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Merge pull request #669 from vpisarev:fast_lin_svm

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Vadim Pisarevsky 2013-03-21 12:22:35 +04:00 committed by OpenCV Buildbot
commit 321070ccf0
3 changed files with 92 additions and 12 deletions
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2
modules/ml/include/opencv2/ml/ml.hpp
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@ -534,6 +534,8 @@ protected:
virtual void write_params( CvFileStorage* fs ) const;
virtual void read_params( CvFileStorage* fs, CvFileNode* node );
void optimize_linear_svm();
CvSVMParams params;
CvMat* class_labels;
int var_all;

55
modules/ml/src/svm.cpp
View File

@ -1517,6 +1517,7 @@ bool CvSVM::do_train( int svm_type, int sample_count, int var_count, const float
}
}
optimize_linear_svm();
ok = true;
__END__;
@ -1524,6 +1525,59 @@ bool CvSVM::do_train( int svm_type, int sample_count, int var_count, const float
return ok;
}
void CvSVM::optimize_linear_svm()
{
// we optimize only linear SVM: compress all the support vectors into one.
if( params.kernel_type != LINEAR )
return;
int class_count = class_labels ? class_labels->cols :
params.svm_type == CvSVM::ONE_CLASS ? 1 : 0;
int i, df_count = class_count > 1 ? class_count*(class_count-1)/2 : 1;
CvSVMDecisionFunc* df = decision_func;
for( i = 0; i < df_count; i++ )
{
int sv_count = df[i].sv_count;
if( sv_count != 1 )
break;
}
// if every decision functions uses a single support vector;
// it's already compressed. skip it then.
if( i == df_count )
return;
int var_count = get_var_count();
int sample_size = (int)(var_count*sizeof(sv[0][0]));
float** new_sv = (float**)cvMemStorageAlloc(storage, df_count*sizeof(new_sv[0]));
for( i = 0; i < df_count; i++ )
{
new_sv[i] = (float*)cvMemStorageAlloc(storage, sample_size);
float* dst = new_sv[i];
memset(dst, 0, sample_size);
int j, k, sv_count = df[i].sv_count;
for( j = 0; j < sv_count; j++ )
{
const float* src = class_count > 1 ? sv[df[i].sv_index[j]] : sv[j];
double a = df[i].alpha[j];
for( k = 0; k < var_count; k++ )
dst[k] = (float)(dst[k] + src[k]*a);
}
df[i].sv_count = 1;
df[i].alpha[0] = 1.;
if( class_count > 1 )
df[i].sv_index[0] = i;
}
sv = new_sv;
sv_total = df_count;
}
bool CvSVM::train( const CvMat* _train_data, const CvMat* _responses,
const CvMat* _var_idx, const CvMat* _sample_idx, CvSVMParams _params )
{
@ -2516,6 +2570,7 @@ void CvSVM::read( CvFileStorage* fs, CvFileNode* svm_node )
CV_NEXT_SEQ_ELEM( df_node->data.seq->elem_size, reader );
}
optimize_linear_svm();
create_kernel();
__END__;

47
samples/cpp/letter_recog.cpp
View File

@ -131,7 +131,7 @@ int build_rtrees_classifier( char* data_filename,
printf( "Could not read the classifier %s\n", filename_to_load );
return -1;
}
printf( "The classifier %s is loaded.\n", data_filename );
printf( "The classifier %s is loaded.\n", filename_to_load );
}
else
{
@ -262,7 +262,7 @@ int build_boost_classifier( char* data_filename,
printf( "Could not read the classifier %s\n", filename_to_load );
return -1;
}
printf( "The classifier %s is loaded.\n", data_filename );
printf( "The classifier %s is loaded.\n", filename_to_load );
}
else
{
@ -403,7 +403,7 @@ int build_mlp_classifier( char* data_filename,
printf( "Could not read the classifier %s\n", filename_to_load );
return -1;
}
printf( "The classifier %s is loaded.\n", data_filename );
printf( "The classifier %s is loaded.\n", filename_to_load );
}
else
{
@ -639,10 +639,11 @@ int build_nbayes_classifier( char* data_filename )
}
static
int build_svm_classifier( char* data_filename )
int build_svm_classifier( char* data_filename, const char* filename_to_save, const char* filename_to_load )
{
CvMat* data = 0;
CvMat* responses = 0;
CvMat* train_resp = 0;
CvMat train_data;
int nsamples_all = 0, ntrain_samples = 0;
int var_count;
@ -666,13 +667,29 @@ int build_svm_classifier( char* data_filename )
ntrain_samples = (int)(nsamples_all*0.1);
var_count = data->cols;
// train classifier
printf( "Training the classifier (may take a few minutes)...\n");
cvGetRows( data, &train_data, 0, ntrain_samples );
CvMat* train_resp = cvCreateMat( ntrain_samples, 1, CV_32FC1);
for (int i = 0; i < ntrain_samples; i++)
train_resp->data.fl[i] = responses->data.fl[i];
svm.train(&train_data, train_resp, 0, 0, param);
// Create or load Random Trees classifier
if( filename_to_load )
{
// load classifier from the specified file
svm.load( filename_to_load );
ntrain_samples = 0;
if( svm.get_var_count() == 0 )
{
printf( "Could not read the classifier %s\n", filename_to_load );
return -1;
}
printf( "The classifier %s is loaded.\n", filename_to_load );
}
else
{
// train classifier
printf( "Training the classifier (may take a few minutes)...\n");
cvGetRows( data, &train_data, 0, ntrain_samples );
train_resp = cvCreateMat( ntrain_samples, 1, CV_32FC1);
for (int i = 0; i < ntrain_samples; i++)
train_resp->data.fl[i] = responses->data.fl[i];
svm.train(&train_data, train_resp, 0, 0, param);
}
// classification
std::vector<float> _sample(var_count * (nsamples_all - ntrain_samples));
@ -691,7 +708,10 @@ int build_svm_classifier( char* data_filename )
CvMat *result = cvCreateMat(1, nsamples_all - ntrain_samples, CV_32FC1);
printf("Classification (may take a few minutes)...\n");
double t = (double)cvGetTickCount();
svm.predict(&sample, result);
t = (double)cvGetTickCount() - t;
printf("Prediction type: %gms\n", t/(cvGetTickFrequency()*1000.));
int true_resp = 0;
for (int i = 0; i < nsamples_all - ntrain_samples; i++)
@ -702,6 +722,9 @@ int build_svm_classifier( char* data_filename )
printf("true_resp = %f%%\n", (float)true_resp / (nsamples_all - ntrain_samples) * 100);
if( filename_to_save )
svm.save( filename_to_save );
cvReleaseMat( &train_resp );
cvReleaseMat( &result );
cvReleaseMat( &data );
@ -772,7 +795,7 @@ int main( int argc, char *argv[] )
method == 4 ?
build_nbayes_classifier( data_filename) :
method == 5 ?
build_svm_classifier( data_filename ):
build_svm_classifier( data_filename, filename_to_save, filename_to_load ):
-1) < 0)
{
help();