| /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
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| 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
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| /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,
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