KNearest Class

Creates instance by raw pointer cv::ml::KNearest*

Algorithm type, one of KNearest::Types.

Gets or sets a memory address allocated by AllocMemory.

Gets or sets the byte length of the allocated memory

Native pointer of OpenCV structure

Gets or sets a handle which allocates using cvSetData.

Default number of neighbors to use in predict method.

Parameter for KDTree implementation

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

Whether classification or regression model should be trained.

Gets a value indicating whether this instance has been disposed.

Gets or sets a value indicating whether you permit disposing this instance.

Pins the object to be allocated by cvSetData.

Allocates the specified size of memory.

Computes error on the training or test dataset

Creates the empty model

Releases the resources

Releases the resources

Releases managed resources

releases unmanaged resources

(Inherited from StatModel.)

Determines whether the specified object is equal to the current object.

Destructor

Finds the neighbors and predicts responses for input vectors.

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

Serves as the default hash function.

Gets the Type of the current instance.

Returns the number of variables in training samples

Returns true if the model is classifier

Returns true if the model is trained

Loads and creates a serialized model from a file.

Loads algorithm from a String.

Creates a shallow copy of the current Object.

Notifies the allocated size of memory.

Predicts response(s) for the provided sample(s)

Reads algorithm parameters from a file storage

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage fs).

If this object is disposed, then ObjectDisposedException is thrown.

Returns a string that represents the current object.

Trains the statistical model

Trains the statistical model

Stores algorithm parameters in a file storage

Data pointer