Package org.opencv.ml

Class LogisticRegression


  • public class LogisticRegression
    extends StatModel
    Implements Logistic Regression classifier. SEE: REF: ml_intro_lr
    • Constructor Detail

      • LogisticRegression

        protected LogisticRegression​(long addr)
    • Method Detail

      • get_learnt_thetas

        public Mat get_learnt_thetas()
        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.
        Returns:
        automatically generated
      • create

        public static LogisticRegression create()
        Creates empty model. Creates Logistic Regression model with parameters given.
        Returns:
        automatically generated
      • load

        public static LogisticRegression load​(java.lang.String filepath,
                                              java.lang.String nodeName)
        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
        Returns:
        automatically generated
      • load

        public static LogisticRegression load​(java.lang.String filepath)
        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
        Returns:
        automatically generated
      • getTermCriteria

        public TermCriteria getTermCriteria()
        SEE: setTermCriteria
        Returns:
        automatically generated
      • getLearningRate

        public double getLearningRate()
        SEE: setLearningRate
        Returns:
        automatically generated
      • predict

        public float predict​(Mat samples,
                             Mat results,
                             int flags)
        Predicts responses for input samples and returns a float type.
        Overrides:
        predict in class StatModel
        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.
        Returns:
        automatically generated
      • predict

        public float predict​(Mat samples,
                             Mat results)
        Predicts responses for input samples and returns a float type.
        Overrides:
        predict in class StatModel
        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.
        Returns:
        automatically generated
      • predict

        public float predict​(Mat samples)
        Predicts responses for input samples and returns a float type.
        Overrides:
        predict in class StatModel
        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.
        Returns:
        automatically generated
      • getIterations

        public int getIterations()
        SEE: setIterations
        Returns:
        automatically generated
      • getMiniBatchSize

        public int getMiniBatchSize()
        SEE: setMiniBatchSize
        Returns:
        automatically generated
      • getRegularization

        public int getRegularization()
        SEE: setRegularization
        Returns:
        automatically generated
      • getTrainMethod

        public int getTrainMethod()
        SEE: setTrainMethod
        Returns:
        automatically generated
      • setIterations

        public void setIterations​(int val)
        getIterations SEE: getIterations
        Parameters:
        val - automatically generated
      • setLearningRate

        public void setLearningRate​(double val)
        getLearningRate SEE: getLearningRate
        Parameters:
        val - automatically generated
      • setMiniBatchSize

        public void setMiniBatchSize​(int val)
        getMiniBatchSize SEE: getMiniBatchSize
        Parameters:
        val - automatically generated
      • setRegularization

        public void setRegularization​(int val)
        getRegularization SEE: getRegularization
        Parameters:
        val - automatically generated
      • setTermCriteria

        public void setTermCriteria​(TermCriteria val)
        getTermCriteria SEE: getTermCriteria
        Parameters:
        val - automatically generated
      • setTrainMethod

        public void setTrainMethod​(int val)
        getTrainMethod SEE: getTrainMethod
        Parameters:
        val - automatically generated
      • finalize

        protected void finalize()
                         throws java.lang.Throwable
        Overrides:
        finalize in class StatModel
        Throws:
        java.lang.Throwable