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61 lines
1.1 KiB
ReStructuredText
61 lines
1.1 KiB
ReStructuredText
Clustering
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==========
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.. highlight:: python
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.. index:: KMeans2
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.. _KMeans2:
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KMeans2
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-------
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.. function:: KMeans2(samples,nclusters,labels,termcrit)-> None
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Splits set of vectors by a given number of clusters.
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:param samples: Floating-point matrix of input samples, one row per sample
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:type samples: :class:`CvArr`
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:param nclusters: Number of clusters to split the set by
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:type nclusters: int
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:param labels: Output integer vector storing cluster indices for every sample
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:type labels: :class:`CvArr`
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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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:type termcrit: :class:`CvTermCriteria`
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The function
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``cvKMeans2``
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implements a k-means algorithm that finds the
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centers of
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``nclusters``
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clusters and groups the input samples
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around the clusters. On output,
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:math:`\texttt{labels}_i`
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contains a cluster index for
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samples stored in the i-th row of the
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``samples``
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matrix.
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