Purpose: 2nd review cycle.
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@ -22,7 +22,7 @@ kmeans
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:param termcrit: Flag to specify the maximum number of iterations and/or the desired accuracy. The accuracy is specified as ``termcrit.epsilon``. As soon as each of the cluster centers moves by less than ``termcrit.epsilon`` on some iteration, the algorithm stops.
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:param termcrit: Flag to specify the maximum number of iterations and/or the desired accuracy. The accuracy is specified as ``termcrit.epsilon``. As soon as each of the cluster centers moves by less than ``termcrit.epsilon`` on some iteration, the algorithm stops.
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:param attempts: Flag to specify 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 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).
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:param flags: Flag that can take the following values:
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:param flags: Flag that can take the following values:
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@ -30,18 +30,18 @@ kmeans
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* **KMEANS_PP_CENTERS** Use ``kmeans++`` center initialization by Arthur and Vassilvitskii.
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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, 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).
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* **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.
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:param centers: Output matrix of the cluster centers, one row per each cluster center.
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:param centers: 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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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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centers of ``clusterCount`` clusters and groups the input samples
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around the clusters. On output,
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around the clusters. As an output,
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:math:`\texttt{labels}_i` contains a 0-based cluster index for
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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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the sample stored in the
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:math:`i^{th}` row of the ``samples`` matrix.
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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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The function returns the compactness measure that is computed as
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.. math::
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.. math::
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@ -65,9 +65,9 @@ partition
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:param vec: Set of elements stored as a vector.
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:param vec: Set of elements stored as a vector.
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: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]`` .
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: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]`` .
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: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.
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: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.
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The generic function ``partition`` implements an
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The generic function ``partition`` implements an
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:math:`O(N^2)` algorithm for
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:math:`O(N^2)` algorithm for
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@ -5,13 +5,11 @@ XML/YAML Persistence
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.. index:: FileStorage
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.. index:: FileStorage
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.. _FileStorage:
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FileStorage
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FileStorage
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-----------
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-----------
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.. c:type:: FileStorage
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.. ocv:class:: FileStorage
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XML/YAML file storage class ::
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XML/YAML file storage class. ::
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class FileStorage
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class FileStorage
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{
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{
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@ -53,7 +51,7 @@ XML/YAML file storage class ::
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// (see DataType) without any headers
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// (see DataType) without any headers
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void writeRaw( const string& fmt, const uchar* vec, size_t len );
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void writeRaw( const string& fmt, const uchar* vec, size_t len );
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// writes an old-style object (CvMat, CvMatND etc.)
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// writes an old-style object (CvMat, CvMatND, etc.)
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void writeObj( const string& name, const void* obj );
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void writeObj( const string& name, const void* obj );
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// returns the default object name from the filename
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// returns the default object name from the filename
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@ -68,13 +66,12 @@ XML/YAML file storage class ::
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.. index:: FileNode
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.. index:: FileNode
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.. _FileNode:
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FileNode
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FileNode
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--------
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--------
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.. c:type:: FileNode
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.. ocv:class:: FileNode
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XML/YAML file node class ::
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XML/YAML file node class. ::
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class CV_EXPORTS FileNode
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class CV_EXPORTS FileNode
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{
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{
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@ -118,13 +115,11 @@ XML/YAML file node class ::
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.. index:: FileNodeIterator
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.. index:: FileNodeIterator
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.. _FileNodeIterator:
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FileNodeIterator
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FileNodeIterator
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----------------
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----------------
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.. c:type:: FileNodeIterator
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.. ocv:class:: FileNodeIterator
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XML/YAML file node iterator class ::
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XML/YAML file node iterator class. ::
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class CV_EXPORTS FileNodeIterator
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class CV_EXPORTS FileNodeIterator
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{
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{
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