/// Predict the belief vector that will result if the specified action is performed
void TransitionModel::anticipateNextBeliefs(const GVec& beliefs, const GVec& actions, GVec& anticipatedBeliefs)
{
if(tutor)
tutor->transition(beliefs, actions, anticipatedBeliefs);
else
{
GAssert(beliefs.size() + actions.size() == model.layer(0).inputs());
buf.resize(beliefs.size() + actions.size());
buf.put(0, beliefs);
buf.put(beliefs.size(), actions);
model.forwardProp(buf);
anticipatedBeliefs.copy(beliefs);
anticipatedBeliefs.addScaled(2.0, model.outputLayer().activation());
anticipatedBeliefs.clip(-1.0, 1.0);
}
}
/// Refines the beliefs to correspond with actual observations
void ObservationModel::calibrateBeliefs(GVec& beliefs, const GVec& observations)
{
if(tutor)
tutor->observations_to_state(observations, beliefs);
else
{
GNeuralNetLayer& layIn = encoder.outputLayer();
for(size_t i = 0; i < calibrationIters; i++) {
decoder.forwardProp(beliefs);
decoder.backpropagate(observations);
decoder.layer(0).backPropError(&layIn);
beliefs.addScaled(decoder.learningRate(), layIn.error());
beliefs.clip(-1.0, 1.0);
}
}
}
// virtual
void GWag::trainInner(const GMatrix& features, const GMatrix& labels)
{
GNeuralNetLearner* pTemp = NULL;
std::unique_ptr<GNeuralNetLearner> hTemp;
size_t weights = 0;
GVec pWeightBuf;
GVec pWeightBuf2;
for(size_t i = 0; i < m_models; i++)
{
m_pNN->train(features, labels);
if(pTemp)
{
// Average m_pNN with pTemp
if(!m_noAlign)
m_pNN->nn().align(pTemp->nn());
pTemp->nn().weightsToVector(pWeightBuf.data());
m_pNN->nn().weightsToVector(pWeightBuf2.data());
pWeightBuf *= (double(i) / (i + 1));
pWeightBuf.addScaled(1.0 / (i + 1), pWeightBuf2);
pTemp->nn().vectorToWeights(pWeightBuf.data());
}
else
{
// Copy the m_pNN
GDom doc;
GDomNode* pNode = m_pNN->serialize(&doc);
GLearnerLoader ll;
pTemp = new GNeuralNetLearner(pNode);
hTemp.reset(pTemp);
weights = pTemp->nn().weightCount();
pWeightBuf.resize(weights);
pWeightBuf2.resize(weights);
}
}
pTemp->nn().weightsToVector(pWeightBuf.data());
m_pNN->nn().vectorToWeights(pWeightBuf.data());
}