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 &centers)

    :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(oclMat &dists, oclMat &labels, const oclMat &src, const oclMat &centers, int distType = NORM_L2SQR, const oclMat &indices = oclMat())

    :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.

    :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 indices: Optional source indices. If not empty:

            * only the indexed source samples will be processed
            * outputs, i.e., ``dists`` and ``labels``, have the same size of indices
            * outputs are in the same order of indices instead of the order of src

The method is a utility function which maybe used for multiple clustering algorithms such as K-means.