Package org.opencv.ml

Class KNearest

java.lang.Object
org.opencv.core.Algorithm
org.opencv.ml.StatModel
org.opencv.ml.KNearest
public class KNearest
extends StatModel
The class implements K-Nearest Neighbors model SEE: REF: ml_intro_knn

  • Field Details

  • Constructor Details

  • Method Details

    • __fromPtr__

      public static KNearest __fromPtr__​(long addr)

    • getDefaultK

      public int getDefaultK()
      SEE: setDefaultK
      Returns:
      automatically generated

    • setDefaultK

      public void setDefaultK​(int val)
      getDefaultK SEE: getDefaultK
      Parameters:
      val - automatically generated

    • getIsClassifier

      public boolean getIsClassifier()
      SEE: setIsClassifier
      Returns:
      automatically generated

    • setIsClassifier

      public void setIsClassifier​(boolean val)
      getIsClassifier SEE: getIsClassifier
      Parameters:
      val - automatically generated

    • getEmax

      public int getEmax()
      SEE: setEmax
      Returns:
      automatically generated

    • setEmax

      public void setEmax​(int val)
      getEmax SEE: getEmax
      Parameters:
      val - automatically generated

    • getAlgorithmType

      public int getAlgorithmType()
      SEE: setAlgorithmType
      Returns:
      automatically generated

    • setAlgorithmType

      public void setAlgorithmType​(int val)
      getAlgorithmType SEE: getAlgorithmType
      Parameters:
      val - automatically generated

    • findNearest

      public float findNearest​(Mat samples, int k, Mat results, Mat neighborResponses, Mat dist)
      Finds the neighbors and predicts responses for input vectors.
      Parameters:
      samples - Input samples stored by rows. It is a single-precision floating-point matrix of <number_of_samples> * k size.
      k - Number of used nearest neighbors. Should be greater than 1.
      results - Vector with results of prediction (regression or classification) for each input sample. It is a single-precision floating-point vector with <number_of_samples> elements.
      neighborResponses - Optional output values for corresponding neighbors. It is a single- precision floating-point matrix of <number_of_samples> * k size.
      dist - Optional output distances from the input vectors to the corresponding neighbors. It is a single-precision floating-point matrix of <number_of_samples> * k size. For each input vector (a row of the matrix samples), the method finds the k nearest neighbors. In case of regression, the predicted result is a mean value of the particular vector's neighbor responses. In case of classification, the class is determined by voting. For each input vector, the neighbors are sorted by their distances to the vector. In case of C++ interface you can use output pointers to empty matrices and the function will allocate memory itself. If only a single input vector is passed, all output matrices are optional and the predicted value is returned by the method. The function is parallelized with the TBB library.
      Returns:
      automatically generated

    • findNearest

      public float findNearest​(Mat samples, int k, Mat results, Mat neighborResponses)
      Finds the neighbors and predicts responses for input vectors.
      Parameters:
      samples - Input samples stored by rows. It is a single-precision floating-point matrix of <number_of_samples> * k size.
      k - Number of used nearest neighbors. Should be greater than 1.
      results - Vector with results of prediction (regression or classification) for each input sample. It is a single-precision floating-point vector with <number_of_samples> elements.
      neighborResponses - Optional output values for corresponding neighbors. It is a single- precision floating-point matrix of <number_of_samples> * k size. is a single-precision floating-point matrix of <number_of_samples> * k size. For each input vector (a row of the matrix samples), the method finds the k nearest neighbors. In case of regression, the predicted result is a mean value of the particular vector's neighbor responses. In case of classification, the class is determined by voting. For each input vector, the neighbors are sorted by their distances to the vector. In case of C++ interface you can use output pointers to empty matrices and the function will allocate memory itself. If only a single input vector is passed, all output matrices are optional and the predicted value is returned by the method. The function is parallelized with the TBB library.
      Returns:
      automatically generated

    • findNearest

      public float findNearest​(Mat samples, int k, Mat results)
      Finds the neighbors and predicts responses for input vectors.
      Parameters:
      samples - Input samples stored by rows. It is a single-precision floating-point matrix of <number_of_samples> * k size.
      k - Number of used nearest neighbors. Should be greater than 1.
      results - Vector with results of prediction (regression or classification) for each input sample. It is a single-precision floating-point vector with <number_of_samples> elements. precision floating-point matrix of <number_of_samples> * k size. is a single-precision floating-point matrix of <number_of_samples> * k size. For each input vector (a row of the matrix samples), the method finds the k nearest neighbors. In case of regression, the predicted result is a mean value of the particular vector's neighbor responses. In case of classification, the class is determined by voting. For each input vector, the neighbors are sorted by their distances to the vector. In case of C++ interface you can use output pointers to empty matrices and the function will allocate memory itself. If only a single input vector is passed, all output matrices are optional and the predicted value is returned by the method. The function is parallelized with the TBB library.
      Returns:
      automatically generated

    • create

      public static KNearest create()
      Creates the empty model The static method creates empty %KNearest classifier. It should be then trained using StatModel::train method.
      Returns:
      automatically generated

    • load

      public static KNearest load​(String filepath)
      Loads and creates a serialized knearest from a file Use KNearest::save to serialize and store an KNearest to disk. Load the KNearest from this file again, by calling this function with the path to the file.
      Parameters:
      filepath - path to serialized KNearest
      Returns:
      automatically generated

    • finalize

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