ml.Machine Learning — OpenCV 2.4.13.0 documentation

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# ml.Machine Learning

## ocl::KNearestNeighbour

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.

C++:`` 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)

  Parameters: | isRegression – Type of the problem: `true` for regression and `false` for classification. maxK – Number of maximum neighbors that may be passed to the method `CvKNearest::find_nearest()`. 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 `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.

C++:`` void `ocl::KNearestNeighbour::``find_nearest`(const oclMat& samples, int k, oclMat& lables)

  Parameters: | samples – Input samples stored by rows. It is a single-precision floating-point matrix of number\_of\_samples \times number\_of\_features size. k – Number of used nearest neighbors. It must satisfy constraint: k \le `CvKNearest::get_max_k()`. 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.

C++:`` double `ocl::``kmeans`(const oclMat& src, int K, oclMat& bestLabels, TermCriteria criteria, int attemps, int flags, oclMat& centers)

  Parameters: | src – Floating-point matrix of input samples, one row per sample. K – Number of clusters to split the set by. bestLabels – Input/output integer array that stores the cluster indices for every sample. 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. 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). 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. 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`.

C++:`` void `ocl::``distanceToCenters`(const oclMat& src, const oclMat& centers, Mat& dists, Mat& labels, int distType=NORM_L2SQR)

  Parameters: | src – Floating-point matrix of input samples. One row per sample. centers – Floating-point matrix of center candidates. One row per center. distType – Distance metric to calculate distances. Supports `NORM_L1` and `NORM_L2SQR`. dists – The output distances calculated from each sample to the best matched center. 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.

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