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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/README b/README new file mode 100644 --- /dev/null +++ b/README diff --git a/Setup.hs b/Setup.hs new file mode 100644 --- /dev/null +++ b/Setup.hs @@ -0,0 +1,2 @@ +import Distribution.Simple +main = defaultMain diff --git a/neet.cabal b/neet.cabal new file mode 100644 --- /dev/null +++ b/neet.cabal @@ -0,0 +1,35 @@ +-- Initial neet.cabal generated by cabal init. For further documentation, +-- see http://haskell.org/cabal/users-guide/ + +name: neet +version: 0.1.0.0 +synopsis: A NEAT library for Haskell +-- description: +homepage: https://github.com/raymoo/NEET +bug-reports: https://github.com/raymoo/NEET/issues +license: GPL-3 +license-file: LICENSE +author: Leon Medvinsky +maintainer: lmedvinsky@hotmail.com +copyright: Copyright (C) 2015 Leon Medvinsky +category: AI +build-type: Simple +extra-source-files: README +cabal-version: >=1.10 +description: + This package provides functions for creating, training, and using NEAT neural + networks. + + +library + exposed-modules: Neet.Genome, Control.Monad.Fresh.Class, Neet.Parameters, Neet.Network, + Neet.Species, Neet.Population, Neet.Examples.XOR + -- other-modules: + other-extensions: FunctionalDependencies, MultiParamTypeClasses + build-depends: base >=4.7 && <4.8, MonadRandom >=0.4 && <0.5, + containers >= 0.5 && < 0.6, multimap >= 1.2 && < 1.3, + transformers == 0.4.*, graphviz == 2999.17.* + hs-source-dirs: src + default-language: Haskell2010 + + ghc-options: -Wall -fwarn-tabs diff --git a/src/Control/Monad/Fresh/Class.hs b/src/Control/Monad/Fresh/Class.hs new file mode 100644 --- /dev/null +++ b/src/Control/Monad/Fresh/Class.hs @@ -0,0 +1,54 @@ +{- +Copyright (C) 2015 Leon Medvinsky + +This program is free software; you can redistribute it and/or +modify it under the terms of the GNU General Public License +as published by the Free Software Foundation; either version 3 +of the License, or (at your option) any later version. + +This program is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU General Public License for more details. + +You should have received a copy of the GNU General Public License +along with this program; if not, write to the Free Software +Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. +-} + +{-# LANGUAGE FunctionalDependencies #-} +{-# LANGUAGE MultiParamTypeClasses #-} + +{-| +Module : Control.Monad.Fresh.Class +Description : Monads that provide fresh values +Copyright : (c) Leon Medvinsky, 2015 + +License : GPL-3 +Maintainer : lmedvinsky@hotmail.com +Stability : experimental +Portability : portable +-} + +module Control.Monad.Fresh.Class (MonadFresh(..), nFresh) where + + + +import Control.Monad + + +-- | A class for monads that can give fresh values. +-- +-- At least for Eq s, an instance of 'MonadFresh' should satisfy +-- +-- @ +-- ('==') '<$>' 'fresh' '<*>' 'fresh' ≡ 'fresh' '*>' 'fresh' '*>' 'pure' 'False' +-- @ +-- +-- and something similar for any number of fresh. +class Monad m => MonadFresh s m | m -> s where + fresh :: m s + + +nFresh :: MonadFresh s m => Int -> m [s] +nFresh n = replicateM n fresh diff --git a/src/Neet/Examples/XOR.hs b/src/Neet/Examples/XOR.hs new file mode 100644 --- /dev/null +++ b/src/Neet/Examples/XOR.hs @@ -0,0 +1,70 @@ +{- +Copyright (C) 2015 Leon Medvinsky + +This program is free software; you can redistribute it and/or +modify it under the terms of the GNU General Public License +as published by the Free Software Foundation; either version 3 +of the License, or (at your option) any later version. + +This program is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU General Public License for more details. + +You should have received a copy of the GNU General Public License +along with this program; if not, write to the Free Software +Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. +-} + +{-| +Module : Neet.Examples.XOR +Description : Testing the algorithm on XOR +Copyright : (c) Leon Medvinsky, 2015 + +License : GPL-3 +Maintainer : lmedvinsky@hotmail.com +Stability : experimental +Portability : ghc +-} + +module Neet.Examples.XOR (xorFit, andFit, orFit) where + + +import Neet.Genome +import Neet.Network + + +boolQuestions :: [[Double]] +boolQuestions = [ [0, 0] + , [0, 1] + , [1, 0] + , [1, 1] + ] + +xorAnswers :: [Double] +xorAnswers = [0, 1, 1, 0] + +sampleFit :: [[Double]] -> [Double] -> Genome -> Double +sampleFit questions answers g = (fromIntegral (length answers) - sumDiffs)**2 + where net = mkPhenotype g + try samp = head . getOutput $ snapshot net samp + responses = map try questions + sumDiffs = sum $ zipWith (\x y -> abs (x - y)) responses answers + +xorFit :: Genome -> Double +xorFit = sampleFit boolQuestions xorAnswers + +andAnswers :: [Double] +andAnswers = [0, 0, 0, 1] + + +andFit :: Genome -> Double +andFit = sampleFit boolQuestions andAnswers + + +orAnswers :: [Double] +orAnswers = [0, 1, 1, 1] + + +orFit :: Genome -> Double +orFit = sampleFit boolQuestions orAnswers diff --git a/src/Neet/Genome.hs b/src/Neet/Genome.hs new file mode 100644 --- /dev/null +++ b/src/Neet/Genome.hs @@ -0,0 +1,431 @@ +{- +Copyright (C) 2015 Leon Medvinsky + +This program is free software; you can redistribute it and/or +modify it under the terms of the GNU General Public License +as published by the Free Software Foundation; either version 3 +of the License, or (at your option) any later version. + +This program is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU General Public License for more details. + +You should have received a copy of the GNU General Public License +along with this program; if not, write to the Free Software +Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. +-} + +{-| +Module : Neet.Genome +Description : Encodings NEAT genomes +Copyright : (c) Leon Medvinsky, 2015 + +License : GPL-3 +Maintainer : lmedvinsky@hotmail.com +Stability : experimental +Portability : ghc +-} + +{-# LANGUAGE RecordWildCards #-} +{-# LANGUAGE FlexibleContexts #-} +{-# LANGUAGE OverloadedStrings #-} +{-# LANGUAGE GeneralizedNewtypeDeriving #-} + +module Neet.Genome ( -- * Genes + NodeId(..) + , NodeType(..) + , NodeGene(..) + , ConnGene(..) + , InnoId(..) + , ConnSig + -- * Genome + , Genome(..) + -- ** Construction + , fullConn + -- ** Breeding + , mutate + , crossover + , breed + -- ** Distance + , distance + -- ** Visualization + , renderGenome + ) where + +import Control.Applicative +import Control.Monad +import Control.Monad.Random +import Data.Map.Strict (Map) +import qualified Data.Traversable as T +import qualified Data.Map.Strict as M + +import qualified Data.Set as S + +import Data.Maybe + +import Control.Monad.Fresh.Class +import Neet.Parameters + +import Data.GraphViz +import Data.GraphViz.Attributes.Complete + +-- | The IDs node genes use to refer to nodes. +newtype NodeId = NodeId Int + deriving (Show, Eq, Ord, PrintDot) + + +-- | Types of nodes +data NodeType = Input | Hidden | Output + deriving (Show, Eq) + + +-- | Node genes +data NodeGene = NodeGene { nodeType :: NodeType + , yHint :: Rational -- ^ A hint for recurrency + } + deriving (Show) + + +-- | Connection genes +data ConnGene = ConnGene { connIn :: NodeId + , connOut :: NodeId + , connWeight :: Double + , connEnabled :: Bool + , connRec :: Bool -- ^ A hint for recurrency + } + deriving (Show) + + +-- | Innovation IDs +newtype InnoId = InnoId Int + deriving (Show, Eq, Ord) + + +-- | A NEAT genome. The innovation numbers are stored in here, and not the genes, +-- to prevent data duplication. +data Genome = + Genome { nodeGenes :: Map NodeId NodeGene + , connGenes :: Map InnoId ConnGene + , nextNode :: NodeId + } + deriving (Show) + + +-- | Takes the number of inputs, the number of outputs, and gives a genome with +-- the inputs fully connected to the outputs with random weights. The order of +-- the connections are deterministic, so when generating a population, you +-- can just start the innovation number at (iSize + 1) * oSize, since the network +-- includes an additional input for the bias. +fullConn :: MonadRandom m => Parameters -> Int -> Int -> m Genome +fullConn Parameters{..} iSize oSize = do + let inCount = iSize + 1 + inIDs = map NodeId [1..inCount] + outIDs = map NodeId [inCount + 1..oSize + inCount] + inputGenes = zip inIDs $ repeat (NodeGene Input 0) + outputGenes = zip outIDs $ repeat (NodeGene Output 1) + nodeGenes = M.fromList $ inputGenes ++ outputGenes + nextNode = NodeId $ inCount + oSize + 1 + nodePairs = (,) <$> inIDs <*> outIDs + conns <- zipWith (\(inN, outN) w -> ConnGene inN outN w True False) nodePairs `liftM` getRandomRs (-weightRange,weightRange) + let connGenes = M.fromList $ zip (map InnoId [1..]) conns + return $ Genome{..} + + +-- | Mutate the weights - perturb or make entirely new weights +mutateWeights :: MonadRandom m => Parameters -> Genome -> m Genome +mutateWeights Parameters{..} gen@Genome{..} = do + roll <- getRandomR (0,1) + if roll > mutWeightRate + then return gen + else setConns gen `liftM` T.mapM mutOne connGenes + where setConns g cs = g { connGenes = cs } + mutOne conn = do + roll <- getRandomR (0,1) + let newWeight + | roll <= newWeightRate = getRandomR (-weightRange,weightRange) + | otherwise = do + pert <- getRandomR (-pertAmount,pertAmount) + return $ connWeight conn + pert + w <- newWeight + return $ conn { connWeight = w } + + +-- | Signature of a connection, used in matching innovations fromthe same generation. +data ConnSig = ConnSig NodeId NodeId + deriving (Show, Eq, Ord) + + +-- | Get a 'ConnSig' +toConnSig :: ConnGene -> ConnSig +toConnSig gene = ConnSig (connIn gene) (connOut gene) + +-- | Adds a single connection, updating the innovation context +addConn :: MonadFresh InnoId m => ConnGene -> + (Map ConnSig InnoId, Map InnoId ConnGene) -> + m (Map ConnSig InnoId, Map InnoId ConnGene) +addConn conn (innos, conns) = case M.lookup siggy innos of + Just inno -> return (innos, M.insert inno conn conns) + Nothing -> do + newInno <- fresh + return (M.insert siggy newInno innos, M.insert newInno conn conns) + where siggy = toConnSig conn + + +-- | Mutation of additional connection. 'Map' parameter is context of previous +-- innovations. This could be global, or per species generation. +mutateConn :: (MonadFresh InnoId m, MonadRandom m) => + Parameters -> Map ConnSig InnoId -> Genome -> m (Map ConnSig InnoId, Genome) +mutateConn params innos g = do + roll <- getRandomR (0,1) + if roll > addConnRate params + then return (innos, g) + else case allowed of + [] -> return (innos, g) + _ -> do + (innos', conns') <- addRandConn innos (connGenes g) + return $ (innos', g { connGenes = conns' }) + + where + -- | Which connections are already filled up by genes. Value is a dummy + -- value because taken is only used in difference anyway. + taken :: Map ConnSig Bool + taken = M.fromList . map (\c -> (toConnSig c, True)) . M.elems . connGenes $ g + + -- | Whether a gene is an input gene + notInput (NodeGene Input _) = False + notInput _ = True + + -- | The genome's nodes, in an assoc list + nodes = M.toList $ nodeGenes g + + -- | Nodes that are not input + nonInputs = filter (notInput . snd) nodes + + -- | Make a pair of 'ConnSig' and the recurrentness + makePair (n1,g1) (n2,g2) = (ConnSig n1 n2, yHint g2 <= yHint g1) + + -- | Possible input -> output pairs + candidates = M.fromList $ makePair <$> nodes <*> nonInputs + + -- | Which pairs are not taken + allowed = M.toList $ M.difference candidates taken + + -- | Picks one of the available pairs + pickOne :: MonadRandom m => m (ConnSig, Bool) + pickOne = uniform allowed + + pickWeight :: MonadRandom m => m Double + pickWeight = let r = weightRange params in getRandomR (-r,r) + + -- | Randomly chooses one of the available connections and creates a + -- gene for it + addRandConn :: (MonadRandom m, MonadFresh InnoId m) => + Map ConnSig InnoId -> Map InnoId ConnGene -> + m (Map ConnSig InnoId, Map InnoId ConnGene) + addRandConn innos' conns = do + (ConnSig inNode outNode, recc) <- pickOne + w <- pickWeight + let newConn = ConnGene inNode outNode w True recc + addConn newConn (innos',conns) + + +-- | Mutation of additional node. +mutateNode :: (MonadRandom m, MonadFresh InnoId m) => + Parameters -> Map ConnSig InnoId -> + Genome -> m (Map ConnSig InnoId, Genome) +mutateNode params innos g = do + roll <- getRandomR (0,1) + if roll <= addNodeRate params then addRandNode else return (innos, g) + where conns = connGenes g + nodes = nodeGenes g + + -- | Pick one of the 'InnoId' 'ConnGene' pairs from conns + pickConn :: MonadRandom m => m (InnoId, ConnGene) + pickConn = uniform $ M.toList conns + + -- | What will the new node's ID be + newId = nextNode g + + -- | What should 'nextNode' be updated to + newNextNode = case newId of NodeId x -> NodeId (x + 1) + + -- | Takes a connection gene and its associated InnoID, and splits + -- it with a node + addNode :: MonadFresh InnoId m => + InnoId -> ConnGene -> m (Map ConnSig InnoId, Genome) + addNode inno gene = do + let ConnSig inId outId = toConnSig gene + + -- | Gene of the input node of this connection + inGene = nodes M.! inId + + -- | Gene of the output node of this connection + outGene = nodes M.! outId + + -- | The new node gene + newGene = NodeGene Hidden ((yHint inGene + yHint outGene) / 2) + + -- | The new map of nodes, after inserting the new one + newNodes = M.insert newId newGene nodes + + -- | The disabled version of the old connection + disabledConn = gene { connEnabled = False } + + -- | The gene for the connection between the input and the new node + backGene = ConnGene inId newId 1 True (connRec gene) + + -- | The gene for the connection between the new node and the output + forwardGene = ConnGene newId outId (connWeight gene) True (connRec gene) + + (innos', newConns) <- + addConn backGene >=> addConn forwardGene $ (innos, conns) + + return $ (innos', g { nodeGenes = newNodes + , connGenes = M.insert inno disabledConn newConns + , nextNode = newNextNode + }) + + -- | Pick an available connection randomly and make a gene for it + addRandNode :: (MonadRandom m, MonadFresh InnoId m) => m (Map ConnSig InnoId, Genome) + addRandNode = + pickConn >>= uncurry addNode + + +-- | Mutates the genome, using the specified parameters and innovation context. +mutate :: (MonadRandom m, MonadFresh InnoId m) => Parameters -> Map ConnSig InnoId -> + Genome -> m (Map ConnSig InnoId, Genome) +mutate params innos g = do + g' <- mutateWeights params g + uncurry (mutateNode params) >=> uncurry (mutateConn params) $ (innos, g') + + +-- | Super left biased merge -- loners on the right map don't get in +superLeft :: Ord k => (a -> b -> c) -> (a -> c) -> Map k a -> Map k b -> Map k c +superLeft comb mk = M.mergeWithKey (\_ a b -> Just $ comb a b) (M.map mk) (const M.empty) + + +-- | Choose between two alternatives with coin chance +flipCoin :: MonadRandom m => a -> a -> m a +flipCoin a1 a2 = uniform [a1, a2] + + +-- | Crossover on just the connections. Put the fittest map first. +crossConns :: MonadRandom m => Parameters -> Map InnoId ConnGene -> Map InnoId ConnGene -> + m (Map InnoId ConnGene) +crossConns params m1 m2 = T.sequence $ superLeft flipConn return m1 m2 + where flipConn c1 c2 = do + if connEnabled c1 && connEnabled c2 + then flipCoin c1 c2 + else do + c <- flipCoin c1 c2 + roll <- getRandomR (0,1) + let enabled + | roll <= disableChance params = False + | otherwise = True + return c { connEnabled = enabled } + + +-- | Crossover on just nodes +crossNodes :: MonadRandom m => Map NodeId NodeGene -> Map NodeId NodeGene -> + m (Map NodeId NodeGene) +crossNodes m1 m2 = T.sequence $ superLeft flipCoin return m1 m2 + + +-- | Crossover. The first argument is the fittest genome. +crossover :: MonadRandom m => Parameters -> Genome -> Genome -> m Genome +crossover params g1 g2 = Genome `liftM` newNodes `ap` newConns `ap` return newNextNode + where newNextNode = max (nextNode g1) (nextNode g2) + newConns = crossConns params (connGenes g1) (connGenes g2) + newNodes = crossNodes (nodeGenes g1) (nodeGenes g2) + + +-- | Breed two genomes together +breed :: (MonadRandom m, MonadFresh InnoId m) => + Parameters -> Map ConnSig InnoId -> Genome -> Genome -> + m (Map ConnSig InnoId, Genome) +breed params innos g1 g2 = + crossover params g1 g2 >>= mutate params innos + + +-- | Gets differences where they exist +differences :: Map InnoId ConnGene -> Map InnoId ConnGene -> Map InnoId Double +differences = M.mergeWithKey (\_ c1 c2 -> Just $ oneDiff c1 c2) (const M.empty) (const M.empty) + where oneDiff c1 c2 = abs $ connWeight c1 - connWeight c2 + + +-- | Genetic distance between two genomes +distance :: Parameters -> Genome -> Genome -> Double +distance params g1 g2 = c1 * exFactor + c2 * disFactor + c3 * weightFactor + where DistParams c1 c2 c3 _ = distParams params + + conns1 = connGenes g1 + conns2 = connGenes g2 + + weightDiffs = differences conns1 conns2 + + weightFactor = M.foldl (+) 0 weightDiffs / fromIntegral (M.size weightDiffs) + + ids1 = M.keysSet conns1 + ids2 = M.keysSet conns2 + + -- | The lower of the top bounds of innovation numbers + edge = min (S.findMax ids1) (S.findMax ids2) + + -- | Excess and Disjoint + exJoints = (ids1 `S.difference` ids2) `S.union` (ids2 `S.difference` ids1) + + (excess, disjoint) = S.partition (<= edge) exJoints + + exFactor = fromIntegral $ S.size excess + + disFactor = fromIntegral $ S.size disjoint + + +graphParams :: GraphvizParams NodeId NodeGene Double Rational Rational +graphParams = + Params { isDirected = True + , globalAttributes = [ GraphAttrs [ RankDir FromLeft + , Splines LineEdges + ] + , NodeAttrs [ FixedSize SetNodeSize + ] + ] + , clusterBy = categorizer + , isDotCluster = const True + , clusterID = iderizer + , fmtCluster = clusterizer + , fmtNode = const [] + , fmtEdge = \(_,_,w) -> [ toLabel w ] + } + where categorizer (nId, ng) = C (yHint ng) (N (nId, yHint ng)) + iderizer 0 = Str "Input Layer" + iderizer 1 = Str "Output Layer" + iderizer rat = Num (Dbl $ fromRational rat) + whiteAttr = Color [WC (X11Color White) Nothing] + blueAttr = Color [WC (X11Color Blue4) Nothing ] + redAttr = Color [WC (X11Color Red2) Nothing ] + greenAttr = Color [WC (X11Color SeaGreen) Nothing ] + solidAttr = Style [ SItem Solid [] ] + circAttr = Shape Circle + clusterizer 0 = [ GraphAttrs [ whiteAttr, rank MinRank ] + , NodeAttrs [ solidAttr, blueAttr, circAttr ] + ] + clusterizer 1 = [ GraphAttrs [ whiteAttr, rank MaxRank ] + , NodeAttrs [ solidAttr, redAttr, circAttr ] + ] + clusterizer _ = [ GraphAttrs [ whiteAttr ] + , NodeAttrs [ solidAttr, greenAttr, circAttr ] + ] + + +-- | This graph produced is ugly and janky and will have bugs, like hidden nodes +-- occasionally appearing with output nodes, and weird clustering overall. If you +-- see some problems in the graph, confirm with the Show instance or something +-- else that there really is a problem. +renderGenome :: Genome -> IO () +renderGenome g = runGraphvizCanvas Dot graph Xlib + where nodes = M.toList . nodeGenes $ g + edges = mapMaybe mkEdge . M.elems . connGenes $ g + mkEdge ConnGene{..} = if connEnabled then Just (connIn, connOut, connWeight) else Nothing + graph = graphElemsToDot graphParams nodes edges diff --git a/src/Neet/Network.hs b/src/Neet/Network.hs new file mode 100644 --- /dev/null +++ b/src/Neet/Network.hs @@ -0,0 +1,141 @@ +{- +Copyright (C) 2015 Leon Medvinsky + +This program is free software; you can redistribute it and/or +modify it under the terms of the GNU General Public License +as published by the Free Software Foundation; either version 3 +of the License, or (at your option) any later version. + +This program is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU General Public License for more details. + +You should have received a copy of the GNU General Public License +along with this program; if not, write to the Free Software +Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. +-} + +{-| +Module : Neet.Network +Description : Networks produced from NEAT Genomes +Copyright : (c) Leon Medvinsky, 2015 + +License : GPL-3 +Maintainer : lmedvinsky@hotmail.com +Stability : experimental +Portability : ghc +-} + +{-# LANGUAGE RecordWildCards #-} +module Neet.Network ( + -- * Sigmoid + modSig + -- * Network + , Network(..) + -- ** Neuron + , Neuron(..) + -- ** Construction + , mkPhenotype + -- ** Updates + , stepNeuron + , stepNetwork + , snapshot + -- ** Output + , getOutput + ) where + +import Data.Map (Map) +import Data.Set (Set) +import qualified Data.Set as S + +import qualified Data.Map as M +import Data.List (foldl') + +import Neet.Genome + + +-- | Modified sigmoid function from the original NEAT paper +modSig :: Double -> Double +modSig d = 1 / (1 + exp (-4.9 * d)) + + +-- | A single neuron +data Neuron = + Neuron { activation :: Double -- ^ The current activation + , connections :: Map NodeId Double -- ^ The inputs to this Neuron + , yHint :: Rational -- ^ Visualization height + } + deriving (Show) + + +-- | Sparse recurrent network, like those made by NEAT +data Network = + Network { netInputs :: [NodeId] -- ^ Which nodes are inputs + , netOutputs :: [NodeId] -- ^ Which nodes are outputs + , netState :: Map NodeId Neuron + , netDepth :: Int -- ^ Upper bound on depth + } + deriving (Show) + + +-- | Takes the previous step's activations and current inputs and gives a +-- function to update a neuron. +stepNeuron :: Map NodeId Double -> Neuron -> Neuron +stepNeuron acts (Neuron _ conns yh) = Neuron (modSig weightedSum) conns yh + where oneFactor nId w = (acts M.! nId) * w + weightedSum = M.foldlWithKey' (\acc k w -> acc + oneFactor k w) 0 conns + + +-- | Steps a network one step. Takes the network and the current input, minus +-- the bias. +stepNetwork :: Network -> [Double] -> Network +stepNetwork net@Network{..} ins = net { netState = newNeurons } + where pairs = zip netInputs (ins ++ [1]) + + acts = M.map activation netState + + -- | The previous state, except updated to have new inputs + modState = foldl' (flip $ uncurry M.insert) acts pairs + + newNeurons = M.map (stepNeuron modState) netState + + +-- | Steps a network for at least its depth +snapshot :: Network -> [Double] -> Network +snapshot net = go (netDepth net - 1) + where go 0 _ = net + go n ds = stepNetwork (go (n - 1) ds) ds + + +mkPhenotype :: Genome -> Network +mkPhenotype Genome{..} = (M.foldl' addConn nodeHusk connGenes) { netInputs = ins + , netOutputs = outs + , netDepth = dep } + where addNode n@(Network _ _ s _) nId (NodeGene _ yh) = + n { netState = M.insert nId (Neuron 0 M.empty yh) s + } + + ins = M.keys . M.filter (\ng -> nodeType ng == Input) $ nodeGenes + outs = M.keys . M.filter (\ng -> nodeType ng == Output) $ nodeGenes + + -- | Network without connections added + nodeHusk = M.foldlWithKey' addNode (Network [] [] M.empty 0) nodeGenes + + depthSet :: Set Rational + depthSet = M.foldl' (flip S.insert) S.empty $ M.map Neet.Genome.yHint nodeGenes + + dep = S.size depthSet + + addConn2Node nId w (Neuron a cs yh) = Neuron a (M.insert nId w cs) yh + + addConn net@Network{ netState = s } ConnGene{..} + | not connEnabled = net + | otherwise = + let newS = M.adjust (addConn2Node connIn connWeight) connOut s + in net { netState = newS } + + +-- | Gets the output of the current state +getOutput :: Network -> [Double] +getOutput Network{..} = map (activation . (netState M.!)) netOutputs diff --git a/src/Neet/Parameters.hs b/src/Neet/Parameters.hs new file mode 100644 --- /dev/null +++ b/src/Neet/Parameters.hs @@ -0,0 +1,86 @@ +{- +Copyright (C) 2015 Leon Medvinsky + +This program is free software; you can redistribute it and/or +modify it under the terms of the GNU General Public License +as published by the Free Software Foundation; either version 3 +of the License, or (at your option) any later version. + +This program is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU General Public License for more details. + +You should have received a copy of the GNU General Public License +along with this program; if not, write to the Free Software +Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. +-} + +{-| +Module : Neet.Parameters +Description : Learning parameters +Copyright : (c) Leon Medvinsky, 2015 + +License : GPL-3 +Maintainer : lmedvinsky@hotmail.com +Stability : experimental +Portability : portable +-} + +module Neet.Parameters (Parameters(..), DistParams(..), defParams, defDP, smallParams ) where + + +-- | The genetic parameters +data Parameters = + Parameters { mutWeightRate :: Double -- ^ How often weights are mutated + , newWeightRate :: Double -- ^ How often weights are replaced if mutated + , pertAmount :: Double -- ^ Max amount of perturbation + , weightRange :: Double -- ^ A new max is between negative this and positive this + , addConnRate :: Double -- ^ How often new connections are made + , addNodeRate :: Double -- ^ How often new nodes are added + , largeSize :: Int -- ^ The minimum size for a species to be considered large + , disableChance :: Double -- ^ How likely that a disabled parent results + -- in a disabled child + , distParams :: DistParams -- ^ Parameters for the distance function + , dropTime :: Maybe Int -- ^ Drop a species if it doesn't improve for this long, + -- and it hasn't hosted the most successful genome. + , noCrossover :: Double -- ^ Percent of population that mutates without crossover + } + deriving (Show) + + +data DistParams = + DistParams { dp1 :: Double -- ^ Coefficient to the number of excess genes + , dp2 :: Double -- ^ Coefficient to the number of disjoint genes + , dp3 :: Double -- ^ Coefficient to the average weight differences + , delta_t :: Double -- ^ How close counts as the same species + } + deriving (Show) + + +-- | The parameters used in the original NEAT paper, except the perturbation amount +-- and threshold for size. +defParams :: Parameters +defParams = + Parameters { mutWeightRate = 0.8 + , newWeightRate = 0.1 + , pertAmount = 0.1 -- This value I made up + , weightRange = 10 -- This one too + , addConnRate = 0.3 + , addNodeRate = 0.03 + , largeSize = 20 + , disableChance = 0.75 + , distParams = defDP + , dropTime = Just 15 + , noCrossover = 0.25 + } + + +-- | Parameters used in the paper for small populations +smallParams :: Parameters +smallParams = defParams { addConnRate = 0.05 } + + +-- | Parameters used for distance in the paper +defDP :: DistParams +defDP = DistParams 1 1 0.4 3 diff --git a/src/Neet/Population.hs b/src/Neet/Population.hs new file mode 100644 --- /dev/null +++ b/src/Neet/Population.hs @@ -0,0 +1,363 @@ +{- +Copyright (C) 2015 Leon Medvinsky + +This program is free software; you can redistribute it and/or +modify it under the terms of the GNU General Public License +as published by the Free Software Foundation; either version 3 +of the License, or (at your option) any later version. + +This program is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU General Public License for more details. + +You should have received a copy of the GNU General Public License +along with this program; if not, write to the Free Software +Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. +-} + +{-| +Module : Neet.Population +Description : Population of NEAT organisms +Copyright : (c) Leon Medvinsky, 2015 + +License : GPL-3 +Maintainer : lmedvinsky@hotmail.com +Stability : experimental +Portability : ghc +-} + +{-# LANGUAGE DeriveFunctor #-} +{-# LANGUAGE GeneralizedNewtypeDeriving #-} +{-# LANGUAGE MultiParamTypeClasses #-} +{-# LANGUAGE RecordWildCards #-} +{-# LANGUAGE FlexibleContexts #-} +{-# LANGUAGE BangPatterns #-} +module Neet.Population ( + Population(..) + -- * PopM + , PopM + , PopContext + , runPopM + -- * Construction + , PopSettings(..) + , newPop + -- * Training + , trainOnce + , trainN + , trainUntil + ) where + +import Neet.Species +import Neet.Genome +import Neet.Parameters + +import Data.MultiMap (MultiMap) +import qualified Data.MultiMap as MM + +import Data.Map (Map) +import qualified Data.Map as M + +import Data.List (foldl', maximumBy) + +import Data.Maybe + +import Control.Monad.Random +import Control.Monad.Fresh.Class +import Control.Monad.Trans.State + +import Control.Applicative +import Control.Monad + + + +import Data.Function + +newtype SpecId = SpecId Int + deriving (Show, Eq, Ord) + + +-- | A NEAT Population +data Population = + Population { popSize :: Int -- ^ Size of the population + , popSpecs :: !(Map SpecId Species) -- ^ The species + , popBScore :: !Double -- ^ Best score so far + , popBOrg :: !Genome -- ^ Best genome so far + , popBSpec :: !SpecId -- ^ Id of the species that hosted the best score + , popCont :: !PopContext -- ^ Tracking state and fresh values + , nextSpec :: !SpecId -- ^ The next species ID + , popParams :: Parameters -- ^ Parameters for large species + , popParamsS :: Parameters -- ^ Parameters for small species + } + deriving (Show) + + +data PopContext = + PC { nextInno :: InnoId + , randGen :: StdGen + } + deriving (Show) + +-- | Custom state monad +newtype PopM a = PopM (State PopContext a) + deriving (Functor, Applicative, Monad) + + +instance MonadRandom PopM where + getRandom = PopM . state $ \s -> + let (r, gen) = random (randGen s) + in (r, s { randGen = gen }) + getRandoms = PopM . state $ \s -> + let (g1, g2) = split $ randGen s + in (randoms g1, s { randGen = g2 }) + getRandomR range = PopM . state $ \s -> + let (r, gen) = randomR range (randGen s) + in (r, s { randGen = gen }) + getRandomRs range = PopM . state $ \s -> + let (g1, g2) = split $ randGen s + in (randomRs range g1, s { randGen = g2 }) + + +instance MonadFresh InnoId PopM where + fresh = PopM . state $ \s -> + let inno@(InnoId x) = nextInno s + in (inno, s { nextInno = InnoId $ x + 1 }) + + +runPopM :: PopM a -> PopContext -> (a, PopContext) +runPopM (PopM ma) = runState ma + + +-- | Settings for creating a new population +data PopSettings = + PS { psSize :: Int -- ^ How big the population should be + , psInputs :: Int -- ^ Number of inputs + , psOutputs :: Int -- ^ Number of outputs + , psParams :: Parameters -- ^ Parameters for large species + , psParamsS :: Parameters -- ^ Parameters for small species + } + deriving (Show) + + +newtype SpecM a = SM (State SpecId a) + deriving (Functor, Applicative, Monad) + + +instance MonadFresh SpecId SpecM where + fresh = SM . state $ \s@(SpecId x) -> (s, SpecId $ x + 1) + + +runSpecM :: SpecM a -> SpecId -> (a, SpecId) +runSpecM (SM ma) = runState ma + + +-- | Buckets for speciation +data SpecBucket = + SB SpecId Genome [Genome] + + +-- | Speciation helper +shuttleOrgs :: MonadFresh SpecId m => + Parameters -> [SpecBucket] -> [Genome] -> m [SpecBucket] +shuttleOrgs p@Parameters{..} buckets = foldM shutOne buckets + where DistParams{..} = distParams + shutOne :: MonadFresh SpecId m => [SpecBucket] -> Genome -> m [SpecBucket] + shutOne (SB sId rep gs:bs) g + | distance p g rep <= delta_t = return $ SB sId rep (g:gs) : bs + | otherwise = liftM (SB sId rep gs :) $ shutOne bs g + shutOne [] g = do + newId <- fresh + return $ [SB newId g [g]] + + +zipWithDefaults :: (a -> b -> Maybe c) -> (a -> Maybe c) -> (b -> Maybe c) -> [a] -> [b] -> [c] +zipWithDefaults _ _ db [] bs = mapMaybe db bs +zipWithDefaults _ da _ as [] = mapMaybe da as +zipWithDefaults f da db (a:as) (b:bs) = + case f a b of + Just res -> res : zipWithDefaults f da db as bs + Nothing -> zipWithDefaults f da db as bs + + +-- | Speciation function +speciate :: MonadFresh SpecId m => + Parameters -> Map SpecId Species -> [Genome] -> m (Map SpecId Species) +speciate params specs gens = do + filled <- fill + let zipped = zipWithDefaults mkSpecies (const Nothing) newSpecies specL filled + return $ M.fromList zipped + where oneBucket (k, Species _ (rep:_) _ _) = SB k rep [] + oneBucket _ = error "(speciate) Empty species!" + assocs = M.toList specs + specL = map snd assocs + buckets = map oneBucket assocs + fill = shuttleOrgs params buckets gens + mkSpecies (Species _ _ scr imp) (SB sId _ gs) + | null gs = Nothing + | otherwise = Just $ (sId, Species (length gs) gs scr imp) + newSpecies (SB _ _ []) = Nothing + newSpecies (SB sId _ (g:gs)) = Just $ (sId, newSpec g gs) + + +-- | Generates a fully connected starter population, given a seed. +newPop :: Int -> PopSettings -> Population +newPop seed PS{..} = fst $ runPopM generate initCont + where popSize = psSize + popBScore = 0 + popBSpec = SpecId 1 + initCont = PC (InnoId $ psInputs * psOutputs + 2) (mkStdGen seed) + popParams = psParams + popParamsS = psParamsS + generateGens = replicateM psSize (fullConn psParams psInputs psOutputs) + generate = do + gens <- generateGens + let (popSpecs, nextSpec) = runSpecM (speciate psParams M.empty gens) (SpecId 1) + popBOrg = head gens + popCont <- PopM get + return Population{..} + + +-- | Advances the population one generation with the fitness function. +trainOnce :: (Genome -> Double) -> Population -> Population +trainOnce f pop = generated + where params = popParams pop + paramsS = popParamsS pop + + chooseParams :: Species -> Parameters + chooseParams s = if specSize s >= largeSize params then params else paramsS + {-# INLINE chooseParams #-} + + initSpecs = popSpecs pop + + -- | Map to fitness data from runFitTest + fits = M.map (\sp -> (sp, runFitTest f sp)) initSpecs + + -- | Whether a species deserves to live (did it improve recently?) + eugenics :: SpecId -> (Species, (MultiMap Double Genome, SpecScore, Double)) -> + Maybe (Species, MultiMap Double Genome, Double) + eugenics sId (sp, (fitmap, ss, adj)) + | maybe False (lastImprovement nSpec >=) (dropTime params) + && sId /= popBSpec pop = Nothing + | otherwise = Just (nSpec, fitmap, adj) + where nSpec = updateSpec ss sp + + -- | Species that have improved recently enough. + masterRace :: Map SpecId (Species, MultiMap Double Genome, Double) + masterRace = M.mapMaybeWithKey eugenics fits + + -- | toList'd version of masterRace + masterList :: [(SpecId,(Species, MultiMap Double Genome, Double))] + masterList = M.toList masterRace + + -- | The best + idVeryBest :: (SpecId, Species) + idVeryBest = maximumBy (compare `on` (bestScore . specScore . snd)) $ map clean masterList + where clean (sId,(sp, _, _)) = (sId,sp) + + veryBest = snd idVeryBest + + bestId = fst idVeryBest + + -- | Species to make buckets of + masterSpec :: Map SpecId Species + masterSpec = M.map (\(s,_,_) -> s) masterRace + + totalFitness = M.foldl' (+) 0 . M.map (\(_,_,x) -> x) $ masterRace + + totalSize = popSize pop + + dubSize = fromIntegral totalSize + + -- | Distribution of species. + candSpecs :: MonadRandom m => [(Parameters, Int, m Genome)] + candSpecs = zip3 ps realShares pickers + where initShares = map share masterList + share (_,(_, _, adj)) = round $ adj / totalFitness * dubSize + remaining = totalSize - foldl' (+) 0 initShares + distributeRem _ [] = error "Should run out of numbers first" + distributeRem n l@(x:xs) + | n > 0 = x + 1 : distributeRem (n - 1) xs + | otherwise = l + realShares = distributeRem remaining initShares + pickers :: MonadRandom m => [m Genome] + pickers = map picker masterList + where picker (_,(s, mmap, _)) = + let numToTake = specSize s `div` 5 + 1 + desc = M.toDescList $ MM.toMap mmap + toPairs (k, vs) = map (\v -> (k,v)) vs + culled = take numToTake $ desc >>= toPairs + in fromList . map (\(d,g) -> (g, toRational d)) $ culled + ps = map (\(_,(s,_,_)) -> chooseParams s) masterList + + applyN :: Monad m => Int -> (a -> m a) -> a -> m a + applyN 0 _ x = return x + applyN n h !x = h x >>= applyN (n - 1) h + + -- | Generate the genomes for a species + specGens :: (MonadFresh InnoId m, MonadRandom m) => + (Parameters, Int, m Genome) -> m [Genome] + specGens (p, n, gen) = liftM snd $ applyN n genOne (M.empty, []) + where genOne (innos, gs) = do + roll <- getRandomR (0,1) + if roll <= noCrossover p + then do + parent <- gen + (innos', g) <- mutate p innos parent + return (innos', g:gs) + else do + mom <- gen + dad <- gen + (innos', g) <- breed p innos mom dad + return (innos', g:gs) + + allGens :: (MonadRandom m, MonadFresh InnoId m) => m [Genome] + allGens = liftM concat $ mapM specGens candSpecs + + genNewSpecies :: (MonadRandom m, MonadFresh InnoId m) => m (Map SpecId Species, SpecId) + genNewSpecies = do + gens <- allGens + return $ runSpecM (speciate params masterSpec gens) (nextSpec pop) + + generated :: Population + generated = fst $ runPopM generate (popCont pop) + + generate :: PopM Population + generate = do + (specs, nextSpec') <- genNewSpecies + let bScoreNow = (bestScore . specScore) veryBest + bOrgNow = (bestGen . specScore) veryBest + bSpecNow = bestId + (bScore, bOrg, bSpec) = + if bScoreNow > popBScore pop + then (bScoreNow, bOrgNow, bSpecNow) + else (popBScore pop, popBOrg pop, popBSpec pop) + cont' <- PopM get + return pop { popSpecs = specs + , popBScore = bScore + , popBOrg = bOrg + , popBSpec = bSpec + , popCont = cont' + , nextSpec = nextSpec' + } + + +-- | Train the population n times. Values less than 1 return the original. +trainN :: Int -> (Genome -> Double) -> Population -> Population +trainN n f p + | n <= 0 = p + | otherwise = applyN n (trainOnce f) p + where applyN 0 _ !x = x + applyN n' h !x = applyN (n' - 1) h (h x) + + +-- | Train until the given fitness (first parameter) is reached, or the max number +-- of generations (second parameter) is reached. Also gives generations needed. +trainUntil :: Double -> Int -> (Genome -> Double) -> Population -> (Population, Int) +trainUntil goal n f p + | n <= 0 = (p, 0) + | otherwise = go n p + where go 0 !p' = (p', n) + go n' !p' + | reached = (p', n - n') + | otherwise = go (n' - 1) (trainOnce f p') + where reached = popBScore p' >= goal diff --git a/src/Neet/Species.hs b/src/Neet/Species.hs new file mode 100644 --- /dev/null +++ b/src/Neet/Species.hs @@ -0,0 +1,94 @@ +{- +Copyright (C) 2015 Leon Medvinsky + +This program is free software; you can redistribute it and/or +modify it under the terms of the GNU General Public License +as published by the Free Software Foundation; either version 3 +of the License, or (at your option) any later version. + +This program is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU General Public License for more details. + +You should have received a copy of the GNU General Public License +along with this program; if not, write to the Free Software +Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. +-} + +{-| +Module : Neet.Species +Description : NEAT Species +Copyright : (c) Leon Medvinsky, 2015 + +License : GPL-3 +Maintainer : lmedvinsky@hotmail.com +Stability : experimental +Portability : ghc +-} + +{-# LANGUAGE RecordWildCards #-} +module Neet.Species ( + Species(..) + , SpecScore(..) + -- * Construction + , newSpec + -- * Update/Fitness + , runFitTest + , updateSpec + ) where + + +import Neet.Genome +import Data.MultiMap (MultiMap) +import qualified Data.MultiMap as MM +import Data.List (foldl') + +-- | A NEAT Species. +data Species = + Species { specSize :: Int + , specOrgs :: [Genome] -- ^ All the organisms in this species + , specScore :: SpecScore + , lastImprovement :: Int -- ^ Number of gens ago the best score improved + } + + +-- | Scoring data +data SpecScore = SpecScore { bestScore :: Double, bestGen :: Genome } + + +instance Show Species where + show (Species siz _ (SpecScore scr _) lastImprov) = + "Species {specSize = " ++ show siz ++ + ", specOrgs = <...>, bestScore = " ++ show scr ++ + ", bestGen = <...>" ++ + ", lastImprovement = " ++ show lastImprov ++ "}" + + +-- | Creates a new 'Species' with starter stats from a 'Genome' and the rest +newSpec :: Genome -> [Genome] -> Species +newSpec gen gens = Species (length gens + 1) (gen:gens) (SpecScore 0 gen) 0 + + +-- | Output the result of testing fitness. Last value is the total adjusted fitness +runFitTest :: (Genome -> Double) -> Species -> (MultiMap Double Genome, SpecScore, Double) +runFitTest f Species{..} = (mmap, ss, totF / dubSize) + where dubSize = fromIntegral specSize :: Double + (mmap, totF) = foldl' accumOne (MM.empty, 0) $ map calcOne specOrgs + calcOne g = let fitness = f g in (fitness, g) + accumOne (accM, accA) (fit, g) = (MM.insert fit g accM, accA + fit) + ss = case MM.findMaxWithValues mmap of + Nothing -> error "(runFitTest) folding fitness resulted in empty map!" + Just (scr, (x:_)) -> SpecScore scr x + _ -> error "(runFitTest) MultiMap had a key with empty list!" + + +-- | Takes a new SpecScore and updates the metadata of a species +updateSpec :: SpecScore -> Species -> Species +updateSpec ss spec = spec { specScore = newScr + , lastImprovement = li + } + where oldScr = specScore spec + (newScr, li) + | bestScore ss > bestScore oldScr = (ss, 0) + | otherwise = (oldScr, lastImprovement spec + 1)