Package org.opencv.ml

Class LogisticRegression

java.lang.Object
org.opencv.core.Algorithm
org.opencv.ml.StatModel
org.opencv.ml.LogisticRegression
public class LogisticRegression
extends StatModel
Implements Logistic Regression classifier. SEE: REF: ml_intro_lr

  • Field Details

  • Constructor Details

  • Method Details

    • __fromPtr__

      public static LogisticRegression __fromPtr__​(long addr)

    • getLearningRate

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

    • setLearningRate

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

    • getIterations

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

    • setIterations

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

    • getRegularization

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

    • setRegularization

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

    • getTrainMethod

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

    • setTrainMethod

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

    • getMiniBatchSize

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

    • setMiniBatchSize

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

    • getTermCriteria

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

    • setTermCriteria

      public void setTermCriteria​(TermCriteria val)
      getTermCriteria SEE: getTermCriteria
      Parameters:
      val - 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

    • 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​(String filepath, 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​(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

    • finalize

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