| /external/opencv3/samples/cpp/tutorial_code/ml/introduction_to_svm/ |
| H A D | introduction_to_svm.cpp | 27 // Train the SVM
29 Ptr<SVM> svm = SVM::create();
30 svm->setType(SVM::C_SVC);
31 svm->setKernel(SVM::LINEAR);
38 // Show the decision regions given by the SVM
79 imshow("SVM Simple Example", image); // show it to the user
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| /external/opencv3/modules/ml/test/ |
| H A D | test_svmtrainauto.cpp | 46 using cv::ml::SVM;
73 cv::Ptr<SVM> svm = SVM::create();
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| H A D | test_mltests2.cpp | 50 return SVM::C_SVC;
52 return SVM::NU_SVC;
54 return SVM::ONE_CLASS;
56 return SVM::EPS_SVR;
58 return SVM::NU_SVR;
65 return SVM::LINEAR;
67 return SVM::POLY;
69 return SVM::RBF;
71 return SVM::SIGMOID;
330 Ptr<SVM>
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| H A D | test_save_load.cpp | 201 model = Algorithm::load<SVM>(filename);
266 Ptr<cv::ml::SVM> svm;
274 Ptr<cv::ml::SVM> svm1, svm2, svm3;
276 svm1 = Algorithm::load<SVM>("SVM45_X_38-1.xml");
277 svm2 = Algorithm::load<SVM>("SVM45_X_38-2.xml");
280 svm3 = Algorithm::load<SVM>(tname);
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| H A D | test_precomp.hpp | 34 using cv::ml::SVM;
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| /external/opencv3/modules/ml/include/opencv2/ |
| H A D | ml.hpp | 479 class CV_EXPORTS_W SVM : public StatModel class in namespace:cv::ml
490 /** Type of a %SVM formulation.
491 See SVM::Types. Default value is SVM::C_SVC. */
498 For SVM::POLY, SVM::RBF, SVM::SIGMOID or SVM::CHI2. Default value is 1. */
505 For SVM::POLY or SVM
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| /external/opencv3/modules/java/src/ |
| H A D | ml+SVM.java | 10 // C++: class SVM
11 //javadoc: SVM
12 public class SVM extends StatModel { class in inherits:StatModel
14 protected SVM(long addr) { super(addr); } method in class:SVM
42 //javadoc: SVM::getType()
56 //javadoc: SVM::setType(val)
70 //javadoc: SVM::getGamma()
84 //javadoc: SVM::setGamma(val)
98 //javadoc: SVM::getCoef0()
112 //javadoc: SVM
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| H A D | ml.cpp | 487 Ptr<cv::ml::SVM>* me = (Ptr<cv::ml::SVM>*) self; //TODO: check for NULL
512 Ptr<cv::ml::SVM>* me = (Ptr<cv::ml::SVM>*) self; //TODO: check for NULL
537 Ptr<cv::ml::SVM>* me = (Ptr<cv::ml::SVM>*) self; //TODO: check for NULL
562 Ptr<cv::ml::SVM>* me = (Ptr<cv::ml::SVM>*) self; //TODO: check for NULL
587 Ptr<cv::ml::SVM>* me = (Ptr<cv::ml::SVM>*) sel
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| /external/opencv3/samples/cpp/tutorial_code/ml/non_linear_svms/ |
| H A D | non_linear_svms.cpp | 81 Ptr<SVM> svm = SVM::create();
82 svm->setType(SVM::C_SVC);
84 svm->setKernel(SVM::LINEAR);
141 imshow("SVM for Non-Linear Training Data", I); // show it to the user
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| /external/opencv3/samples/cpp/ |
| H A D | train_HOG.cpp | 14 void get_svm_detector(const Ptr<SVM>& svm, vector< float > & hog_detector );
24 void get_svm_detector(const Ptr<SVM>& svm, vector< float > & hog_detector )
321 Ptr<SVM> svm = SVM::create();
322 /* Default values to train SVM */
327 svm->setKernel(SVM::LINEAR);
331 svm->setType(SVM::EPS_SVR); // C_SVC; // EPSILON_SVR; // may be also NU_SVR; // do regression task
357 Ptr<SVM> svm;
364 // Load the trained SVM.
365 svm = StatModel::load<SVM>( "my_people_detecto
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| H A D | points_classifier.cpp | 127 Ptr<SVM> svm = SVM::create();
128 svm->setType(SVM::C_SVC);
129 svm->setKernel(SVM::POLY); //SVM::LINEAR;
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| H A D | letter_recog.cpp | 34 " [-boost|-mlp|-knearest|-nbayes|-svm] # to use boost/mlp/knearest/SVM classifier instead of default Random Trees\n" );
486 Ptr<SVM> model;
494 model = load_classifier<SVM>(filename_to_load);
504 model = SVM::create();
505 model->setType(SVM::C_SVC);
506 model->setKernel(SVM::LINEAR);
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| /external/opencv3/samples/python2/ |
| H A D | letter_recog.py | 25 Models: RTrees, KNearest, Boost, SVM, MLP
106 class SVM(LetterStatModel): class in inherits:LetterStatModel
108 self.model = cv2.SVM()
149 models = [RTrees, KNearest, Boost, SVM, MLP] # NBayes
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| H A D | digits_video.py | 27 model = SVM()
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| H A D | digits.py | 4 SVM and KNearest digit recognition.
7 Then it trains a SVM and KNearest classifiers on it and evaluates
87 class SVM(StatModel): class in inherits:StatModel
176 print 'training SVM...'
177 model = SVM(C=2.67, gamma=5.383)
180 cv2.imshow('SVM test', vis)
181 print 'saving SVM as "digits_svm.dat"...'
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| H A D | digits_adjust.py | 5 Grid search is used to find the best parameters for SVM and KNearest classifiers.
6 SVM adjustment follows the guidelines given in
15 --model {svm|knearest} - select the classifier (SVM is the default)
105 print 'adjusting SVM (may take a long time) ...'
110 score = cross_validate(SVM, params, samples, labels)
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| /external/opencv3/modules/ml/src/ |
| H A D | svm.cpp | 105 // SVM training parameters
121 svmType = SVM::C_SVC;
122 kernelType = SVM::RBF;
151 /////////////////////////////////////// SVM kernel ///////////////////////////////////////
152 class SVMKernelImpl : public SVM::Kernel
307 case SVM::LINEAR:
310 case SVM::RBF:
313 case SVM::POLY:
316 case SVM::SIGMOID:
319 case SVM
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| /external/opencv3/apps/traincascade/ |
| H A D | old_ml.hpp | 167 // SVM params type
290 // SVM training parameters
456 // SVM model
460 // SVM type
463 // SVM kernel type
466 // SVM params type
2044 typedef CvSVM SVM; typedef in namespace:cv
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