Documentation
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Index ¶
Constants ¶
const ( // MaxFitness Used as max value which we add error too to get an organism's fitness MaxFitness = 1000.0 // FitnessThreshold is the fitness value for which an organism is considered to have won the experiment // TODO update to value which beats the environment (not yet determined) FitnessThreshold = 55 )
const ( RightSide = Side("right") LeftSide = Side("left") BothSide = Side("both") )
Variables ¶
This section is empty.
Functions ¶
func EvaluatePredictions ¶
func EvaluatePredictions(predictions []float64, leftObj VisualObject, rightObj VisualObject) float64
EvaluatePredictions returns the loss between predictions and ground truth of leftObj and rightObj
func ParseVisualObjectConfig ¶
ParseVisualObjectConfig parses config semantically in the format (config = "x1 x2 \n x3 x4") to [ [x1, x2], [x3, x4] ] where if xi == "o" => xi = 1
Types ¶
type CPPNNetworkBuilder ¶
type CPPNNetworkBuilder struct {
UseLEO bool
SubstrateLayout cppn.EvolvableSubstrateLayout
NodesActivation utils.NodeActivationType
Context *eshyperneat.ESHyperNEATContext
// New Graph Builder generator function.
// Note: Graph Builder is used to store graphs in a user-friendly format
NewGraphBuilder func() *cppn.GraphBuilder
}
CPPNNetworkBuilder handles the building of Network's using CPPN network's to query connections/topology by the esHyperNEAT algorithm
func (*CPPNNetworkBuilder) CreateANNFromCPPN ¶
func (builder *CPPNNetworkBuilder) CreateANNFromCPPN(cppnNetwork *network.Network) (network.NetworkSolver, error)
CreateANNFromCPPN creates a NetworkSolver (ANN) by querying the cppnNetwork by the ESHyperNeat Algorithm
func (*CPPNNetworkBuilder) CreateANNFromCPPNOrganism ¶
func (builder *CPPNNetworkBuilder) CreateANNFromCPPNOrganism(cppnOrganism *genetics.Organism) (network.NetworkSolver, error)
CreateANNFromCPPNOrganism creates a NetworkSolver (ANN) by querying the Organism.Phenotype cppnNetwork by the ESHyperNeat Algorithm
type Environment ¶
type Environment struct {
VisualObjects []VisualObject
}
Environment holds the dataset and evaluation methods for the modular retina experiment
func NewRetinaEnvironment ¶
func NewRetinaEnvironment() Environment
NewRetinaEnvironment creates a new Retina Environment with a dataset of all possible Visual Object
type GenerationEvaluator ¶
type GenerationEvaluator struct {
OutputPath string
Environment *Environment
CPPNNetworkBuilder *CPPNNetworkBuilder
}
GenerationEvaluator holds the Retina environment and setup instructions. it implemens the GenerationEvaluator interface by implementing GenerationEvaluate
func (GenerationEvaluator) GenerationEvaluate ¶
func (eval GenerationEvaluator) GenerationEvaluate(population *genetics.Population, epoch *experiments.Generation, context *neat.NeatContext) (err error)
GenerationEvaluate evaluates a population of organisms and prints their performance on the retina experiment
func (GenerationEvaluator) OrganismEvaluate ¶
func (eval GenerationEvaluator) OrganismEvaluate(organism *genetics.Organism, context *neat.NeatContext) (bool, error)
OrganismEvaluate evalutes an individual phenotype network with retina experiment and returns true if its won
type VisualObject ¶
type VisualObject struct {
// Setup visual object
Side Side
Size int
Config string
// Inner computed values from Config parsing
Data [][]float64
}
VisualObject represents a left, right, or both, object classified by retina
func CreateRetinaDataset ¶
func CreateRetinaDataset() []VisualObject
CreateRetinaDataset returns the 16 possible permuations of visual objects
func NewVisualObject ¶
func NewVisualObject(side Side, size int, config string) VisualObject
NewVisualObject creates a new VisuaObject by first parsing the config string into a VisualObject
func (*VisualObject) Flatten ¶
func (obj *VisualObject) Flatten() []float64
Flatten returns a 1D array from the values in the 2D obj
func (*VisualObject) String ¶
func (obj *VisualObject) String() string
Source Files