ml.Machine Learning
=============================
.. highlight:: cpp
ocl::KNearestNeighbour
--------------------------
.. ocv:class:: ocl::KNearestNeighbour : public ocl::CvKNearest
The class implements K-Nearest Neighbors model as described in the beginning of this section.
ocl::KNearestNeighbour
--------------------------
Computes the weighted sum of two arrays. ::
class CV_EXPORTS KNearestNeighbour: public CvKNearest
{
public:
KNearestNeighbour();
~KNearestNeighbour();
bool train(const Mat& trainData, Mat& labels, Mat& sampleIdx = Mat().setTo(Scalar::all(0)),
bool isRegression = false, int max_k = 32, bool updateBase = false);
void clear();
void find_nearest(const oclMat& samples, int k, oclMat& lables);
private:
/* hidden */
};
ocl::KNearestNeighbour::train
---------------------------------
Trains the model.
.. ocv:function:: bool ocl::KNearestNeighbour::train(const Mat& trainData, Mat& labels, Mat& sampleIdx = Mat().setTo(Scalar::all(0)), bool isRegression = false, int max_k = 32, bool updateBase = false)
:param isRegression: Type of the problem: ``true`` for regression and ``false`` for classification.
:param maxK: Number of maximum neighbors that may be passed to the method :ocv:func:`CvKNearest::find_nearest`.
:param updateBase: Specifies whether the model is trained from scratch (``update_base=false``), or it is updated using the new training data (``update_base=true``). In the latter case, the parameter ``maxK`` must not be larger than the original value.
The method trains the K-Nearest model. It follows the conventions of the generic :ocv:func:`CvStatModel::train` approach with the following limitations:
* Only ``CV_ROW_SAMPLE`` data layout is supported.
* Input variables are all ordered.
* Output variables can be either categorical ( ``is_regression=false`` ) or ordered ( ``is_regression=true`` ).
* Variable subsets (``var_idx``) and missing measurements are not supported.
ocl::KNearestNeighbour::find_nearest
----------------------------------------
Finds the neighbors and predicts responses for input vectors.
.. ocv:function:: void ocl::KNearestNeighbour::find_nearest(const oclMat& samples, int k, oclMat& lables )
:param samples: Input samples stored by rows. It is a single-precision floating-point matrix of :math:`number\_of\_samples \times number\_of\_features` size.
:param k: Number of used nearest neighbors. It must satisfy constraint: :math:`k \le` :ocv:func:`CvKNearest::get_max_k`.
:param labels: 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.
ocl::kmeans
---------------
Finds centers of clusters and groups input samples around the clusters.
.. ocv:function:: double ocl::kmeans(const oclMat &src, int K, oclMat &bestLabels, TermCriteria criteria, int attemps, int flags, oclMat ¢ers)
:param src: Floating-point matrix of input samples, one row per sample.
:param K: Number of clusters to split the set by.
:param bestLabels: Input/output integer array that stores the cluster indices for every sample.
:param criteria: The algorithm termination criteria, that is, the maximum number of iterations and/or the desired accuracy. The accuracy is specified as ``criteria.epsilon``. As soon as each of the cluster centers moves by less than ``criteria.epsilon`` on some iteration, the algorithm stops.
:param attempts: Flag to specify the number of times the algorithm is executed using different initial labellings. The algorithm returns the labels that yield the best compactness (see the last function parameter).
:param flags: Flag that can take the following values:
* **KMEANS_RANDOM_CENTERS** Select random initial centers in each attempt.
* **KMEANS_PP_CENTERS** Use ``kmeans++`` center initialization by Arthur and Vassilvitskii [Arthur2007].
* **KMEANS_USE_INITIAL_LABELS** During the first (and possibly the only) attempt, use the user-supplied labels instead of computing them from the initial centers. For the second and further attempts, use the random or semi-random centers. Use one of ``KMEANS_*_CENTERS`` flag to specify the exact method.
:param centers: Output matrix of the cluster centers, one row per each cluster center.
ocl::distanceToCenters
----------------------
For each samples in ``source``, find its closest neighour in ``centers``.
.. ocv:function:: void ocl::distanceToCenters(const oclMat &src, const oclMat ¢ers, Mat &dists, Mat &labels, int distType = NORM_L2SQR)
:param src: Floating-point matrix of input samples. One row per sample.
:param centers: Floating-point matrix of center candidates. One row per center.
:param distType: Distance metric to calculate distances. Supports ``NORM_L1`` and ``NORM_L2SQR``.
:param dists: The output distances calculated from each sample to the best matched center.
:param labels: The output index of best matched center for each row of sample.
The method is a utility function which maybe used for multiple clustering algorithms such as K-means.