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minor changes in documentation

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This commit is contained in:
Alexander Shishkov
2012-04-13 07:31:10 +00:00
parent b6dac61e96
commit d77fb60017
4 changed files with 20 additions and 6 deletions
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8
modules/core/doc/clustering.rst
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@@ -11,9 +11,9 @@ Finds centers of clusters and groups input samples around the clusters.
.. ocv:pyfunction:: cv2.kmeans(data, K, criteria, attempts, flags[, bestLabels[, centers]]) -> retval, bestLabels, centers
.. ocv:cfunction:: int cvKMeans2(const CvArr* samples, int nclusters, 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 clusterCount, CvArr* labels, CvTermCriteria criteria, int attempts=1, CvRNG* rng=0, int flags=0, CvArr* centers=0, double* compactness=0)
.. ocv:pyoldfunction:: cv.KMeans2(samples, nclusters, labels, criteria)-> None
.. ocv:pyoldfunction:: cv.KMeans2(samples, clusterCount, labels, criteria)-> None
:param samples: Floating-point matrix of input samples, one row per sample.
@@ -25,6 +25,8 @@ Finds centers of clusters and groups input samples around the clusters.
:param attempts: Flag to specify the number of times the algorithm is executed using different initial labelings. 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.
@@ -35,6 +37,8 @@ Finds centers of clusters and groups input samples around the clusters.
: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 ``clusterCount`` clusters and groups the input samples
around the clusters. As an output,