LogisticRegression

Objective-C

@interface LogisticRegression : StatModel

Swift

class LogisticRegression : StatModel

Implements Logistic Regression classifier.

See

REF: ml_intro_lr

Member of Ml

Methods

  • This function returns the trained parameters arranged across rows.

     For a two class classification problem, it returns a row matrix. It returns learnt parameters of
 the Logistic Regression as a matrix of type CV_32F.

    Declaration

    Objective-C

    - (nonnull Mat *)get_learnt_thetas;

    Swift

    func get_learnt_thetas() -> Mat

  • Creates empty model.

     Creates Logistic Regression model with parameters given.

    Declaration

    Objective-C

    + (nonnull LogisticRegression *)create;

    Swift

    class func create() -> 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

    Declaration

    Objective-C

    + (nonnull LogisticRegression *)load:(nonnull NSString *)filepath
                            nodeName:(nonnull NSString *)nodeName;

    Swift

    class func load(filepath: String, nodeName: String) -> LogisticRegression

    Parameters

    filepath

    path to serialized LogisticRegression
    nodeName

    name of node containing the classifier

  • 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

    Declaration

    Objective-C

    + (nonnull LogisticRegression *)load:(nonnull NSString *)filepath;

    Swift

    class func load(filepath: String) -> LogisticRegression

    Parameters

    filepath

    path to serialized LogisticRegression

  • See

    -setTermCriteria:

    Declaration

    Objective-C

    - (nonnull TermCriteria *)getTermCriteria;

    Swift

    func getTermCriteria() -> TermCriteria

  • See

    -setLearningRate:

    Declaration

    Objective-C

    - (double)getLearningRate;

    Swift

    func getLearningRate() -> Double

  • Predicts responses for input samples and returns a float type.

    Declaration

    Objective-C

    - (float)predict:(nonnull Mat *)samples
         results:(nonnull Mat *)results
           flags:(int)flags;

    Swift

    func predict(samples: Mat, results: Mat, flags: Int32) -> Float

    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.

  • Predicts responses for input samples and returns a float type.

    Declaration

    Objective-C

    - (float)predict:(nonnull Mat *)samples results:(nonnull Mat *)results;

    Swift

    func predict(samples: Mat, results: Mat) -> Float

    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.

  • Predicts responses for input samples and returns a float type.

    Declaration

    Objective-C

    - (float)predict:(nonnull Mat *)samples;

    Swift

    func predict(samples: Mat) -> Float

    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.

  • See

    -setIterations:

    Declaration

    Objective-C

    - (int)getIterations;

    Swift

    func getIterations() -> Int32

  • See

    -setMiniBatchSize:

    Declaration

    Objective-C

    - (int)getMiniBatchSize;

    Swift

    func getMiniBatchSize() -> Int32

  • See

    -setRegularization:

    Declaration

    Objective-C

    - (int)getRegularization;

    Swift

    func getRegularization() -> Int32

  • See

    -setTrainMethod:

    Declaration

    Objective-C

    - (int)getTrainMethod;

    Swift

    func getTrainMethod() -> Int32

  • getIterations - see: -getIterations:

    Declaration

    Objective-C

    - (void)setIterations:(int)val;

    Swift

    func setIterations(val: Int32)

  • getLearningRate - see: -getLearningRate:

    Declaration

    Objective-C

    - (void)setLearningRate:(double)val;

    Swift

    func setLearningRate(val: Double)

  • getMiniBatchSize - see: -getMiniBatchSize:

    Declaration

    Objective-C

    - (void)setMiniBatchSize:(int)val;

    Swift

    func setMiniBatchSize(val: Int32)

  • getRegularization - see: -getRegularization:

    Declaration

    Objective-C

    - (void)setRegularization:(int)val;

    Swift

    func setRegularization(val: Int32)

  • getTermCriteria - see: -getTermCriteria:

    Declaration

    Objective-C

    - (void)setTermCriteria:(nonnull TermCriteria *)val;

    Swift

    func setTermCriteria(val: TermCriteria)

  • getTrainMethod - see: -getTrainMethod:

    Declaration

    Objective-C

    - (void)setTrainMethod:(int)val;

    Swift

    func setTrainMethod(val: Int32)