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Package pyopencv :: Module pyopencvext :: Class CvSVM
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Class CvSVM

 object --+        
          |        
??.instance --+    
              |    
    CvStatModel --+
                  |
                 CvSVM
Instance Methods [hide private]
 
__init__(...)
__init__( (object)arg1) -> None :
 
clear(...)
clear( (CvSVM)arg1) -> None :
 
get_params(...)
get_params( (CvSVM)arg1) -> CvSVMParams :
 
get_support_vector(...)
get_support_vector( (CvSVM)arg1, (object)i) -> object :
 
get_support_vector_count(...)
get_support_vector_count( (CvSVM)arg1) -> int :
 
get_var_count(...)
get_var_count( (CvSVM)arg1) -> int :
 
load(...)
load( (CvSVM)arg1, (str)filename [, (str)name=None]) -> None :
 
predict(...)
predict( (CvSVM)inst, (Mat)_sample [, (bool)returnDFVal=False]) -> object :
 
read(...)
read( (CvSVM)inst, (FileStorage)storage, (FileNode)node) -> None :
 
save(...)
save( (CvSVM)arg1, (str)filename [, (str)name=None]) -> None :
 
train(...)
train( (CvSVM)inst, (Mat)_train_data, (Mat)_responses [, (Mat)_var_idx=Mat() [, (Mat)_sample_idx=Mat() [, (CvSVMParams)_params=<pyopencv.pyopencvext.CvSVMParams object at 0x02B825A8>]]]) -> object :
 
train_auto(...)
train_auto( (CvSVM)inst, (Mat)_train_data, (Mat)_responses, (Mat)_var_idx, (Mat)_sample_idx, (CvSVMParams)_params [, (object)k_fold=10 [, (CvParamGrid)C_grid=CvParamGrid(min_val=0.10000000000000001, max_val=500.0, step=5.0) [, (CvParamGrid)gamma_grid=CvParamGrid(min_val=1.0000000000000001e-05, max_val=0.59999999999999998, step=15.0) [, (CvParamGrid)p_grid=CvParamGrid(min_val=0.01, max_val=100.0, step=7.0) [, (CvParamGrid)nu_grid=CvParamGrid(min_val=0.01, max_val=0.20000000000000001, step=3.0) [, (CvParamGrid)coef_grid=CvParamGrid(min_val=0.10000000000000001, max_val=300.0, step=14.0) [, (CvParamGrid)degree_grid=CvParamGrid(min_val=0.01, max_val=4.0, step=7.0)]]]]]]]) -> object :
 
write(...)
write( (CvSVM)inst, (FileStorage)storage, (str)name) -> None :

Inherited from CvStatModel: __reduce__

Inherited from unreachable.instance: __new__

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __reduce_ex__, __repr__, __setattr__, __sizeof__, __str__, __subclasshook__

Static Methods [hide private]
 
get_default_grid(...)
get_default_grid( (object)param_id) -> CvParamGrid :
Class Variables [hide private]
  C = 0
  COEF = 4
  C_SVC = 100
  DEGREE = 5
  EPS_SVR = 103
  GAMMA = 1
  LINEAR = 0
  NU = 3
  NU_SVC = 101
  NU_SVR = 104
  ONE_CLASS = 102
  P = 2
  POLY = 1
  RBF = 2
  SIGMOID = 3
  __instance_size__ = 144
Properties [hide private]
  this

Inherited from object: __class__

Method Details [hide private]

__init__(...)
(Constructor)

  

__init__( (object)arg1) -> None :

    C++ signature :
        void __init__(_object*)

__init__( (object)arg1, (Mat)_train_data, (Mat)_responses [, (Mat)_var_idx=Mat() [, (Mat)_sample_idx=Mat() [, (CvSVMParams)_params=<pyopencv.pyopencvext.CvSVMParams object at 0x02B82738>]]]) -> None :

    C++ signature :
        void __init__(_object*,cv::Mat,cv::Mat [,cv::Mat=Mat() [,cv::Mat=Mat() [,CvSVMParams=<pyopencv.pyopencvext.CvSVMParams object at 0x02B82738>]]])
Overrides: object.__init__

clear(...)

  

clear( (CvSVM)arg1) -> None :

    C++ signature :
        void clear(CvSVM {lvalue})

clear( (CvSVM)arg1) -> None :

    C++ signature :
        void clear(CvSVM_wrapper {lvalue})
Overrides: CvStatModel.clear

get_default_grid(...)
Static Method

  

get_default_grid( (object)param_id) -> CvParamGrid :

    C++ signature :
        CvParamGrid get_default_grid(int)

get_params(...)

  

get_params( (CvSVM)arg1) -> CvSVMParams :

    C++ signature :
        CvSVMParams get_params(CvSVM {lvalue})

get_params( (CvSVM)arg1) -> CvSVMParams :

    C++ signature :
        CvSVMParams get_params(CvSVM_wrapper {lvalue})

get_support_vector(...)

  

get_support_vector( (CvSVM)arg1, (object)i) -> object :

    C++ signature :
        boost::python::api::object get_support_vector(CvSVM_wrapper {lvalue},int)

get_support_vector_count(...)

  

get_support_vector_count( (CvSVM)arg1) -> int :

    C++ signature :
        int get_support_vector_count(CvSVM {lvalue})

get_support_vector_count( (CvSVM)arg1) -> int :

    C++ signature :
        int get_support_vector_count(CvSVM_wrapper {lvalue})

get_var_count(...)

  

get_var_count( (CvSVM)arg1) -> int :

    C++ signature :
        int get_var_count(CvSVM {lvalue})

load(...)

  

load( (CvSVM)arg1, (str)filename [, (str)name=None]) -> None :

    C++ signature :
        void load(CvSVM {lvalue},char const* [,char const*=None])

load( (CvSVM)arg1, (str)filename [, (str)name=None]) -> None :

    C++ signature :
        void load(CvSVM_wrapper {lvalue},char const* [,char const*=None])
Overrides: CvStatModel.load

predict(...)

  

predict( (CvSVM)inst, (Mat)_sample [, (bool)returnDFVal=False]) -> object :
    
    Argument '_sample':
        C/C++ type: ::CvMat const *.
        Python type: Mat.

    C++ signature :
        boost::python::api::object predict(CvSVM,cv::Mat {lvalue} [,bool=False])

predict( (CvSVM)arg1, (Mat)_sample [, (bool)returnDFVal=False]) -> float :

    C++ signature :
        float predict(CvSVM {lvalue},cv::Mat [,bool=False])

predict( (CvSVM)arg1, (Mat)_sample [, (bool)returnDFVal=False]) -> float :

    C++ signature :
        float predict(CvSVM_wrapper {lvalue},cv::Mat [,bool=False])

read(...)

  

read( (CvSVM)inst, (FileStorage)storage, (FileNode)node) -> None :
    
    Argument 'node':
        C/C++ type: ::CvFileNode *.
        Python type: FileNode.
    Argument 'storage':
        C/C++ type: ::CvFileStorage *.
        Python type: FileStorage.

    C++ signature :
        void read(CvSVM {lvalue},cv::FileStorage {lvalue},cv::FileNode {lvalue})
Overrides: CvStatModel.read

save(...)

  

save( (CvSVM)arg1, (str)filename [, (str)name=None]) -> None :

    C++ signature :
        void save(CvSVM {lvalue},char const* [,char const*=None])

save( (CvSVM)arg1, (str)filename [, (str)name=None]) -> None :

    C++ signature :
        void save(CvSVM_wrapper {lvalue},char const* [,char const*=None])
Overrides: CvStatModel.save

train(...)

  

train( (CvSVM)inst, (Mat)_train_data, (Mat)_responses [, (Mat)_var_idx=Mat() [, (Mat)_sample_idx=Mat() [, (CvSVMParams)_params=<pyopencv.pyopencvext.CvSVMParams object at 0x02B825A8>]]]) -> object :
    
    Argument '_sample_idx':
        C/C++ type: ::CvMat const *.
        Python type: Mat.
    Argument '_train_data':
        C/C++ type: ::CvMat const *.
        Python type: Mat.
    Argument '_var_idx':
        C/C++ type: ::CvMat const *.
        Python type: Mat.
    Argument '_responses':
        C/C++ type: ::CvMat const *.
        Python type: Mat.

    C++ signature :
        boost::python::api::object train(CvSVM {lvalue},cv::Mat {lvalue},cv::Mat {lvalue} [,cv::Mat=Mat() [,cv::Mat=Mat() [,CvSVMParams=<pyopencv.pyopencvext.CvSVMParams object at 0x02B825A8>]]])

train( (CvSVM)arg1, (Mat)_train_data, (Mat)_responses [, (Mat)_var_idx=Mat() [, (Mat)_sample_idx=Mat() [, (CvSVMParams)_params=<pyopencv.pyopencvext.CvSVMParams object at 0x02B82670>]]]) -> bool :

    C++ signature :
        bool train(CvSVM {lvalue},cv::Mat,cv::Mat [,cv::Mat=Mat() [,cv::Mat=Mat() [,CvSVMParams=<pyopencv.pyopencvext.CvSVMParams object at 0x02B82670>]]])

train( (CvSVM)arg1, (Mat)_train_data, (Mat)_responses [, (Mat)_var_idx=Mat() [, (Mat)_sample_idx=Mat() [, (CvSVMParams)_params=<pyopencv.pyopencvext.CvSVMParams object at 0x02B82670>]]]) -> bool :

    C++ signature :
        bool train(CvSVM_wrapper {lvalue},cv::Mat,cv::Mat [,cv::Mat=Mat() [,cv::Mat=Mat() [,CvSVMParams=<pyopencv.pyopencvext.CvSVMParams object at 0x02B82670>]]])

train_auto(...)

  

train_auto( (CvSVM)inst, (Mat)_train_data, (Mat)_responses, (Mat)_var_idx, (Mat)_sample_idx, (CvSVMParams)_params [, (object)k_fold=10 [, (CvParamGrid)C_grid=CvParamGrid(min_val=0.10000000000000001, max_val=500.0, step=5.0) [, (CvParamGrid)gamma_grid=CvParamGrid(min_val=1.0000000000000001e-05, max_val=0.59999999999999998, step=15.0) [, (CvParamGrid)p_grid=CvParamGrid(min_val=0.01, max_val=100.0, step=7.0) [, (CvParamGrid)nu_grid=CvParamGrid(min_val=0.01, max_val=0.20000000000000001, step=3.0) [, (CvParamGrid)coef_grid=CvParamGrid(min_val=0.10000000000000001, max_val=300.0, step=14.0) [, (CvParamGrid)degree_grid=CvParamGrid(min_val=0.01, max_val=4.0, step=7.0)]]]]]]]) -> object :
    
    Argument '_sample_idx':
        C/C++ type: ::CvMat const *.
        Python type: Mat.
    Argument '_train_data':
        C/C++ type: ::CvMat const *.
        Python type: Mat.
    Argument '_var_idx':
        C/C++ type: ::CvMat const *.
        Python type: Mat.
    Argument '_responses':
        C/C++ type: ::CvMat const *.
        Python type: Mat.

    C++ signature :
        boost::python::api::object train_auto(CvSVM {lvalue},cv::Mat {lvalue},cv::Mat {lvalue},cv::Mat {lvalue},cv::Mat {lvalue},CvSVMParams [,int=10 [,CvParamGrid=CvParamGrid(min_val=0.10000000000000001, max_val=500.0, step=5.0) [,CvParamGrid=CvParamGrid(min_val=1.0000000000000001e-05, max_val=0.59999999999999998, step=15.0) [,CvParamGrid=CvParamGrid(min_val=0.01, max_val=100.0, step=7.0) [,CvParamGrid=CvParamGrid(min_val=0.01, max_val=0.20000000000000001, step=3.0) [,CvParamGrid=CvParamGrid(min_val=0.10000000000000001, max_val=300.0, step=14.0) [,CvParamGrid=CvParamGrid(min_val=0.01, max_val=4.0, step=7.0)]]]]]]])

train_auto( (CvSVM)arg1, (Mat)_train_data, (Mat)_responses, (Mat)_var_idx, (Mat)_sample_idx, (CvSVMParams)_params [, (object)k_fold=10 [, (CvParamGrid)C_grid=CvParamGrid(min_val=0.10000000000000001, max_val=500.0, step=5.0) [, (CvParamGrid)gamma_grid=CvParamGrid(min_val=1.0000000000000001e-05, max_val=0.59999999999999998, step=15.0) [, (CvParamGrid)p_grid=CvParamGrid(min_val=0.01, max_val=100.0, step=7.0) [, (CvParamGrid)nu_grid=CvParamGrid(min_val=0.01, max_val=0.20000000000000001, step=3.0) [, (CvParamGrid)coef_grid=CvParamGrid(min_val=0.10000000000000001, max_val=300.0, step=14.0) [, (CvParamGrid)degree_grid=CvParamGrid(min_val=0.01, max_val=4.0, step=7.0)]]]]]]]) -> bool :

    C++ signature :
        bool train_auto(CvSVM {lvalue},cv::Mat,cv::Mat,cv::Mat,cv::Mat,CvSVMParams [,int=10 [,CvParamGrid=CvParamGrid(min_val=0.10000000000000001, max_val=500.0, step=5.0) [,CvParamGrid=CvParamGrid(min_val=1.0000000000000001e-05, max_val=0.59999999999999998, step=15.0) [,CvParamGrid=CvParamGrid(min_val=0.01, max_val=100.0, step=7.0) [,CvParamGrid=CvParamGrid(min_val=0.01, max_val=0.20000000000000001, step=3.0) [,CvParamGrid=CvParamGrid(min_val=0.10000000000000001, max_val=300.0, step=14.0) [,CvParamGrid=CvParamGrid(min_val=0.01, max_val=4.0, step=7.0)]]]]]]])

train_auto( (CvSVM)arg1, (Mat)_train_data, (Mat)_responses, (Mat)_var_idx, (Mat)_sample_idx, (CvSVMParams)_params [, (object)k_fold=10 [, (CvParamGrid)C_grid=CvParamGrid(min_val=0.10000000000000001, max_val=500.0, step=5.0) [, (CvParamGrid)gamma_grid=CvParamGrid(min_val=1.0000000000000001e-05, max_val=0.59999999999999998, step=15.0) [, (CvParamGrid)p_grid=CvParamGrid(min_val=0.01, max_val=100.0, step=7.0) [, (CvParamGrid)nu_grid=CvParamGrid(min_val=0.01, max_val=0.20000000000000001, step=3.0) [, (CvParamGrid)coef_grid=CvParamGrid(min_val=0.10000000000000001, max_val=300.0, step=14.0) [, (CvParamGrid)degree_grid=CvParamGrid(min_val=0.01, max_val=4.0, step=7.0)]]]]]]]) -> bool :

    C++ signature :
        bool train_auto(CvSVM_wrapper {lvalue},cv::Mat,cv::Mat,cv::Mat,cv::Mat,CvSVMParams [,int=10 [,CvParamGrid=CvParamGrid(min_val=0.10000000000000001, max_val=500.0, step=5.0) [,CvParamGrid=CvParamGrid(min_val=1.0000000000000001e-05, max_val=0.59999999999999998, step=15.0) [,CvParamGrid=CvParamGrid(min_val=0.01, max_val=100.0, step=7.0) [,CvParamGrid=CvParamGrid(min_val=0.01, max_val=0.20000000000000001, step=3.0) [,CvParamGrid=CvParamGrid(min_val=0.10000000000000001, max_val=300.0, step=14.0) [,CvParamGrid=CvParamGrid(min_val=0.01, max_val=4.0, step=7.0)]]]]]]])

write(...)

  

write( (CvSVM)inst, (FileStorage)storage, (str)name) -> None :
    
    Argument 'storage':
        C/C++ type: ::CvFileStorage *.
        Python type: FileStorage.

    C++ signature :
        void write(CvSVM,cv::FileStorage {lvalue},char const*)
Overrides: CvStatModel.write

Property Details [hide private]

this

Get Method:
unreachable(...)

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