/external/opencv3/modules/java/src/ |
ml+StatModel.java | 10 // C++: class StatModel 11 //javadoc: StatModel 12 public class StatModel extends Algorithm { 14 protected StatModel(long addr) { super(addr); } 28 //javadoc: StatModel::getVarCount() 42 //javadoc: StatModel::empty() 56 //javadoc: StatModel::isTrained() 70 //javadoc: StatModel::isClassifier() 91 //javadoc: StatModel::train(samples, layout, responses) 112 //javadoc: StatModel::predict(samples, results, flags [all...] |
ml+NormalBayesClassifier.java | 11 public class NormalBayesClassifier extends StatModel {
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ml+KNearest.java | 11 public class KNearest extends StatModel {
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ml+DTrees.java | 11 public class DTrees extends StatModel {
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ml+LogisticRegression.java | 12 public class LogisticRegression extends StatModel {
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ml+ANN_MLP.java | 12 public class ANN_MLP extends StatModel {
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ml+EM.java | 12 public class EM extends StatModel {
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ml+SVM.java | 12 public class SVM extends StatModel {
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ml.cpp | [all...] |
/external/opencv3/modules/ml/src/ |
inner_functions.cpp | 53 bool StatModel::empty() const { return !isTrained(); } 55 int StatModel::getVarCount() const { return 0; } 57 bool StatModel::train( const Ptr<TrainData>&, int ) 63 bool StatModel::train( InputArray samples, int layout, InputArray responses ) 68 float StatModel::calcError( const Ptr<TrainData>& data, bool testerr, OutputArray _resp ) const
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/external/opencv3/modules/ml/test/ |
test_precomp.hpp | 31 using cv::ml::StatModel; 65 Ptr<StatModel> model;
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test_save_load.cpp | 191 Ptr<StatModel> model; 217 model->predict(input, output, StatModel::RAW_OUTPUT | (isTree ? DTrees::PREDICT_SUM : 0));
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test_mltests2.cpp | 131 float ann_calc_error( Ptr<StatModel> ann, Ptr<TrainData> _data, map<int, int>& cls_map, int type, vector<float> *resp_labels )
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/external/opencv3/modules/ml/include/opencv2/ |
ml.hpp | 66 ground is defined by the class cv::ml::StatModel that all the other ML classes are derived from. 102 /** @brief The structure represents the logarithmic grid range of statmodel parameters. 104 It is used for optimizing statmodel accuracy by varying model parameters, the accuracy estimate 115 double minVal; //!< Minimum value of the statmodel parameter. Default value is 0. 116 double maxVal; //!< Maximum value of the statmodel parameter. Default value is 0. 117 /** @brief Logarithmic step for iterating the statmodel parameter. 119 The grid determines the following iteration sequence of the statmodel parameter values: 133 of this class into StatModel::train. 290 class CV_EXPORTS_W StatModel : public Algorithm 338 The method uses StatModel::predict to compute the error. For regression models the error i [all...] |
/external/opencv3/samples/python2/ |
digits.py | 68 class StatModel(object): 74 class KNearest(StatModel): 87 class SVM(StatModel):
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/external/opencv3/samples/cpp/ |
points_classifier.cpp | 85 static void predict_and_paint(const Ptr<StatModel>& model, Mat& dst) 105 Ptr<NormalBayesClassifier> normalBayesClassifier = StatModel::train<NormalBayesClassifier>(prepare_train_data()); 191 Ptr<GBTrees> gbtrees = StatModel::train<GBTrees>(prepare_train_data(), params);
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tree_engine.cpp | 21 static void train_and_print_errs(Ptr<StatModel> model, const Ptr<TrainData>& data)
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logistic_regression.cpp | 167 Ptr<LogisticRegression> lr2 = StatModel::load<LogisticRegression>(saveFilename);
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letter_recog.cpp | 88 Ptr<T> model = StatModel::load<T>( filename_to_load ); 118 static void test_and_save_classifier(const Ptr<StatModel>& model, 314 float s = model->predict( temp_sample, noArray(), StatModel::RAW_OUTPUT );
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train_HOG.cpp | 365 svm = StatModel::load<SVM>( "my_people_detector.yml" );
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/cts/apps/CtsVerifier/libs/ |
opencv3-android.jar | |
/external/opencv3/apps/traincascade/ |
old_ml.hpp | 89 /* A structure, representing the lattice range of statmodel parameters. 90 It is used for optimizing statmodel parameters by cross-validation method. 158 /* The structure, representing the grid range of statmodel parameters. 159 It is used for optimizing statmodel accuracy by varying model parameters, [all...] |