/**
*
* ~~~~~ Back Propogation ~~~~~
*
* Train the network by 'back propogation'
* see http://en.wikipedia.org/wiki/Backpropagation
*/
std::vector<double> _backPropogation ( std::vector<double> input, std::vector<double> expected, Network& net )
{
// get the result of feeding the input into the network
std::vector<double> output = net.feedForward(input);
ActFunction actf = net.activate();
double rate = net.rate();
// ~~~~~ loop backwards over the layers ~~~~~
//
// as we descend though the layers, the difference between the output and the expected
// result is propogated through each layer; while the change in weights (deltas) is computed
// on for each layer.
for(auto layer = net.rbegin(); layer != net.rend(); ++layer)
{
// input and output for each layer
std::vector<double> layer_input = (*layer)->getInput();
std::vector<double> layer_output = (*layer)->getOutput();
// iterate over the neurons in a vector
auto out = layer_output.begin();
for (auto neuron = (*layer)->begin(); neuron != (*layer)->end(); ++neuron, ++out)
{
// the output layer is handled a bit differently, as it can be compared directly with the
// expected answer
auto ex = expected.begin();
auto in = layer_input.begin();
for (auto weight = (*neuron).begin(); weight != (*neuron).end(); ++weight, ++in, ++ex )
{
// calculate the deltas of the weights
double delta = rate * ((*ex) - (*in)) * actf.dydx((*out)) * (*in);
(*weight) -= delta;
}
}
// propogate the expected value down the chain by
// recalculating the layer's output with the new weights
expected = (*layer)->feedForward( layer_input );
}
return expected;
}
bool xor_evaluate(Organism *org) {
Network *net;
double out[4]; //The four outputs
double this_out; //The current output
int count;
double errorsum;
bool success; //Check for successful activation
int numnodes; /* Used to figure out how many nodes
should be visited during activation */
int net_depth; //The max depth of the network to be activated
int relax; //Activates until relaxation
//The four possible input combinations to xor
//The first number is for biasing
double in[4][3]={{1.0,0.0,0.0},
{1.0,0.0,1.0},
{1.0,1.0,0.0},
{1.0,1.0,1.0}};
net=org->net;
numnodes=((org->gnome)->nodes).size();
net_depth=net->max_depth();
//TEST CODE: REMOVE
//cout<<"ACTIVATING: "<<org->gnome<<endl;
//cout<<"DEPTH: "<<net_depth<<endl;
//Load and activate the network on each input
for(count=0;count<=3;count++) {
net->load_sensors(in[count]);
//Relax net and get output
success=net->activate();
//use depth to ensure relaxation
for (relax=0;relax<=net_depth;relax++) {
success=net->activate();
this_out=(*(net->outputs.begin()))->activation;
}
out[count]=(*(net->outputs.begin()))->activation;
net->flush();
}
if (success) {
errorsum=(fabs(out[0])+fabs(1.0-out[1])+fabs(1.0-out[2])+fabs(out[3]));
org->fitness=pow((4.0-errorsum),2);
org->error=errorsum;
}
else {
//The network is flawed (shouldnt happen)
errorsum=999.0;
org->fitness=0.001;
}
#ifndef NO_SCREEN_OUT
cout<<"Org "<<(org->gnome)->genome_id<<" error: "<<errorsum<<" ["<<out[0]<<" "<<out[1]<<" "<<out[2]<<" "<<out[3]<<"]"<<endl;
cout<<"Org "<<(org->gnome)->genome_id<<" fitness: "<<org->fitness<<endl;
#endif
// if (errorsum<0.05) {
//if (errorsum<0.2) {
if ((out[0]<0.5)&&(out[1]>=0.5)&&(out[2]>=0.5)&&(out[3]<0.5)) {
org->winner=true;
return true;
}
else {
org->winner=false;
return false;
}
}
