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Commit 3dc03531 authored by Maria Dimashova's avatar Maria Dimashova
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added CvEM read/write (#1032)

parent bd33e0a3
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Showing with 332 additions and 18 deletions
modules/ml/include/opencv2/ml/ml.hpp
View file @ 3dc03531
......@@ -609,6 +609,7 @@ public:
CV_OUT cv::Mat* labels=0 );
CV_WRAP virtual float predict( const cv::Mat& sample, CV_OUT cv::Mat* probs=0 ) const;
CV_WRAP virtual double calcLikelihood( const cv::Mat &sample ) const;
CV_WRAP int getNClusters() const;
CV_WRAP cv::Mat getMeans() const;
......@@ -616,7 +617,8 @@ public:
CV_WRAP cv::Mat getWeights() const;
CV_WRAP cv::Mat getProbs() const;
CV_WRAP inline double getLikelihood() const { return log_likelihood; };
CV_WRAP inline double getLikelihood() const { return log_likelihood; }
CV_WRAP inline double getLikelihoodDelta() const { return log_likelihood_delta; }
#endif
CV_WRAP virtual void clear();
......@@ -627,12 +629,19 @@ public:
const CvMat* get_weights() const;
const CvMat* get_probs() const;
inline double get_log_likelihood () const { return log_likelihood; };
inline double get_log_likelihood() const { return log_likelihood; }
inline double get_log_likelihood_delta() const { return log_likelihood_delta; }
// inline const CvMat * get_log_weight_div_det () const { return log_weight_div_det; };
// inline const CvMat * get_inv_eigen_values () const { return inv_eigen_values; };
// inline const CvMat ** get_cov_rotate_mats () const { return cov_rotate_mats; };
virtual void read( CvFileStorage* fs, CvFileNode* node );
virtual void write( CvFileStorage* fs, const char* name ) const;
virtual void write_params( CvFileStorage* fs ) const;
virtual void read_params( CvFileStorage* fs, CvFileNode* node );
protected:
virtual void set_params( const CvEMParams& params,
......@@ -645,6 +654,7 @@ protected:
const CvMat* means );
CvEMParams params;
double log_likelihood;
double log_likelihood_delta;
CvMat* means;
CvMat** covs;
......
modules/ml/src/em.cpp
View file @ 3dc03531
......@@ -115,6 +115,221 @@ void CvEM::clear()
}
}
void CvEM::read( CvFileStorage* fs, CvFileNode* node )
{
bool ok = false;
CV_FUNCNAME( "CvEM::read" );
__BEGIN__;
clear();
size_t data_size;
CvEMParams _params;
CvSeqReader reader;
CvFileNode* em_node = 0;
CvFileNode* tmp_node = 0;
CvSeq* seq = 0;
CvMat **tmp_covs = 0;
CvMat **tmp_cov_rotate_mats = 0;
read_params( fs, node );
em_node = cvGetFileNodeByName( fs, node, "cvEM" );
if( !em_node )
CV_ERROR( CV_StsBadArg, "cvEM tag not found" );
CV_CALL( weights = (CvMat*)cvReadByName( fs, em_node, "weights" ));
CV_CALL( means = (CvMat*)cvReadByName( fs, em_node, "means" ));
CV_CALL( log_weight_div_det = (CvMat*)cvReadByName( fs, em_node, "log_weight_div_det" ));
CV_CALL( inv_eigen_values = (CvMat*)cvReadByName( fs, em_node, "inv_eigen_values" ));
// Size of all the following data
data_size = _params.nclusters*2*sizeof(CvMat*);
CV_CALL( tmp_covs = (CvMat**)cvAlloc( data_size ));
memset( tmp_covs, 0, data_size );
tmp_cov_rotate_mats = tmp_covs + params.nclusters;
CV_CALL( tmp_node = cvGetFileNodeByName( fs, em_node, "covs" ));
seq = tmp_node->data.seq;
if( !CV_NODE_IS_SEQ(tmp_node->tag) || seq->total != params.nclusters)
CV_ERROR( CV_StsParseError, "Missing or invalid sequence of covariance matrices" );
CV_CALL( cvStartReadSeq( seq, &reader, 0 ));
for( int i = 0; i < params.nclusters; i++ )
{
CV_CALL( tmp_covs[i] = (CvMat*)cvRead( fs, (CvFileNode*)reader.ptr ));
CV_NEXT_SEQ_ELEM( seq->elem_size, reader );
}
CV_CALL( tmp_node = cvGetFileNodeByName( fs, em_node, "cov_rotate_mats" ));
seq = tmp_node->data.seq;
if( !CV_NODE_IS_SEQ(tmp_node->tag) || seq->total != params.nclusters)
CV_ERROR( CV_StsParseError, "Missing or invalid sequence of rotated cov. matrices" );
CV_CALL( cvStartReadSeq( seq, &reader, 0 ));
for( int i = 0; i < params.nclusters; i++ )
{
CV_CALL( tmp_cov_rotate_mats[i] = (CvMat*)cvRead( fs, (CvFileNode*)reader.ptr ));
CV_NEXT_SEQ_ELEM( seq->elem_size, reader );
}
covs = tmp_covs;
cov_rotate_mats = tmp_cov_rotate_mats;
ok = true;
__END__;
if (!ok)
clear();
}
void CvEM::read_params( CvFileStorage *fs, CvFileNode *node)
{
CV_FUNCNAME( "CvEM::read_params");
__BEGIN__;
size_t data_size;
CvEMParams _params;
CvSeqReader reader;
CvFileNode* param_node = 0;
CvFileNode* tmp_node = 0;
CvSeq* seq = 0;
const char * start_step_name = 0;
const char * cov_mat_type_name = 0;
param_node = cvGetFileNodeByName( fs, node, "params" );
if( !param_node )
CV_ERROR( CV_StsBadArg, "params tag not found" );
CV_CALL( start_step_name = cvReadStringByName( fs, param_node, "start_step", 0 ) );
CV_CALL( cov_mat_type_name = cvReadStringByName( fs, param_node, "cov_mat_type", 0 ) );
if( start_step_name )
_params.start_step = strcmp( start_step_name, "START_E_STEP" ) == 0 ? START_E_STEP :
strcmp( start_step_name, "START_M_STEP" ) == 0 ? START_M_STEP :
strcmp( start_step_name, "START_AUTO_STEP" ) == 0 ? START_AUTO_STEP : 0;
else
CV_CALL( _params.start_step = cvReadIntByName( fs, param_node, "start_step", -1 ) );
if( cov_mat_type_name )
_params.cov_mat_type = strcmp( cov_mat_type_name, "COV_MAT_SPHERICAL" ) == 0 ? COV_MAT_SPHERICAL :
strcmp( cov_mat_type_name, "COV_MAT_DIAGONAL" ) == 0 ? COV_MAT_DIAGONAL :
strcmp( cov_mat_type_name, "COV_MAT_GENERIC" ) == 0 ? COV_MAT_GENERIC : 0;
else
CV_CALL( _params.cov_mat_type = cvReadIntByName( fs, param_node, "cov_mat_type", -1) );
CV_CALL( _params.nclusters = cvReadIntByName( fs, param_node, "nclusters", -1 ));
CV_CALL( _params.weights = (CvMat*)cvReadByName( fs, param_node, "weights" ));
CV_CALL( _params.means = (CvMat*)cvReadByName( fs, param_node, "means" ));
data_size = _params.nclusters*sizeof(CvMat*);
CV_CALL( _params.covs = (const CvMat**)cvAlloc( data_size ));
memset( _params.covs, 0, data_size );
CV_CALL( tmp_node = cvGetFileNodeByName( fs, param_node, "covs" ));
seq = tmp_node->data.seq;
if( !CV_NODE_IS_SEQ(tmp_node->tag) || seq->total != _params.nclusters)
CV_ERROR( CV_StsParseError, "Missing or invalid sequence of covariance matrices" );
CV_CALL( cvStartReadSeq( seq, &reader, 0 ));
for( int i = 0; i < _params.nclusters; i++ )
{
CV_CALL( _params.covs[i] = (CvMat*)cvRead( fs, (CvFileNode*)reader.ptr ));
CV_NEXT_SEQ_ELEM( seq->elem_size, reader );
}
params = _params;
__END__;
}
void CvEM::write_params( CvFileStorage* fs ) const
{
CV_FUNCNAME( "CvEM::write_params" );
__BEGIN__;
const char* cov_mat_type_name =
(params.cov_mat_type == COV_MAT_SPHERICAL) ? "COV_MAT_SPHERICAL" :
(params.cov_mat_type == COV_MAT_DIAGONAL) ? "COV_MAT_DIAGONAL" :
(params.cov_mat_type == COV_MAT_GENERIC) ? "COV_MAT_GENERIC" : 0;
const char* start_step_name =
(params.start_step == START_E_STEP) ? "START_E_STEP" :
(params.start_step == START_M_STEP) ? "START_M_STEP" :
(params.start_step == START_AUTO_STEP) ? "START_AUTO_STEP" : 0;
CV_CALL( cvStartWriteStruct( fs, "params", CV_NODE_MAP ) );
if( cov_mat_type_name )
{
CV_CALL( cvWriteString( fs, "cov_mat_type", cov_mat_type_name) );
}
else
{
CV_CALL( cvWriteInt( fs, "cov_mat_type", params.cov_mat_type ) );
}
if( start_step_name )
{
CV_CALL( cvWriteString( fs, "start_step", start_step_name) );
}
else
{
CV_CALL( cvWriteInt( fs, "cov_mat_type", params.start_step ) );
}
CV_CALL( cvWriteInt( fs, "nclusters", params.nclusters ));
CV_CALL( cvWrite( fs, "weights", weights ));
CV_CALL( cvWrite( fs, "means", means ));
CV_CALL( cvStartWriteStruct( fs, "covs", CV_NODE_SEQ ));
for( int i = 0; i < params.nclusters; i++ )
CV_CALL( cvWrite( fs, NULL, covs[i] ));
CV_CALL( cvEndWriteStruct( fs ) );
// Close params struct
CV_CALL( cvEndWriteStruct( fs ) );
__END__;
}
void CvEM::write( CvFileStorage* fs, const char* name ) const
{
CV_FUNCNAME( "CvEM::write" );
__BEGIN__;
CV_CALL( cvStartWriteStruct( fs, name, CV_NODE_MAP, CV_TYPE_NAME_ML_EM ) );
write_params(fs);
CV_CALL( cvStartWriteStruct( fs, "cvEM", CV_NODE_MAP ) );
CV_CALL( cvWrite( fs, "means", means ) );
CV_CALL( cvWrite( fs, "weights", weights ) );
CV_CALL( cvWrite( fs, "log_weight_div_det", log_weight_div_det ) );
CV_CALL( cvWrite( fs, "inv_eigen_values", inv_eigen_values ) );
CV_CALL( cvStartWriteStruct( fs, "covs", CV_NODE_SEQ ));
for( int i = 0; i < params.nclusters; i++ )
CV_CALL( cvWrite( fs, NULL, covs[i] ));
CV_CALL( cvEndWriteStruct( fs ));
CV_CALL( cvStartWriteStruct( fs, "cov_rotate_mats", CV_NODE_SEQ ));
for( int i = 0; i < params.nclusters; i++ )
CV_CALL( cvWrite( fs, NULL, cov_rotate_mats[i] ));
CV_CALL( cvEndWriteStruct( fs ) );
// close cvEM
CV_CALL( cvEndWriteStruct( fs ) );
// close top level
CV_CALL( cvEndWriteStruct( fs ) );
__END__;
}
void CvEM::set_params( const CvEMParams& _params, const CvVectors& train_data )
{
......@@ -203,6 +418,78 @@ void CvEM::set_params( const CvEMParams& _params, const CvVectors& train_data )
__END__;
}
/****************************************************************************************/
double CvEM::calcLikelihood( const cv::Mat &input_sample ) const
{
const CvMat _input_sample = input_sample;
const CvMat* _sample = &_input_sample ;
float* sample_data = 0;
int cov_mat_type = params.cov_mat_type;
int i, dims = means->cols;
int nclusters = params.nclusters;
cvPreparePredictData( _sample, dims, 0, params.nclusters, 0, &sample_data );
// allocate memory and initializing headers for calculating
cv::AutoBuffer<double> buffer(nclusters + dims);
CvMat expo = cvMat(1, nclusters, CV_64F, &buffer[0] );
CvMat diff = cvMat(1, dims, CV_64F, &buffer[nclusters] );
// calculate the probabilities
for( int k = 0; k < nclusters; k++ )
{
const double* mean_k = (const double*)(means->data.ptr + means->step*k);
const double* w = (const double*)(inv_eigen_values->data.ptr + inv_eigen_values->step*k);
double cur = log_weight_div_det->data.db[k];
CvMat* u = cov_rotate_mats[k];
// cov = u w u' --> cov^(-1) = u w^(-1) u'
if( cov_mat_type == COV_MAT_SPHERICAL )
{
double w0 = w[0];
for( i = 0; i < dims; i++ )
{
double val = sample_data[i] - mean_k[i];
cur += val*val*w0;
}
}
else
{
for( i = 0; i < dims; i++ )
diff.data.db[i] = sample_data[i] - mean_k[i];
if( cov_mat_type == COV_MAT_GENERIC )
cvGEMM( &diff, u, 1, 0, 0, &diff, CV_GEMM_B_T );
for( i = 0; i < dims; i++ )
{
double val = diff.data.db[i];
cur += val*val*w[i];
}
}
expo.data.db[k] = cur;
}
// probability = (2*pi)^(-dims/2)*exp( -0.5 * cur )
cvConvertScale( &expo, &expo, -0.5 );
double factor = -double(dims)/2.0 * log(2.0*M_PI);
cvAndS( &expo, cvScalar(factor), &expo );
// Calculate the log-likelihood of the given sample -
// see Alex Smola's blog http://blog.smola.org/page/2 for
// details on the log-sum-exp trick
double mini,maxi,retval;
cvMinMaxLoc( &expo, &mini, &maxi, 0, 0 );
CvMat *flp = cvCloneMat(&expo);
cvSubS( &expo, cvScalar(maxi), flp);
cvExp( flp, flp );
CvScalar ss = cvSum( flp );
retval = log(ss.val[0]) + maxi;
cvReleaseMat(&flp);
if( sample_data != _sample->data.fl )
cvFree( &sample_data );
return retval;
}
/****************************************************************************************/
float
......@@ -219,12 +506,12 @@ CvEM::predict( const CvMat* _sample, CvMat* _probs ) const
cvPreparePredictData( _sample, dims, 0, params.nclusters, _probs, &sample_data );
// allocate memory and initializing headers for calculating
// allocate memory and initializing headers for calculating
cv::AutoBuffer<double> buffer(nclusters + dims);
CvMat expo = cvMat(1, nclusters, CV_64F, &buffer[0] );
CvMat diff = cvMat(1, dims, CV_64F, &buffer[nclusters] );
// calculate the probabilities
// calculate the probabilities
for( int k = 0; k < nclusters; k++ )
{
const double* mean_k = (const double*)(means->data.ptr + means->step*k);
......@@ -260,12 +547,17 @@ CvEM::predict( const CvMat* _sample, CvMat* _probs ) const
cls = k;
opt = cur;
}
/* probability = (2*pi)^(-dims/2)*exp( -0.5 * cur ) */
}
// probability = (2*pi)^(-dims/2)*exp( -0.5 * cur )
cvConvertScale( &expo, &expo, -0.5 );
double factor = -double(dims)/2.0 * log(2.0*M_PI);
cvAndS( &expo, cvScalar(factor), &expo );
// Calculate the posterior probability of each component
// given the sample data.
if( _probs )
{
cvConvertScale( &expo, &expo, -0.5 );
cvExp( &expo, &expo );
if( _probs->cols == 1 )
cvReshape( &expo, &expo, 0, nclusters );
......@@ -336,6 +628,7 @@ bool CvEM::train( const CvMat* _samples, const CvMat* _sample_idx,
init_em( train_data );
log_likelihood = run_em( train_data );
if( log_likelihood <= -DBL_MAX/10000. )
EXIT;
......@@ -497,8 +790,11 @@ void CvEM::init_auto( const CvVectors& train_data )
if( nclusters > 1 )
{
CV_CALL( labels = cvCreateMat( 1, nsamples, CV_32SC1 ));
// Not fully executed in case means are already given
kmeans( train_data, nclusters, labels, cvTermCriteria( CV_TERMCRIT_ITER,
params.means ? 1 : 10, 0.5 ), params.means );
CV_CALL( cvSortSamplesByClasses( (const float**)train_data.data.fl,
labels, class_ranges->data.i ));
}
......@@ -855,18 +1151,18 @@ double CvEM::run_em( const CvVectors& train_data )
else
cvTranspose( cvGetDiag( covs[k], &diag ), w );
w_data = w->data.db;
for( j = 0, det = 1.; j < dims; j++ )
det *= w_data[j];
if( det < min_det_value )
for( j = 0, det = 0.; j < dims; j++ )
det += std::log(w_data[j]);
if( det < std::log(min_det_value) )
{
if( start_step == START_AUTO_STEP )
det = min_det_value;
det = std::log(min_det_value);
else
EXIT;
}
log_det->data.db[k] = det;
}
else
else // spherical
{
d = cvTrace(covs[k]).val[0]/(double)dims;
if( d < min_variation )
......@@ -881,10 +1177,12 @@ double CvEM::run_em( const CvVectors& train_data )
}
}
cvLog( log_det, log_det );
if( is_spherical )
{
cvLog( log_det, log_det );
cvScale( log_det, log_det, dims );
}
}
for( n = 0; n < params.term_crit.max_iter; n++ )
{
......@@ -952,9 +1250,12 @@ double CvEM::run_em( const CvVectors& train_data )
}
_log_likelihood+=sum_max_val;
// check termination criteria
//if( fabs( (_log_likelihood - prev_log_likelihood) / prev_log_likelihood ) < params.term_crit.epsilon )
if( fabs( (_log_likelihood - prev_log_likelihood) ) < params.term_crit.epsilon )
// Check termination criteria. Use the same termination criteria as it is used in MATLAB
log_likelihood_delta = _log_likelihood - prev_log_likelihood;
// if( n>0 )
// fprintf(stderr, "iter=%d, ll=%0.5f (delta=%0.5f, goal=%0.5f)\n",
// n, _log_likelihood, delta, params.term_crit.epsilon * fabs( _log_likelihood));
if ( log_likelihood_delta > 0 && log_likelihood_delta < params.term_crit.epsilon * std::fabs( _log_likelihood) )
break;
prev_log_likelihood = _log_likelihood;
}
......@@ -1009,11 +1310,12 @@ double CvEM::run_em( const CvVectors& train_data )
}
else
{
// Det. of general NxN cov. matrix is the prod. of the eig. vals
if( is_general )
cvSVD( cov, w, cov_rotate_mats[k], 0, CV_SVD_U_T );
cvMaxS( w, min_variation, w );
for( j = 0, det = 1.; j < dims; j++ )
det *= w_data[j];
for( j = 0, det = 0.; j < dims; j++ )
det += std::log( w_data[j] );
log_det->data.db[k] = det;
}
}
......@@ -1021,9 +1323,11 @@ double CvEM::run_em( const CvVectors& train_data )
cvConvertScale( weights, weights, 1./(double)nsamples, 0 );
cvMaxS( weights, DBL_MIN, weights );
cvLog( log_det, log_det );
if( is_spherical )
{
cvLog( log_det, log_det );
cvScale( log_det, log_det, dims );
}
} // end of iteration process
//log_weight_div_det[k] = -2*log(weights_k/det(Sigma_k))^0.5) = -2*log(weights_k) + log(det(Sigma_k)))
......
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