68a8a11161
It supports NORM_L1 distance types now and can use user provided indices. Also fixed a bug of kmeans where distance pointers should be float instead of double. NORM_L2 changed to NORM_L2SQR, Accuracy and Perf tests are added added ROI support in accuracy test of distanceToCenters
113 lines
5.4 KiB
ReStructuredText
113 lines
5.4 KiB
ReStructuredText
ml.Machine Learning
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=============================
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.. highlight:: cpp
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ocl::KNearestNeighbour
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--------------------------
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.. ocv:class:: ocl::KNearestNeighbour : public ocl::CvKNearest
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The class implements K-Nearest Neighbors model as described in the beginning of this section.
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ocl::KNearestNeighbour
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--------------------------
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Computes the weighted sum of two arrays. ::
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class CV_EXPORTS KNearestNeighbour: public CvKNearest
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{
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public:
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KNearestNeighbour();
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~KNearestNeighbour();
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bool train(const Mat& trainData, Mat& labels, Mat& sampleIdx = Mat().setTo(Scalar::all(0)),
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bool isRegression = false, int max_k = 32, bool updateBase = false);
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void clear();
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void find_nearest(const oclMat& samples, int k, oclMat& lables);
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private:
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/* hidden */
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};
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ocl::KNearestNeighbour::train
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---------------------------------
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Trains the model.
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.. 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)
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:param isRegression: Type of the problem: ``true`` for regression and ``false`` for classification.
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:param maxK: Number of maximum neighbors that may be passed to the method :ocv:func:`CvKNearest::find_nearest`.
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: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.
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The method trains the K-Nearest model. It follows the conventions of the generic :ocv:func:`CvStatModel::train` approach with the following limitations:
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* Only ``CV_ROW_SAMPLE`` data layout is supported.
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* Input variables are all ordered.
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* Output variables can be either categorical ( ``is_regression=false`` ) or ordered ( ``is_regression=true`` ).
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* Variable subsets (``var_idx``) and missing measurements are not supported.
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ocl::KNearestNeighbour::find_nearest
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----------------------------------------
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Finds the neighbors and predicts responses for input vectors.
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.. ocv:function:: void ocl::KNearestNeighbour::find_nearest(const oclMat& samples, int k, oclMat& lables )
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: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.
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:param k: Number of used nearest neighbors. It must satisfy constraint: :math:`k \le` :ocv:func:`CvKNearest::get_max_k`.
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: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.
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ocl::kmeans
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---------------
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Finds centers of clusters and groups input samples around the clusters.
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.. ocv:function:: double ocl::kmeans(const oclMat &src, int K, oclMat &bestLabels, TermCriteria criteria, int attemps, int flags, oclMat ¢ers)
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:param src: Floating-point matrix of input samples, one row per sample.
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:param K: Number of clusters to split the set by.
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:param bestLabels: Input/output integer array that stores the cluster indices for every sample.
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: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.
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: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).
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:param flags: Flag that can take the following values:
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* **KMEANS_RANDOM_CENTERS** Select random initial centers in each attempt.
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* **KMEANS_PP_CENTERS** Use ``kmeans++`` center initialization by Arthur and Vassilvitskii [Arthur2007].
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* **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.
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:param centers: Output matrix of the cluster centers, one row per each cluster center.
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ocl::distanceToCenters
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----------------------
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For each samples in ``source``, find its closest neighour in ``centers``.
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.. ocv:function:: void ocl::distanceToCenters(oclMat &dists, oclMat &labels, const oclMat &src, const oclMat ¢ers, int distType = NORM_L2SQR, const oclMat &indices = oclMat())
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:param dists: The output distances calculated from each sample to the best matched center.
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:param labels: The output index of best matched center for each row of sample.
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:param src: Floating-point matrix of input samples. One row per sample.
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:param centers: Floating-point matrix of center candidates. One row per center.
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:param distType: Distance metric to calculate distances. Supports ``NORM_L1`` and ``NORM_L2SQR``.
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:param indices: Optional source indices. If not empty:
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* only the indexed source samples will be processed
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* outputs, i.e., ``dists`` and ``labels``, have the same size of indices
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* outputs are in the same order of indices instead of the order of src
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The method is a utility function which maybe used for multiple clustering algorithms such as K-means.
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