void mexFunctionTest(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
{
if (nrhs < 2 || nrhs > 3)
{
mexPrintf("Usage: [score] = SQBTreesPredict( model, feats, [maxIters] )\n");
mexPrintf("\tfeats must be of type SINGLE\n");
mexPrintf("\tmaxIters is optional and limits the amount of weak learners evaluated for classification.");
mexErrMsgTxt("Incorrect input format.\n");
}
if (nlhs != 1)
mexErrMsgTxt("Two output args expected");
#define mFeats (prhs[1])
#define mModel (prhs[0])
#define mMaxIters (prhs[2])
MatlabInputMatrix<FeatsType> pFeats( mFeats, 0, 0, "feats" );
//const unsigned maxIters = pMaxIters.data()[0];
TreeBoosterType TB;
// load model
TB.loadFromMatlab( mModel );
unsigned maxIters = TB.numWeakLearners();
if (nrhs >= 3)
{
MatlabInputMatrix<unsigned int> pMaxIters( mMaxIters, 1, 1, "maxiters" );
unsigned inputMaxIters = pMaxIters.data()[0];
if (inputMaxIters <= 0)
mexErrMsgTxt("maxIters must be higher than zero.");
if (inputMaxIters > maxIters)
mexPrintf("-- WARNING: maxIters is greater than the number of weaklearners used!\n");
else
{
maxIters = inputMaxIters;
mexPrintf("Limiting number of weak learners to %d\n", (int)maxIters);
}
}
// for now just copy the values
gFeatArrayType feats = Eigen::Map< const gFeatArrayType >( pFeats.data(), pFeats.rows(), pFeats.cols() );
TreeBoosterType::ResponseArrayType newScores;
TB.predict( TreeBoosterType::SampleListType(feats),
TreeBoosterType::FeatureValueObjectType(feats),
newScores,
maxIters );
MatlabOutputMatrix<double> outMatrix( &plhs[0], feats.rows(), 1 );
for (unsigned i=0; i < feats.rows(); i++)
outMatrix.data()[i] = newScores.coeff(i);
}
void mexFunctionTest(TreeBoosterType &TB) //int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
{
printf("Testing!\n");
// if (nrhs < 2 || nrhs > 3)
// {
// mexPrintf("Usage: [score] = SQBTreesPredict( model, feats, [maxIters] )\n");
// mexPrintf("\tfeats must be of type SINGLE\n");
// mexPrintf("\tmaxIters is optional and limits the amount of weak learners evaluated for classification.");
// mexErrMsgTxt("Incorrect input format.\n");
// }
// if (nlhs != 1)
// mexErrMsgTxt("Two output args expected");
// #define mFeats (prhs[1])
// #define mModel (prhs[0])
// #define mMaxIters (prhs[2])
// MatlabInputMatrix<FeatsType> pFeats( mFeats, 0, 0, "feats" );
// //const unsigned maxIters = pMaxIters.data()[0];
// TreeBoosterType TB;
// // load model
static const char *input_file =
"/cvlabdata1/home/pglowack/Work/Vaa3D-BuiltWithDefaultScripts/vaa3d_tools/"
"bigneuron_ported/AmosSironi_PrzemyslawGlowacki/SQBTree_plugin/aaaaaaaa.cfg";
libconfig::Config cfg;
// Read the file. If there is an error, report it and exit.
try
{
cfg.readFile(input_file);
}
catch(const libconfig::FileIOException &fioex)
{
std::cerr << "I/O error while reading file." << std::endl;
// return(EXIT_FAILURE);
}
catch(const libconfig::ParseException &pex)
{
std::cerr << "Parse error at " << pex.getFile() << ":" << pex.getLine()
<< " - " << pex.getError() << std::endl;
// return(EXIT_FAILURE);
}
libconfig::Setting &root = cfg.getRoot();
libconfig::Setting ®ressor = root["regressor"];
TB.loadFromLibconfig(regressor);
unsigned maxIters = TB.numWeakLearners();
// if (nrhs >= 3)
// {
// MatlabInputMatrix<unsigned int> pMaxIters( mMaxIters, 1, 1, "maxiters" );
// unsigned inputMaxIters = pMaxIters.data()[0];
// if (inputMaxIters <= 0)
// mexErrMsgTxt("maxIters must be higher than zero.");
// if (inputMaxIters > maxIters)
// mexPrintf("-- WARNING: maxIters is greater than the number of weaklearners used!\n");
// else
// {
// maxIters = inputMaxIters;
// mexPrintf("Limiting number of weak learners to %d\n", (int)maxIters);
// }
// }
// for now just copy the values
FeatsType testFeaturesArray[] = {3, 1, 2,
6, 3, 4,
-1, -2, -4,
-2, -3, -1
};
unsigned int testFeaturesColsNo = 3;
unsigned int testFeaturesRowsNo = 4;
gFeatArrayType feats = Eigen::Map< const gFeatArrayType >( testFeaturesArray, testFeaturesRowsNo, testFeaturesColsNo );
TreeBoosterType::ResponseArrayType newScores;
TB.predict( TreeBoosterType::SampleListType(feats),
TreeBoosterType::FeatureValueObjectType(feats),
newScores,
maxIters );
for (unsigned i=0; i < feats.rows(); i++) {
std::cout << newScores.coeff(i) << std::endl;
// outMatrix.data()[i] = newScores.coeff(i);
}
// MatlabOutputMatrix<double> outMatrix( &plhs[0], feats.rows(), 1 );
// for (unsigned i=0; i < feats.rows(); i++)
// outMatrix.data()[i] = newScores.coeff(i);
//}
//void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
//{
//#ifdef SQBTREES_TRAIN
// mexFunctionTrain(nlhs, plhs, nrhs, prhs);
//#else
// mexFunctionTest(nlhs, plhs, nrhs, prhs);
//#endif
}