#define SVM 0 // support vectors machine
#define DT 1 // decision tree
#define BT 0 // ADA Boost
-#define GBT 1 // gradient boosted trees
+#define GBT 0 // gradient boosted trees
#define RF 0 // random forest
#define ERT 0 // extremely randomized trees
#define ANN 0 // artificial neural networks
0.05f, // shrinkage
0.6f, // subsample_portion
2, // max_depth
- true // use_surrogates )
+ false // use_surrogates )
);
gbtrees.train( trainSamples, CV_ROW_SAMPLE, trainClasses, Mat(), Mat(), var_types, Mat(), params );
#if DT
find_decision_boundary_DT();
- namedWindow( "DT", 1 );
+ namedWindow( "DT", WINDOW_AUTOSIZE );
imshow( "DT", imgDst );
#endif
#if BT
find_decision_boundary_BT();
- namedWindow( "BT", 1 );
+ namedWindow( "BT", WINDOW_AUTOSIZE );
imshow( "BT", imgDst);
#endif
#if GBT
find_decision_boundary_GBT();
- namedWindow( "GBT", 1 );
+ namedWindow( "GBT", WINDOW_AUTOSIZE );
imshow( "GBT", imgDst);
#endif
#if RF
find_decision_boundary_RF();
- namedWindow( "RF", 1 );
+ namedWindow( "RF", WINDOW_AUTOSIZE );
imshow( "RF", imgDst);
#endif
#if ERT
find_decision_boundary_ERT();
- namedWindow( "ERT", 1 );
+ namedWindow( "ERT", WINDOW_AUTOSIZE );
imshow( "ERT", imgDst);
#endif