Type Definition opencv::types::PtrOfANN_MLP

type PtrOfANN_MLP = Ptr<dyn ANN_MLP>;

Implementations

impl PtrOfANN_MLP

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

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

Trait Implementations

impl ANN_MLP for PtrOfANN_MLP

pub fn as_raw_ANN_MLP(&self) -> *const c_void

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

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

Sets training method and common parameters.

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

Returns current training method

pub fn set_activation_function(
    &mut self,
    typ: 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.

pub fn set_layer_sizes(&mut self, _layer_sizes: &dyn ToInputArray) -> 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.

pub 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.

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

Termination criteria of the training algorithm. You can specify the maximum number of iterations (maxCount) and/or how much the error could change between the iterations to make the algorithm continue (epsilon). Default value is TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, 0.01).

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

Termination criteria of the training algorithm. You can specify the maximum number of iterations (maxCount) and/or how much the error could change between the iterations to make the algorithm continue (epsilon). Default value is TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, 0.01).

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

BPROP: Strength of the weight gradient term. The recommended value is about 0.1. Default value is 0.1.

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

BPROP: Strength of the weight gradient term. The recommended value is about 0.1. Default value is 0.1.

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

BPROP: Strength of the momentum term (the difference between weights on the 2 previous iterations). This parameter provides some inertia to smooth the random fluctuations of the weights. It can vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough. Default value is 0.1.

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

BPROP: Strength of the momentum term (the difference between weights on the 2 previous iterations). This parameter provides some inertia to smooth the random fluctuations of the weights. It can vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough. Default value is 0.1.

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

RPROP: Initial value

inline formula

of update-values

inline formula

. Default value is 0.1.

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

RPROP: Initial value

inline formula

of update-values

inline formula

. Default value is 0.1.

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

RPROP: Increase factor

inline formula

. It must be >1. Default value is 1.2.

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

RPROP: Increase factor

inline formula

. It must be >1. Default value is 1.2.

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

RPROP: Decrease factor

inline formula

. It must be <1. Default value is 0.5.

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

RPROP: Decrease factor

inline formula

. It must be <1. Default value is 0.5.

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

RPROP: Update-values lower limit

inline formula

. It must be positive. Default value is FLT_EPSILON.

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

RPROP: Update-values lower limit

inline formula

. It must be positive. Default value is FLT_EPSILON.

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

RPROP: Update-values upper limit

inline formula

. It must be >1. Default value is 50.

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

RPROP: Update-values upper limit

inline formula

. It must be >1. Default value is 50.

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

ANNEAL: Update initial temperature. It must be >=0. Default value is 10.

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

ANNEAL: Update initial temperature. It must be >=0. Default value is 10.

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

ANNEAL: Update final temperature. It must be >=0 and less than initialT. Default value is 0.1.

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

ANNEAL: Update final temperature. It must be >=0 and less than initialT. Default value is 0.1.

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

ANNEAL: Update cooling ratio. It must be >0 and less than 1. Default value is 0.95.

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

ANNEAL: Update cooling ratio. It must be >0 and less than 1. Default value is 0.95.

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

ANNEAL: Update iteration per step. It must be >0 . Default value is 10.

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

ANNEAL: Update iteration per step. It must be >0 . Default value is 10.

pub fn set_anneal_energy_rng(&mut self, rng: &RNG) -> Result<()>

Set/initialize anneal RNG

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

impl AlgorithmTrait for PtrOfANN_MLP

pub fn as_raw_Algorithm(&self) -> *const c_void

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

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

Clears the algorithm state

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

Stores algorithm parameters in a file storage

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

simplified API for language bindings Stores algorithm parameters in a file storage

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

Reads algorithm parameters from a file storage

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

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

pub 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).

pub 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 StatModel for PtrOfANN_MLP

pub fn as_raw_StatModel(&self) -> *const c_void

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

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

Returns the number of variables in training samples

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

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

Returns true if the model is trained

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

Returns true if the model is classifier

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

Trains the statistical model

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

Trains the statistical model

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

Computes error on the training or test dataset

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

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