Copyright	(c) Leon Medvinsky, 2015
License	GPL-3
Maintainer	lmedvinsky@hotmail.com
Stability	experimental
Portability	ghc
Safe Haskell	None
Language	Haskell2010

Neet.Genome

Contents

Genes
Genome

Description

Synopsis

Genes

newtype NodeId Source

The IDs node genes use to refer to nodes.

Constructors

NodeId Int

Instances

Eq NodeId
Ord NodeId
Show NodeId
PrintDot NodeId

data NodeType Source

Types of nodes

Constructors

Input
Hidden
Output

Instances

Eq NodeType
Show NodeType

data NodeGene Source

Node genes

Constructors

NodeGene
Fields nodeType :: NodeType yHint :: Rational A hint for recurrency

Instances

Show NodeGene

data ConnGene Source

Connection genes

Constructors

ConnGene
Fields connIn :: NodeId connOut :: NodeId connWeight :: Double connEnabled :: Bool connRec :: Bool A hint for recurrency

Instances

Show ConnGene

newtype InnoId Source

Innovation IDs

Constructors

InnoId Int

Instances

Eq InnoId
Ord InnoId
Show InnoId
MonadFresh InnoId PopM

data ConnSig Source

Signature of a connection, used in matching innovations fromthe same generation.

Instances

Eq ConnSig
Ord ConnSig
Show ConnSig

Genome

data Genome Source

A NEAT genome. The innovation numbers are stored in here, and not the genes, to prevent data duplication.

Constructors

Genome
Fields nodeGenes :: Map NodeId NodeGene connGenes :: Map InnoId ConnGene nextNode :: NodeId

Instances

Show Genome

Construction

fullConn :: MonadRandom m => Parameters -> Int -> Int -> m Genome Source

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.

Breeding

mutate :: (MonadRandom m, MonadFresh InnoId m) => Parameters -> Map ConnSig InnoId -> Genome -> m (Map ConnSig InnoId, Genome) Source

Mutates the genome, using the specified parameters and innovation context.

crossover :: MonadRandom m => Parameters -> Genome -> Genome -> m Genome Source

Crossover. The first argument is the fittest genome.

breed :: (MonadRandom m, MonadFresh InnoId m) => Parameters -> Map ConnSig InnoId -> Genome -> Genome -> m (Map ConnSig InnoId, Genome) Source

Breed two genomes together

Distance

distance :: Parameters -> Genome -> Genome -> Double Source

Genetic distance between two genomes

Visualization

renderGenome :: Genome -> IO () Source

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.