void CuriosityLoop::makePrediction(const matrix::Matrix& s, const matrix::Matrix& m){
matrix::Matrix sm = s.above(m);
matrix::Matrix f;
f.set(1,1);
f.val(0,0) = 1;
sm = sm.above(f);
matrix::Matrix pwMod = predictorWeights;
for(int i = 0; i < predictorWeights.getM(); i++){//to
for(int j = 0; j < predictorWeights.getN(); j++){//from
if(pInput.val(j,0) == 1 && pOutput.val(i,0) == 1)
pwMod.val(i,j) = predictorWeights.val(i,j); //Transposes the weight matrix.
else
pwMod.val(i,j) = 0;
// pwMod.val(i,j) = predictorWeights.val(i,j)*predictorMask.val(i,j);
}
}
matrix::Matrix a = pwMod*sm; //Make prediction here.
this->prediction = a; //The prediction is stored here.
//************************UNRESTRICTED PREDICTOR CODE ****************************
//ALSO MAKE PREDICTIONS FOR THE UNRESTRICTED PREDICTOR
matrix::Matrix uPwMod = uPredictorWeights;
for(int i = 0; i < uPredictorWeights.getM(); i++){//to
for(int j = 0; j < uPredictorWeights.getN(); j++){//from
if(uPInput.val(j,0) == 1 && uPOutput.val(i,0) == 1)
uPwMod.val(i,j) = uPredictorWeights.val(i,j);
else
uPwMod.val(i,j) = 0;
// pwMod.val(i,j) = predictorWeights.val(i,j)*predictorMask.val(i,j);
}
}
matrix::Matrix uA = uPwMod*sm; //Make prediction here.
this->uPrediction = uA; //The prediction is stored here.
//************************UNRESTRICTED PREDICTOR CODE ****************************
// cout << predictorWeights.getM() << " " << predictorWeights.getN() << "= pw \n";
// cout << sm.getM() << " " << sm.getN() << " = sm\n";
// cout << pwMod.getM() << " " << pwMod.getN() << " = a\n";
// for(int i = 0; i < prediction.getM(); i++){
// for(int j = 0; j < pwMod.getN(); j++){
// cout << prediction.val(i,0) << " ";
// }
// cout << " = precd\n";
// }
};
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;
};
