void InvertNChannelControllerHebbH::learnHebb(const matrix::Matrix& context_sensors, const matrix::Matrix& h_update){
// preprocess context sensors
Matrix c_sensors = context_sensors;
for (int i=0;i<number_context_sensors;i++){
if (c_sensors.val(i,0)<0.15) {
c_sensors.val(i,0)=0; // IR's should only have positive values
}
}
// adapt hebbian weights
for (uint i=0; i<number_motors; i++){
for (uint j=0; j<(uint)number_context_sensors; j++){
if (i==j){ // TODO: remove (it is just for testing)
double dp= eps_hebb* h_update.val(i,0) * c_sensors.val(j,0) *(1 - pow(p.val(i,j),2));
// std::cout<<eps_hebb<<"*"<<h_update.val(i,0)<<" * "<<c_sensors.val(j,0)<<std::endl;
p.val(i,j)+=dp;
}
}
}
/*
// remove this !!! (just a test)
for (int i=0; i<number_motors; i++){
for (int j=0; j<number_context_sensors; j++){
if ((j==0) || (j==1)){
p.val(i,j)=-0.1;
} else {
p.val(i,j)=0.1;
}
}
}
*/
}
/**
* predict the update of h based on the actual context sensors
* @param context_sensors prediction is based on these sensors
*/
matrix::Matrix InvertNChannelControllerHebbH::predictHebb(const matrix::Matrix& context_sensors){
// preprocess context sensors
Matrix c_sensors = context_sensors;
for (int i=0;i<number_context_sensors;i++){
if (c_sensors.val(i,0)<0.15) {
c_sensors.val(i,0)=0; // IR's should only have positive values
}
}
Matrix pred_h_update(number_motors,1) ;
for (unsigned int k = 0; k < number_motors; k++) {
pred_h_update.val(k,0)=0;
}
for (uint i=0; i<number_motors; i++){
for (uint j=0; j<(uint)number_context_sensors; j++){
pred_h_update.val(i,0)+= p.val(i,j) * context_sensors.val(j,0);
}
}
return pred_h_update;
}
static void keepMatrixTraceUp(matrix::Matrix& m){
int l = std::min((short unsigned int)2,std::min(m.getM(), m.getN()));
for(int i=0; i<l; i++){
if(m.val(i,i)<0.8) m.val(i,i)+=0.001;
}
}
double CuriosityLoop::updatePrediction(const matrix::Matrix& smHist, const matrix::Matrix& s, const matrix::Matrix& m, int phase){
matrix::Matrix sm = s.above(m);
matrix::Matrix f;
f.set(1,1);
f.val(0,0) = 1;
sm = sm.above(f);
//1. Go through the predictions of this predictor determining the prediction errors at each dimension.
matrix::Matrix error;
error.set(smHist.getM(), 1);
prediction_error = 0;
for(int i = 0; i < prediction.getM(); i++){
if(pOutput.val(i,0) == 1){
error.val(i,0) = prediction.val(i,0) - sm.val(i,0);
prediction_error = prediction_error + pow(error.val(i,0),2);
// cout << error << "predictionError\n";
}
else{
// cout << "This dimension is not predicted, and does not count towards the error\n";
error.val(i,0) = 0;
//prediction_error = prediction_error + error.val(i,0);
}
}
parent_error.val(phase,0) = prediction_error;
//2. Change the weights by the delta rule.
for(int i = 0; i < prediction.getM(); i++){//to
for(int j = 0; j < predictorWeights.getN(); j++){//from
// predictorWeights.val(i,j) = predictorWeights.val(i,j) - 0.00001*error.val(i,0)*smHist.val(j,0);
predictorWeights.val(i,j) = predictorWeights.val(i,j) - 0.0001*error.val(i,0)*smHist.val(j,0);
if(predictorWeights.val(i,j) > 10)
predictorWeights.val(i,j) = 10;
else if(predictorWeights.val(i,j) < -10)
predictorWeights.val(i,j) = -10;
}
}
prediction_error_time_average = 0.9999*prediction_error_time_average + (1-0.9999)*prediction_error;
//Update the fitness of this predictor based on the instantaneous reduction / increase in prediction error.
this->fitness = 0.1 + 100*(prediction_error_time_average - old_prediction_error_time_average);
old_prediction_error_time_average = prediction_error_time_average;
//cout << fitness << " ";
//Improve the method of determining this gradient later!
//UPDATE THE UNRESTRICTED PREDICTOR NOW AS WELL, ALWAYS...
//1. Go through the predictions of this UNRESTRICTED predictor determining the prediction errors at each dimension.
matrix::Matrix uError;
uError.set(smHist.getM(), 1);
uPrediction_error = 0;
for(int i = 0; i < uPrediction.getM(); i++){
if(uPOutput.val(i,0) == 1){
uError.val(i,0) = uPrediction.val(i,0) - sm.val(i,0);
uPrediction_error = uPrediction_error + pow(uError.val(i,0),2);
// cout << error << "predictionError\n";
}
else{
// cout << "This dimension is not predicted, and does not count towards the error\n";
uError.val(i,0) = 0;
//prediction_error = prediction_error + error.val(i,0);
}
}
//cout << "phase = " << phase << "\n";
offspring_error.val(phase,0) = uPrediction_error;
//2. Change the weights by the delta rule.
for(int i = 0; i < uPrediction.getM(); i++){
for(int j = 0; j < uPredictorWeights.getN(); j++){
uPredictorWeights.val(i,j) = uPredictorWeights.val(i,j) - 0.0001*uError.val(i,0)*smHist.val(j,0);
if(uPredictorWeights.val(i,j) > 10)
uPredictorWeights.val(i,j) = 10;
else if(uPredictorWeights.val(i,j) < -10)
uPredictorWeights.val(i,j) = -10;
}
}
//************************UNRESTRICTED PREDICTOR CODE ****************************
return this->fitness;
};
