// virtual
GMatrix* GGraphCutTransducer::transduceInner(const GMatrix& features1, const GMatrix& labels1, const GMatrix& features2)
{
// Use k-NN to compute a distance metric with good scale factors for prediction
GKNN knn;
knn.setNeighborCount(m_neighborCount);
//knn.setOptimizeScaleFactors(true);
knn.train(features1, labels1);
GRowDistanceScaled* pMetric = knn.metric();
// Merge the features into one dataset and build a kd-tree
GMatrix both(features1.relation().clone());
GReleaseDataHolder hBoth(&both);
both.reserve(features1.rows() + features2.rows());
for(size_t i = 0; i < features1.rows(); i++)
both.takeRow((double*)features1[i]);
for(size_t i = 0; i < features2.rows(); i++)
both.takeRow((double*)features2[i]);
GRowDistanceScaled metric2;
GKdTree neighborFinder(&both, m_neighborCount, &metric2, false);
GVec::copy(metric2.scaleFactors(), pMetric->scaleFactors(), features1.cols());
// Transduce
GMatrix* pOut = new GMatrix(labels1.relation().clone());
Holder<GMatrix> hOut(pOut);
pOut->newRows(features2.rows());
pOut->setAll(0);
for(size_t lab = 0; lab < labels1.cols(); lab++)
{
// Use max-flow/min-cut graph-cut to separate out each label value
int valueCount = (int)labels1.relation().valueCount(lab);
for(int val = 1; val < valueCount; val++)
{
// Add neighborhood edges
GGraphCut gc(features1.rows() + features2.rows() + 2);
for(size_t i = 0; i < both.rows(); i++)
{
neighborFinder.neighbors(m_pNeighbors, m_pDistances, i);
for(size_t j = 0; j < m_neighborCount; j++)
{
if(m_pNeighbors[j] >= both.rows())
continue;
gc.addEdge(2 + i, 2 + m_pNeighbors[j], (float)(1.0 / std::max(sqrt(m_pDistances[j]), 1e-9))); // connect neighbors
}
}
// Add source and sink edges
for(size_t i = 0; i < features1.rows(); i++)
{
if((int)labels1[i][0] == val)
gc.addEdge(0, 2 + i, 1e12f); // connect to source
else
gc.addEdge(1, 2 + i, 1e12f); // connect to sink
}
// Cut
gc.cut(0, 1);
// Label the unlabeled rows
for(size_t i = 0; i < features2.rows(); i++)
{
if(gc.isSource(2 + features1.rows() + i))
pOut->row(i)[lab] = (double)val;
}
}
}
return hOut.release();
}
virtual void initVector(double* pVector)
{
GRowDistanceScaled* pMetric = m_pLearner->metric();
GVec::copy(pVector, pMetric->scaleFactors(), relation()->size());
}
