Package org.opencv.ml

## Class KNearest

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

Fields
Modifier and Type Field Description
static int BRUTE_FORCE
static int KDTREE
• ### Fields inherited from class org.opencv.ml.StatModel

COMPRESSED_INPUT, PREPROCESSED_INPUT, RAW_OUTPUT, UPDATE_MODEL
• ### Fields inherited from class org.opencv.core.Algorithm

nativeObj
• ### Constructor Summary

Constructors
Modifier Constructor Description
protected  KNearest​(long addr)
• ### Method Summary

All Methods
Modifier and Type Method Description
static KNearest __fromPtr__​(long addr)
static KNearest create()
Creates the empty model The static method creates empty %KNearest classifier.
protected void finalize()
float findNearest​(Mat samples, int k, Mat results)
Finds the neighbors and predicts responses for input vectors.
float findNearest​(Mat samples, int k, Mat results, Mat neighborResponses)
Finds the neighbors and predicts responses for input vectors.
float findNearest​(Mat samples, int k, Mat results, Mat neighborResponses, Mat dist)
Finds the neighbors and predicts responses for input vectors.
int getAlgorithmType()
SEE: setAlgorithmType
int getDefaultK()
SEE: setDefaultK
int getEmax()
SEE: setEmax
boolean getIsClassifier()
SEE: setIsClassifier
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.
void setAlgorithmType​(int val)
getAlgorithmType SEE: getAlgorithmType
void setDefaultK​(int val)
getDefaultK SEE: getDefaultK
void setEmax​(int val)
getEmax SEE: getEmax
void setIsClassifier​(boolean val)
getIsClassifier SEE: getIsClassifier
• ### Methods inherited from class org.opencv.ml.StatModel

calcError, empty, getVarCount, isClassifier, isTrained, predict, predict, predict, train, train, train
• ### Methods inherited from class org.opencv.core.Algorithm

clear, getDefaultName, getNativeObjAddr, save
• ### Methods inherited from class java.lang.Object

clone, equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
• ### Field Detail

• #### BRUTE_FORCE

public static final int BRUTE_FORCE
Constant Field Values
• #### KDTREE

public static final int KDTREE
Constant Field Values
• ### Constructor Detail

• #### KNearest

protected KNearest​(long addr)
• ### Method Detail

• #### __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 &lt;number_of_samples&gt; * 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 &lt;number_of_samples&gt; elements.
neighborResponses - Optional output values for corresponding neighbors. It is a single- precision floating-point matrix of &lt;number_of_samples&gt; * k size.
dist - Optional output distances from the input vectors to the corresponding neighbors. It is a single-precision floating-point matrix of &lt;number_of_samples&gt; * 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 &lt;number_of_samples&gt; * 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 &lt;number_of_samples&gt; elements.
neighborResponses - Optional output values for corresponding neighbors. It is a single- precision floating-point matrix of &lt;number_of_samples&gt; * k size. is a single-precision floating-point matrix of &lt;number_of_samples&gt; * 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 &lt;number_of_samples&gt; * 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 &lt;number_of_samples&gt; elements. precision floating-point matrix of &lt;number_of_samples&gt; * k size. is a single-precision floating-point matrix of &lt;number_of_samples&gt; * 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

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
• #### finalize

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