changed GBT parameters in sample
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@ -272,6 +272,7 @@ void find_decision_boundary_GBT()
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Mat trainSamples, trainClasses;
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prepare_train_data( trainSamples, trainClasses );
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trainClasses.convertTo( trainClasses, CV_32FC1 );
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// learn classifier
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CvGBTrees gbtrees;
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@ -279,13 +280,13 @@ void find_decision_boundary_GBT()
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Mat var_types( 1, trainSamples.cols + 1, CV_8UC1, Scalar(CV_VAR_ORDERED) );
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var_types.at<uchar>( trainSamples.cols ) = CV_VAR_CATEGORICAL;
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CvGBTreesParams params( CvGBTrees::SQUARED_LOSS, // loss_function_type
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CvGBTreesParams params( CvGBTrees::DEVIANCE_LOSS, // loss_function_type
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100, // weak_count
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0.05f, // shrinkage
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0.6f, // subsample_portion
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0.1f, // shrinkage
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1.0f, // subsample_portion
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2, // max_depth
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false // use_surrogates )
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);
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);
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gbtrees.train( trainSamples, CV_ROW_SAMPLE, trainClasses, Mat(), Mat(), var_types, Mat(), params );
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@ -315,10 +316,6 @@ void find_decision_boundary_RF()
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// learn classifier
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CvRTrees rtrees;
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Mat var_types( 1, trainSamples.cols + 1, CV_8UC1, Scalar(CV_VAR_ORDERED) );
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var_types.at<uchar>( trainSamples.cols ) = CV_VAR_CATEGORICAL;
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CvRTParams params( 4, // max_depth,
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2, // min_sample_count,
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0.f, // regression_accuracy,
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@ -332,7 +329,7 @@ void find_decision_boundary_RF()
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CV_TERMCRIT_ITER // termcrit_type
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);
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rtrees.train( trainSamples, CV_ROW_SAMPLE, trainClasses, Mat(), Mat(), var_types, Mat(), params );
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rtrees.train( trainSamples, CV_ROW_SAMPLE, trainClasses, Mat(), Mat(), Mat(), Mat(), params );
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Mat testSample(1, 2, CV_32FC1 );
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for( int y = 0; y < img.rows; y += testStep )
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