ml_KNearest
import { ml_KNearest } from '@banou/opencv-wasm'Use after await initOpenCV(). See the initialization and named imports guide.
Native object: release it with using or delete(). Factories can return null; check before calling methods. Inherits ml_StatModel.
K-Nearest Neighbour Classifier *
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The class implements K-Nearest Neighbors model
See: ml_intro_knn
Constructors and members
static create
Creates the empty model
The static method creates empty %KNearest classifier. It should be then trained using StatModel::train method.
create(): ml_KNearest | null;The ml_KNearest | null result.
static 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.
load(filepath: EmbindString): ml_KNearest | null;filepathpath to serialized KNearest
The ml_KNearest | null result.
clone
Create another handle to the same native object. This retains the object without copying its pixels or algorithm state; dispose both handles separately.
clone(): this;The this result.
getDefaultK
Default number of neighbors to use in predict method. See: setDefaultK
getDefaultK(): number;The number result.
setDefaultK
Default number of neighbors to use in predict method. See: setDefaultK See: getDefaultK
setDefaultK(val: number): void;valval argument (number).
getIsClassifier
Whether classification or regression model should be trained. See: setIsClassifier
getIsClassifier(): boolean;The boolean result.
setIsClassifier
Whether classification or regression model should be trained. See: setIsClassifier See: getIsClassifier
setIsClassifier(val: boolean): void;valval argument (boolean).
getEmax
Parameter for KDTree implementation. See: setEmax
getEmax(): number;The number result.
setEmax
Parameter for KDTree implementation. See: setEmax See: getEmax
setEmax(val: number): void;valval argument (number).
getAlgorithmType
%Algorithm type, one of KNearest::Types. See: setAlgorithmType
getAlgorithmType(): number;The number result.
setAlgorithmType
%Algorithm type, one of KNearest::Types. See: setAlgorithmType See: getAlgorithmType
setAlgorithmType(val: number): void;valval argument (number).
findNearest
Finds the neighbors and predicts responses for input vectors.
findNearest(samples: Mat, k: number, results: Mat, neighborResponses: Mat, dist: Mat): number;3 available overloads
findNearest(samples: Mat, k: number, results: Mat): number;findNearest(samples: Mat, k: number, results: Mat, neighborResponses: Mat): number;findNearest(samples: Mat, k: number, results: Mat, neighborResponses: Mat, dist: Mat): number;samplesInput samples stored by rows. It is a single-precision floating-point matrix of
<number_of_samples> * ksize.kNumber of used nearest neighbors. Should be greater than 1.
resultsOutput destination, filled by the native operation. 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.neighborResponsesOutput destination, filled by the native operation. Optional output values for corresponding neighbors. It is a single- precision floating-point matrix of
<number_of_samples> * ksize.distOutput destination, filled by the native operation. Optional output distances from the input vectors to the corresponding neighbors. It is a single-precision floating-point matrix of
<number_of_samples> * ksize.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.
The number result.
These signatures describe this package. Upstream documentation can mention optional backends that are absent from this build. Check runtime compatibility before choosing a backend or file format.