int register_all_quick_manual(lua_State* tolua_S)
{
if (nullptr == tolua_S)
return 0;
extendFunctions(tolua_S);
extendNode(tolua_S);
CCLOG("Quick-Cocos2d-x C++ support ready.");
return 0;
}
void LuaEngine::extendLuaObject()
{
if ( NULL == _stack || NULL == _stack->getLuaState())
return;
lua_State* lua_S = _stack->getLuaState();
extendNode(lua_S);
extendMenuItem(lua_S);
extendLayer(lua_S);
extendControl(lua_S);
extendWebsocket(lua_S);
extendGLNode(lua_S);
extendScrollView(lua_S);
extendDrawNode(lua_S);
_stack->clean();
}
AlphaReal TreeLearner::run()
{
set< int > tmpIdx, idxPos, idxNeg, origIdx;
//ScalarLearner* pCurrentBaseLearner = 0;
ScalarLearner* pTmpBaseLearner = 0;
int ib = 0;
vector< int > tmpVector( 2, -1 );
_pTrainingData->getIndexSet( origIdx );
_pScalaWeakHypothesisSource->setTrainingData(_pTrainingData);
//train the first learner
NodePoint parentNode, nodeLeft, nodeRight;
parentNode._idx = 0;
parentNode._parentIdx = -1;
parentNode._learnerIdxSet = origIdx;
calculateEdgeImprovement( parentNode );
// insert the root
if ( parentNode._edgeImprovement <= 0.0 ) // the constant is the best, in this case the treelearner is equivalent to the constant learner
{
_baseLearners.push_back( parentNode._constantLearner );
_idxPairs.push_back( tmpVector );
this->_alpha = parentNode._constantLearner->getAlpha();
ib++;
delete parentNode._learner;
return parentNode._constantEnergy;
}
_baseLearners.push_back( parentNode._learner );
_idxPairs.push_back( tmpVector );
ib++;
// put the first two children into the priority queue
extendNode( parentNode, nodeLeft, nodeRight );
calculateEdgeImprovement( nodeLeft );
calculateEdgeImprovement( nodeRight );
priority_queue< NodePoint, vector<NodePoint>, greater_first_tree<NodePoint> > pq;
pq.push(nodeLeft);
pq.push(nodeRight);
while ( ! pq.empty() )
{
NodePoint currentNode = pq.top();
pq.pop();
if ( _verbose > 3 ) {
cout << "Current edge imporvement: " << currentNode._edgeImprovement << endl;
}
if (currentNode._edgeImprovement>0)
{
_baseLearners.push_back( currentNode._learner );
_idxPairs.push_back( tmpVector );
//_baseLearners[ib] = currentNode._learner;
delete currentNode._constantLearner;
} else {
_baseLearners.push_back(currentNode._constantLearner);
_idxPairs.push_back( tmpVector );
//_baseLearners[ib] = currentNode._constantLearner;
delete currentNode._learner;
continue;
}
_idxPairs[ currentNode._parentIdx ][ currentNode._leftOrRightChild ] = ib;
currentNode._idx = ib;
ib++;
if (ib >= _numBaseLearners) break;
extendNode( currentNode, nodeLeft, nodeRight );
calculateEdgeImprovement( nodeLeft );
calculateEdgeImprovement( nodeRight );
pq.push(nodeLeft);
pq.push(nodeRight);
}
while ( ! pq.empty() )
{
NodePoint currentNode = pq.top();
pq.pop();
if (_verbose>3) cout << "Discarded node's edge improvement: " << currentNode._edgeImprovement << endl;
if (currentNode._learner) delete currentNode._learner;
delete currentNode._constantLearner;
}
_id = _baseLearners[0]->getId();
for(int ib = 1; ib < _baseLearners.size(); ++ib)
_id += "_x_" + _baseLearners[ib]->getId();
//calculate alpha
this->_alpha = 0.0;
AlphaReal eps_min = 0.0, eps_pls = 0.0;
//_pTrainingData->clearIndexSet();
_pTrainingData->loadIndexSet( origIdx );
for( int i = 0; i < _pTrainingData->getNumExamples(); i++ ) {
vector< Label> l = _pTrainingData->getLabels( i );
for( vector< Label >::iterator it = l.begin(); it != l.end(); it++ ) {
AlphaReal result = this->classify( _pTrainingData, i, it->idx );
if ( ( result * it->y ) < 0 ) eps_min += it->weight;
if ( ( result * it->y ) > 0 ) eps_pls += it->weight;
}
}
// set the smoothing value to avoid numerical problem
// when theta=0.
setSmoothingVal( (AlphaReal)(1.0 / _pTrainingData->getNumExamples() * 0.01 ) );
this->_alpha = getAlpha( eps_min, eps_pls );
// calculate the energy (sum of the energy of the leaves
AlphaReal energy = this->getEnergy( eps_min, eps_pls );
return energy;
}
