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neat.hpp
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neat.hpp
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/**
* @file neat.hpp
* @author Bang Liu
*
* Definition of NEAT class.
*/
#ifndef MLPACK_METHODS_NE_NEAT_HPP
#define MLPACK_METHODS_NE_NEAT_HPP
#include <cstddef>
#include <cstdio>
#include <mlpack/core.hpp>
#include "link_gene.hpp"
#include "neuron_gene.hpp"
#include "genome.hpp"
#include "species.hpp"
#include "population.hpp"
#include "tasks.hpp"
#include "parameters.hpp"
namespace mlpack {
namespace ne {
struct LinkInnovation {
ssize_t fromNeuronId;
ssize_t toNeuronId;
ssize_t newLinkInnovId;
};
struct NeuronInnovation {
ssize_t splitLinkInnovId;
ssize_t newNeuronId;
ssize_t newInputLinkInnovId;
ssize_t newOutputLinkInnovId;
};
/**
* This class implements NEAT algorithm.
*/
template<typename TaskType>
class NEAT {
public:
// Default constructor.
// Parametric constructor.
// Destructor.
// Check whether a link innovation already exist.
ssize_t CheckLinkInnovation(ssize_t fromNeuronId, ssize_t toNeuronId) {
for (ssize_t i=0; i<aLinkInnovations.size(); ++i) {
if (aLinkInnovations[i].fromNeuronId == fromNeuronId &&
aLinkInnovations[i].toNeuronId == toNeuronId) {
return i;
}
}
return -1; // -1 means no match found, a new innovation.
}
// Check whether a neuron innovation already exist.
ssize_t CheckNeuronInnovation(ssize_t splitLinkInnovId) {
for (ssize_t i=0; i<aNeuronInnovations.size(); ++i) {
if (aNeuronInnovations[i].splitLinkInnovId == splitLinkInnovId) {
return i;
}
}
return -1;
}
// Add a new link innovation.
LinkInnovation AddLinkInnovation(ssize_t fromNeuronId, ssize_t toNeuronId) {
LinkInnovation linkInnov;
linkInnov.fromNeuronId = fromNeuronId;
linkInnov.toNeuronId = toNeuronId;
linkInnov.newLinkInnovId = aNextLinkInnovId++;
aLinkInnovations.push_back(linkInnov);
return linkInnov;
}
// Add a new neuron innovation.
NeuronInnovation AddNeuronInnovation(ssize_t splitLinkInnovId) {
NeuronInnovation neuronInnov;
neuronInnov.splitLinkInnovId = splitLinkInnovId;
neuronInnov.newNeuronId = aNextNeuronId++;
neuronInnov.newInputLinkInnovId = aNextLinkInnovId++;
neuronInnov.newOutputLinkInnovId = aNextLinkInnovId++;
aNeuronInnovations.push_back(neuronInnov);
// TODO: do we need to add two link innovations ???
return neuronInnov;
}
// Check if link exist or not.
ssize_t IsLinkExist(Genome& genome, ssize_t fromNeuronId, ssize_t toNeuronId) {
for (ssize_t i=0; i<genome.NumLink(); ++i) {
if (genome.aLinkGenes[i].FromNeuronId() == fromNeuronId &&
genome.aLinkGenes[i].ToNeuronId() == toNeuronId) {
return i;
}
}
return -1; // -1 means not exist.
}
// Mutate: add new link to genome.
// TODO: what if created looped link? It will influence the depth calculation in genome class!!
void MutateAddLink(Genome& genome, double mutateAddLinkProb) {
// Whether mutate or not.
double p = mlpack::math::Random();
if (p > mutateAddLinkProb) return;
// Select from neuron
ssize_t fromNeuronIdx = mlpack::math::RandInt(0, genome.aNeuronGenes.size());
ssize_t fromNeuronId = genome.aNeuronGenes[fromNeuronIdx].Id();
// Select to neuron which cannot be input.
ssize_t toNeuronIdx = mlpack::math::RandInt(genome.NumInput(), genome.aNeuronGenes.size());
ssize_t toNeuronId = genome.aNeuronGenes[toNeuronIdx].Id();
// Check link already exist or not.
ssize_t linkIdx = IsLinkExist(genome, fromNeuronId, toNeuronId);
if (linkIdx != -1) {
genome.aLinkGenes[linkIdx].Enabled(true);
return;
}
// Check innovation already exist or not.
ssize_t innovIdx = CheckLinkInnovation(fromNeuronId, toNeuronId);
if (innovIdx != -1) {
LinkGene linkGene(fromNeuronId,
toNeuronId,
aLinkInnovations[innovIdx].newLinkInnovId,
mlpack::math::RandNormal(0, 1), // TODO: make the distribution an argument for control?
true);
genome.AddLink(linkGene);
return;
}
// If new link and new innovation, create it, push new innovation.
LinkInnovation linkInnov = AddLinkInnovation(fromNeuronId, toNeuronId);
LinkGene linkGene(fromNeuronId,
toNeuronId,
linkInnov.newLinkInnovId,
mlpack::math::RandNormal(0, 1), // TODO: make the distribution an argument for control?
true);
genome.AddLink(linkGene);
}
// Mutate: add new neuron to genome.
void MutateAddNeuron(Genome& genome, double mutateAddNeuronProb) {
// Whether mutate or not.
double p = mlpack::math::Random();
if (p > mutateAddNeuronProb) return;
// No link.
if (genome.NumLink() == 0) return;
// Select link to split.
ssize_t linkIdx = mlpack::math::RandInt(0, genome.NumLink());
if (!genome.aLinkGenes[linkIdx].Enabled()) return;
genome.aLinkGenes[linkIdx].Enabled(false);
// Check innovation already exist or not.
ssize_t splitLinkInnovId = genome.aLinkGenes[linkIdx].InnovationId();
ssize_t innovIdx = CheckNeuronInnovation(splitLinkInnovId);
if (innovIdx != -1) {
NeuronGene neuronGene(aNeuronInnovations[innovIdx].newNeuronId,
HIDDEN,
SIGMOID, // TODO: make it random??
0,
0);
genome.AddHiddenNeuron(neuronGene);
LinkGene inputLink(genome.aLinkGenes[linkIdx].FromNeuronId(),
aNeuronInnovations[innovIdx].newNeuronId,
aNeuronInnovations[innovIdx].newInputLinkInnovId,
1,
true);
LinkGene outputLink(aNeuronInnovations[innovIdx].newNeuronId,
genome.aLinkGenes[linkIdx].ToNeuronId(),
aNeuronInnovations[innovIdx].newOutputLinkInnovId,
genome.aLinkGenes[linkIdx].Weight(),
true);
genome.AddLink(inputLink);
genome.AddLink(outputLink);
return;
}
// If new innovation, create.
NeuronInnovation neuronInnov = AddNeuronInnovation(splitLinkInnovId);
NeuronGene neuronGene(neuronInnov.newNeuronId,
HIDDEN,
SIGMOID, // TODO: make it random??
0,
0);
genome.AddHiddenNeuron(neuronGene);
LinkGene inputLink(genome.aLinkGenes[linkIdx].FromNeuronId(),
neuronInnov.newNeuronId,
neuronInnov.newInputLinkInnovId,
1,
true);
LinkGene outputLink(neuronInnov.newNeuronId,
genome.aLinkGenes[linkIdx].ToNeuronId(),
neuronInnov.newOutputLinkInnovId,
genome.aLinkGenes[linkIdx].Weight(),
true);
genome.AddLink(inputLink);
genome.AddLink(outputLink);
}
// Crossover link weights.
// NOTICE: assume momGenome is the better genome.
// NOTICE: assume childGenome is empty.
// NOTICE: in the NEAT paper, disabled links also can crossover, calculate distance, etc.
// Is it really a good idea???
// If not, we will need to change CrossoverLinkAndNeuron, and Disjoint, and WeightDiff.
void CrossoverLinkAndNeuron(Genome& momGenome, Genome& dadGenome, Genome& childGenome) {
// Add input and output neuron genes to child genome.
for (ssize_t i=0; i<(momGenome.NumInput() + momGenome.NumOutput()); ++i) {
childGenome.aNeuronGenes.push_back(momGenome.aNeuronGenes[i]);
}
// Iterate to add link genes and neuron genes to child genome.
for (ssize_t i=0; i<momGenome.NumLink(); ++i) {
ssize_t innovId = momGenome.aLinkGenes[i].InnovationId();
ssize_t idx = dadGenome.GetLinkIndex(innovId);
bool linkContainedInDad = (idx != -1);
double randNum = mlpack::math::Random();
if (!linkContainedInDad) { // exceed or disjoint
childGenome.AddLink(momGenome.aLinkGenes[i]);
// Add from neuron
ssize_t idxInChild = childGenome.GetNeuronIndex(momGenome.aLinkGenes[i].FromNeuronId());
ssize_t idxInParent = momGenome.GetNeuronIndex(momGenome.aLinkGenes[i].FromNeuronId());
if (idxInChild == -1) {
childGenome.AddHiddenNeuron(momGenome.aNeuronGenes[idxInParent]);
}
// Add to neuron
idxInChild = childGenome.GetNeuronIndex(momGenome.aLinkGenes[i].ToNeuronId());
idxInParent = momGenome.GetNeuronIndex(momGenome.aLinkGenes[i].ToNeuronId());
if (idxInChild == -1) {
childGenome.AddHiddenNeuron(momGenome.aNeuronGenes[idxInParent]);
}
continue;
}
if (linkContainedInDad && randNum < 0.5) {
childGenome.AddLink(momGenome.aLinkGenes[i]);
// Add from neuron
ssize_t idxInChild = childGenome.GetNeuronIndex(momGenome.aLinkGenes[i].FromNeuronId());
ssize_t idxInParent = momGenome.GetNeuronIndex(momGenome.aLinkGenes[i].FromNeuronId());
if (idxInChild == -1) {
childGenome.AddHiddenNeuron(momGenome.aNeuronGenes[idxInParent]);
}
// Add to neuron
idxInChild = childGenome.GetNeuronIndex(momGenome.aLinkGenes[i].ToNeuronId());
idxInParent = momGenome.GetNeuronIndex(momGenome.aLinkGenes[i].ToNeuronId());
if (idxInChild == -1) {
childGenome.AddHiddenNeuron(momGenome.aNeuronGenes[idxInParent]);
}
continue;
}
if (linkContainedInDad && randNum >= 0.5) {
childGenome.AddLink(dadGenome.aLinkGenes[idx]);
// Add from neuron TODO: make it a function?? check whether crossover is correct.
ssize_t idxInChild = childGenome.GetNeuronIndex(dadGenome.aLinkGenes[idx].FromNeuronId());
ssize_t idxInParent = dadGenome.GetNeuronIndex(dadGenome.aLinkGenes[idx].FromNeuronId());
if (idxInChild == -1) {
childGenome.AddHiddenNeuron(dadGenome.aNeuronGenes[idxInParent]);
}
// Add to neuron
idxInChild = childGenome.GetNeuronIndex(dadGenome.aLinkGenes[idx].ToNeuronId());
idxInParent = dadGenome.GetNeuronIndex(dadGenome.aLinkGenes[idx].ToNeuronId());
if (idxInChild == -1) {
childGenome.AddHiddenNeuron(dadGenome.aNeuronGenes[idxInParent]);
}
continue;
}
}
}
void Crossover(Genome& genome1, Genome& genome2, Genome& childGenome) {
if (Species::CompareGenome(genome1, genome2)) { // genome1 is better
CrossoverLinkAndNeuron(genome1, genome2, childGenome);
} else {
CrossoverLinkAndNeuron(genome2, genome1, childGenome);
}
}
// Measure two genomes' disjoint (including exceed).
// TODO: we can seperate into disjoint and exceed.
// But currently maybe it is enough.
double Disjoint(Genome& genome1, Genome& genome2) {
double numDisjoint = 0;
for (ssize_t i=0; i<genome1.NumLink(); ++i) {
ssize_t innovId = genome1.aLinkGenes[i].InnovationId();
bool linkContainedInGenome2 = genome2.ContainLink(innovId);
if (!linkContainedInGenome2) {
++numDisjoint;
}
}
for (ssize_t i=0; i<genome2.NumLink(); ++i) {
ssize_t innovId = genome2.aLinkGenes[i].InnovationId();
bool linkContainedInGenome1 = genome1.ContainLink(innovId);
if (!linkContainedInGenome1) {
++numDisjoint;
}
}
ssize_t largerGenomeSize = std::max(genome1.NumLink(), genome2.NumLink());
double deltaD = numDisjoint / largerGenomeSize;
return deltaD;
}
// Measure two genomes' weight difference.
double WeightDiff(Genome& genome1, Genome& genome2) {
double deltaW = 0;
ssize_t coincident = 0;
for (ssize_t i=0; i<genome1.NumLink(); ++i) {
ssize_t innovId = genome1.aLinkGenes[i].InnovationId();
ssize_t idx = genome2.GetLinkIndex(innovId);
bool linkContainedInGenome2 = (idx != -1);
if (linkContainedInGenome2) {
deltaW += std::abs(genome1.aLinkGenes[i].Weight() - genome2.aLinkGenes[idx].Weight());
++coincident;
}
}
deltaW = deltaW / coincident;
return deltaW;
}
// Whether two genome belong to same species or not.
bool IsSameSpecies(Genome& genome1, Genome& genome2) {
double deltaD = Disjoint(genome1, genome2);
double deltaW = WeightDiff(genome1, genome2);
double delta = aCoeffDisjoint * deltaD + aCoeffWeightDiff * deltaW;
if (delta < aCompatThreshold) {
return true;
} else {
return false;
}
}
// Add genome to existing species or create new species.
void AddGenomeToSpecies(Genome& genome) {
for (ssize_t i=0; i<aPopulation.NumSpecies(); ++i) {
if (aPopulation.aSpecies[i].SpeciesSize() > 0) {
if (IsSameSpecies(aPopulation.aSpecies[i].aGenomes[0], genome)) { // each first genome in species is the representative genome.
aPopulation.aSpecies[i].AddGenome(genome);
return;
}
}
}
Species newSpecies = Species();
newSpecies.AddGenome(genome);
newSpecies.Id(aPopulation.NextSpeciesId()); // NOTICE: changed species id.
newSpecies.StaleAge(0);
aPopulation.AddSpecies(newSpecies);
}
// Remove stale species.
void RemoveStaleSpecies(Population& population, ssize_t staleAgeThreshold) {
for (ssize_t i=0; i<population.NumSpecies(); ++i) {
if (population.aSpecies[i].StaleAge() > staleAgeThreshold) {
population.RemoveSpecies(i);
}
}
}
// Set adjusted fitness.
void AdjustFitness(Population& population) {
for (ssize_t i=0; i<population.NumSpecies(); ++i) {
if (population.aSpecies[i].SpeciesSize() > 0) {
for (ssize_t j=0; j<population.aSpecies[i].SpeciesSize(); ++j) {
double fitness = population.aSpecies[i].aGenomes[j].Fitness();
ssize_t speciesSize = population.aSpecies[i].SpeciesSize();
double adjustedFitness = fitness / speciesSize;
population.aSpecies[i].aGenomes[j].AdjustedFitness(adjustedFitness);
}
}
}
}
// Distribute genomes into species.
void Speciate() {
}
// Initialize population.
void InitPopulation() {
aPopulation = Population(aSeedGenome, aPopulationSize);
}
// Reproduce next generation of population.
void Reproduce() {
}
// Evolve.
void Evolve() {
}
private:
// Task.
TaskType aTask;
// Seed genome. It is used for init population.
Genome aSeedGenome;
// Population to evolve.
Population aPopulation;
// Population size.
ssize_t aPopulationSize;
// List of link innovations.
std::vector<LinkInnovation> aLinkInnovations;
// List of neuron innovations.
std::vector<NeuronInnovation> aNeuronInnovations;
// Next neuron id.
ssize_t aNextNeuronId;
// Next link id.
ssize_t aNextLinkInnovId;
// Max number of generation to evolve.
ssize_t aMaxGeneration;
// Efficient for disjoint.
double aCoeffDisjoint;
// Efficient for weight difference.
double aCoeffWeightDiff;
// Threshold for judge whether belong to same species.
double aCompatThreshold;
// Threshold for species stale age.
ssize_t aStaleAgeThreshold;
};
} // namespace ne
} // namespace mlpack
#endif // MLPACK_METHODS_NE_NEAT_HPP