I have data with 21 features and the number of classes is dependent of my goal.
When I formulate my problem with 2 classes the following code works. But when I formulate my problem with 3 classes I get segmentation fault on the training method. The only thing that changes is the number of classes.
This is my code:
Mat trainSamples, trainClasses;
float priors[] = { 1 , 1};
Mat var_type = Mat(21 + 1, 1, CV_8U );
var_type.setTo(Scalar(CV_VAR_NUMERICAL) ); // all inputs are numerical
var_type.at(21, 0) = CV_VAR_CATEGORICAL;
CvRTrees *rtrees;
rtrees = new CvRTrees [1];
CvRTParams params( 25, // max_depth,
2000, // min_sample_count,
0, // regression_accuracy,
false, // use_surrogates,
2, // max_categories,
priors, // priors,
false, // calc_var_importance,
4, // nactive_vars,
2000, // max_num_of_trees_in_the_forest,
0.000f, // forest_accuracy,
CV_TERMCRIT_ITER | CV_TERMCRIT_EPS // termcrit_type
);
load_feat(trainSamples, trainClasses, "data_model.csv"); //loading samples and corresponding class
feat_standardization(trainSamples, &standard_feat); //samples standardization
rtrees[0].train( trainSamples, CV_ROW_SAMPLE, trainClasses, Mat(), Mat(), var_type, Mat(), params );
Even when I change the max_categories parameter to 3 it does not work either.
I hope that someone can enlighten me with this issue.
Thank you.
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