| /external/opencv3/samples/cpp/tutorial_code/ml/introduction_to_svm/ |
| introduction_to_svm.cpp | 27 // Train the SVM
29 Ptr<SVM> svm = SVM::create(); local
30 svm->setType(SVM::C_SVC);
31 svm->setKernel(SVM::LINEAR);
32 svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6));
35 svm->train(trainingDataMat, ROW_SAMPLE, labelsMat)
[all...] |
| /external/opencv3/modules/ml/test/ |
| test_svmtrainauto.cpp | 46 using cv::ml::SVM;
73 cv::Ptr<SVM> svm = SVM::create(); local
74 svm->trainAuto( data, 10 ); // 2-fold cross validation.
78 float result0 = svm->predict( test_point0 );
81 float result1 = svm->predict( test_point1 );
|
| 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;
59 CV_Error( CV_StsBadArg, "incorrect svm type string" );
65 return SVM::LINEAR;
67 return SVM::POLY;
69 return SVM::RBF;
71 return SVM::SIGMOID
[all...] |
| test_save_load.cpp | 201 model = Algorithm::load<SVM>(filename);
266 Ptr<cv::ml::SVM> svm;
267 string filename = tempfile("svm.xml");
268 ASSERT_THROW(svm.save(filename.c_str()), Exception);
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);
|
| test_precomp.hpp | 20 #define CV_SVM "svm"
34 using cv::ml::SVM;
|
| /external/opencv3/modules/ml/include/opencv2/ |
| ml.hpp | 479 class CV_EXPORTS_W SVM : public StatModel
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::SIGMOID. Default value is 0.*
[all...] |
| /external/opencv3/modules/java/src/ |
| ml+SVM.java | 10 // C++: class SVM
11 //javadoc: SVM
12 public class SVM extends StatModel {
14 protected SVM(long addr) { super(addr); }
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::setCoef0(val
[all...] |
| 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>*) self; //TODO: check for NUL
[all...] |
| /external/opencv3/samples/cpp/tutorial_code/ml/non_linear_svms/ |
| non_linear_svms.cpp | 78 //------------------------ 3. Train the svm ----------------------------------------------------
81 Ptr<SVM> svm = SVM::create(); local
82 svm->setType(SVM::C_SVC);
83 svm->setC(0.1);
84 svm->setKernel(SVM::LINEAR);
85 svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, (int)1e7, 1e-6))
[all...] |
| /external/opencv3/samples/cpp/ |
| 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 )
27 Mat sv = svm->getSupportVectors();
31 double rho = svm->getDecisionFunction(0, alpha, svidx);
321 Ptr<SVM> svm = SVM::create(); local
322 /* Default values to train SVM */
357 Ptr<SVM> svm; local
[all...] |
| points_classifier.cpp | 7 #define _OCL_SVM_ 1 // select whether using ocl::svm method or not, default is using
127 Ptr<SVM> svm = SVM::create(); local
128 svm->setType(SVM::C_SVC);
129 svm->setKernel(SVM::POLY); //SVM::LINEAR;
130 svm->setDegree(0.5)
[all...] |
| 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);
557 else if( strcmp(argv[i], "-svm") == 0)
|
| /external/opencv3/samples/python2/ |
| letter_recog.py | 25 Models: RTrees, KNearest, Boost, SVM, MLP
106 class SVM(LetterStatModel):
108 self.model = cv2.SVM()
149 models = [RTrees, KNearest, Boost, SVM, MLP] # NBayes
|
| 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):
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"...'
|
| digits_video.py | 27 model = SVM()
|
| 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
13 digits_adjust.py [--model {svm|knearest}] [--cloud] [--env <PiCloud environment>]
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)
152 args.setdefault('--model', 'svm')
154 if args['--model'] not in ['svm', 'knearest']:
|
| /external/opencv3/modules/ml/src/ |
| 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::CHI2
[all...] |
| /external/opencv3/apps/traincascade/ |
| old_ml.hpp | 122 #define CV_TYPE_NAME_ML_SVM "opencv-ml-svm"
167 // SVM params type
290 // SVM training parameters
456 // SVM model
460 // SVM type
463 // SVM kernel type
466 // SVM params type
[all...] |
| /cts/apps/CtsVerifier/libs/ |
| opencv3-android.jar | |
