[−][src]Trait opencv::ml::LogisticRegression
Required methods
fn as_raw_LogisticRegression(&self) -> *mut c_void
Provided methods
fn get_learning_rate(&self) -> Result<f64>
@see setLearningRate
fn set_learning_rate(&mut self, val: f64) -> Result<()>
@copybrief getLearningRate @see getLearningRate
fn get_iterations(&self) -> Result<i32>
@see setIterations
fn set_iterations(&mut self, val: i32) -> Result<()>
@copybrief getIterations @see getIterations
fn get_regularization(&self) -> Result<i32>
@see setRegularization
fn set_regularization(&mut self, val: i32) -> Result<()>
@copybrief getRegularization @see getRegularization
fn get_train_method(&self) -> Result<i32>
@see setTrainMethod
fn set_train_method(&mut self, val: i32) -> Result<()>
@copybrief getTrainMethod @see getTrainMethod
fn get_mini_batch_size(&self) -> Result<i32>
@see setMiniBatchSize
fn set_mini_batch_size(&mut self, val: i32) -> Result<()>
@copybrief getMiniBatchSize @see getMiniBatchSize
fn get_term_criteria(&self) -> Result<TermCriteria>
@see setTermCriteria
fn set_term_criteria(&mut self, val: &TermCriteria) -> Result<()>
@copybrief getTermCriteria @see getTermCriteria
fn predict(&self, samples: &Mat, results: &mut Mat, flags: i32) -> Result<f32>
Predicts responses for input samples and returns a float type.
Parameters
- samples: The input data for the prediction algorithm. Matrix [m x n], where each row contains variables (features) of one object being classified. Should have data type CV_32F.
- results: Predicted labels as a column matrix of type CV_32S.
- flags: Not used.
C++ default parameters
- results: noArray()
- flags: 0
fn get_learnt_thetas(&self) -> Result<Mat>
This function returns the trained parameters arranged across rows.
For a two class classifcation problem, it returns a row matrix. It returns learnt parameters of the Logistic Regression as a matrix of type CV_32F.
Methods
impl<'_> dyn LogisticRegression + '_
[src]
pub fn create() -> Result<PtrOfLogisticRegression>
[src]
Creates empty model.
Creates Logistic Regression model with parameters given.
pub fn load(filepath: &str, node_name: &str) -> Result<PtrOfLogisticRegression>
[src]
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()