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 mexFunctionTrain(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
{
if (nrhs != 4)
{
mexPrintf("Usage: [model] = SQBTreesTrain( feats, labels, maxIters, options )\n");
mexPrintf("\tfeats must be of type SINGLE\n");
mexPrintf("\tOptions contains the following fields:\n");
mexPrintf("\t\t.loss = 'exploss', 'squaredloss' or 'logloss'\n");
mexPrintf("\t\t.shrinkageFactor = between 0 and 1. For instance, 0.02\n");
mexPrintf("\t\t.subsamplingFactor = between 0 and 1. Initial suggestion: 0.5\n");
mexPrintf("\t\t.maxTreeDepth >= 0. UINT32. Initial suggestion: 2\n");
mexPrintf("\t\t.disableLineSearch, UINT32. If != 0 then line search is disabled and shrinkageFactor is used as the step length.\n");
mexPrintf("\t\t.mtry, UINT32. Num of variables to search at each split, randomly chosen (as in Random Forests). Optional, otherwise it equals number of columns in feats.\n");
mexPrintf("\t\t.randSeed. UINT32. If not provided, time is used to generate a seed.\n");
mexPrintf("\t\t.verboseOutput, UINT32. If != 0 then verbose output is enabled (default = false if not specified).\n");
mexErrMsgTxt("Incorrect input format\n");
}
if (nlhs != 1)
mexErrMsgTxt("One output arg expected");
#define mFeats (prhs[0])
#define mLabels (prhs[1])
#define mMaxIters (prhs[2])
#define mOptions (prhs[3])
MatlabInputMatrix<FeatsType> pFeats( mFeats, 0, 0, "feats" );
MatlabInputMatrix<WeightsType> pLabels( mLabels, pFeats.rows(), 1, "labels" );
MatlabInputMatrix<unsigned int> pMaxIters( mMaxIters, 1, 1, "maxiters" );
const unsigned maxIters = pMaxIters.data()[0];
TreeBoosterType TB;
// we will use a random sampler
SQB::TreeBoosterNaiveResampler< TreeBoosterType::ResamplerBaseObjectType::WeightsArrayType,
TreeBoosterType::ResamplerBaseObjectType::LabelsArrayType > resampler;
TB.setResamplerObject( &resampler );
// read options, a bit messy right now
{
mxArray *pLoss = mxGetField( mOptions, 0, "loss" );
if (pLoss == NULL) mexErrMsgTxt("options.loss not found");
mxArray *pSF = mxGetField( mOptions, 0, "shrinkageFactor" );
if (pSF == NULL) mexErrMsgTxt("options.shrinkageFactor not found");
mxArray *pSubSampFact = mxGetField( mOptions, 0, "subsamplingFactor" );
if (pSubSampFact == NULL) mexErrMsgTxt("options.subsamplingFactor not found");
mxArray *pMaxDepth = mxGetField( mOptions, 0, "maxTreeDepth" );
if (pMaxDepth == NULL) mexErrMsgTxt("options.maxTreeDepth not found");
mxArray *pRandSeed = mxGetField( mOptions, 0, "randSeed" );
const bool usingCustomRandSeed = (pRandSeed != NULL);
mxArray *pDisableLineSearch = mxGetField( mOptions, 0, "disableLineSearch" );
const bool disableLineSearchSpecified = (pDisableLineSearch != NULL);
mxArray *pVerboseOutput = mxGetField( mOptions, 0, "verboseOutput" );
const bool verboseOutputSpecified = (pVerboseOutput != NULL);
mxArray *pMTry = mxGetField( mOptions, 0, "mtry" );
const bool mtrySpecified = (pMTry != NULL);
char lossName[40];
if ( mxGetString(pLoss, lossName, sizeof(lossName)) != 0 )
mexErrMsgTxt("Error reading options.loss");
if ( mxGetClassID(pSF) != mxDOUBLE_CLASS )
mexErrMsgTxt("options.shrinkageFactor must be double");
if ( mxGetNumberOfElements(pSF) != 1 )
mexErrMsgTxt("options.shrinkageFactor must be a scalar");
{
const double sf = ((double *)mxGetData(pSF))[0];
if ( sf <= 0 || sf > 1) mexErrMsgTxt("Shrinkage factor must be in (0,1]");
TB.setShrinkageFactor( sf );
}
if (strcmp(lossName, "exploss") == 0)
TB.setLoss( SQB::ExpLoss );
else if ( strcmp(lossName, "logloss") == 0 )
TB.setLoss( SQB::LogLoss );
else if ( strcmp(lossName, "squaredloss") == 0 )
TB.setLoss( SQB::SquaredLoss );
else
mexErrMsgTxt("options.loss contains an invalid value");
if ( mxGetClassID(pSubSampFact) != mxDOUBLE_CLASS )
mexErrMsgTxt("options.subsamplingFactor must be double");
if ( mxGetNumberOfElements(pSubSampFact) != 1 )
mexErrMsgTxt("options.subsamplingFactor must be a scalar");
{
const double ss = ((double *)mxGetData(pSubSampFact))[0];
if ( ss <= 0 || ss > 1 ) mexErrMsgTxt("Subsampling factor must be in (0,1]");
resampler.setResamplingFactor( ss );
}
if ( mxGetClassID(pMaxDepth) != mxUINT32_CLASS )
mexErrMsgTxt("options.pMaxDepth must be UINT32");
if ( mxGetNumberOfElements(pMaxDepth) != 1 )
mexErrMsgTxt("options.maxDepth must be a scalar");
{
const unsigned maxDepth = ((unsigned int *)mxGetData(pMaxDepth))[0];
if (maxDepth < 0) // not gonna happen if it is unsigned
mexErrMsgTxt("Minimum maxDepth is 0");
TB.setMaxTreeDepth( maxDepth );
}
if ( disableLineSearchSpecified )
{
if ( mxGetClassID(pDisableLineSearch) != mxUINT32_CLASS )
mexErrMsgTxt("options.disableLineSearch must be UINT32");
if ( mxGetNumberOfElements(pDisableLineSearch) != 1 )
mexErrMsgTxt("options.disableLineSearch must be a scalar");
TB.setDisableLineSearch(((unsigned int *)mxGetData(pDisableLineSearch))[0] != 0);
}
else
TB.setDisableLineSearch(false);
if ( verboseOutputSpecified )
{
if ( mxGetClassID(pVerboseOutput) != mxUINT32_CLASS )
mexErrMsgTxt("options.verboseOutput must be UINT32");
if ( mxGetNumberOfElements(pVerboseOutput) != 1 )
mexErrMsgTxt("options.verboseOutput must be a scalar");
TB.setVerboseOutput(((unsigned int *)mxGetData(pVerboseOutput))[0] != 0);
}
else
TB.setVerboseOutput(false);
if ( mtrySpecified )
{
if ( mxGetClassID(pMTry) != mxUINT32_CLASS )
mexErrMsgTxt("options.mtry must be UINT32");
if ( mxGetNumberOfElements(pMTry) != 1 )
mexErrMsgTxt("options.mtry must be a scalar");
TB.setMTry(((unsigned int *)mxGetData(pMTry))[0]);
}
else
TB.setDisableLineSearch(false);
if (usingCustomRandSeed)
{
if ( mxGetClassID(pRandSeed) != mxUINT32_CLASS )
mexErrMsgTxt("options.randSeed must be UINT32");
if ( mxGetNumberOfElements(pRandSeed) != 1 )
mexErrMsgTxt("options.randSeed must be a scalar");
TB.setRandSeed( ((unsigned int *)mxGetData(pRandSeed))[0] );
} else
{
TB.setRandSeed( time(NULL) );
}
}
{
// for now just copy the values
gFeatArrayType feats = Eigen::Map< const gFeatArrayType >( pFeats.data(), pFeats.rows(), pFeats.cols() );
TreeBoosterType::ResponseArrayType labels = Eigen::Map< const TreeBoosterType::ResponseArrayType >( pLabels.data(), pLabels.rows() );
TB.printOptionsSummary();
TB.learn( TreeBoosterType::SampleListType(feats),
TreeBoosterType::FeatureListType(feats),
TreeBoosterType::FeatureValueObjectType(feats),
TreeBoosterType::ClassifierResponseValueObjectType(labels),
maxIters );
TB.printOptionsSummary();
}
plhs[0] = TB.saveToMatlab();
#undef mFeats
#undef mLabels
#undef mMaxIters
#undef mPredFeats
}
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
}
void mexFunctionTrain(TreeBoosterType &TB/*int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]*/)
{
// if (nrhs != 4)
// {
// mexPrintf("Usage: [model] = SQBTreesTrain( feats, labels, maxIters, options )\n");
// mexPrintf("\tfeats must be of type SINGLE\n");
// mexPrintf("\tOptions contains the following fields:\n");
// mexPrintf("\t\t.loss = 'exploss', 'squaredloss' or 'logloss'\n");
// mexPrintf("\t\t.shrinkageFactor = between 0 and 1. For instance, 0.02\n");
// mexPrintf("\t\t.subsamplingFactor = between 0 and 1. Initial suggestion: 0.5\n");
// mexPrintf("\t\t.maxTreeDepth >= 0. UINT32. Initial suggestion: 2\n");
// mexPrintf("\t\t.disableLineSearch, UINT32. If != 0 then line search is disabled and shrinkageFactor is used as the step length.\n");
// mexPrintf("\t\t.mtry, UINT32. Num of variables to search at each split, randomly chosen (as in Random Forests). Optional, otherwise it equals number of columns in feats.\n");
// mexPrintf("\t\t.randSeed. UINT32. If not provided, time is used to generate a seed.\n");
// mexPrintf("\t\t.verboseOutput, UINT32. If != 0 then verbose output is enabled (default = false if not specified).\n");
// mexErrMsgTxt("Incorrect input format\n");
// }
// if (nlhs != 1)
// mexErrMsgTxt("One output arg expected");
// #define mFeats (prhs[0])
// #define mLabels (prhs[1])
// #define mMaxIters (prhs[2])
// #define mOptions (prhs[3])
// MatlabInputMatrix<FeatsType> pFeats( mFeats, 0, 0, "feats" );
// MatlabInputMatrix<WeightsType> pLabels( mLabels, pFeats.rows(), 1, "labels" );
// MatlabInputMatrix<unsigned int> pMaxIters( mMaxIters, 1, 1, "maxiters" );
// const unsigned maxIters = pMaxIters.data()[0];
// TreeBoosterType TB;
// we will use a random sampler
SQB::TreeBoosterNaiveResampler< TreeBoosterType::ResamplerBaseObjectType::WeightsArrayType,
TreeBoosterType::ResamplerBaseObjectType::LabelsArrayType > resampler;
TB.setResamplerObject( &resampler );
// // read options, a bit messy right now
// {
// mxArray *pLoss = mxGetField( mOptions, 0, "loss" );
// if (pLoss == NULL) mexErrMsgTxt("options.loss not found");
// mxArray *pSF = mxGetField( mOptions, 0, "shrinkageFactor" );
// if (pSF == NULL) mexErrMsgTxt("options.shrinkageFactor not found");
// mxArray *pSubSampFact = mxGetField( mOptions, 0, "subsamplingFactor" );
// if (pSubSampFact == NULL) mexErrMsgTxt("options.subsamplingFactor not found");
// mxArray *pMaxDepth = mxGetField( mOptions, 0, "maxTreeDepth" );
// if (pMaxDepth == NULL) mexErrMsgTxt("options.maxTreeDepth not found");
// mxArray *pRandSeed = mxGetField( mOptions, 0, "randSeed" );
// const bool usingCustomRandSeed = (pRandSeed != NULL);
// mxArray *pDisableLineSearch = mxGetField( mOptions, 0, "disableLineSearch" );
// const bool disableLineSearchSpecified = (pDisableLineSearch != NULL);
// mxArray *pVerboseOutput = mxGetField( mOptions, 0, "verboseOutput" );
// const bool verboseOutputSpecified = (pVerboseOutput != NULL);
// mxArray *pMTry = mxGetField( mOptions, 0, "mtry" );
// const bool mtrySpecified = (pMTry != NULL);
// char lossName[40];
// if ( mxGetString(pLoss, lossName, sizeof(lossName)) != 0 )
// mexErrMsgTxt("Error reading options.loss");
// if ( mxGetClassID(pSF) != mxDOUBLE_CLASS )
// mexErrMsgTxt("options.shrinkageFactor must be double");
// if ( mxGetNumberOfElements(pSF) != 1 )
// mexErrMsgTxt("options.shrinkageFactor must be a scalar");
// {
// const double sf = ((double *)mxGetData(pSF))[0];
// if ( sf <= 0 || sf > 1) mexErrMsgTxt("Shrinkage factor must be in (0,1]");
// TB.setShrinkageFactor( sf );
// }
// if (strcmp(lossName, "exploss") == 0)
// TB.setLoss( SQB::ExpLoss );
// else if ( strcmp(lossName, "logloss") == 0 )
// TB.setLoss( SQB::LogLoss );
// else if ( strcmp(lossName, "squaredloss") == 0 )
// TB.setLoss( SQB::SquaredLoss );
// else
// mexErrMsgTxt("options.loss contains an invalid value");
// if ( mxGetClassID(pSubSampFact) != mxDOUBLE_CLASS )
// mexErrMsgTxt("options.subsamplingFactor must be double");
// if ( mxGetNumberOfElements(pSubSampFact) != 1 )
// mexErrMsgTxt("options.subsamplingFactor must be a scalar");
// {
// const double ss = ((double *)mxGetData(pSubSampFact))[0];
// if ( ss <= 0 || ss > 1 ) mexErrMsgTxt("Subsampling factor must be in (0,1]");
// resampler.setResamplingFactor( ss );
// }
// if ( mxGetClassID(pMaxDepth) != mxUINT32_CLASS )
// mexErrMsgTxt("options.pMaxDepth must be UINT32");
// if ( mxGetNumberOfElements(pMaxDepth) != 1 )
// mexErrMsgTxt("options.maxDepth must be a scalar");
// {
// const unsigned maxDepth = ((unsigned int *)mxGetData(pMaxDepth))[0];
// if (maxDepth < 0) // not gonna happen if it is unsigned
// mexErrMsgTxt("Minimum maxDepth is 0");
// TB.setMaxTreeDepth( maxDepth );
// }
// if ( disableLineSearchSpecified )
// {
// if ( mxGetClassID(pDisableLineSearch) != mxUINT32_CLASS )
// mexErrMsgTxt("options.disableLineSearch must be UINT32");
// if ( mxGetNumberOfElements(pDisableLineSearch) != 1 )
// mexErrMsgTxt("options.disableLineSearch must be a scalar");
// TB.setDisableLineSearch(((unsigned int *)mxGetData(pDisableLineSearch))[0] != 0);
// }
// else
// TB.setDisableLineSearch(false);
// if ( verboseOutputSpecified )
// {
// if ( mxGetClassID(pVerboseOutput) != mxUINT32_CLASS )
// mexErrMsgTxt("options.verboseOutput must be UINT32");
// if ( mxGetNumberOfElements(pVerboseOutput) != 1 )
// mexErrMsgTxt("options.verboseOutput must be a scalar");
// TB.setVerboseOutput(((unsigned int *)mxGetData(pVerboseOutput))[0] != 0);
// }
// else
// TB.setVerboseOutput(false);
// if ( mtrySpecified )
// {
// if ( mxGetClassID(pMTry) != mxUINT32_CLASS )
// mexErrMsgTxt("options.mtry must be UINT32");
// if ( mxGetNumberOfElements(pMTry) != 1 )
// mexErrMsgTxt("options.mtry must be a scalar");
// TB.setMTry(((unsigned int *)mxGetData(pMTry))[0]);
// }
// else
// TB.setDisableLineSearch(false);
// if (usingCustomRandSeed)
// {
// if ( mxGetClassID(pRandSeed) != mxUINT32_CLASS )
// mexErrMsgTxt("options.randSeed must be UINT32");
// if ( mxGetNumberOfElements(pRandSeed) != 1 )
// mexErrMsgTxt("options.randSeed must be a scalar");
// TB.setRandSeed( ((unsigned int *)mxGetData(pRandSeed))[0] );
// } else
// {
// TB.setRandSeed( time(NULL) );
// }
// }
{
//pFeats.data() returns const FeatsType*
//pFeats.rows() and pFeats.cols() return unsigned int
//pLabels.data() returns SQB::TreeBoosterWeightsType (i.e. double)
//pLabels.rows() returns unsigned int
// // for now just copy the values
// gFeatArrayType feats = Eigen::Map< const gFeatArrayType >( pFeats.data(), pFeats.rows(), pFeats.cols() );
// TreeBoosterType::ResponseArrayType labels = Eigen::Map< const TreeBoosterType::ResponseArrayType >( pLabels.data(), pLabels.rows() );
// TB.printOptionsSummary();
// TB.learn( TreeBoosterType::SampleListType(feats),
// TreeBoosterType::FeatureListType(feats),
// TreeBoosterType::FeatureValueObjectType(feats),
// TreeBoosterType::ClassifierResponseValueObjectType(labels),
// maxIters );
// TB.printOptionsSummary();
const unsigned maxIters = 200;
// for now just copy the values
FeatsType featuresArray[] = {3, 1, 2,
6, 3, 4,
-1, -2, -4,
-2, -3, -1
};
unsigned int featuresColsNo = 3;
unsigned int featuresRowsNo = 4;
WeightsType labelsArray[] = {1,
1,
-1,
-1};
unsigned int labelsRowsNo = featuresRowsNo;
gFeatArrayType feats = Eigen::Map< const gFeatArrayType >( featuresArray, featuresRowsNo, featuresColsNo );
TreeBoosterType::ResponseArrayType labels = Eigen::Map< const TreeBoosterType::ResponseArrayType >( labelsArray, labelsRowsNo );
TB.printOptionsSummary();
TB.learn( TreeBoosterType::SampleListType(feats),
TreeBoosterType::FeatureListType(feats),
TreeBoosterType::FeatureValueObjectType(feats),
TreeBoosterType::ClassifierResponseValueObjectType(labels),
maxIters );
std::cout << "Hu!" << std::endl << std::flush;
TB.printOptionsSummary();
}
// libconfig::Config cfg;
// libconfig::Setting &root = cfg.getRoot();
// // Add some settings to the configuration.
// libconfig::Setting &address = root.add("regressor", libconfig::Setting::TypeList);
libconfig::Config cfg;
libconfig::Setting &root = cfg.getRoot();
// // Add some settings to the configuration.
// libconfig::Setting &address = root.add("address", libconfig::Setting::TypeGroup);
// address.add("street", libconfig::Setting::TypeString) = "1 Woz Way";
// address.add("city", libconfig::Setting::TypeString) = "San Jose";
// address.add("state", libconfig::Setting::TypeString) = "CA";
// address.add("zip", libconfig::Setting::TypeInt) = 95110;
// libconfig::Setting &array = root.add("array", libconfig::Setting::TypeArray);
// for(int i = 0; i < 10; ++i)
// array.add(libconfig::Setting::TypeInt) = 10 * i;
// root.add(libconfig::Setting::TypeList);
libconfig::Setting ®ressor = root.add("regressor", libconfig::Setting::TypeList);
TB.saveToLibconfig(regressor);
static const char *output_file =
"/cvlabdata1/home/pglowack/Work/Vaa3D-BuiltWithDefaultScripts/vaa3d_tools/"
"bigneuron_ported/AmosSironi_PrzemyslawGlowacki/SQBTree_plugin/aaaaaaaa.cfg";
// Write out the new configuration.
try
{
cfg.writeFile(output_file);
std::cerr << "New configuration successfully written to: " << output_file
<< std::endl;
}
catch(const libconfig::FileIOException &fioex)
{
std::cerr << "I/O error while writing file: " << output_file << std::endl;
//return(EXIT_FAILURE);
}
// plhs[0] = TB.saveToMatlab();
//#undef mFeats
//#undef mLabels
//#undef mMaxIters
//#undef mPredFeats
}
