NEAT::GeneticPopulation * ImageExperiment::createInitialPopulation(
int populationSize)
{
cout << "Creating Image Experiment initial population..." << endl;
GeneticPopulation *population = new GeneticPopulation();
vector<GeneticNodeGene> genes;
genes.push_back(GeneticNodeGene("Bias", "NetworkSensor", 0, false));
genes.push_back(GeneticNodeGene("X", "NetworkSensor", 0, false));
genes.push_back(GeneticNodeGene("Y", "NetworkSensor", 0, false));
//genes.push_back(GeneticNodeGene("Gauss","HiddenNode", 0.5, false, ACTIVATION_FUNCTION_GAUSSIAN));
genes.push_back(GeneticNodeGene("XOUT", "NetworkOutputNode", 1, false, ACTIVATION_FUNCTION_SIGMOID));
genes.push_back(GeneticNodeGene("YOUT", "NetworkOutputNode", 1, false, ACTIVATION_FUNCTION_SIGMOID));
for (int a=0;a<populationSize;a++)
{
shared_ptr<GeneticIndividual> individual(new GeneticIndividual(genes,true,1.0));
for (int b=0;b<0;b++)
{
individual->testMutate();
}
population->addIndividual(individual);
}
cout << "Finished creating population\n";
return population;
}
NEAT::GeneticPopulation* AtariNoGeomNoiseExperiment::createInitialPopulation(int populationSize) {
GeneticPopulation *population = new GeneticPopulation();
vector<GeneticNodeGene> genes;
for (int y=0; y<substrate_height; y++) {
for (int x=0; x<substrate_width; x++) {
Node node(x, y, 0);
genes.push_back(GeneticNodeGene(nameLookup[node],"NetworkSensor",0,false));
}
}
// Output Layer
for (int i=0; i<numActions; i++) {
Node node(i, 0, 2);
genes.push_back(GeneticNodeGene(nameLookup[node],"NetworkOutputNode",1,false,
ACTIVATION_FUNCTION_SIGMOID));
}
// Bias node
genes.push_back(GeneticNodeGene("Bias","NetworkSensor",0,false));
for (int a=0; a<populationSize; a++) {
shared_ptr<GeneticIndividual> individual(new GeneticIndividual(genes,true,1.0));
for (int b=0; b<10; b++) {
individual->testMutate();
}
population->addIndividual(individual);
}
cout << "Finished creating population\n";
return population;
}
NEAT::GeneticPopulation* XorExperiment::createInitialPopulation(int populationSize)
{
//Make stdout unbuffered. cliche needs that.
//setvbuf (stdout, NULL, _IONBF, 0);
GeneticPopulation *population = new GeneticPopulation();
vector<GeneticNodeGene> genes;
genes.push_back(GeneticNodeGene("Bias","NetworkSensor",0,false));
genes.push_back(GeneticNodeGene("X1","NetworkSensor",0,false));
genes.push_back(GeneticNodeGene("X2","NetworkSensor",0,false));
genes.push_back(GeneticNodeGene("Output","NetworkOutputNode",1,false,ACTIVATION_FUNCTION_SIGMOID));
for (int a=0;a<populationSize;a++)
{
shared_ptr<GeneticIndividual> individual(new GeneticIndividual(genes,true,1.0));
for (int b=0;b<0;b++)
{
individual->testMutate();
}
population->addIndividual(individual);
}
cout << "Finished creating population\n";
return population;
}
NEAT::GeneticPopulation* AtariPixelExperiment::createInitialPopulation(int populationSize) {
GeneticPopulation *population = new GeneticPopulation();
vector<GeneticNodeGene> genes;
// Input Nodes
genes.push_back(GeneticNodeGene("Bias","NetworkSensor",0,false)); // TODO: Check if this helps or not
genes.push_back(GeneticNodeGene("X1","NetworkSensor",0,false));
genes.push_back(GeneticNodeGene("Y1","NetworkSensor",0,false));
genes.push_back(GeneticNodeGene("X2","NetworkSensor",0,false));
genes.push_back(GeneticNodeGene("Y2","NetworkSensor",0,false));
// Output Nodes
for (int i=0; i<numColors; ++i) {
genes.push_back(GeneticNodeGene("Output_Input" + boost::lexical_cast<std::string>(i) +
"_Processing",
"NetworkOutputNode",1,false,
ACTIVATION_FUNCTION_SIGMOID));
}
genes.push_back(GeneticNodeGene("Output_Processing_Output","NetworkOutputNode",1,false,
ACTIVATION_FUNCTION_SIGMOID));
for (int a=0; a<populationSize; a++) {
shared_ptr<GeneticIndividual> individual(new GeneticIndividual(genes,true,1.0));
for (int b=0;b<10;b++) {
individual->testMutate();
}
population->addIndividual(individual);
}
cout << "Finished creating population\n";
return population;
}
GeneticPopulation* ModNeatExperiment7::createInitialPopulation(int populationSize) {
GeneticPopulation *population = new GeneticPopulation();
vector<GeneticNodeGene> genes;
// Set-up the CCPPN which encodes the substrate weights
////// CCPPN encoding a 3 layered sandwich substrate's weights
// Configuration of the CCPPN's Input layer
genes.push_back(GeneticNodeGene("X1", "NetworkSensor", 0, false)); // position on the X axis in the substrate's link start layers
genes.push_back(GeneticNodeGene("Y1", "NetworkSensor", 0, false)); // position on the Y axis in the substrate's link start layers
genes.push_back(GeneticNodeGene("Z1", "NetworkSensor", 0, false)); // position on the Z axis in the substrate's link start layers
genes.push_back(GeneticNodeGene("X2", "NetworkSensor", 0, false)); // position on the X axis in the substrate's link end layers
genes.push_back(GeneticNodeGene("Y2", "NetworkSensor", 0, false)); // position on the Y axis in the substrate's link end layers
genes.push_back(GeneticNodeGene("Z2", "NetworkSensor", 0, false)); // position on the Z axis in the substrate's link end layers
#ifdef USE_BIASES
genes.push_back(GeneticNodeGene("Bias", "NetworkSensor", 0, false)); // bias for the CPPN
#endif // USE_BIASES
#ifdef USE_DELTAS
genes.push_back(GeneticNodeGene("DeltaX", "NetworkSensor", 0, false)); // offset between the two nodes on the X axis
genes.push_back(GeneticNodeGene("DeltaY", "NetworkSensor", 0, false)); // offset between the two nodes on the Y axis
genes.push_back(GeneticNodeGene("DeltaZ", "NetworkSensor", 0, false)); // offset between the two nodes on the Z axis
#endif // USE_DELTAS
#ifdef MODULAR_HNEAT
screen << "Laying out a heterogeneous controller CPPN" << endl;
genes.push_back(GeneticNodeGene("T1", "NetworkSensor", 0, false)); // position in the body
genes.push_back(GeneticNodeGene("T2", "NetworkSensor", 0, false)); // offset between the two nodes on the Z axis
#ifdef USE_DELTAS
genes.push_back(GeneticNodeGene("DeltaT", "NetworkSensor", 0, false)); // offset between the two nodes on the Z axis
#endif // USE_DELTAS
#endif // MODULAR_HNEAT
#ifndef MODULAR_HNEAT
screen << "Laying out a homogeneous controller CPPN" << endl;
#endif
// Configuration of the CCPPN's Output layer
genes.push_back(GeneticNodeGene("Output", "NetworkOutputNode", 1, false, ACTIVATION_FUNCTION_SIGMOID)); // weight for link between layers A and B
#ifdef USE_BIASES
genes.push_back(GeneticNodeGene("Bias_out", "NetworkOutputNode", 1, false, ACTIVATION_FUNCTION_SIGMOID)); // bias for the second sandwich layer
#endif // USE_BIASES
// Create the initial population
for (int a = 0; a < populationSize; a++) {
shared_ptr<GeneticIndividual> individual(new GeneticIndividual(genes, true, 1.0));
for (int b = 0; b < 0; b++) {
individual->testMutate();
}
population->addIndividual(individual);
}
screen << "Finished creating population\n";
// store population and individual size
popSize = population->getIndividualCount();
// screen << "\n individual count: " << popSize << "\n";
return population;
}
bool GeneticIndividual::mutateAddNode(int fromNodeID,int toNodeID)
{
if (fromNodeID>=0&&fromNodeID==toNodeID)
{
throw CREATE_LOCATEDEXCEPTION_INFO("Error: Tried to create a node within a loop connection");
}
GeneticLinkGene *randomLink;
bool allowAddNodeToRecurrentConnection
= (
Globals::getSingleton()->getParameterValue("AllowAddNodeToRecurrentConnection")>
Globals::getSingleton()->getRandom().getRandomDouble()
);
int chances=0;
//cout << __FILE__ << ": Trying to add a node\n";
int tmpFromNodeID,tmpToNodeID;
int randomLinkIndex;
while (true)
{
chances++;
//cout << "Trying to add node...\n";
if (chances>=200000)
{
//cout << "Blown second chance limit. Giving up\n";
return false;
}
if (chances>=100000)
{
randomLinkIndex = (randomLinkIndex+1)%links.size();
//cout << "Blown chance limit. incrementing over all possible links\n";
}
else
{
//This loop ensures that you don't get a recurrent or loop connection when you add complexity.
randomLinkIndex = Globals::getSingleton()->getRandom().getRandomInt(int(links.size()));
}
if (fromNodeID>=0)
{
randomLink = getLink(fromNodeID,toNodeID);
tmpFromNodeID = fromNodeID;
tmpToNodeID = toNodeID;
}
else
{
randomLink = &links[randomLinkIndex];
tmpFromNodeID = randomLink->getFromNodeID();
tmpToNodeID = randomLink->getToNodeID();
//cout << "IDs: " << tmpFromNodeID << " , " << tmpToNodeID << endl;
}
if (tmpFromNodeID==tmpToNodeID)
{
//loop link, try again
//cout << "Loop link, ignore\n";
continue;
}
GeneticNodeGene *fromNode=NULL,*toNode=NULL;
for (int a=0;a<(int)nodes.size();a++)
{
//This loop goes through and gets the sum of the two nodes' drawing positions for averaging.
if (nodes[a].getID()==tmpFromNodeID)
fromNode = &nodes[a];
if (nodes[a].getID()==tmpToNodeID)
toNode = &nodes[a];
}
if (!fromNode->isEnabled() || !toNode->isEnabled())
{
//Disabled nodes, don't add anything.
//cout << "One of the nodes is disabled...\n";
continue;
}
if (fromNode->getDrawingPosition()>=toNode->getDrawingPosition())
{
//Recurrent connection.
//cout << "Recurrent connection. Ignoring\n";
if (!allowAddNodeToRecurrentConnection)
continue;
}
if(fromNode->isTopologyFrozen() && toNode->isTopologyFrozen())
{
//cout << "Topology frozen on both nodes. Ignoring\n";
//Don't add links between two frozen topology nodes
continue;
}
double newPosition = (fromNode->getDrawingPosition()+toNode->getDrawingPosition())/2.0;
randomLink->setAge(0);
randomLink->setWeight(randomLink->getWeight()/2.0);
bool randomActivation=false;
if (Globals::getSingleton()->getParameterValue("ExtraActivationFunctions")>Globals::getSingleton()->getRandom().getRandomDouble())
randomActivation=true;
GeneticNodeGene newNode = GeneticNodeGene("","HiddenNode",newPosition,randomActivation);
GeneticLinkGene sourceLink = GeneticLinkGene(randomLink->getFromNodeID(),newNode.getID(),1.0);
GeneticLinkGene destLink = GeneticLinkGene(newNode.getID(),randomLink->getToNodeID(),randomLink->getWeight()/2.0);
addNode(newNode);
addLink(sourceLink);
addLink(destLink);
if (Globals::getSingleton()->getParameterValue("AddBiasToHiddenNodes")>Globals::getSingleton()->getRandom().getRandomDouble())
{
int biasNodeID=-1;
for (int a=0;a<(int)nodes.size();a++)
{
if (nodes[a].getName()==string("Bias"))
{
biasNodeID=(int)a;
break;
}
}
if (biasNodeID==-1)
{
throw CREATE_LOCATEDEXCEPTION_INFO("Error, tried to add link from bias node when biad node didn't exist!");
}
if (randomLink->getFromNodeID()==biasNodeID)
{
//Oops, the from link was already the bias node, don't bother
}
else
{
GeneticLinkGene biasLink = GeneticLinkGene(biasNodeID,newNode.getID(),0.0);
addLink(biasLink);
}
}
return true;
}
}
NEAT::GeneticPopulation* AtariFTNeatExperiment::createInitialPopulation(int populationSize) {
GeneticPopulation *population = new GeneticPopulation();
vector<GeneticNodeGene> genes;
vector<GeneticLinkGene> links;
clock_t start = clock();
vector<int> inputNodeIndexes;
vector<int> hiddenNodeIndexes;
vector<int> outputNodeIndexes;
int nodeCounter = 0;
// One input layer for each object class and an extra for the self object
for (int i=0; i<=numObjClasses; ++i) {
for (int y=0; y<substrate_height; y++) {
for (int x=0; x<substrate_width; x++) {
int xmod = substrate_width*i+x;
Node node(xmod, y, 0);
genes.push_back(GeneticNodeGene(nameLookup[node],"NetworkSensor",0,false));
inputNodeIndexes.push_back(nodeCounter);
nodeCounter++;
}
}
}
// Processing Layer
for (int y=0; y<substrate_height; y++) {
for (int x=0; x<substrate_width; x++) {
Node node(x, y, 1);
genes.push_back(GeneticNodeGene(nameLookup[node],"NetworkOutputNode",1,false,
ACTIVATION_FUNCTION_SIGMOID));
hiddenNodeIndexes.push_back(nodeCounter);
nodeCounter++;
}
}
// Output Layer
for (int i=0; i<numActions; i++) {
Node node(i, 0, 2);
genes.push_back(GeneticNodeGene(nameLookup[node],"NetworkOutputNode",1,false,
ACTIVATION_FUNCTION_SIGMOID));
outputNodeIndexes.push_back(nodeCounter);
nodeCounter++;
}
// Bias node
// genes.push_back(GeneticNodeGene("Bias","NetworkSensor",0,false));
// inputNodeIndexes.push_back(nodeCounter);
// nodeCounter++;
clock_t end = clock();
cout << "Created " << nodeCounter << " nodes in " << float(end-start)/CLOCKS_PER_SEC << " seconds." << endl;
start = clock();
// [Links] Input --> Processing
for (int z=0; z<inputNodeIndexes.size(); z++) {
for (int q=0; q<hiddenNodeIndexes.size(); q++) {
int fromNodeIndex = inputNodeIndexes[z];
int toNodeIndex = hiddenNodeIndexes[q];
links.push_back(GeneticLinkGene(genes[fromNodeIndex].getID(),genes[toNodeIndex].getID()));
}
}
// [Links] Processing --> Output
for (int z=0; z<hiddenNodeIndexes.size(); z++) {
for (int q=0; q<outputNodeIndexes.size(); q++) {
int fromNodeIndex = hiddenNodeIndexes[z];
int toNodeIndex = outputNodeIndexes[q];
links.push_back(GeneticLinkGene(genes[fromNodeIndex].getID(),genes[toNodeIndex].getID()));
}
}
end = clock();
cout << "Created " << links.size() << " links in " << float(end-start)/CLOCKS_PER_SEC << " seconds." << endl;
for (int a=0; a<populationSize; a++) {
shared_ptr<GeneticIndividual> individual(new GeneticIndividual(genes,links));
for (int b=0;b<10;b++) {
individual->testMutate();
}
population->addIndividual(individual);
}
cout << "Finished creating population\n";
return population;
}
NEAT::GeneticPopulation* CoCheckersExperiment::createInitialPopulation(int populationSize)
{
GeneticPopulation *population = new GeneticPopulation(
shared_ptr<CoCheckersExperiment>((CoCheckersExperiment*)this->clone())
);
vector<GeneticNodeGene> genes;
genes.push_back(GeneticNodeGene("Bias","NetworkSensor",0,false));
genes.push_back(GeneticNodeGene("X1","NetworkSensor",0,false));
genes.push_back(GeneticNodeGene("Y1","NetworkSensor",0,false));
genes.push_back(GeneticNodeGene("X2","NetworkSensor",0,false));
genes.push_back(GeneticNodeGene("Y2","NetworkSensor",0,false));
genes.push_back(GeneticNodeGene("DeltaX","NetworkSensor",0,false));
genes.push_back(GeneticNodeGene("DeltaY","NetworkSensor",0,false));
genes.push_back(GeneticNodeGene("Output_ab","NetworkOutputNode",1,false,ACTIVATION_FUNCTION_SIGMOID));
genes.push_back(GeneticNodeGene("Output_bc","NetworkOutputNode",1,false,ACTIVATION_FUNCTION_SIGMOID));
genes.push_back(GeneticNodeGene("Output_ac","NetworkOutputNode",1,false,ACTIVATION_FUNCTION_SIGMOID));
#if CHECKERS_EXPERIMENT_ENABLE_BIASES
genes.push_back(GeneticNodeGene("Bias_b","NetworkOutputNode",1,false,ACTIVATION_FUNCTION_SIGMOID));
genes.push_back(GeneticNodeGene("Bias_c","NetworkOutputNode",1,false,ACTIVATION_FUNCTION_SIGMOID));
#endif
for (int a=0;a<populationSize;a++)
{
shared_ptr<GeneticIndividual> individual(new GeneticIndividual(genes,true,1.0));
for (int b=0;b<0;b++)
{
individual->testMutate();
}
population->addIndividual(individual);
}
shared_ptr<NEAT::CoEvoGeneticGeneration> coEvoGeneration =
static_pointer_cast<NEAT::CoEvoGeneticGeneration>(population->getGeneration());
//Create the tests
for (int a=0;a<(population->getIndividualCount()/10);a++)
{
shared_ptr<NEAT::GeneticIndividual> individual(new GeneticIndividual(genes,true,1.0));
//This function clones the individual to make a test
coEvoGeneration->addTest(individual);
}
coEvoGeneration->bootstrap();
cout << "Finished creating population\n";
return population;
}
