void Model::Predict_gbt( const SampleSet& samples, SampleSet& outError )
{
int true_resp = 0;
CvGBTrees *model = (CvGBTrees*)m_pModel;
for (int i = 0; i < samples.N(); i++)
{
float ret;
ret = model->predict(samples.GetSampleAt(i), cv::Mat(), cv::Range::all());
if (ret != samples.GetLabelAt(i))
{
outError.Add(samples.GetSampleAt(i), samples.GetLabelAt(i));
}
else
{
true_resp++;
}
}
printf("%d %d",samples.N(), true_resp);
}
void find_decision_boundary_GBT()
{
img.copyTo( imgDst );
Mat trainSamples, trainClasses;
prepare_train_data( trainSamples, trainClasses );
// learn classifier
CvGBTrees gbtrees;
Mat var_types( 1, trainSamples.cols + 1, CV_8UC1, Scalar(CV_VAR_ORDERED) );
var_types.at<uchar>( trainSamples.cols ) = CV_VAR_CATEGORICAL;
CvGBTreesParams params( CvGBTrees::DEVIANCE_LOSS, // loss_function_type
100, // weak_count
0.1f, // shrinkage
1.0f, // subsample_portion
2, // max_depth
false // use_surrogates )
);
gbtrees.train( trainSamples, CV_ROW_SAMPLE, trainClasses, Mat(), Mat(), var_types, Mat(), params );
Mat testSample(1, 2, CV_32FC1 );
for( int y = 0; y < img.rows; y += testStep )
{
for( int x = 0; x < img.cols; x += testStep )
{
testSample.at<float>(0) = (float)x;
testSample.at<float>(1) = (float)y;
int response = (int)gbtrees.predict( testSample );
circle( imgDst, Point(x,y), 2, classColors[response], 1 );
}
}
}
