTs but then no functor instance were
possible:
> data Population a where
> Pop :: Individual a => NonEmpty a -> Population a
-}
newtype Population a = Pop { unPop :: NonEmpty a }
newtype Population a = Pop {unPop :: NonEmpty a}
deriving (Foldable, Functor, Semigroup, Show, Traversable)
instance (Arbitrary i) => Arbitrary (Population i) where
arbitrary = Pop <$> arbitrary
{-|
Selects one individual from the population using proportionate selection.
-}
proportionate1 :: (Individual i, MonadRandom m) => Population i -> m i
proportionate1 pop =
sequence ((\ i -> (, i) <$> fitness i) <$> pop) >>=
sample . fromWeightedList . NE.toList . unPop
-- TODO Perhaps use stochastic acceptance for performance?
sequence ((\i -> (,i) <$> fitness i) <$> pop)
>>= sample . fromWeightedList . NE.toList . unPop
-- TODO Perhaps use stochastic acceptance for performance?
{-|
Selects @n@ individuals from the population using proportionate selection.
@ -98,12 +106,13 @@ Selects @n@ individuals from the population using proportionate selection.
-- TODO Perhaps use Data.Vector.Sized for the result?
proportionate
:: (Individual i, MonadRandom m)
=> N -> Population i -> m (NonEmpty i)
=> N
-> Population i
-> m (NonEmpty i)
proportionate n pop
| n > 1 = (<|) <$> proportionate1 pop <*> proportionate (n - 1) pop
| otherwise = (:|) <$> proportionate1 pop <*> return []
{-|
Produce offspring circularly.
-}
@ -113,7 +122,6 @@ children nX (i1 :| [i2]) = children2 nX i1 i2
children nX (i1 :| i2 : is') =
(<>) <$> children2 nX i1 i2 <*> children nX (NE.fromList is')
children2 :: (Individual i, MonadRandom m) => N -> i -> i -> m (NonEmpty i)
children2 nX i1 i2 = do
-- TODO Add crossover probability?
@ -122,43 +130,63 @@ children2 nX i1 i2 = do
i6 <- mutate i4
return $ i5 :| [i6]
{-|
The @k@ worst individuals in the population.
The @k@ best individuals in the population when comparing using the supplied
function.
-}
bestBy :: (Individual i, Monad m) => N -> (i -> m R) -> Population i -> m [i]
bestBy k f =
fmap (NE.take k . fmap fst . NE.sortBy (comparing (Down . snd))) .
traverse (\ i -> (i, ) <$> f i) . unPop
fmap (NE.take k . fmap fst . NE.sortBy (comparing (Down . snd)))
. traverse (\i -> (i,) <$> f i)
. unPop
-- TODO no trivial instance for worst
-- prop_worstLength :: Int -> Population Int -> Property
-- prop_worstLength k pop = monadicIO $ (k ==) . length <$> worst k pop
{-|
The @k@ worst individuals in the population.
-}
worst :: (Individual i, Monad m) => N -> Population i -> m [i]
worst = flip bestBy (fmap (1 /) . fitness)
{-|
The @k@ best individuals in the population.
-}
bests :: (Individual i, Monad m) => N -> Population i -> m [i]
bests = flip bestBy fitness
{-|
Runs the GA and prints the @nResult@ best individuals.
-}
ga' nParents nX pop term nResult = do
pop <- ga nParents nX pop term
res <- bests nResult pop
sequence $ putText . pretty <$> res
{-|
Runs the GA, using in each iteration
- @nParents@ parents for creating @nParents@ children and
- @nX@-point crossover.
It terminates after the termination criterion is fulfilled.
-}
ga
:: (Individual i, MonadRandom m, Monad m)
=> N -> N -> Population i -> Termination i -> m (Population i)
=> N
-> N
-> Population i
-> Termination i
-> m (Population i)
ga nParents nX pop term = ga' nParents nX pop term 0
where
ga'
:: (Individual i, MonadRandom m, Monad m)
=> N -> N -> Population i -> Termination i -> N -> m (Population i)
=> N
-> N
-> Population i
-> Termination i
-> N
-> m (Population i)
ga' nParents nX pop term t = do
-- trace (show t <> ": " <> show (length pop)) $ return ()
is <- proportionate nParents pop
@ -172,22 +200,17 @@ ga nParents nX pop term = ga' nParents nX pop term 0
-- replace fitness proportionally
-- let pop' = Pop <$> proportionate (length pop) (pop <> Pop is')
if term pop' t
then
return pop'
else
ga' nParents nX pop' term (t + 1)
then return pop'
else ga' nParents nX pop' term (t + 1)
-- * Termination criteria
{-|
Termination decisions may take into account the current population and the
current iteration number.
-}
type Termination i = Population i -> N -> Bool
{-|
Termination after a number of steps.
-}