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Searched
refs:KNearest
(Results
1 - 14
of
14
) sorted by null
/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
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/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
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/cts/apps/CtsVerifier/libs/
opencv3-android.jar
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