Class cv::ml::KNearest#

The class implements K-Nearest Neighbors model. View details

#include <opencv2/ml.hpp>

Collaboration diagram for cv::ml::KNearest:

Public Types#

Implementations of KNearest algorithm.

enum Types {
    BRUTE_FORCE =1,
    KDTREE =2
}

Public Member Functions#

Public Member Functions inherited from cv::ml::StatModel

Return	Name	Description
`float`	`calcError(` `const Ptr< TrainData > & data,` `bool test,` `OutputArray resp )`	Computes error on the training or test dataset.
`bool`	`empty()`	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
`int`	`getVarCount()`	Returns the number of variables in training samples.
`bool`	`isClassifier()`	Returns true if the model is classifier.
`bool`	`isTrained()`	Returns true if the model is trained.
`float`	`predict(` `InputArray samples,` `OutputArray results = noArray(),` `int flags = 0 )`	Predicts response(s) for the provided sample(s)
`bool`	`train(` `const Ptr< TrainData > & trainData,` `int flags = 0 )`	Trains the statistical model.
`bool`	`train(` `InputArray samples,` `int layout,` `InputArray responses )`	Trains the statistical model.

Public Member Functions inherited from cv::Algorithm

Return	Name	Description
	`Algorithm()`
	`~Algorithm()`
`void`	`clear()`	Clears the algorithm state.
`bool`	`empty()`	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
`String`	`getDefaultName()`
`void`	`read(const FileNode & fn)`	Reads algorithm parameters from a file storage.
`void`	`save(const String & filename)`
`void`	`write(` `const Ptr< FileStorage > & fs,` `const String & name = String() )`
`void`	`write(FileStorage & fs)`	Stores algorithm parameters in a file storage.
`void`	`write(` `FileStorage & fs,` `const String & name )`

Static Public Member Functions#

Static Public Member Functions inherited from cv::ml::StatModel

Return	Name	Description
`static Ptr< _Tp >`	`train(` `const Ptr< TrainData > & data,` `int flags = 0 )`	Create and train model with default parameters.

Static Public Member Functions inherited from cv::Algorithm

Return	Name	Description
`static Ptr< _Tp >`	`load(` `const String & filename,` `const String & objname = String() )`	Loads algorithm from the file.
`static Ptr< _Tp >`	`loadFromString(` `const String & strModel,` `const String & objname = String() )`	Loads algorithm from a String.
`static Ptr< _Tp >`	`read(const FileNode & fn)`	Reads algorithm from the file node.

Additional Inherited Members#

Detailed Description#

The class implements K-Nearest Neighbors model.

See also

ml_intro_knn

Member Enumeration Documentation#

enum Types

Implementations of KNearest algorithm.

BRUTE_FORCE
Python: cv.ml.KNearest_BRUTE_FORCE

KDTREE
Python: cv.ml.KNearest_KDTREE

Member Function Documentation#

findNearest()#

float cv::ml::KNearest::findNearest(
InputArray samples,
int k,
OutputArray results,
OutputArray neighborResponses = noArray(),
OutputArray dist = noArray() )

Python:

cv.ml.KNearest.findNearest(samples, k[, results[, neighborResponses[, dist]]]) -> retval, results, neighborResponses, dist

Finds the neighbors and predicts responses for input vectors.

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.

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.

Here is the call graph for this function:

getAlgorithmType()#

int cv::ml::KNearest::getAlgorithmType()

Python:

cv.ml.KNearest.getAlgorithmType() -> retval

Algorithm type, one of KNearest::Types.

See also

setAlgorithmType

getDefaultK()#

int cv::ml::KNearest::getDefaultK()

Python:

cv.ml.KNearest.getDefaultK() -> retval

Default number of neighbors to use in predict method.

See also

setDefaultK

getEmax()#

int cv::ml::KNearest::getEmax()

Python:

cv.ml.KNearest.getEmax() -> retval

Parameter for KDTree implementation.

See also

setEmax

getIsClassifier()#

bool cv::ml::KNearest::getIsClassifier()

Python:

cv.ml.KNearest.getIsClassifier() -> retval

Whether classification or regression model should be trained.

See also

setIsClassifier

setAlgorithmType()#

void cv::ml::KNearest::setAlgorithmType(int val)

Python:

cv.ml.KNearest.setAlgorithmType(val)

See also

getAlgorithmType

setDefaultK()#

void cv::ml::KNearest::setDefaultK(int val)

Python:

cv.ml.KNearest.setDefaultK(val)

See also

getDefaultK

setEmax()#

void cv::ml::KNearest::setEmax(int val)

Python:

cv.ml.KNearest.setEmax(val)

See also

getEmax

setIsClassifier()#

void cv::ml::KNearest::setIsClassifier(bool val)

Python:

cv.ml.KNearest.setIsClassifier(val)

See also

getIsClassifier

create()#

static Ptr< KNearest > cv::ml::KNearest::create()

Python:

cv.ml.KNearest.create() -> retval

cv.ml.KNearest_create() -> retval

Creates the empty model.

The static method creates empty KNearest classifier. It should be then trained using StatModel::train method.

load()#

static Ptr< KNearest > cv::ml::KNearest::load(const String & filepath)

Python:

cv.ml.KNearest.load(filepath) -> retval

cv.ml.KNearest_load(filepath) -> retval

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

Source file#

The documentation for this class was generated from the following file:

opencv2/ml.hpp