Package org.opencv.ml
Class KNearest
java.lang.Object
org.opencv.core.Algorithm
org.opencv.ml.StatModel
org.opencv.ml.KNearest
The class implements K-Nearest Neighbors model
SEE: REF: ml_intro_knn
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Field Summary
FieldsFields inherited from class org.opencv.ml.StatModel
COMPRESSED_INPUT, PREPROCESSED_INPUT, RAW_OUTPUT, UPDATE_MODEL
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionstatic 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
SEE: setAlgorithmTypeint
SEE: setDefaultKint
getEmax()
SEE: setEmaxboolean
SEE: setIsClassifierstatic KNearest
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: getAlgorithmTypevoid
setDefaultK
(int val) getDefaultK SEE: getDefaultKvoid
setEmax
(int val) getEmax SEE: getEmaxvoid
setIsClassifier
(boolean val) getIsClassifier SEE: getIsClassifierMethods 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
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Field Details
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BRUTE_FORCE
public static final int BRUTE_FORCE- See Also:
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KDTREE
public static final int KDTREE- See Also:
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Constructor Details
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KNearest
protected KNearest(long addr)
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Method Details
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__fromPtr__
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getDefaultK
public int getDefaultK()SEE: setDefaultK- Returns:
- automatically generated
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setDefaultK
public void setDefaultK(int val) getDefaultK SEE: getDefaultK- Parameters:
val
- automatically generated
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getIsClassifier
public boolean getIsClassifier()SEE: setIsClassifier- Returns:
- automatically generated
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setIsClassifier
public void setIsClassifier(boolean val) getIsClassifier SEE: getIsClassifier- Parameters:
val
- automatically generated
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getEmax
public int getEmax()SEE: setEmax- Returns:
- automatically generated
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setEmax
public void setEmax(int val) getEmax SEE: getEmax- Parameters:
val
- automatically generated
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getAlgorithmType
public int getAlgorithmType()SEE: setAlgorithmType- Returns:
- automatically generated
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setAlgorithmType
public void setAlgorithmType(int val) getAlgorithmType SEE: getAlgorithmType- Parameters:
val
- automatically generated
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findNearest
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
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findNearest
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
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findNearest
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
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create
Creates the empty model The static method creates empty %KNearest classifier. It should be then trained using StatModel::train method.- Returns:
- automatically generated
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load
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
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finalize
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