82 lines
4.3 KiB
ReStructuredText
82 lines
4.3 KiB
ReStructuredText
Clustering
|
|
==========
|
|
|
|
.. highlight:: cpp
|
|
|
|
kmeans
|
|
------
|
|
Finds centers of clusters and groups input samples around the clusters.
|
|
|
|
.. ocv:function:: double kmeans( InputArray data, int K, InputOutputArray bestLabels, TermCriteria criteria, int attempts, int flags, OutputArray centers=noArray() )
|
|
|
|
.. ocv:pyfunction:: cv2.kmeans(data, K, criteria, attempts, flags[, bestLabels[, centers]]) -> retval, bestLabels, centers
|
|
|
|
.. ocv:cfunction:: int cvKMeans2( const CvArr* samples, int cluster_count, CvArr* labels, CvTermCriteria termcrit, int attempts=1, CvRNG* rng=0, int flags=0, CvArr* _centers=0, double* compactness=0 )
|
|
|
|
.. ocv:pyoldfunction:: cv.KMeans2(samples, nclusters, labels, termcrit, attempts=1, flags=0, centers=None) -> float
|
|
|
|
:param samples: Floating-point matrix of input samples, one row per sample.
|
|
|
|
:param cluster_count: Number of clusters to split the set by.
|
|
|
|
:param labels: 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 rng: CvRNG state initialized by RNG().
|
|
|
|
: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.
|
|
|
|
:param compactness: The returned value that is described below.
|
|
|
|
The function ``kmeans`` implements a k-means algorithm that finds the
|
|
centers of ``cluster_count`` clusters and groups the input samples
|
|
around the clusters. As an output,
|
|
:math:`\texttt{labels}_i` contains a 0-based cluster index for
|
|
the sample stored in the
|
|
:math:`i^{th}` row of the ``samples`` matrix.
|
|
|
|
The function returns the compactness measure that is computed as
|
|
|
|
.. math::
|
|
|
|
\sum _i \| \texttt{samples} _i - \texttt{centers} _{ \texttt{labels} _i} \| ^2
|
|
|
|
after every attempt. The best (minimum) value is chosen and the
|
|
corresponding labels and the compactness value are returned by the function.
|
|
Basically, you can use only the core of the function, set the number of
|
|
attempts to 1, initialize labels each time using a custom algorithm, pass them with the
|
|
( ``flags`` = ``KMEANS_USE_INITIAL_LABELS`` ) flag, and then choose the best (most-compact) clustering.
|
|
|
|
partition
|
|
-------------
|
|
Splits an element set into equivalency classes.
|
|
|
|
.. ocv:function:: template<typename _Tp, class _EqPredicate> int partition( const vector<_Tp>& vec, vector<int>& labels, _EqPredicate predicate=_EqPredicate())
|
|
|
|
:param vec: Set of elements stored as a vector.
|
|
|
|
:param labels: Output vector of labels. It contains as many elements as ``vec``. Each label ``labels[i]`` is a 0-based cluster index of ``vec[i]`` .
|
|
|
|
:param predicate: Equivalence predicate (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 in the same class, and returns ``false`` if they may or may not be in the same class.
|
|
|
|
The generic function ``partition`` implements an
|
|
:math:`O(N^2)` algorithm for
|
|
splitting a set of
|
|
:math:`N` elements into one or more equivalency classes, as described in
|
|
http://en.wikipedia.org/wiki/Disjoint-set_data_structure
|
|
. The function
|
|
returns the number of equivalency classes.
|
|
|
|
.. [Arthur2007] Arthur and S. Vassilvitskii. k-means++: the advantages of careful seeding, Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, 2007
|