Opencv - RTrees algorithm : adding weight to class - c++

Question

I am using OpenCV's implementation of Random Forest algorithm (i.e. RTrees) and am facing a little problem when setting parameters.
I have 5 classes and 3 variables and I want to add weight to classes because the samples sizes for each classes vary a lot.
I took a look at the documentation here and here and it seems that the priors array is the solution, but when I try to give it 5 weights (for my 5 classes) it gives me the following error :
OpenCV Error: One of arguments' values is out of range (Every class weight should be positive) in CvDTreeTrainData::set_data, file /home/sguinard/dev/opencv-2.4.13/modules/ml/src/tree.cpp, line 644
terminate called after throwing an instance of 'cv::Exception'
what(): /home/sguinard/dev/opencv-2.4.13/modules/ml/src/tree.cpp:644: error: (-211) Every class weight should be positive in function CvDTreeTrainData::set_data
If I understand well, it's due to the fact that the priors array have 5 elements. And when I try to give it only 3 elements (as my number of variables) everything works.
According to the documentation, this array should be used to add weight to classes but it actually seems that it is used to add weight to variables...
So, does anyone knows how to add weight to classes on OpenCV's RTrees algorithm ? (I'm working with OpenCV 2.4.13 in c++)
Thanks in advance !
Here is my code :
cv::Mat RandomForest(cv::Mat train_data, cv::Mat response_data, cv::Mat sample_data, int size, int size_predict, float weights[5])
{
#undef CV_TERMCRIT_ITER
#define CV_TERMCRIT_ITER 10
#define ATTRIBUTES_PER_SAMPLE 3
cv::RandomTrees RFTree;
float priors[] = {1,1,1};
CvRTParams RFParams = CvRTParams(25, // max depth
500, // min sample count
0, // regression accuracy: N/A here
false, // compute surrogate split, no missing data
5, // max number of categories (use sub-optimal algorithm for larger numbers)
//priors
weights, // the array of priors (use weights or priors)
true,//false, // calculate variable importance
2, // number of variables randomly selected at node and used to find the best split(s).
100, // max number of trees in the forest
0.01f, // forrest accuracy
CV_TERMCRIT_ITER | CV_TERMCRIT_EPS // termination cirteria
);
cv::Mat varIdx = cv::Mat();
cv::Mat vartype( train_data.cols + 1, 1, CV_8U );
vartype.setTo(cv::Scalar::all(CV_VAR_NUMERICAL));
vartype.at<uchar>(ATTRIBUTES_PER_SAMPLE, 0) = CV_VAR_CATEGORICAL;
cv::Mat sampleIdx = cv::Mat();
cv::Mat missingdatamask = cv::Mat();
for (int i=0; i!=train_data.rows; ++i)
{
for (int j=0; j!=train_data.cols; ++j)
{
if(train_data.at<float>(i,j)<0
|| train_data.at<float>(i,j)>10000
|| !float(train_data.at<float>(i,j)))
{train_data.at<float>(i,j)=0;}
}
}
// Training
std::cout << "Training ....." << std::flush;
bool train = RFTree.train(train_data,
CV_ROW_SAMPLE,//tflag,
response_data,//responses,
varIdx,
sampleIdx,
vartype,
missingdatamask,
RFParams);
if (train){std::cout << " Done" << std::endl;}
else{std::cout << " Failed" << std::endl;return cv::Mat();}
std::cout << "Variable Importance : " << std::endl;
cv::Mat VI = RFTree.getVarImportance();
for (int i=0; i!=VI.cols; ++i){std::cout << VI.at<float>(i) << " - " << std::flush;}
std::cout << std::endl;
std::cout << "Predicting ....." << std::flush;
cv::Mat predict(1,sample_data.rows,CV_32F);
float max = 0;
for (int i=0; i!=sample_data.rows; ++i)
{
predict.at<float>(i) = RFTree.predict(sample_data.row(i));
if (predict.at<float>(i)>max){max=predict.at<float>(i);/*std::cout << predict.at<float>(i) << "-"<< std::flush;*/}
}
// Personnal test due to an error I got (everyone sent to 0)
if (max==0){std::cout << " Failed ... Max value = 0" << std::endl;return cv::Mat();}
std::cout << " Done ... Max value = " << max << std::endl;
return predict;
}