// virtual
void GKNN::enableIncrementalLearning(sp_relation& pRelation, int labelDims, double* pMins, double* pRanges)
{
clear();
m_pRelation = pRelation;
m_labelDims = labelDims;
m_featureDims = pRelation->size() - m_labelDims;
m_pInstances = new GData(m_pRelation);
GMixedRelation* pMixedRel = new GMixedRelation();
pMixedRel->addAttrs(pRelation.get(), 0, pRelation->size() - labelDims);
sp_relation pRelInputs = pMixedRel;
m_pDistanceMetric->init(pRelInputs);
// Allocate some other buffers
int maxOutputValueCount = 0;
for(int n = 0; n < m_labelDims; n++)
maxOutputValueCount = MAX(maxOutputValueCount, pRelation->valueCount(m_featureDims + n));
m_pValueCounts = new double[maxOutputValueCount];
// Scale factor optimization
if(m_optimizeScaleFactors)
{
m_pCritic = new GKnnScaleFactorCritic(this, m_labelDims, m_featureDims);
m_pScaleFactorOptimizer = new GMomentumGreedySearch(m_pCritic);
}
else
{
m_pCritic = NULL;
m_pScaleFactorOptimizer = NULL;
}
}
void transition(GArgReader& args)
{
// Load the input data
GMatrix* pActions = loadData(args.pop_string());
Holder<GMatrix> hActions(pActions);
GMatrix* pState = loadData(args.pop_string());
Holder<GMatrix> hState(pState);
if(pState->rows() != pActions->rows())
ThrowError("Expected the same number of rows in both datasets");
// Parse options
bool delta = false;
while(args.size() > 0)
{
if(args.if_pop("-delta"))
delta = true;
else
ThrowError("Invalid option: ", args.peek());
}
// Make the output data
size_t actionDims = pActions->cols();
size_t stateDims = pState->cols();
GMixedRelation* pRelation = new GMixedRelation();
sp_relation pRel = pRelation;
pRelation->addAttrs(pActions->relation().get());
pRelation->addAttrs(stateDims + stateDims, 0);
GMatrix* pTransition = new GMatrix(pRel);
pTransition->newRows(pActions->rows() - 1);
for(size_t i = 0; i < pActions->rows() - 1; i++)
{
double* pOut = pTransition->row(i);
GVec::copy(pOut, pActions->row(i), actionDims);
GVec::copy(pOut + actionDims, pState->row(i), stateDims);
GVec::copy(pOut + actionDims + stateDims, pState->row(i + 1), stateDims);
if(delta)
GVec::subtract(pOut + actionDims + stateDims, pState->row(i), stateDims);
}
pTransition->print(cout);
}
GIncrementalLearnerQAgent::GIncrementalLearnerQAgent(sp_relation& pObsControlRelation, GIncrementalLearner* pQTable, int actionDims, double* pInitialState, GRand* pRand, GAgentActionIterator* pActionIterator, double softMaxThresh)
: GQLearner(pObsControlRelation, actionDims, pInitialState, pRand, pActionIterator)
{
// Enable incremental learning
m_pQTable = pQTable;
GMixedRelation* pQRelation = new GMixedRelation();
pQRelation->addAttrs(pObsControlRelation.get());
pQRelation->addAttr(0); // the Q-value
sp_relation pRelQtable;
pRelQtable = pQRelation;
pQTable->enableIncrementalLearning(pRelQtable, 1, NULL, NULL);
// Init other stuff
m_pBuf = new double[pQRelation->size()];
m_softMaxThresh = softMaxThresh;
m_pActionIterator = pActionIterator;
pActionIterator->reset(pInitialState);
}