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

fn as_raw_ANN_MLP(&self) -> *const c_void

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

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

Sets training method and common parameters.

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

Returns current training method

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.

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.

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.

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).

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).

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.

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.

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.

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.

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

RPROP: Initial value

inline formula

of update-values

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. Default value is 0.1.

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

RPROP: Initial value

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of update-values

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. Default value is 0.1.

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

RPROP: Increase factor

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

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

RPROP: Increase factor

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

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

RPROP: Decrease factor

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

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

RPROP: Decrease factor

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

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

RPROP: Update-values lower limit

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. It must be positive. Default value is FLT_EPSILON.

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

RPROP: Update-values lower limit

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. It must be positive. Default value is FLT_EPSILON.

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

RPROP: Update-values upper limit

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

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

RPROP: Update-values upper limit

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

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

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

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

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

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.

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.

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.

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.

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

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

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

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

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

Set/initialize anneal RNG

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

impl AlgorithmTrait for PtrOfANN_MLP

fn as_raw_Algorithm(&self) -> *const c_void

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

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

Clears the algorithm state

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

Stores algorithm parameters in a file storage

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

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

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

Reads algorithm parameters from a file storage

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 StatModel for PtrOfANN_MLP

fn as_raw_StatModel(&self) -> *const c_void

fn as_raw_mut_StatModel(&mut 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: &Ptr<dyn TrainData>,
    flags: i32
) -> Result<bool>

Trains the statistical model

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

Trains the statistical model

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

Computes error on the training or test dataset

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

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