Struct opencv::types::PtrOfANN_MLP

pub struct PtrOfANN_MLP { /* fields omitted */ }

Methods

impl PtrOfANN_MLP

pub fn as_raw_PtrOfANN_MLP(&self) -> *mut c_void

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

Trait Implementations

impl Algorithm for PtrOfANN_MLP

fn as_raw_Algorithm(&self) -> *mut c_void

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

Clears the algorithm state

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 ANN_MLP for PtrOfANN_MLP

fn as_raw_ANN_MLP(&self) -> *mut c_void

fn set_train_method(
    &mut self,
    method: i32,
    param1: f64,
    param2: f64
) -> Result<()>

Sets training method and common parameters. ## Parameters * method: Default value is ANN_MLP::RPROP. See ANN_MLP::TrainingMethods. * param1: passed to setRpropDW0 for ANN_MLP::RPROP and to setBackpropWeightScale for ANN_MLP::BACKPROP and to initialT for ANN_MLP::ANNEAL. * param2: passed to setRpropDWMin for ANN_MLP::RPROP and to setBackpropMomentumScale for ANN_MLP::BACKPROP and to finalT for ANN_MLP::ANNEAL.

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

Returns current training method

fn set_activation_function(
    &mut self,
    _type: i32,
    param1: f64,
    param2: f64
) -> Result<()>

Initialize the activation function for each neuron. Currently the default and the only fully supported activation function is ANN_MLP::SIGMOID_SYM. ## Parameters * type: The type of activation function. See ANN_MLP::ActivationFunctions. * param1: The first parameter of the activation function, inline formula. Default value is 0. * param2: The second parameter of the activation function, inline formula. Default value is 0.

fn set_layer_sizes(&mut self, _layer_sizes: &Mat) -> Result<()>

Integer vector specifying the number of neurons in each layer including the input and output layers. The very first element specifies the number of elements in the input layer. The last element - number of elements in the output layer. Default value is empty Mat. ## See also getLayerSizes

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

Integer vector specifying the number of neurons in each layer including the input and output layers. The very first element specifies the number of elements in the input layer. The last element - number of elements in the output layer. ## See also setLayerSizes

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

@see setTermCriteria

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

@copybrief getTermCriteria @see getTermCriteria

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

@see setBackpropWeightScale

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

@copybrief getBackpropWeightScale @see getBackpropWeightScale

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

@see setBackpropMomentumScale

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

@copybrief getBackpropMomentumScale @see getBackpropMomentumScale

fn get_rprop_dw0(&self) -> Result<f64>

@see setRpropDW0

fn set_rprop_dw0(&mut self, val: f64) -> Result<()>

@copybrief getRpropDW0 @see getRpropDW0

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

@see setRpropDWPlus

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

@copybrief getRpropDWPlus @see getRpropDWPlus

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

@see setRpropDWMinus

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

@copybrief getRpropDWMinus @see getRpropDWMinus

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

@see setRpropDWMin

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

@copybrief getRpropDWMin @see getRpropDWMin

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

@see setRpropDWMax

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

@copybrief getRpropDWMax @see getRpropDWMax

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

@see setAnnealInitialT

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

@copybrief getAnnealInitialT @see getAnnealInitialT

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

@see setAnnealFinalT

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

@copybrief getAnnealFinalT @see getAnnealFinalT

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

@see setAnnealCoolingRatio

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

@copybrief getAnnealCoolingRatio @see getAnnealCoolingRatio

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

@see setAnnealItePerStep

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

@copybrief getAnnealItePerStep @see getAnnealItePerStep

fn get_weights(&self, layer_idx: i32) -> Result<Mat>

impl StatModel for PtrOfANN_MLP

fn as_raw_StatModel(&self) -> *mut 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 train_with_data(
    &mut self,
    train_data: &PtrOfTrainData,
    flags: i32
) -> Result<bool>

Trains the statistical model

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

Trains the statistical model

fn calc_error(
    &self,
    data: &PtrOfTrainData,
    test: bool,
    resp: &mut Mat
) -> Result<f32>

Computes error on the training or test dataset

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

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

impl Send for PtrOfANN_MLP

impl Drop for PtrOfANN_MLP

fn drop(&mut self)

Executes the destructor for this type.