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5ff1fababc
ml: refactored tests * use parametrized tests where appropriate * use stable theRNG in most tests * use modern style with EXPECT_/ASSERT_ checks
52 lines
1.5 KiB
C++
52 lines
1.5 KiB
C++
#ifndef __OPENCV_TEST_PRECOMP_HPP__
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#define __OPENCV_TEST_PRECOMP_HPP__
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#include "opencv2/ts.hpp"
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#include <opencv2/ts/cuda_test.hpp> // EXPECT_MAT_NEAR
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#include "opencv2/ml.hpp"
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#include "opencv2/core/core_c.h"
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#include <fstream>
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using std::ifstream;
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namespace opencv_test {
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using namespace cv::ml;
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#define CV_NBAYES "nbayes"
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#define CV_KNEAREST "knearest"
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#define CV_SVM "svm"
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#define CV_EM "em"
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#define CV_ANN "ann"
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#define CV_DTREE "dtree"
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#define CV_BOOST "boost"
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#define CV_RTREES "rtrees"
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#define CV_ERTREES "ertrees"
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#define CV_SVMSGD "svmsgd"
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using cv::Ptr;
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using cv::ml::StatModel;
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using cv::ml::TrainData;
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using cv::ml::NormalBayesClassifier;
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using cv::ml::SVM;
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using cv::ml::KNearest;
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using cv::ml::ParamGrid;
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using cv::ml::ANN_MLP;
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using cv::ml::DTrees;
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using cv::ml::Boost;
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using cv::ml::RTrees;
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using cv::ml::SVMSGD;
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void defaultDistribs( Mat& means, vector<Mat>& covs, int type=CV_32FC1 );
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void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const Mat& _means, const vector<Mat>& covs, int dataType, int labelType );
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int maxIdx( const vector<int>& count );
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bool getLabelsMap( const Mat& labels, const vector<int>& sizes, vector<int>& labelsMap, bool checkClusterUniq=true );
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bool calcErr( const Mat& labels, const Mat& origLabels, const vector<int>& sizes, float& err, bool labelsEquivalent = true, bool checkClusterUniq=true );
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// used in LR test
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bool calculateError( const Mat& _p_labels, const Mat& _o_labels, float& error);
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} // namespace
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#endif
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