Package org.opencv.ml

Class KNearest

  • public class KNearest
extends StatModel
    The class implements K-Nearest Neighbors model SEE: REF: ml_intro_knn

    • Constructor Detail

      • KNearest

        protected KNearest​(long addr)

    • Method Detail

      • __fromPtr__

        public static KNearest __fromPtr__​(long addr)

      • 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​(java.lang.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

      • getIsClassifier

        public boolean getIsClassifier()
        SEE: setIsClassifier
        Returns:
        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

      • getAlgorithmType

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

      • getDefaultK

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

      • getEmax

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

      • setAlgorithmType

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

      • setDefaultK

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

      • setEmax

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

      • setIsClassifier

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

      • finalize

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