Struct opencv::ml::SVM

pub struct SVM { /* private fields */ }

Support Vector Machines.

See also

[ml_intro_svm]

Implementations

impl SVM

pub fn get_default_grid(param_id: i32) -> Result<ParamGrid>

Generates a grid for %SVM parameters.

Parameters

• param_id: %SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is generated for the parameter with this ID.

The function generates a grid for the specified parameter of the %SVM algorithm. The grid may be passed to the function SVM::trainAuto.

pub fn get_default_grid_ptr(param_id: i32) -> Result<Ptr<ParamGrid>>

Generates a grid for %SVM parameters.

Parameters

• param_id: %SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is generated for the parameter with this ID.

The function generates a grid pointer for the specified parameter of the %SVM algorithm. The grid may be passed to the function SVM::trainAuto.

pub fn create() -> Result<Ptr<SVM>>

Creates empty model. Use StatModel::train to train the model. Since %SVM has several parameters, you may want to find the best parameters for your problem, it can be done with SVM::trainAuto.

pub fn load(filepath: &str) -> Result<Ptr<SVM>>

Loads and creates a serialized svm from a file

Use SVM::save to serialize and store an SVM to disk. Load the SVM from this file again, by calling this function with the path to the file.

Parameters

• filepath: path to serialized svm

Trait Implementations

impl AlgorithmTrait for SVM

fn as_raw_mut_Algorithm(&mut self) -> *mut c_void

fn clear(&mut self) -> Result<()>

Clears the algorithm state

fn read(&mut self, fn_: &FileNode) -> Result<()>

Reads algorithm parameters from a file storage

impl AlgorithmTraitConst for SVM

fn as_raw_Algorithm(&self) -> *const c_void

fn write(&self, fs: &mut FileStorage) -> Result<()>

Stores algorithm parameters in a file storage

fn write_1(&self, fs: &mut FileStorage, name: &str) -> Result<()>

Stores algorithm parameters in a file storage

fn write_with_name(&self, fs: &Ptr<FileStorage>, name: &str) -> Result<()>

@deprecated

fn write_with_name_def(&self, fs: &Ptr<FileStorage>) -> Result<()>

👎Deprecated:

Note

Deprecated: ## Note This alternative version of AlgorithmTraitConst::write_with_name function uses the following default values for its arguments:

fn empty(&self) -> Result<bool>

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

fn save(&self, filename: &str) -> Result<()>

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

fn get_default_name(&self) -> Result<String>

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

impl Boxed for SVM

unsafe fn from_raw(ptr: *mut c_void) -> Self

Wrap the specified raw pointer

fn into_raw(self) -> *mut c_void

Return an the underlying raw pointer while consuming this wrapper.

fn as_raw(&self) -> *const c_void

Return the underlying raw pointer.

fn as_raw_mut(&mut self) -> *mut c_void

Return the underlying mutable raw pointer

impl Debug for SVM

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Drop for SVM

fn drop(&mut self)

Executes the destructor for this type.

impl From<SVM> for Algorithm

fn from(s: SVM) -> Self

Converts to this type from the input type.

impl From<SVM> for StatModel

fn from(s: SVM) -> Self

Converts to this type from the input type.

impl SVMTrait for SVM

fn as_raw_mut_SVM(&mut self) -> *mut c_void

fn set_type(&mut self, val: i32) -> Result<()>

Type of a %SVM formulation. See SVM::Types. Default value is SVM::C_SVC.

fn set_gamma(&mut self, val: f64) -> Result<()>

Parameter inline formula of a kernel function. For SVM::POLY, SVM::RBF, SVM::SIGMOID or SVM::CHI2. Default value is 1.

fn set_coef0(&mut self, val: f64) -> Result<()>

Parameter coef0 of a kernel function. For SVM::POLY or SVM::SIGMOID. Default value is 0.

fn set_degree(&mut self, val: f64) -> Result<()>

Parameter degree of a kernel function. For SVM::POLY. Default value is 0.

fn set_c(&mut self, val: f64) -> Result<()>

Parameter C of a %SVM optimization problem. For SVM::C_SVC, SVM::EPS_SVR or SVM::NU_SVR. Default value is 0.

fn set_nu(&mut self, val: f64) -> Result<()>

Parameter inline formula of a %SVM optimization problem. For SVM::NU_SVC, SVM::ONE_CLASS or SVM::NU_SVR. Default value is 0.

fn set_p(&mut self, val: f64) -> Result<()>

Parameter inline formula of a %SVM optimization problem. For SVM::EPS_SVR. Default value is 0.

fn set_class_weights(&mut self, val: &Mat) -> Result<()>

Optional weights in the SVM::C_SVC problem, assigned to particular classes. They are multiplied by C so the parameter C of class i becomes classWeights(i) * C. Thus these weights affect the misclassification penalty for different classes. The larger weight, the larger penalty on misclassification of data from the corresponding class. Default value is empty Mat.

fn set_term_criteria(&mut self, val: TermCriteria) -> Result<()>

Termination criteria of the iterative %SVM training procedure which solves a partial case of constrained quadratic optimization problem. You can specify tolerance and/or the maximum number of iterations. Default value is TermCriteria( TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, FLT_EPSILON );

fn set_kernel(&mut self, kernel_type: i32) -> Result<()>

Initialize with one of predefined kernels. See SVM::KernelTypes.

fn set_custom_kernel(&mut self, _kernel: &Ptr<SVM_Kernel>) -> Result<()>

Initialize with custom kernel. See SVM::Kernel class for implementation details

fn train_auto( &mut self, data: &Ptr<TrainData>, k_fold: i32, cgrid: ParamGrid, gamma_grid: ParamGrid, p_grid: ParamGrid, nu_grid: ParamGrid, coeff_grid: ParamGrid, degree_grid: ParamGrid, balanced: bool ) -> Result<bool>

Trains an %SVM with optimal parameters.

fn train_auto_def(&mut self, data: &Ptr<TrainData>) -> Result<bool>

Trains an %SVM with optimal parameters.

fn train_auto_with_data( &mut self, samples: &impl ToInputArray, layout: i32, responses: &impl ToInputArray, k_fold: i32, cgrid: Ptr<ParamGrid>, gamma_grid: Ptr<ParamGrid>, p_grid: Ptr<ParamGrid>, nu_grid: Ptr<ParamGrid>, coeff_grid: Ptr<ParamGrid>, degree_grid: Ptr<ParamGrid>, balanced: bool ) -> Result<bool>

Trains an %SVM with optimal parameters

fn train_auto_with_data_def( &mut self, samples: &impl ToInputArray, layout: i32, responses: &impl ToInputArray ) -> Result<bool>

Trains an %SVM with optimal parameters

impl SVMTraitConst for SVM

fn as_raw_SVM(&self) -> *const c_void

fn get_type(&self) -> Result<i32>

Type of a %SVM formulation. See SVM::Types. Default value is SVM::C_SVC.

fn get_gamma(&self) -> Result<f64>

Parameter inline formula of a kernel function. For SVM::POLY, SVM::RBF, SVM::SIGMOID or SVM::CHI2. Default value is 1.

fn get_coef0(&self) -> Result<f64>

Parameter coef0 of a kernel function. For SVM::POLY or SVM::SIGMOID. Default value is 0.

fn get_degree(&self) -> Result<f64>

Parameter degree of a kernel function. For SVM::POLY. Default value is 0.

fn get_c(&self) -> Result<f64>

Parameter C of a %SVM optimization problem. For SVM::C_SVC, SVM::EPS_SVR or SVM::NU_SVR. Default value is 0.

fn get_nu(&self) -> Result<f64>

Parameter inline formula of a %SVM optimization problem. For SVM::NU_SVC, SVM::ONE_CLASS or SVM::NU_SVR. Default value is 0.

fn get_p(&self) -> Result<f64>

Parameter inline formula of a %SVM optimization problem. For SVM::EPS_SVR. Default value is 0.

fn get_class_weights(&self) -> Result<Mat>

Optional weights in the SVM::C_SVC problem, assigned to particular classes. They are multiplied by C so the parameter C of class i becomes classWeights(i) * C. Thus these weights affect the misclassification penalty for different classes. The larger weight, the larger penalty on misclassification of data from the corresponding class. Default value is empty Mat.

fn get_term_criteria(&self) -> Result<TermCriteria>

Termination criteria of the iterative %SVM training procedure which solves a partial case of constrained quadratic optimization problem. You can specify tolerance and/or the maximum number of iterations. Default value is TermCriteria( TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, FLT_EPSILON );

fn get_kernel_type(&self) -> Result<i32>

Type of a %SVM kernel. See SVM::KernelTypes. Default value is SVM::RBF.

fn get_support_vectors(&self) -> Result<Mat>

Retrieves all the support vectors

fn get_uncompressed_support_vectors(&self) -> Result<Mat>

Retrieves all the uncompressed support vectors of a linear %SVM

fn get_decision_function( &self, i: i32, alpha: &mut impl ToOutputArray, svidx: &mut impl ToOutputArray ) -> Result<f64>

Retrieves the decision function

impl StatModelTrait for SVM

fn as_raw_mut_StatModel(&mut self) -> *mut c_void

fn train_with_data( &mut self, train_data: &Ptr<TrainData>, flags: i32 ) -> Result<bool>

Trains the statistical model

fn train_with_data_def(&mut self, train_data: &Ptr<TrainData>) -> Result<bool>

Trains the statistical model

fn train( &mut self, samples: &impl ToInputArray, layout: i32, responses: &impl ToInputArray ) -> Result<bool>

Trains the statistical model

impl StatModelTraitConst for SVM

fn as_raw_StatModel(&self) -> *const c_void

fn get_var_count(&self) -> Result<i32>

Returns the number of variables in training samples

fn empty(&self) -> Result<bool>

fn is_trained(&self) -> Result<bool>

Returns true if the model is trained

fn is_classifier(&self) -> Result<bool>

Returns true if the model is classifier

fn calc_error( &self, data: &Ptr<TrainData>, test: bool, resp: &mut impl ToOutputArray ) -> Result<f32>

Computes error on the training or test dataset

fn predict( &self, samples: &impl ToInputArray, results: &mut impl ToOutputArray, flags: i32 ) -> Result<f32>

Predicts response(s) for the provided sample(s)

fn predict_def(&self, samples: &impl ToInputArray) -> Result<f32>

Predicts response(s) for the provided sample(s)

impl TryFrom<StatModel> for SVM

type Error = Error

The type returned in the event of a conversion error.

fn try_from(s: StatModel) -> Result<Self>

Performs the conversion.

impl Send for SVM