diff --git a/modules/ml/include/opencv2/ml/ml.hpp b/modules/ml/include/opencv2/ml/ml.hpp
index dc7a4048a..6612d2ea1 100644
--- a/modules/ml/include/opencv2/ml/ml.hpp
+++ b/modules/ml/include/opencv2/ml/ml.hpp
@@ -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;
diff --git a/modules/ml/src/svm.cpp b/modules/ml/src/svm.cpp
index 9cbc46be8..3c970f201 100644
--- a/modules/ml/src/svm.cpp
+++ b/modules/ml/src/svm.cpp
@@ -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__;
diff --git a/samples/cpp/letter_recog.cpp b/samples/cpp/letter_recog.cpp
index 49a55fc62..74d5971ca 100644
--- a/samples/cpp/letter_recog.cpp
+++ b/samples/cpp/letter_recog.cpp
@@ -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();