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Fixed more documentation & source discrepancies

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This commit is contained in:
Andrey Kamaev
2012-05-28 11:22:43 +00:00
parent a61b730238
commit 71625ad458
20 changed files with 1037 additions and 954 deletions
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14
modules/legacy/doc/expectation_maximization.rst
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@@ -8,7 +8,7 @@ This section describes obsolete ``C`` interface of EM algorithm. Details of the
CvEMParams
----------
.. ocv:class:: CvEMParams
.. ocv:struct:: CvEMParams
Parameters of the EM algorithm. All parameters are public. You can initialize them by a constructor and then override some of them directly if you want.
@@ -18,10 +18,10 @@ The constructors
.. ocv:function:: CvEMParams::CvEMParams()
.. ocv:function:: CvEMParams::CvEMParams( int nclusters, int cov_mat_type=CvEM::COV_MAT_DIAGONAL, int start_step=CvEM::START_AUTO_STEP, CvTermCriteria term_crit=cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, FLT_EPSILON), const CvMat* probs=0, const CvMat* weights=0, const CvMat* means=0, const CvMat** covs=0 )
.. ocv:function:: CvEMParams::CvEMParams( int nclusters, int cov_mat_type=EM::COV_MAT_DIAGONAL, int start_step=EM::START_AUTO_STEP, CvTermCriteria term_crit=cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, FLT_EPSILON), const CvMat* probs=0, const CvMat* weights=0, const CvMat* means=0, const CvMat** covs=0 )
:param nclusters: The number of mixture components in the Gaussian mixture model. Some of EM implementation could determine the optimal number of mixtures within a specified value range, but that is not the case in ML yet.
:param cov_mat_type: Constraint on covariance matrices which defines type of matrices. Possible values are:
* **CvEM::COV_MAT_SPHERICAL** A scaled identity matrix :math:`\mu_k * I`. There is the only parameter :math:`\mu_k` to be estimated for each matrix. The option may be used in special cases, when the constraint is relevant, or as a first step in the optimization (for example in case when the data is preprocessed with PCA). The results of such preliminary estimation may be passed again to the optimization procedure, this time with ``cov_mat_type=CvEM::COV_MAT_DIAGONAL``.
@@ -30,7 +30,7 @@ The constructors
* **CvEM::COV_MAT_GENERIC** A symmetric positively defined matrix. The number of free parameters in each matrix is about :math:`d^2/2`. It is not recommended to use this option, unless there is pretty accurate initial estimation of the parameters and/or a huge number of training samples.
:param start_step: The start step of the EM algorithm:
:param start_step: The start step of the EM algorithm:
* **CvEM::START_E_STEP** Start with Expectation step. You need to provide means :math:`a_k` of mixture components to use this option. Optionally you can pass weights :math:`\pi_k` and covariance matrices :math:`S_k` of mixture components.
* **CvEM::START_M_STEP** Start with Maximization step. You need to provide initial probabilities :math:`p_{i,k}` to use this option.
@@ -40,7 +40,7 @@ The constructors
:param probs: Initial probabilities :math:`p_{i,k}` of sample :math:`i` to belong to mixture component :math:`k`. It is a floating-point matrix of :math:`nsamples \times nclusters` size. It is used and must be not NULL only when ``start_step=CvEM::START_M_STEP``.
:param weights: Initial weights :math:`\pi_k` of mixture components. It is a floating-point vector with :math:`nclusters` elements. It is used (if not NULL) only when ``start_step=CvEM::START_E_STEP``.
:param weights: Initial weights :math:`\pi_k` of mixture components. It is a floating-point vector with :math:`nclusters` elements. It is used (if not NULL) only when ``start_step=CvEM::START_E_STEP``.
:param means: Initial means :math:`a_k` of mixture components. It is a floating-point matrix of :math:`nclusters \times dims` size. It is used used and must be not NULL only when ``start_step=CvEM::START_E_STEP``.
@@ -62,7 +62,7 @@ With another constructor it is possible to override a variety of parameters from
CvEM
----
.. ocv:class:: CvEM
.. ocv:class:: CvEM : public CvStatModel
The class implements the EM algorithm as described in the beginning of the section :ref:`ML_Expectation Maximization`.
@@ -71,7 +71,7 @@ CvEM::train
-----------
Estimates the Gaussian mixture parameters from a sample set.
.. ocv:function:: void CvEM::train( const Mat& samples, const Mat& sample_idx=Mat(), CvEMParams params=CvEMParams(), Mat* labels=0 )
.. ocv:function:: bool CvEM::train( const Mat& samples, const Mat& sampleIdx=Mat(), CvEMParams params=CvEMParams(), Mat* labels=0 )
.. ocv:function:: bool CvEM::train( const CvMat* samples, const CvMat* sampleIdx=0, CvEMParams params=CvEMParams(), CvMat* labels=0 )