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integrated parallel SVM prediction; fixed warnings after meanshift integration

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This commit is contained in:
Vadim Pisarevsky
2011-04-19 16:20:44 +00:00
parent 537a36115f
commit 17a2480a21
5 changed files with 133 additions and 10 deletions
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82
samples/cpp/letter_recog.cpp
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@@ -9,7 +9,7 @@
void help()
{
printf("\nThe sample demonstrates how to train Random Trees classifier\n"
"(or Boosting classifier, or MLP, or Knearest, or Nbayes - see main()) using the provided dataset.\n"
"(or Boosting classifier, or MLP, or Knearest, or Nbayes, or Support Vector Machines - see main()) using the provided dataset.\n"
"\n"
"We use the sample database letter-recognition.data\n"
"from UCI Repository, here is the link:\n"
@@ -28,7 +28,7 @@ void help()
"The usage: letter_recog [-data <path to letter-recognition.data>] \\\n"
" [-save <output XML file for the classifier>] \\\n"
" [-load <XML file with the pre-trained classifier>] \\\n"
" [-boost|-mlp|-knearest|-nbayes] # to use boost/mlp/knearest classifier instead of default Random Trees\n" );
" [-boost|-mlp|-knearest|-nbayes|-svm] # to use boost/mlp/knearest/SVM classifier instead of default Random Trees\n" );
}
// This function reads data and responses from the file <filename>
@@ -630,6 +630,78 @@ int build_nbayes_classifier( char* data_filename )
return 0;
}
static
int build_svm_classifier( char* data_filename )
{
CvMat* data = 0;
CvMat* responses = 0;
CvMat train_data;
int nsamples_all = 0, ntrain_samples = 0;
int var_count;
CvSVM svm;
int ok = read_num_class_data( data_filename, 16, &data, &responses );
if( !ok )
{
printf( "Could not read the database %s\n", data_filename );
return -1;
}
////////// SVM parameters ///////////////////////////////
CvSVMParams param;
param.kernel_type=CvSVM::LINEAR;
param.svm_type=CvSVM::C_SVC;
param.C=1;
///////////////////////////////////////////////////////////
printf( "The database %s is loaded.\n", data_filename );
nsamples_all = data->rows;
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);
// classification
float _sample[var_count * (nsamples_all - ntrain_samples)];
CvMat sample = cvMat( nsamples_all - ntrain_samples, 16, CV_32FC1, _sample );
float true_results[nsamples_all - ntrain_samples];
for (int j = ntrain_samples; j < nsamples_all; j++)
{
float *s = data->data.fl + j * var_count;
for (int i = 0; i < var_count; i++)
{
sample.data.fl[(j - ntrain_samples) * var_count + i] = s[i];
}
true_results[j - ntrain_samples] = responses->data.fl[j];
}
CvMat *result = cvCreateMat(1, nsamples_all - ntrain_samples, CV_32FC1);
printf("Classification (may take a few minutes)...\n");
(int)svm.predict(&sample, result);
int true_resp = 0;
for (int i = 0; i < nsamples_all - ntrain_samples; i++)
{
if (result->data.fl[i] == true_results[i])
true_resp++;
}
printf("true_resp = %f%%\n", (float)true_resp / (nsamples_all - ntrain_samples) * 100);
cvReleaseMat( &train_resp );
cvReleaseMat( &result );
cvReleaseMat( &data );
cvReleaseMat( &responses );
return 0;
}
int main( int argc, char *argv[] )
{
char* filename_to_save = 0;
@@ -672,6 +744,10 @@ int main( int argc, char *argv[] )
{
method = 4;
}
else if ( strcmp(argv[i], "-svm") == 0)
{
method = 5;
}
else
break;
}
@@ -687,6 +763,8 @@ int main( int argc, char *argv[] )
build_knearest_classifier( data_filename, 10 ) :
method == 4 ?
build_nbayes_classifier( data_filename) :
method == 5 ?
build_svm_classifier( data_filename ):
-1) < 0)
{
help();