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  /external/opencv3/modules/java/src/
ml+KNearest.java 9 // C++: class KNearest
10 //javadoc: KNearest
11 public class KNearest extends StatModel {
13 protected KNearest(long addr) { super(addr); }
25 //javadoc: KNearest::setDefaultK(val)
39 //javadoc: KNearest::getDefaultK()
53 //javadoc: KNearest::getIsClassifier()
67 //javadoc: KNearest::setIsClassifier(val)
81 //javadoc: KNearest::getEmax()
95 //javadoc: KNearest::setEmax(val
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ml.cpp     [all...]
  /external/opencv3/modules/ml/src/
knearest.cpp 79 bool update = (flags & ml::KNearest::UPDATE_MODEL) != 0 && !samples.empty();
141 int getType() const { return ml::KNearest::BRUTE_FORCE; }
365 int getType() const { return ml::KNearest::KDTREE; }
435 class KNearestImpl : public KNearest
512 Ptr<KNearest> KNearest::create()
  /external/opencv3/modules/ml/test/
test_precomp.hpp 19 #define CV_KNEAREST "knearest"
35 using cv::ml::KNearest;
test_emknearestkmeans.cpp 48 using cv::ml::KNearest;
316 // KNearest default implementation
317 Ptr<KNearest> knearest = KNearest::create(); local
318 knearest->train(trainData, ml::ROW_SAMPLE, trainLabels);
319 knearest->findNearest(testData, 4, bestLabels);
332 // KNearest KDTree implementation
333 Ptr<KNearest> knearestKdt = KNearest::create()
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test_mltests2.cpp 323 model = KNearest::create();
477 model = Algorithm::load<KNearest>( filename );
  /external/opencv3/samples/python2/
letter_recog.py 5 (or Boosting classifier, or MLP, or Knearest, or Support Vector Machines) using the provided dataset.
25 Models: RTrees, KNearest, Boost, SVM, MLP
74 class KNearest(LetterStatModel):
76 self.model = cv2.KNearest()
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
74 class KNearest(StatModel):
170 print 'training KNearest...'
171 model = KNearest(k=4)
174 cv2.imshow('KNearest test', vis)
digits_adjust.py 5 Grid search is used to find the best parameters for SVM and KNearest classifiers.
13 digits_adjust.py [--model {svm|knearest}] [--cloud] [--env <PiCloud environment>]
15 --model {svm|knearest} - select the classifier (SVM is the default)
129 print 'adjusting KNearest ...'
132 err = cross_validate(KNearest, dict(k=k), samples, labels)
154 if args['--model'] not in ['svm', 'knearest']:
160 if args['--model'] == 'knearest':
  /external/opencv3/samples/cpp/
points_classifier.cpp 116 Ptr<KNearest> knn = KNearest::create();
letter_recog.cpp 15 "(or Boosting classifier, or MLP, or Knearest, or Nbayes, or Support Vector Machines - see main()) using the provided dataset.\n"
34 " [-boost|-mlp|-knearest|-nbayes|-svm] # to use boost/mlp/knearest/SVM classifier instead of default Random Trees\n" );
440 Ptr<KNearest> model = KNearest::create();
549 else if( strcmp(argv[i], "-knearest") == 0 || strcmp(argv[i], "-knn") == 0 )
  /external/opencv3/modules/ml/include/opencv2/
ml.hpp 397 class CV_EXPORTS_W KNearest : public StatModel
419 /** %Algorithm type, one of KNearest::Types. */
456 /** @brief Implementations of KNearest algorithm
466 The static method creates empty %KNearest classifier. It should be then trained using StatModel::train method.
468 CV_WRAP static Ptr<KNearest> create();
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  /external/opencv3/apps/traincascade/
old_ml.hpp     [all...]
  /cts/apps/CtsVerifier/libs/
opencv3-android.jar 

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