void PiMax::setSensorTeaching(const matrix::Matrix& teaching){
assert(teaching.getM() == number_sensors && teaching.getN() == 1);
// calculate the a_teaching,
// that belongs to the distal teaching value by the inverse model.
a_teaching = (A.pseudoInverse() * (teaching-b)).mapP(0.95, clip);
intern_isTeaching=true;
}
void PiMax::setMotorTeaching(const matrix::Matrix& teaching){
assert(teaching.getM() == number_motors && teaching.getN() == 1);
// Note: through the clipping the otherwise effectless
// teaching with old motor value has now an effect,
// namely to drive out of the saturation region.
a_teaching= teaching.mapP(0.95,clip);
intern_isTeaching=true;
}
void SosAvgGrad::setC(const matrix::Matrix& _C){
assert(C.getM() == _C.getM() && C.getN() == _C.getN());
C=_C;
}
void SosAvgGrad::setA(const matrix::Matrix& _A){
assert(A.getM() == _A.getM() && A.getN() == _A.getN());
A=_A;
}
void SosAvgGrad::setS(const matrix::Matrix& _S){
assert(S.getM() == _S.getM() && S.getN() == _S.getN());
S=_S;
}
void PiMax::seth(const matrix::Matrix& _h){
assert(h.getM() == _h.getM() && h.getN() == _h.getN());
h=_h;
}
void PiMax::setA(const matrix::Matrix& _A){
assert(A.getM() == _A.getM() && A.getN() == _A.getN());
A=_A;
}
void PiMax::setC(const matrix::Matrix& _C){
assert(C.getM() == _C.getM() && C.getN() == _C.getN());
C=_C;
}
void RandomDyn::setC(const matrix::Matrix& _C){
assert(C.getM() == _C.getM() && C.getN() == _C.getN());
C=_C;
}
void RandomDyn::seth(const matrix::Matrix& _h){
assert(h.getM() == _h.getM() && h.getN() == _h.getN());
h=_h;
}
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;
}
}
virtual void seth(const matrix::Matrix& _h){
assert(h.getM() == _h.getM() && h.getN() == _h.getN());
h=_h;
}
virtual void setC(const matrix::Matrix& _C){
assert(C.getM() == _C.getM() && C.getN() == _C.getN());
C=_C;
}
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;
};
void SeMoX::setSensorTeaching(const matrix::Matrix& teaching){
assert(teaching.getM() == number_sensors && teaching.getN() == 1);
// calculate the y_teaching, that belongs to the distal teaching value by the inverse model.
y_teaching = (A.pseudoInverse(0.001) * (teaching-B)).mapP(0.95, clip);
intern_useTeaching=true;
}
