neet (empty) → 0.1.0.0
raw patch · 11 files changed
+1950/−0 lines, 11 filesdep +MonadRandomdep +basedep +containerssetup-changed
Dependencies added: MonadRandom, base, containers, graphviz, multimap, transformers
Files
- LICENSE +674/−0
- README +0/−0
- Setup.hs +2/−0
- neet.cabal +35/−0
- src/Control/Monad/Fresh/Class.hs +54/−0
- src/Neet/Examples/XOR.hs +70/−0
- src/Neet/Genome.hs +431/−0
- src/Neet/Network.hs +141/−0
- src/Neet/Parameters.hs +86/−0
- src/Neet/Population.hs +363/−0
- src/Neet/Species.hs +94/−0
+ LICENSE view
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+ README view
+ Setup.hs view
@@ -0,0 +1,2 @@+import Distribution.Simple+main = defaultMain
+ neet.cabal view
@@ -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
+ src/Control/Monad/Fresh/Class.hs view
@@ -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
+ src/Neet/Examples/XOR.hs view
@@ -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
+ src/Neet/Genome.hs view
@@ -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
+ src/Neet/Network.hs view
@@ -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
+ src/Neet/Parameters.hs view
@@ -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
+ src/Neet/Population.hs view
@@ -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
+ src/Neet/Species.hs view
@@ -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)