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Merged the trunk r8467:8507 (inclusive) (big bunch of documentation fixes)

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Andrey Kamaev
2012-05-30 11:13:07 +00:00
parent 052d2dc23a
commit 81a5988015
120 changed files with 5407 additions and 4695 deletions
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10
modules/core/doc/clustering.rst
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@@ -7,17 +7,17 @@ kmeans
------
Finds centers of clusters and groups input samples around the clusters.
.. ocv:function:: double kmeans( InputArray samples, int clusterCount, InputOutputArray labels, TermCriteria criteria, int attempts, int flags, OutputArray centers=noArray() )
.. 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 clusterCount, CvArr* labels, CvTermCriteria criteria, int attempts=1, CvRNG* rng=0, int flags=0, CvArr* centers=0, double* compactness=0)
.. 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, clusterCount, labels, criteria)-> None
.. 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 clusterCount: Number of clusters to split the set by.
: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.
@@ -40,7 +40,7 @@ Finds centers of clusters and groups input samples around the clusters.
:param compactness: The returned value that is described below.
The function ``kmeans`` implements a k-means algorithm that finds the
centers of ``clusterCount`` clusters and groups the input samples
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