77 lines
3.6 KiB
ReStructuredText
77 lines
3.6 KiB
ReStructuredText
Clustering
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==========
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.. index:: kmeans
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.. _kmeans:
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kmeans
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------
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.. c:function:: double kmeans( const Mat\& samples, int clusterCount, Mat\& labels, TermCriteria termcrit, int attempts, int flags, Mat* centers )
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Finds the centers of clusters and groups the input samples around the clusters.
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:param samples: Floating-point matrix of input samples, one row per sample
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:param clusterCount: The number of clusters to split the set by
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:param labels: The input/output integer array that will store the cluster indices for every sample
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:param termcrit: Specifies maximum number of iterations and/or accuracy (distance the centers can move by between subsequent iterations)
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:param attempts: How many times the algorithm is executed using different initial labelings. The algorithm returns the labels that yield the best compactness (see the last function parameter)
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:param flags: It can take the following values:
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* **KMEANS_RANDOM_CENTERS** Random initial centers are selected in each attempt
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* **KMEANS_PP_CENTERS** Use kmeans++ center initialization by Arthur and Vassilvitskii
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* **KMEANS_USE_INITIAL_LABELS** During the first (and possibly the only) attempt, the
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function uses the user-supplied labels instaed of computing them from the initial centers. For the second and further attempts, the function will use the random or semi-random centers (use one of ``KMEANS_*_CENTERS`` flag to specify the exact method)
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:param centers: The output matrix of the cluster centers, one row per each cluster center
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The function ``kmeans`` implements a k-means algorithm that finds the
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centers of ``clusterCount`` clusters and groups the input samples
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around the clusters. On output,
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:math:`\texttt{labels}_i` contains a 0-based cluster index for
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the sample stored in the
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:math:`i^{th}` row of the ``samples`` matrix.
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The function returns the compactness measure, which is computed as
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.. math::
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\sum _i \| \texttt{samples} _i - \texttt{centers} _{ \texttt{labels} _i} \| ^2
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after every attempt; the best (minimum) value is chosen and the
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corresponding labels and the compactness value are returned by the function.
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Basically, the user can use only the core of the function, set the number of
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attempts to 1, initialize labels each time using some custom algorithm and pass them with
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( ``flags`` = ``KMEANS_USE_INITIAL_LABELS`` ) flag, and then choose the best (most-compact) clustering.
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.. index:: partition
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partition
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-------------
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.. c:function:: template<typename _Tp, class _EqPredicate> int
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.. c:function:: partition( const vector<_Tp>\& vec, vector<int>\& labels, _EqPredicate predicate=_EqPredicate())
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Splits an element set into equivalency classes.
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:param vec: The set of elements stored as a vector
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:param labels: The output vector of labels; will contain as many elements as ``vec`` . Each label ``labels[i]`` is 0-based cluster index of ``vec[i]`` :param predicate: The equivalence predicate (i.e. pointer to a boolean function of two arguments or an instance of the class that has the method ``bool operator()(const _Tp& a, const _Tp& b)`` . The predicate returns true when the elements are certainly if the same class, and false if they may or may not be in the same class
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The generic function ``partition`` implements an
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:math:`O(N^2)` algorithm for
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splitting a set of
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:math:`N` elements into one or more equivalency classes, as described in
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http://en.wikipedia.org/wiki/Disjoint-set_data_structure
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. The function
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returns the number of equivalency classes.
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