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merged 2.4 into trunk

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Vadim Pisarevsky
2012-04-30 14:33:52 +00:00
parent 3f1c6d7357
commit d5a0088bbe
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2
modules/ml/doc/ertrees.rst
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@@ -3,7 +3,7 @@ Extremely randomized trees
Extremely randomized trees have been introduced by Pierre Geurts, Damien Ernst and Louis Wehenkel in the article "Extremely randomized trees", 2006 [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.65.7485&rep=rep1&type=pdf]. The algorithm of growing Extremely randomized trees is similar to :ref:`Random Trees` (Random Forest), but there are two differences:
#. Extremely randomized trees don't apply the bagging procedure to constract the training samples for each tree. The same input training set is used to train all trees.
#. Extremely randomized trees don't apply the bagging procedure to construct a set of the training samples for each tree. The same input training set is used to train all trees.
#. Extremely randomized trees pick a node split very extremely (both a variable index and variable splitting value are chosen randomly), whereas Random Forest finds the best split (optimal one by variable index and variable splitting value) among random subset of variables.

236
modules/ml/doc/expectation_maximization.rst
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@@ -1,3 +1,7 @@
.. _ML_Expectation Maximization:
Expectation Maximization
========================
@@ -85,87 +89,67 @@ already a good enough approximation).
*
Bilmes98 J. A. Bilmes. *A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models*. Technical Report TR-97-021, International Computer Science Institute and Computer Science Division, University of California at Berkeley, April 1998.
EM
--
.. ocv:class:: EM
CvEMParams
----------
.. ocv:class:: 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.
The class implements the EM algorithm as described in the beginning of this section. It is inherited from :ocv:class:`Algorithm`.
EM::EM
------
The constructor of the class
CvEMParams::CvEMParams
----------------------
The constructors
.. ocv:function:: EM::EM(int nclusters=EM::DEFAULT_NCLUSTERS, int covMatType=EM::COV_MAT_DIAGONAL, const TermCriteria& termCrit=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, EM::DEFAULT_MAX_ITERS, FLT_EPSILON) )
.. 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 )
: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 nclusters: The number of mixture components in the Gaussian mixture model. Default value of the parameter is ``EM::DEFAULT_NCLUSTERS=5``. 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:
:param covMatType: 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 earch 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``.
* **EM::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 ``covMatType=EM::COV_MAT_DIAGONAL``.
* **CvEM::COV_MAT_DIAGONAL** A diagonal matrix with positive diagonal elements. The number of free parameters is ``d`` for each matrix. This is most commonly used option yielding good estimation results.
* **EM::COV_MAT_DIAGONAL** A diagonal matrix with positive diagonal elements. The number of free parameters is ``d`` for each matrix. This is most commonly used option yielding good estimation results.
* **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.
* **EM::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 termCrit: The termination criteria of the EM algorithm. The EM algorithm can be terminated by the number of iterations ``termCrit.maxCount`` (number of M-steps) or when relative change of likelihood logarithm is less than ``termCrit.epsilon``. Default maximum number of iterations is ``EM::DEFAULT_MAX_ITERS=100``.
:param start_step: The start step of the EM algorithm:
EM::train
---------
Estimates the Gaussian mixture parameters from a samples set.
* **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.
* **CvEM::START_AUTO_STEP** Start with Expectation step. You need not provide any parameters because they will be estimated by the k-means algorithm.
.. ocv:function:: bool EM::train(InputArray samples, OutputArray logLikelihoods=noArray(), OutputArray labels=noArray(), OutputArray probs=noArray())
:param term_crit: The termination criteria of the EM algorithm. The EM algorithm can be terminated by the number of iterations ``term_crit.max_iter`` (number of M-steps) or when relative change of likelihood logarithm is less than ``term_crit.epsilon``.
.. ocv:function:: bool EM::trainE(InputArray samples, InputArray means0, InputArray covs0=noArray(), InputArray weights0=noArray(), OutputArray logLikelihoods=noArray(), OutputArray labels=noArray(), OutputArray probs=noArray())
.. ocv:function:: bool EM::trainM(InputArray samples, InputArray probs0, OutputArray logLikelihoods=noArray(), OutputArray labels=noArray(), OutputArray probs=noArray())
: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 samples: Samples from which the Gaussian mixture model will be estimated. It should be a one-channel matrix, each row of which is a sample. If the matrix does not have ``CV_64F`` type it will be converted to the inner matrix of such type for the further computing.
:param means0: Initial means :math:`a_k` of mixture components. It is a one-channel matrix of :math:`nclusters \times dims` size. If the matrix does not have ``CV_64F`` type it will be converted to the inner matrix of such type for the further computing.
: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 covs0: The vector of initial covariance matrices :math:`S_k` of mixture components. Each of covariance matrices is a one-channel matrix of :math:`dims \times dims` size. If the matrices do not have ``CV_64F`` type they will be converted to the inner matrices of such type for the further computing.
:param weights0: Initial weights :math:`\pi_k` of mixture components. It should be a one-channel floating-point matrix with :math:`1 \times nclusters` or :math:`nclusters \times 1` size.
:param probs0: Initial probabilities :math:`p_{i,k}` of sample :math:`i` to belong to mixture component :math:`k`. It is a one-channel floating-point matrix of :math:`nsamples \times nclusters` size.
: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``.
:param logLikelihoods: The optional output matrix that contains a likelihood logarithm value for each sample. It has :math:`nsamples \times 1` size and ``CV_64FC1`` type.
:param covs: Initial covariance matrices :math:`S_k` of mixture components. Each of covariance matrices is a valid square floating-point matrix of :math:`dims \times dims` size. It is used (if not NULL) only when ``start_step=CvEM::START_E_STEP``.
:param labels: The optional output "class label" for each sample: :math:`\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N` (indices of the most probable mixture component for each sample). It has :math:`nsamples \times 1` size and ``CV_32SC1`` type.
:param probs: The optional output matrix that contains posterior probabilities of each Gaussian mixture component given the each sample. It has :math:`nsamples \times nclusters` size and ``CV_64FC1`` type.
The default constructor represents a rough rule-of-the-thumb:
Three versions of training method differ in the initialization of Gaussian mixture model parameters and start step:
::
* **train** - Starts with Expectation step. Initial values of the model parameters will be estimated by the k-means algorithm.
CvEMParams() : nclusters(10), cov_mat_type(1/*CvEM::COV_MAT_DIAGONAL*/),
start_step(0/*CvEM::START_AUTO_STEP*/), probs(0), weights(0), means(0), covs(0)
{
term_crit=cvTermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, FLT_EPSILON );
}
* **trainE** - Starts with Expectation step. You need to provide initial means :math:`a_k` of mixture components. Optionally you can pass initial weights :math:`\pi_k` and covariance matrices :math:`S_k` of mixture components.
* **trainM** - Starts with Maximization step. You need to provide initial probabilities :math:`p_{i,k}` to use this option.
With another constructor it is possible to override a variety of parameters from a single number of mixtures (the only essential problem-dependent parameter) to initial values for the mixture parameters.
CvEM
----
.. ocv:class:: CvEM
The class implements the EM algorithm as described in the beginning of this section.
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 CvMat* samples, const CvMat* sampleIdx=0, CvEMParams params=CvEMParams(), CvMat* labels=0 )
.. ocv:pyfunction:: cv2.EM.train(samples[, sampleIdx[, params]]) -> retval, labels
:param samples: Samples from which the Gaussian mixture model will be estimated.
:param sample_idx: Mask of samples to use. All samples are used by default.
:param params: Parameters of the EM algorithm.
:param labels: The optional output "class label" for each sample: :math:`\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N` (indices of the most probable mixture component for each sample).
The methods return ``true`` if the Gaussian mixture model was trained successfully, otherwise it returns ``false``.
Unlike many of the ML models, EM is an unsupervised learning algorithm and it does not take responses (class labels or function values) as input. Instead, it computes the
*Maximum Likelihood Estimate* of the Gaussian mixture parameters from an input sample set, stores all the parameters inside the structure:
@@ -178,121 +162,37 @@ Unlike many of the ML models, EM is an unsupervised learning algorithm and it do
The trained model can be used further for prediction, just like any other classifier. The trained model is similar to the
:ocv:class:`CvNormalBayesClassifier`.
For an example of clustering random samples of the multi-Gaussian distribution using EM, see ``em.cpp`` sample in the OpenCV distribution.
EM::predict
-----------
Returns a likelihood logarithm value and an index of the most probable mixture component for the given sample.
.. ocv:function:: Vec2d predict(InputArray sample, OutputArray probs=noArray()) const
:param sample: A sample for classification. It should be a one-channel matrix of :math:`1 \times dims` or :math:`dims \times 1` size.
CvEM::predict
-------------
Returns a mixture component index of a sample.
:param probs: Optional output matrix that contains posterior probabilities of each component given the sample. It has :math:`1 \times nclusters` size and ``CV_64FC1`` type.
.. ocv:function:: float CvEM::predict( const Mat& sample, Mat* probs=0 ) const
The method returns a two-element ``double`` vector. Zero element is a likelihood logarithm value for the sample. First element is an index of the most probable mixture component for the given sample.
.. ocv:function:: float CvEM::predict( const CvMat* sample, CvMat* probs ) const
CvEM::isTrained
---------------
Returns ``true`` if the Gaussian mixture model was trained.
.. ocv:pyfunction:: cv2.EM.predict(sample) -> retval, probs
.. ocv:function:: bool EM::isTrained() const
:param sample: A sample for classification.
:param probs: If it is not null then the method will write posterior probabilities of each component given the sample data to this parameter.
CvEM::getNClusters
------------------
Returns the number of mixture components :math:`M` in the gaussian mixture model.
.. ocv:function:: int CvEM::getNClusters() const
.. ocv:function:: int CvEM::get_nclusters() const
.. ocv:pyfunction:: cv2.EM.getNClusters() -> retval
CvEM::getMeans
------------------
Returns mixture means :math:`a_k`.
.. ocv:function:: Mat CvEM::getMeans() const
.. ocv:function:: const CvMat* CvEM::get_means() const
.. ocv:pyfunction:: cv2.EM.getMeans() -> means
CvEM::getCovs
-------------
Returns mixture covariance matrices :math:`S_k`.
.. ocv:function:: void CvEM::getCovs(std::vector<cv::Mat>& covs) const
.. ocv:function:: const CvMat** CvEM::get_covs() const
.. ocv:pyfunction:: cv2.EM.getCovs([covs]) -> covs
CvEM::getWeights
----------------
Returns mixture weights :math:`\pi_k`.
.. ocv:function:: Mat CvEM::getWeights() const
.. ocv:function:: const CvMat* CvEM::get_weights() const
.. ocv:pyfunction:: cv2.EM.getWeights() -> weights
CvEM::getProbs
--------------
Returns vectors of probabilities for each training sample.
.. ocv:function:: Mat CvEM::getProbs() const
.. ocv:function:: const CvMat* CvEM::get_probs() const
.. ocv:pyfunction:: cv2.EM.getProbs() -> probs
For each training sample :math:`i` (that have been passed to the constructor or to :ocv:func:`CvEM::train`) returns probabilities :math:`p_{i,k}` to belong to a mixture component :math:`k`.
CvEM::getLikelihood
EM::read, EM::write
-------------------
Returns logarithm of likelihood.
.. ocv:function:: double CvEM::getLikelihood() const
.. ocv:function:: double CvEM::get_log_likelihood() const
.. ocv:pyfunction:: cv2.EM.getLikelihood() -> likelihood
CvEM::getLikelihoodDelta
------------------------
Returns difference between logarithm of likelihood on the last iteration and logarithm of likelihood on the previous iteration.
.. ocv:function:: double CvEM::getLikelihoodDelta() const
.. ocv:function:: double CvEM::get_log_likelihood_delta() const
.. ocv:pyfunction:: cv2.EM.getLikelihoodDelta() -> likelihood delta
CvEM::write_params
------------------
Writes used parameters of the EM algorithm to a file storage.
.. ocv:function:: void CvEM::write_params( CvFileStorage* fs ) const
:param fs: A file storage where parameters will be written.
CvEM::read_params
-----------------
Reads parameters of the EM algorithm.
.. ocv:function:: void CvEM::read_params( CvFileStorage* fs, CvFileNode* node )
:param fs: A file storage with parameters of the EM algorithm.
:param node: The parent map. If it is NULL, the function searches a node with parameters in all the top-level nodes (streams), starting with the first one.
The function reads EM parameters from the specified file storage node. For example of clustering random samples of multi-Gaussian distribution using EM see em.cpp sample in OpenCV distribution.
See :ocv:func:`Algorithm::read` and :ocv:func:`Algorithm::write`.
EM::get, EM::set
----------------
See :ocv:func:`Algorithm::get` and :ocv:func:`Algorithm::set`. The following parameters are available:
* ``"nclusters"``
* ``"covMatType"``
* ``"maxIters"``
* ``"epsilon"``
* ``"weights"`` *(read-only)*
* ``"means"`` *(read-only)*
* ``"covs"`` *(read-only)*
..

6
modules/ml/doc/mldata.rst
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@@ -34,7 +34,7 @@ Class for loading the data from a ``.csv`` file.
void mix_train_and_test_idx();
const CvMat* get_var_idx();
void chahge_var_idx( int vi, bool state );
void change_var_idx( int vi, bool state );
const CvMat* get_var_types();
void set_var_types( const char* str );
@@ -158,11 +158,11 @@ The method returns the indices of variables (columns) used in the ``values`` mat
It returns ``0`` if the used subset is not set. It throws an exception if the data has not been loaded from the file yet. Returned matrix is a single-row matrix of the type ``CV_32SC1``. Its column count is equal to the size of the used variable subset.
CvMLData::chahge_var_idx
CvMLData::change_var_idx
------------------------
Enables or disables particular variable in the loaded data
.. ocv:function:: void CvMLData::chahge_var_idx( int vi, bool state )
.. ocv:function:: void CvMLData::change_var_idx( int vi, bool state )
By default, after reading the data set all variables in the ``values`` matrix (see :ocv:func:`CvMLData::get_values`) are used. But you may want to use only a subset of variables and include/exclude (depending on ``state`` value) a variable with the ``vi`` index from the used subset. If the data has not been loaded from the file yet, an exception is thrown.

34
modules/ml/include/opencv2/ml/ml.hpp
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@@ -563,13 +563,13 @@ public:
enum {COV_MAT_SPHERICAL=0, COV_MAT_DIAGONAL=1, COV_MAT_GENERIC=2, COV_MAT_DEFAULT=COV_MAT_DIAGONAL};
// Default parameters
enum {DEFAULT_NCLUSTERS=10, DEFAULT_MAX_ITERS=100};
enum {DEFAULT_NCLUSTERS=5, DEFAULT_MAX_ITERS=100};
// The initial step
enum {START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0};
CV_WRAP EM(int nclusters=EM::DEFAULT_NCLUSTERS, int covMatType=EM::COV_MAT_DIAGONAL,
const TermCriteria& termcrit=TermCriteria(TermCriteria::COUNT+
const TermCriteria& termCrit=TermCriteria(TermCriteria::COUNT+
TermCriteria::EPS,
EM::DEFAULT_MAX_ITERS, FLT_EPSILON));
@@ -577,27 +577,26 @@ public:
CV_WRAP virtual void clear();
CV_WRAP virtual bool train(InputArray samples,
OutputArray logLikelihoods=noArray(),
OutputArray labels=noArray(),
OutputArray probs=noArray(),
OutputArray logLikelihoods=noArray());
OutputArray probs=noArray());
CV_WRAP virtual bool trainE(InputArray samples,
InputArray means0,
InputArray covs0=noArray(),
InputArray weights0=noArray(),
OutputArray logLikelihoods=noArray(),
OutputArray labels=noArray(),
OutputArray probs=noArray(),
OutputArray logLikelihoods=noArray());
OutputArray probs=noArray());
CV_WRAP virtual bool trainM(InputArray samples,
InputArray probs0,
OutputArray logLikelihoods=noArray(),
OutputArray labels=noArray(),
OutputArray probs=noArray(),
OutputArray logLikelihoods=noArray());
OutputArray probs=noArray());
CV_WRAP int predict(InputArray sample,
OutputArray probs=noArray(),
CV_OUT double* logLikelihood=0) const;
CV_WRAP Vec2d predict(InputArray sample,
OutputArray probs=noArray()) const;
CV_WRAP bool isTrained() const;
@@ -613,9 +612,9 @@ protected:
const Mat* weights0);
bool doTrain(int startStep,
OutputArray logLikelihoods,
OutputArray labels,
OutputArray probs,
OutputArray logLikelihoods);
OutputArray probs);
virtual void eStep();
virtual void mStep();
@@ -623,7 +622,7 @@ protected:
void decomposeCovs();
void computeLogWeightDivDet();
void computeProbabilities(const Mat& sample, int& label, Mat* probs, double* logLikelihood) const;
Vec2d computeProbabilities(const Mat& sample, Mat* probs) const;
// all inner matrices have type CV_64FC1
CV_PROP_RW int nclusters;
@@ -635,7 +634,6 @@ protected:
Mat trainProbs;
Mat trainLogLikelihoods;
Mat trainLabels;
Mat trainCounts;
CV_PROP Mat weights;
CV_PROP Mat means;
@@ -2035,7 +2033,7 @@ public:
// returns:
// 0 - OK
// 1 - file can not be opened or is not correct
// -1 - file can not be opened or is not correct
int read_csv( const char* filename );
const CvMat* get_values() const;
@@ -2052,7 +2050,9 @@ public:
void mix_train_and_test_idx();
const CvMat* get_var_idx();
void chahge_var_idx( int vi, bool state ); // state == true to set vi-variable as predictor
void chahge_var_idx( int vi, bool state ); // misspelled (saved for back compitability),
// use change_var_idx
void change_var_idx( int vi, bool state ); // state == true to set vi-variable as predictor
const CvMat* get_var_types();
int get_var_type( int var_idx ) const;

14
modules/ml/src/data.cpp
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@@ -199,10 +199,7 @@ int CvMLData::read_csv(const char* filename)
int type;
token = strtok(buf, str_delimiter);
if (!token)
{
fclose(file);
return -1;
}
break;
for (int i = 0; i < cols_count-1; i++)
{
str_to_flt_elem( token, el_ptr[i], type);
@@ -217,7 +214,7 @@ int CvMLData::read_csv(const char* filename)
str_to_flt_elem( token, el_ptr[cols_count-1], type);
var_types_ptr[cols_count-1] |= type;
cvSeqPush( seq, el_ptr );
if( !fgets_chomp( buf, M, file ) || !strchr( buf, delimiter ) )
if( !fgets_chomp( buf, M, file ) )
break;
}
fclose(file);
@@ -743,7 +740,12 @@ const CvMat* CvMLData::get_var_idx()
void CvMLData::chahge_var_idx( int vi, bool state )
{
CV_FUNCNAME( "CvMLData::get_responses_ptr" );
change_var_idx( vi, state );
}
void CvMLData::change_var_idx( int vi, bool state )
{
CV_FUNCNAME( "CvMLData::change_var_idx" );
__BEGIN__;
int var_count = 0;

247
modules/ml/src/em.cpp
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@@ -44,16 +44,16 @@
namespace cv
{
const double minEigenValue = DBL_MIN;
const double minEigenValue = DBL_EPSILON;
///////////////////////////////////////////////////////////////////////////////////////////////////////
EM::EM(int _nclusters, int _covMatType, const TermCriteria& _criteria)
EM::EM(int _nclusters, int _covMatType, const TermCriteria& _termCrit)
{
nclusters = _nclusters;
covMatType = _covMatType;
maxIters = (_criteria.type & TermCriteria::MAX_ITER) ? _criteria.maxCount : DEFAULT_MAX_ITERS;
epsilon = (_criteria.type & TermCriteria::EPS) ? _criteria.epsilon : 0;
maxIters = (_termCrit.type & TermCriteria::MAX_ITER) ? _termCrit.maxCount : DEFAULT_MAX_ITERS;
epsilon = (_termCrit.type & TermCriteria::EPS) ? _termCrit.epsilon : 0;
}
EM::~EM()
@@ -67,7 +67,6 @@ void EM::clear()
trainProbs.release();
trainLogLikelihoods.release();
trainLabels.release();
trainCounts.release();
weights.release();
means.release();
@@ -82,22 +81,22 @@ void EM::clear()
bool EM::train(InputArray samples,
OutputArray logLikelihoods,
OutputArray labels,
OutputArray probs,
OutputArray logLikelihoods)
OutputArray probs)
{
Mat samplesMat = samples.getMat();
setTrainData(START_AUTO_STEP, samplesMat, 0, 0, 0, 0);
return doTrain(START_AUTO_STEP, labels, probs, logLikelihoods);
return doTrain(START_AUTO_STEP, logLikelihoods, labels, probs);
}
bool EM::trainE(InputArray samples,
InputArray _means0,
InputArray _covs0,
InputArray _weights0,
OutputArray logLikelihoods,
OutputArray labels,
OutputArray probs,
OutputArray logLikelihoods)
OutputArray probs)
{
Mat samplesMat = samples.getMat();
vector<Mat> covs0;
@@ -107,24 +106,24 @@ bool EM::trainE(InputArray samples,
setTrainData(START_E_STEP, samplesMat, 0, !_means0.empty() ? &means0 : 0,
!_covs0.empty() ? &covs0 : 0, _weights0.empty() ? &weights0 : 0);
return doTrain(START_E_STEP, labels, probs, logLikelihoods);
return doTrain(START_E_STEP, logLikelihoods, labels, probs);
}
bool EM::trainM(InputArray samples,
InputArray _probs0,
OutputArray logLikelihoods,
OutputArray labels,
OutputArray probs,
OutputArray logLikelihoods)
OutputArray probs)
{
Mat samplesMat = samples.getMat();
Mat probs0 = _probs0.getMat();
setTrainData(START_M_STEP, samplesMat, !_probs0.empty() ? &probs0 : 0, 0, 0, 0);
return doTrain(START_M_STEP, labels, probs, logLikelihoods);
return doTrain(START_M_STEP, logLikelihoods, labels, probs);
}
int EM::predict(InputArray _sample, OutputArray _probs, double* logLikelihood) const
Vec2d EM::predict(InputArray _sample, OutputArray _probs) const
{
Mat sample = _sample.getMat();
CV_Assert(isTrained());
@@ -136,17 +135,16 @@ int EM::predict(InputArray _sample, OutputArray _probs, double* logLikelihood) c
sample.convertTo(tmp, CV_64FC1);
sample = tmp;
}
sample.reshape(1, 1);
int label;
Mat probs;
if( _probs.needed() )
{
_probs.create(1, nclusters, CV_64FC1);
probs = _probs.getMat();
}
computeProbabilities(sample, label, !probs.empty() ? &probs : 0, logLikelihood);
return label;
return computeProbabilities(sample, !probs.empty() ? &probs : 0);
}
bool EM::isTrained() const
@@ -395,7 +393,7 @@ void EM::computeLogWeightDivDet()
}
}
bool EM::doTrain(int startStep, OutputArray labels, OutputArray probs, OutputArray logLikelihoods)
bool EM::doTrain(int startStep, OutputArray logLikelihoods, OutputArray labels, OutputArray probs)
{
int dim = trainSamples.cols;
// Precompute the empty initial train data in the cases of EM::START_E_STEP and START_AUTO_STEP
@@ -469,12 +467,11 @@ bool EM::doTrain(int startStep, OutputArray labels, OutputArray probs, OutputArr
trainProbs.release();
trainLabels.release();
trainLogLikelihoods.release();
trainCounts.release();
return true;
}
void EM::computeProbabilities(const Mat& sample, int& label, Mat* probs, double* logLikelihood) const
Vec2d EM::computeProbabilities(const Mat& sample, Mat* probs) const
{
// L_ik = log(weight_k) - 0.5 * log(|det(cov_k)|) - 0.5 *(x_i - mean_k)' cov_k^(-1) (x_i - mean_k)]
// q = arg(max_k(L_ik))
@@ -490,7 +487,7 @@ void EM::computeProbabilities(const Mat& sample, int& label, Mat* probs, double*
int dim = sample.cols;
Mat L(1, nclusters, CV_64FC1);
label = 0;
int label = 0;
for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)
{
const Mat centeredSample = sample - means.row(clusterIndex);
@@ -506,16 +503,12 @@ void EM::computeProbabilities(const Mat& sample, int& label, Mat* probs, double*
Lval += w * val * val;
}
CV_DbgAssert(!logWeightDivDet.empty());
Lval = logWeightDivDet.at<double>(clusterIndex) - 0.5 * Lval;
L.at<double>(clusterIndex) = Lval;
L.at<double>(clusterIndex) = logWeightDivDet.at<double>(clusterIndex) - 0.5 * Lval;
if(Lval > L.at<double>(label))
if(L.at<double>(clusterIndex) > L.at<double>(label))
label = clusterIndex;
}
if(!probs && !logLikelihood)
return;
double maxLVal = L.at<double>(label);
Mat expL_Lmax = L; // exp(L_ij - L_iq)
for(int i = 0; i < L.cols; i++)
@@ -530,8 +523,11 @@ void EM::computeProbabilities(const Mat& sample, int& label, Mat* probs, double*
expL_Lmax.copyTo(*probs);
}
if(logLikelihood)
*logLikelihood = std::log(expDiffSum) + maxLVal - 0.5 * dim * CV_LOG2PI;
Vec2d res;
res[0] = std::log(expDiffSum) + maxLVal - 0.5 * dim * CV_LOG2PI;
res[1] = label;
return res;
}
void EM::eStep()
@@ -549,104 +545,122 @@ void EM::eStep()
for(int sampleIndex = 0; sampleIndex < trainSamples.rows; sampleIndex++)
{
Mat sampleProbs = trainProbs.row(sampleIndex);
computeProbabilities(trainSamples.row(sampleIndex), trainLabels.at<int>(sampleIndex),
&sampleProbs, &trainLogLikelihoods.at<double>(sampleIndex));
Vec2d res = computeProbabilities(trainSamples.row(sampleIndex), &sampleProbs);
trainLogLikelihoods.at<double>(sampleIndex) = res[0];
trainLabels.at<int>(sampleIndex) = static_cast<int>(res[1]);
}
}
void EM::mStep()
{
trainCounts.create(1, nclusters, CV_32SC1);
trainCounts = Scalar(0);
// Update means_k, covs_k and weights_k from probs_ik
int dim = trainSamples.cols;
for(int sampleIndex = 0; sampleIndex < trainLabels.rows; sampleIndex++)
trainCounts.at<int>(trainLabels.at<int>(sampleIndex))++;
// Update weights
// not normalized first
reduce(trainProbs, weights, 0, CV_REDUCE_SUM);
if(countNonZero(trainCounts) != (int)trainCounts.total())
// Update means
means.create(nclusters, dim, CV_64FC1);
means = Scalar(0);
const double minPosWeight = trainSamples.rows * DBL_EPSILON;
double minWeight = DBL_MAX;
int minWeightClusterIndex = -1;
for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)
{
clusterTrainSamples();
}
else
{
// Update means_k, covs_k and weights_k from probs_ik
int dim = trainSamples.cols;
if(weights.at<double>(clusterIndex) <= minPosWeight)
continue;
// Update weights
// not normalized first
reduce(trainProbs, weights, 0, CV_REDUCE_SUM);
// Update means
means.create(nclusters, dim, CV_64FC1);
means = Scalar(0);
for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)
if(weights.at<double>(clusterIndex) < minWeight)
{
Mat clusterMean = means.row(clusterIndex);
for(int sampleIndex = 0; sampleIndex < trainSamples.rows; sampleIndex++)
clusterMean += trainProbs.at<double>(sampleIndex, clusterIndex) * trainSamples.row(sampleIndex);
clusterMean /= weights.at<double>(clusterIndex);
minWeight = weights.at<double>(clusterIndex);
minWeightClusterIndex = clusterIndex;
}
// Update covsEigenValues and invCovsEigenValues
covs.resize(nclusters);
covsEigenValues.resize(nclusters);
Mat clusterMean = means.row(clusterIndex);
for(int sampleIndex = 0; sampleIndex < trainSamples.rows; sampleIndex++)
clusterMean += trainProbs.at<double>(sampleIndex, clusterIndex) * trainSamples.row(sampleIndex);
clusterMean /= weights.at<double>(clusterIndex);
}
// Update covsEigenValues and invCovsEigenValues
covs.resize(nclusters);
covsEigenValues.resize(nclusters);
if(covMatType == EM::COV_MAT_GENERIC)
covsRotateMats.resize(nclusters);
invCovsEigenValues.resize(nclusters);
for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)
{
if(weights.at<double>(clusterIndex) <= minPosWeight)
continue;
if(covMatType != EM::COV_MAT_SPHERICAL)
covsEigenValues[clusterIndex].create(1, dim, CV_64FC1);
else
covsEigenValues[clusterIndex].create(1, 1, CV_64FC1);
if(covMatType == EM::COV_MAT_GENERIC)
covsRotateMats.resize(nclusters);
invCovsEigenValues.resize(nclusters);
for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)
covs[clusterIndex].create(dim, dim, CV_64FC1);
Mat clusterCov = covMatType != EM::COV_MAT_GENERIC ?
covsEigenValues[clusterIndex] : covs[clusterIndex];
clusterCov = Scalar(0);
Mat centeredSample;
for(int sampleIndex = 0; sampleIndex < trainSamples.rows; sampleIndex++)
{
if(covMatType != EM::COV_MAT_SPHERICAL)
covsEigenValues[clusterIndex].create(1, dim, CV_64FC1);
else
covsEigenValues[clusterIndex].create(1, 1, CV_64FC1);
centeredSample = trainSamples.row(sampleIndex) - means.row(clusterIndex);
if(covMatType == EM::COV_MAT_GENERIC)
covs[clusterIndex].create(dim, dim, CV_64FC1);
Mat clusterCov = covMatType != EM::COV_MAT_GENERIC ?
covsEigenValues[clusterIndex] : covs[clusterIndex];
clusterCov = Scalar(0);
Mat centeredSample;
for(int sampleIndex = 0; sampleIndex < trainSamples.rows; sampleIndex++)
clusterCov += trainProbs.at<double>(sampleIndex, clusterIndex) * centeredSample.t() * centeredSample;
else
{
centeredSample = trainSamples.row(sampleIndex) - means.row(clusterIndex);
if(covMatType == EM::COV_MAT_GENERIC)
clusterCov += trainProbs.at<double>(sampleIndex, clusterIndex) * centeredSample.t() * centeredSample;
else
double p = trainProbs.at<double>(sampleIndex, clusterIndex);
for(int di = 0; di < dim; di++ )
{
double p = trainProbs.at<double>(sampleIndex, clusterIndex);
for(int di = 0; di < dim; di++ )
{
double val = centeredSample.at<double>(di);
clusterCov.at<double>(covMatType != EM::COV_MAT_SPHERICAL ? di : 0) += p*val*val;
}
double val = centeredSample.at<double>(di);
clusterCov.at<double>(covMatType != EM::COV_MAT_SPHERICAL ? di : 0) += p*val*val;
}
}
if(covMatType == EM::COV_MAT_SPHERICAL)
clusterCov /= dim;
clusterCov /= weights.at<double>(clusterIndex);
// Update covsRotateMats for EM::COV_MAT_GENERIC only
if(covMatType == EM::COV_MAT_GENERIC)
{
SVD svd(covs[clusterIndex], SVD::MODIFY_A + SVD::FULL_UV);
covsEigenValues[clusterIndex] = svd.w;
covsRotateMats[clusterIndex] = svd.u;
}
max(covsEigenValues[clusterIndex], minEigenValue, covsEigenValues[clusterIndex]);
// update invCovsEigenValues
invCovsEigenValues[clusterIndex] = 1./covsEigenValues[clusterIndex];
}
// Normalize weights
weights /= trainSamples.rows;
if(covMatType == EM::COV_MAT_SPHERICAL)
clusterCov /= dim;
clusterCov /= weights.at<double>(clusterIndex);
// Update covsRotateMats for EM::COV_MAT_GENERIC only
if(covMatType == EM::COV_MAT_GENERIC)
{
SVD svd(covs[clusterIndex], SVD::MODIFY_A + SVD::FULL_UV);
covsEigenValues[clusterIndex] = svd.w;
covsRotateMats[clusterIndex] = svd.u;
}
max(covsEigenValues[clusterIndex], minEigenValue, covsEigenValues[clusterIndex]);
// update invCovsEigenValues
invCovsEigenValues[clusterIndex] = 1./covsEigenValues[clusterIndex];
}
for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)
{
if(weights.at<double>(clusterIndex) <= minPosWeight)
{
Mat clusterMean = means.row(clusterIndex);
means.row(minWeightClusterIndex).copyTo(clusterMean);
covs[minWeightClusterIndex].copyTo(covs[clusterIndex]);
covsEigenValues[minWeightClusterIndex].copyTo(covsEigenValues[clusterIndex]);
if(covMatType == EM::COV_MAT_GENERIC)
covsRotateMats[minWeightClusterIndex].copyTo(covsRotateMats[clusterIndex]);
invCovsEigenValues[minWeightClusterIndex].copyTo(invCovsEigenValues[clusterIndex]);
}
}
// Normalize weights
weights /= trainSamples.rows;
}
void EM::read(const FileNode& fn)
@@ -657,29 +671,6 @@ void EM::read(const FileNode& fn)
computeLogWeightDivDet();
}
static Algorithm* createEM()
{
return new EM;
}
static AlgorithmInfo em_info("StatModel.EM", createEM);
AlgorithmInfo* EM::info() const
{
static volatile bool initialized = false;
if( !initialized )
{
EM obj;
em_info.addParam(obj, "nclusters", obj.nclusters);
em_info.addParam(obj, "covMatType", obj.covMatType);
em_info.addParam(obj, "weights", obj.weights);
em_info.addParam(obj, "means", obj.means);
em_info.addParam(obj, "covs", obj.covs);
initialized = true;
}
return &em_info;
}
} // namespace cv
/* End of file. */

80
modules/ml/src/ml_init.cpp Normal file
View File

@@ -0,0 +1,80 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
namespace cv
{
static Algorithm* createEM()
{
return new EM;
}
static AlgorithmInfo em_info("StatModel.EM", createEM);
AlgorithmInfo* EM::info() const
{
static volatile bool initialized = false;
if( !initialized )
{
EM obj;
em_info.addParam(obj, "nclusters", obj.nclusters);
em_info.addParam(obj, "covMatType", obj.covMatType);
em_info.addParam(obj, "maxIters", obj.maxIters);
em_info.addParam(obj, "epsilon", obj.epsilon);
em_info.addParam(obj, "weights", obj.weights, true);
em_info.addParam(obj, "means", obj.means, true);
em_info.addParam(obj, "covs", obj.covs, true);
initialized = true;
}
return &em_info;
}
bool initModule_ml(void)
{
Ptr<Algorithm> em = createEM();
return em->info() != 0;
}
}

133
modules/ml/test/test_emknearestkmeans.cpp
View File

@@ -129,7 +129,7 @@ int maxIdx( const vector<int>& count )
}
static
bool getLabelsMap( const Mat& labels, const vector<int>& sizes, vector<int>& labelsMap )
bool getLabelsMap( const Mat& labels, const vector<int>& sizes, vector<int>& labelsMap, bool checkClusterUniq=true )
{
size_t total = 0, nclusters = sizes.size();
for(size_t i = 0; i < sizes.size(); i++)
@@ -158,21 +158,25 @@ bool getLabelsMap( const Mat& labels, const vector<int>& sizes, vector<int>& lab
startIndex += sizes[clusterIndex];
int cls = maxIdx( count );
CV_Assert( !buzy[cls] );
CV_Assert( !checkClusterUniq || !buzy[cls] );
labelsMap[clusterIndex] = cls;
buzy[cls] = true;
}
for(size_t i = 0; i < buzy.size(); i++)
if(!buzy[i])
return false;
if(checkClusterUniq)
{
for(size_t i = 0; i < buzy.size(); i++)
if(!buzy[i])
return false;
}
return true;
}
static
bool calcErr( const Mat& labels, const Mat& origLabels, const vector<int>& sizes, float& err, bool labelsEquivalent = true )
bool calcErr( const Mat& labels, const Mat& origLabels, const vector<int>& sizes, float& err, bool labelsEquivalent = true, bool checkClusterUniq=true )
{
err = 0;
CV_Assert( !labels.empty() && !origLabels.empty() );
@@ -186,7 +190,7 @@ bool calcErr( const Mat& labels, const Mat& origLabels, const vector<int>& sizes
bool isFlt = labels.type() == CV_32FC1;
if( !labelsEquivalent )
{
if( !getLabelsMap( labels, sizes, labelsMap ) )
if( !getLabelsMap( labels, sizes, labelsMap, checkClusterUniq ) )
return false;
for( int i = 0; i < labels.rows; i++ )
@@ -369,14 +373,14 @@ int CV_EMTest::runCase( int caseIndex, const EM_Params& params,
cv::EM em(params.nclusters, params.covMatType, params.termCrit);
if( params.startStep == EM::START_AUTO_STEP )
em.train( trainData, labels );
em.train( trainData, noArray(), labels );
else if( params.startStep == EM::START_E_STEP )
em.trainE( trainData, *params.means, *params.covs, *params.weights, labels );
em.trainE( trainData, *params.means, *params.covs, *params.weights, noArray(), labels );
else if( params.startStep == EM::START_M_STEP )
em.trainM( trainData, *params.probs, labels );
em.trainM( trainData, *params.probs, noArray(), labels );
// check train error
if( !calcErr( labels, trainLabels, sizes, err , false ) )
if( !calcErr( labels, trainLabels, sizes, err , false, false ) )
{
ts->printf( cvtest::TS::LOG, "Case index %i : Bad output labels.\n", caseIndex );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
@@ -392,11 +396,10 @@ int CV_EMTest::runCase( int caseIndex, const EM_Params& params,
for( int i = 0; i < testData.rows; i++ )
{
Mat sample = testData.row(i);
double likelihood = 0;
Mat probs;
labels.at<int>(i,0) = (int)em.predict( sample, probs, &likelihood );
labels.at<int>(i) = static_cast<int>(em.predict( sample, probs )[1]);
}
if( !calcErr( labels, testLabels, sizes, err, false ) )
if( !calcErr( labels, testLabels, sizes, err, false, false ) )
{
ts->printf( cvtest::TS::LOG, "Case index %i : Bad output labels.\n", caseIndex );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
@@ -519,7 +522,7 @@ protected:
Mat firstResult(samples.rows, 1, CV_32SC1);
for( int i = 0; i < samples.rows; i++)
firstResult.at<int>(i) = em.predict(samples.row(i));
firstResult.at<int>(i) = static_cast<int>(em.predict(samples.row(i))[1]);
// Write out
string filename = tempfile() + ".xml";
@@ -560,7 +563,7 @@ protected:
int errCaseCount = 0;
for( int i = 0; i < samples.rows; i++)
errCaseCount = std::abs(em.predict(samples.row(i)) - firstResult.at<int>(i)) < FLT_EPSILON ? 0 : 1;
errCaseCount = std::abs(em.predict(samples.row(i))[1] - firstResult.at<int>(i)) < FLT_EPSILON ? 0 : 1;
if( errCaseCount > 0 )
{
@@ -572,7 +575,105 @@ protected:
}
};
class CV_EMTest_Classification : public cvtest::BaseTest
{
public:
CV_EMTest_Classification() {}
protected:
virtual void run(int)
{
// This test classifies spam by the following way:
// 1. estimates distributions of "spam" / "not spam"
// 2. predict classID using Bayes classifier for estimated distributions.
CvMLData data;
string dataFilename = string(ts->get_data_path()) + "spambase.data";
if(data.read_csv(dataFilename.c_str()) != 0)
{
ts->printf(cvtest::TS::LOG, "File with spambase dataset cann't be read.\n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
}
Mat values = data.get_values();
CV_Assert(values.cols == 58);
int responseIndex = 57;
Mat samples = values.colRange(0, responseIndex);
Mat responses = values.col(responseIndex);
vector<int> trainSamplesMask(samples.rows, 0);
int trainSamplesCount = (int)(0.5f * samples.rows);
for(int i = 0; i < trainSamplesCount; i++)
trainSamplesMask[i] = 1;
RNG rng(0);
for(size_t i = 0; i < trainSamplesMask.size(); i++)
{
int i1 = rng(static_cast<unsigned>(trainSamplesMask.size()));
int i2 = rng(static_cast<unsigned>(trainSamplesMask.size()));
std::swap(trainSamplesMask[i1], trainSamplesMask[i2]);
}
EM model0(3), model1(3);
Mat samples0, samples1;
for(int i = 0; i < samples.rows; i++)
{
if(trainSamplesMask[i])
{
Mat sample = samples.row(i);
int resp = (int)responses.at<float>(i);
if(resp == 0)
samples0.push_back(sample);
else
samples1.push_back(sample);
}
}
model0.train(samples0);
model1.train(samples1);
Mat trainConfusionMat(2, 2, CV_32SC1, Scalar(0)),
testConfusionMat(2, 2, CV_32SC1, Scalar(0));
const double lambda = 1.;
for(int i = 0; i < samples.rows; i++)
{
Mat sample = samples.row(i);
double sampleLogLikelihoods0 = model0.predict(sample)[0];
double sampleLogLikelihoods1 = model1.predict(sample)[0];
int classID = sampleLogLikelihoods0 >= lambda * sampleLogLikelihoods1 ? 0 : 1;
if(trainSamplesMask[i])
trainConfusionMat.at<int>((int)responses.at<float>(i), classID)++;
else
testConfusionMat.at<int>((int)responses.at<float>(i), classID)++;
}
// std::cout << trainConfusionMat << std::endl;
// std::cout << testConfusionMat << std::endl;
double trainError = (double)(trainConfusionMat.at<int>(1,0) + trainConfusionMat.at<int>(0,1)) / trainSamplesCount;
double testError = (double)(testConfusionMat.at<int>(1,0) + testConfusionMat.at<int>(0,1)) / (samples.rows - trainSamplesCount);
const double maxTrainError = 0.16;
const double maxTestError = 0.19;
int code = cvtest::TS::OK;
if(trainError > maxTrainError)
{
ts->printf(cvtest::TS::LOG, "Too large train classification error (calc = %f, valid=%f).\n", trainError, maxTrainError);
code = cvtest::TS::FAIL_INVALID_TEST_DATA;
}
if(testError > maxTestError)
{
ts->printf(cvtest::TS::LOG, "Too large test classification error (calc = %f, valid=%f).\n", trainError, maxTrainError);
code = cvtest::TS::FAIL_INVALID_TEST_DATA;
}
ts->set_failed_test_info(code);
}
};
TEST(ML_KMeans, accuracy) { CV_KMeansTest test; test.safe_run(); }
TEST(ML_KNearest, accuracy) { CV_KNearestTest test; test.safe_run(); }
TEST(ML_EM, accuracy) { CV_EMTest test; test.safe_run(); }
TEST(ML_EM, save_load) { CV_EMTest_SaveLoad test; test.safe_run(); }
TEST(ML_EM, classification) { CV_EMTest_Classification test; test.safe_run(); }