void printNetwork(NEAT::FastNetwork<double> &testNetwork)
{
return;
cout << "Network links:\n";
cout << testNetwork.getLink("Input0","Hidden1")->weight << endl;
cout << testNetwork.getLink("Input0","Hidden2")->weight << endl;
cout << endl;
cout << testNetwork.getLink("Input1","Hidden1")->weight << endl;
cout << testNetwork.getLink("Input1","Hidden2")->weight << endl;
cout << endl;
cout << testNetwork.getLink("Input2","Hidden1")->weight << endl;
cout << testNetwork.getLink("Input2","Hidden2")->weight << endl;
cout << endl;
cout << testNetwork.getLink("Hidden0","Output0")->weight << endl;
cout << testNetwork.getLink("Hidden1","Output0")->weight << endl;
cout << testNetwork.getLink("Hidden2","Output0")->weight << endl;
cout << endl;
CREATE_PAUSE("");
}
void ShapesExperiment::printNetworkCPPN(shared_ptr<const NEAT::GeneticIndividual> individual)
{
cout << "Printing cppn network" << endl;
ofstream network_file;
network_file.open ("networkCPPN-ThatSolvedTheProblem.txt", ios::trunc );
NEAT::FastNetwork <double> network = individual->spawnFastPhenotypeStack <double>(); //JMC: this creates the network CPPN associated with this individual used to produce the substrate (neural network)
network_file << "num links:" << network.getLinkCount() << endl;
network_file << "num nodes:" << network.getNodeCount() << endl;
int numLinks = network.getLinkCount();
int numNodes = network.getNodeCount();
ActivationFunction activationFunction;
//print out which node corresponds to which integer (e.g. so you can translate a fromNode of 1 to "x1"
map<string,int> localNodeNameToIndex = *network.getNodeNameToIndex();
for( map<string,int>::iterator iter = localNodeNameToIndex.begin(); iter != localNodeNameToIndex.end(); iter++ ) {
network_file << (*iter).first << " is node number: " << (*iter).second << endl;
}
for (size_t a=0;a<numLinks;a++)
{
NetworkIndexedLink <double> link = *network.getLink(a);
network_file << link.fromNode << "->" << link.toNode << " : " << link.weight << endl;
}
for (size_t a=0;a<numNodes;a++)
{
activationFunction = *network.getActivationFunction(a);
network_file << " activation function " << a << ": ";
if(activationFunction == ACTIVATION_FUNCTION_SIGMOID) network_file << "ACTIVATION_FUNCTION_SIGMOID";
if(activationFunction == ACTIVATION_FUNCTION_SIN) network_file << "ACTIVATION_FUNCTION_SIN";
if(activationFunction == ACTIVATION_FUNCTION_COS) network_file << "ACTIVATION_FUNCTION_COS";
if(activationFunction == ACTIVATION_FUNCTION_GAUSSIAN) network_file << "ACTIVATION_FUNCTION_GAUSSIAN";
if(activationFunction == ACTIVATION_FUNCTION_SQUARE) network_file << "ACTIVATION_FUNCTION_SQUARE";
if(activationFunction == ACTIVATION_FUNCTION_ABS_ROOT) network_file << "ACTIVATION_FUNCTION_ABS_ROOT";
if(activationFunction == ACTIVATION_FUNCTION_LINEAR) network_file << "ACTIVATION_FUNCTION_LINEAR";
if(activationFunction == ACTIVATION_FUNCTION_ONES_COMPLIMENT) network_file << "ACTIVATION_FUNCTION_ONES_COMPLIMENT";
if(activationFunction == ACTIVATION_FUNCTION_END) network_file << "ACTIVATION_FUNCTION_END";
network_file << endl;
}
network_file.close();
return;
}
