Trait opencv::prelude::LogisticRegression

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pub trait LogisticRegression: LogisticRegressionConst + StatModel {
    // Required method
    fn as_raw_mut_LogisticRegression(&mut self) -> *mut c_void;

    // Provided methods
    fn set_learning_rate(&mut self, val: f64) -> Result<()> { ... }
    fn set_iterations(&mut self, val: i32) -> Result<()> { ... }
    fn set_regularization(&mut self, val: i32) -> Result<()> { ... }
    fn set_train_method(&mut self, val: i32) -> Result<()> { ... }
    fn set_mini_batch_size(&mut self, val: i32) -> Result<()> { ... }
    fn set_term_criteria(&mut self, val: TermCriteria) -> Result<()> { ... }
}
Expand description

Implements Logistic Regression classifier.

See also

[ml_intro_lr]

Required Methods§

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

Provided Methods§

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

Learning rate.

See also

setLearningRate getLearningRate

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

Number of iterations.

See also

setIterations getIterations

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

Kind of regularization to be applied. See LogisticRegression::RegKinds.

See also

setRegularization getRegularization

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

Kind of training method used. See LogisticRegression::Methods.

See also

setTrainMethod getTrainMethod

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

Specifies the number of training samples taken in each step of Mini-Batch Gradient Descent. Will only be used if using LogisticRegression::MINI_BATCH training algorithm. It has to take values less than the total number of training samples.

See also

setMiniBatchSize getMiniBatchSize

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

Termination criteria of the algorithm.

See also

setTermCriteria getTermCriteria

Implementations§

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impl dyn LogisticRegression + '_

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pub fn create() -> Result<Ptr<dyn LogisticRegression>>

Creates empty model.

Creates Logistic Regression model with parameters given.

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pub fn load( filepath: &str, node_name: &str ) -> Result<Ptr<dyn LogisticRegression>>

Loads and creates a serialized LogisticRegression from a file

Use LogisticRegression::save to serialize and store an LogisticRegression to disk. Load the LogisticRegression from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier

Parameters
  • filepath: path to serialized LogisticRegression
  • nodeName: name of node containing the classifier
C++ default parameters
  • node_name: String()

Implementors§