float SVMClassifier::predict( const cv::Mat_<float>& z) const
{
if ( svmp.isLinear())
return (z.dot(linx) - b)/linx.total(); // Normalise by the vector length
double result = -b;
KernelThreadFunc ktf( as, kernel, xs);
ktf.calcResult( 0, numSVs, &z);
result += ktf.getResult();
return result / z.total();
/*
static const int nthreads = boost::thread::hardware_concurrency(); // CPU threads available
const int segSz = numSVs / nthreads;
int rem = numSVs % nthreads;
boost::thread_group tgroup;
vector<KernelThreadFunc*> tobjs(nthreads);
int segOffset = 0;
for ( int i = 0; i < nthreads; ++i)
{
int ssz = segSz;
if ( rem > 0)
{
ssz++;
rem--;
} // end if
tobjs[i] = new KernelThreadFunc( as, kernel, xs);
tgroup.create_thread( boost::bind( &KernelThreadFunc::calcResult, tobjs[i], segOffset, ssz, &z)); // Start thread
segOffset += ssz; // Offset for next thread
} // end for
tgroup.join_all(); // All processing done
// Collect the result for return
for ( int i = 0; i < nthreads; ++i)
{
result += tobjs[i]->getResult();
delete tobjs[i];
} // end for
return result / z.total(); // Normalise by the vector length
*/
} // end predict
/**
* @brief Gabor filtering
* @ref http://en.wikipedia.org/wiki/Gabor_filter
*/
float VO_Gabor::VO_GaborFiltering(const cv::Mat_<float>& iImg)
{
return iImg.dot(this->m_VOWindowFunc->m_MatWindowedKernel);
}
