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320 lines
12 KiB
ReStructuredText
320 lines
12 KiB
ReStructuredText
Expectation Maximization
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========================
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The EM (Expectation Maximization) algorithm estimates the parameters of the multivariate probability density function in the form of a Gaussian mixture distribution with a specified number of mixtures.
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Consider the set of the
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:math:`x_1, x_2,...,x_{N}` : N feature vectors?? from a d-dimensional Euclidean space drawn from a Gaussian mixture:
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.. math::
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p(x;a_k,S_k, \pi _k) = \sum _{k=1}^{m} \pi _kp_k(x), \quad \pi _k \geq 0, \quad \sum _{k=1}^{m} \pi _k=1,
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.. math::
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p_k(x)= \varphi (x;a_k,S_k)= \frac{1}{(2\pi)^{d/2}\mid{S_k}\mid^{1/2}} exp \left \{ - \frac{1}{2} (x-a_k)^TS_k^{-1}(x-a_k) \right \} ,
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where
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:math:`m` is the number of mixtures,
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:math:`p_k` is the normal distribution
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density with the mean
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:math:`a_k` and covariance matrix
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:math:`S_k`,
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:math:`\pi_k` is the weight of the k-th mixture. Given the number of mixtures
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:math:`M` and the samples
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:math:`x_i`,
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:math:`i=1..N` the algorithm finds the
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maximum-likelihood estimates (MLE) of all the mixture parameters,
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that is,
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:math:`a_k`,
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:math:`S_k` and
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:math:`\pi_k` :
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.. math::
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L(x, \theta )=logp(x, \theta )= \sum _{i=1}^{N}log \left ( \sum _{k=1}^{m} \pi _kp_k(x) \right ) \to \max _{ \theta \in \Theta },
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.. math::
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\Theta = \left \{ (a_k,S_k, \pi _k): a_k \in \mathbbm{R} ^d,S_k=S_k^T>0,S_k \in \mathbbm{R} ^{d \times d}, \pi _k \geq 0, \sum _{k=1}^{m} \pi _k=1 \right \} .
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The EM algorithm is an iterative procedure. Each iteration includes
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two steps. At the first step (Expectation step or E-step), you find a
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probability
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:math:`p_{i,k}` (denoted
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:math:`\alpha_{i,k}` in the formula below) of
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sample ``i`` to belong to mixture ``k`` using the currently
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available mixture parameter estimates:
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.. math::
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\alpha _{ki} = \frac{\pi_k\varphi(x;a_k,S_k)}{\sum\limits_{j=1}^{m}\pi_j\varphi(x;a_j,S_j)} .
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At the second step (Maximization step or M-step), the mixture parameter estimates are refined using the computed probabilities:
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.. math::
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\pi _k= \frac{1}{N} \sum _{i=1}^{N} \alpha _{ki}, \quad a_k= \frac{\sum\limits_{i=1}^{N}\alpha_{ki}x_i}{\sum\limits_{i=1}^{N}\alpha_{ki}} , \quad S_k= \frac{\sum\limits_{i=1}^{N}\alpha_{ki}(x_i-a_k)(x_i-a_k)^T}{\sum\limits_{i=1}^{N}\alpha_{ki}}
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Alternatively, the algorithm may start with the M-step when the initial values for
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:math:`p_{i,k}` can be provided. Another alternative when
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:math:`p_{i,k}` are unknown is to use a simpler clustering algorithm to pre-cluster the input samples and thus obtain initial
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:math:`p_{i,k}` . Often (including ML) the
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:ref:`kmeans` algorithm is used for that purpose.
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One of the main problems?? the EM algorithm should deal with is a large number
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of parameters to estimate. The majority of the parameters reside in
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covariance matrices, which are
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:math:`d \times d` elements each
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where
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:math:`d` is the feature space dimensionality. However, in
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many practical problems, the covariance matrices are close to diagonal
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or even to
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:math:`\mu_k*I` , where
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:math:`I` is an identity matrix and
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:math:`\mu_k` is a mixture-dependent "scale" parameter. So, a robust computation
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scheme could start with harder constraints on the covariance
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matrices and then use the estimated parameters as an input for a less
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constrained optimization problem (often a diagonal covariance matrix is
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already a good enough approximation).
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**References:**
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*
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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.
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.. index:: CvEMParams
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.. _CvEMParams:
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CvEMParams
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----------
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.. c:type:: CvEMParams
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Parameters of the EM algorithm ::
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struct CvEMParams
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{
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CvEMParams() : nclusters(10), cov_mat_type(CvEM::COV_MAT_DIAGONAL),
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start_step(CvEM::START_AUTO_STEP), probs(0), weights(0), means(0),
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covs(0)
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{
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term_crit=cvTermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,
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100, FLT_EPSILON );
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}
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CvEMParams( int _nclusters, int _cov_mat_type=1/*CvEM::COV_MAT_DIAGONAL*/,
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int _start_step=0/*CvEM::START_AUTO_STEP*/,
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CvTermCriteria _term_crit=cvTermCriteria(
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CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,
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100, FLT_EPSILON),
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CvMat* _probs=0, CvMat* _weights=0,
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CvMat* _means=0, CvMat** _covs=0 ) :
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nclusters(_nclusters), cov_mat_type(_cov_mat_type),
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start_step(_start_step),
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probs(_probs), weights(_weights), means(_means), covs(_covs),
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term_crit(_term_crit)
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{}
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int nclusters;
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int cov_mat_type;
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int start_step;
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const CvMat* probs;
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const CvMat* weights;
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const CvMat* means;
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const CvMat** covs;
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CvTermCriteria term_crit;
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};
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The structure has two constructors. The default one represents a rough rule-of-the-thumb. With another one 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.
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.. index:: CvEM
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.. _CvEM:
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CvEM
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----
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.. c:type:: CvEM
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EM model ::
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class CV_EXPORTS CvEM : public CvStatModel
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{
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public:
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// Type of covariance matrices
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enum { COV_MAT_SPHERICAL=0, COV_MAT_DIAGONAL=1, COV_MAT_GENERIC=2 };
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// Initial step
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enum { START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0 };
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CvEM();
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CvEM( const CvMat* samples, const CvMat* sample_idx=0,
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CvEMParams params=CvEMParams(), CvMat* labels=0 );
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virtual ~CvEM();
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virtual bool train( const CvMat* samples, const CvMat* sample_idx=0,
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CvEMParams params=CvEMParams(), CvMat* labels=0 );
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virtual float predict( const CvMat* sample, CvMat* probs ) const;
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virtual void clear();
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int get_nclusters() const { return params.nclusters; }
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const CvMat* get_means() const { return means; }
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const CvMat** get_covs() const { return covs; }
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const CvMat* get_weights() const { return weights; }
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const CvMat* get_probs() const { return probs; }
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protected:
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virtual void set_params( const CvEMParams& params,
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const CvVectors& train_data );
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virtual void init_em( const CvVectors& train_data );
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virtual double run_em( const CvVectors& train_data );
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virtual void init_auto( const CvVectors& samples );
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virtual void kmeans( const CvVectors& train_data, int nclusters,
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CvMat* labels, CvTermCriteria criteria,
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const CvMat* means );
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CvEMParams params;
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double log_likelihood;
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CvMat* means;
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CvMat** covs;
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CvMat* weights;
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CvMat* probs;
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CvMat* log_weight_div_det;
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CvMat* inv_eigen_values;
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CvMat** cov_rotate_mats;
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};
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.. index:: CvEM::train
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.. _CvEM::train:
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CvEM::train
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-----------
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.. c:function:: void CvEM::train( const CvMat* samples, const CvMat* sample_idx=0, CvEMParams params=CvEMParams(), CvMat* labels=0 )
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Estimates the Gaussian mixture parameters from a sample set.
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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
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*Maximum Likelihood Estimate* of the Gaussian mixture parameters from an input sample set, stores all the parameters inside the structure:
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:math:`p_{i,k}` in ``probs``,
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:math:`a_k` in ``means`` ,
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:math:`S_k` in ``covs[k]``,
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:math:`\pi_k` in ``weights`` , and optionally computes the output "class label" for each sample:
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:math:`\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N` (indices of the most probable mixture for each sample).
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The trained model can be used further for prediction, just like any other classifier. The trained model is similar to the
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:ref:`Bayes classifier`.
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Example: Clustering random samples of multi-Gaussian distribution using EM ::
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#include "ml.h"
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#include "highgui.h"
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int main( int argc, char** argv )
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{
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const int N = 4;
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const int N1 = (int)sqrt((double)N);
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const CvScalar colors[] = {{0,0,255}},{{0,255,0}},
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{{0,255,255}},{{255,255,0}
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;
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int i, j;
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int nsamples = 100;
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CvRNG rng_state = cvRNG(-1);
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CvMat* samples = cvCreateMat( nsamples, 2, CV_32FC1 );
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CvMat* labels = cvCreateMat( nsamples, 1, CV_32SC1 );
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IplImage* img = cvCreateImage( cvSize( 500, 500 ), 8, 3 );
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float _sample[2];
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CvMat sample = cvMat( 1, 2, CV_32FC1, _sample );
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CvEM em_model;
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CvEMParams params;
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CvMat samples_part;
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cvReshape( samples, samples, 2, 0 );
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for( i = 0; i < N; i++ )
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{
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CvScalar mean, sigma;
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// form the training samples
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cvGetRows( samples, &samples_part, i*nsamples/N,
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(i+1)*nsamples/N );
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mean = cvScalar(((i
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((i/N1)+1.)*img->height/(N1+1));
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sigma = cvScalar(30,30);
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cvRandArr( &rng_state, &samples_part, CV_RAND_NORMAL,
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mean, sigma );
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}
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cvReshape( samples, samples, 1, 0 );
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// initialize model parameters
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params.covs = NULL;
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params.means = NULL;
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params.weights = NULL;
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params.probs = NULL;
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params.nclusters = N;
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params.cov_mat_type = CvEM::COV_MAT_SPHERICAL;
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params.start_step = CvEM::START_AUTO_STEP;
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params.term_crit.max_iter = 10;
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params.term_crit.epsilon = 0.1;
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params.term_crit.type = CV_TERMCRIT_ITER|CV_TERMCRIT_EPS;
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// cluster the data
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em_model.train( samples, 0, params, labels );
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#if 0
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// the piece of code shows how to repeatedly optimize the model
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// with less-constrained parameters
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//(COV_MAT_DIAGONAL instead of COV_MAT_SPHERICAL)
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// when the output of the first stage is used as input for the second one.
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CvEM em_model2;
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params.cov_mat_type = CvEM::COV_MAT_DIAGONAL;
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params.start_step = CvEM::START_E_STEP;
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params.means = em_model.get_means();
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params.covs = (const CvMat**)em_model.get_covs();
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params.weights = em_model.get_weights();
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em_model2.train( samples, 0, params, labels );
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// to use em_model2, replace em_model.predict()
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// with em_model2.predict() below
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#endif
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// classify every image pixel
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cvZero( img );
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for( i = 0; i < img->height; i++ )
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{
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for( j = 0; j < img->width; j++ )
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{
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CvPoint pt = cvPoint(j, i);
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sample.data.fl[0] = (float)j;
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sample.data.fl[1] = (float)i;
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int response = cvRound(em_model.predict( &sample, NULL ));
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CvScalar c = colors[response];
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cvCircle( img, pt, 1, cvScalar(c.val[0]*0.75,
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c.val[1]*0.75,c.val[2]*0.75), CV_FILLED );
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}
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}
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//draw the clustered samples
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for( i = 0; i < nsamples; i++ )
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{
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CvPoint pt;
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pt.x = cvRound(samples->data.fl[i*2]);
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pt.y = cvRound(samples->data.fl[i*2+1]);
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cvCircle( img, pt, 1, colors[labels->data.i[i]], CV_FILLED );
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}
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cvNamedWindow( "EM-clustering result", 1 );
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cvShowImage( "EM-clustering result", img );
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cvWaitKey(0);
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cvReleaseMat( &samples );
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cvReleaseMat( &labels );
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return 0;
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}
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