This commit is contained in:
Gary Bradski 2010-12-04 08:30:46 +00:00
parent 67d8f9ae73
commit ecded116db

View File

@ -3,23 +3,34 @@
#include <cstdio>
/*
The sample demonstrates how to train Random Trees classifier
(or Boosting classifier, or MLP - see main()) using the provided dataset.
We use the sample database letter-recognition.data
from UCI Repository, here is the link:
Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998).
UCI Repository of machine learning databases
[http://www.ics.uci.edu/~mlearn/MLRepository.html].
Irvine, CA: University of California, Department of Information and Computer Science.
The dataset consists of 20000 feature vectors along with the
responses - capital latin letters A..Z.
The first 16000 (10000 for boosting)) samples are used for training
and the remaining 4000 (10000 for boosting) - to test the classifier.
*/
void help()
{
printf("\nThe sample demonstrates how to train Random Trees classifier\n"
"(or Boosting classifier, or MLP - 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"
"\n"
"Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998).\n"
"UCI Repository of machine learning databases\n"
"[http://www.ics.uci.edu/~mlearn/MLRepository.html].\n"
"Irvine, CA: University of California, Department of Information and Computer Science.\n"
"\n"
"The dataset consists of 20000 feature vectors along with the\n"
"responses - capital latin letters A..Z.\n"
"The first 16000 (10000 for boosting)) samples are used for training\n"
"and the remaining 4000 (10000 for boosting) - to test the classifier.\n"
"======================================================\n");
printf("\nThis is letter recognition sample.\n"
"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] # to use boost/mlp classifier instead of default Random Trees\n" );
}
// This function reads data and responses from the file <filename>
static int
read_num_class_data( const char* filename, int var_count,
@ -521,11 +532,7 @@ int main( int argc, char *argv[] )
build_mlp_classifier( data_filename, filename_to_save, filename_to_load ) :
-1) < 0)
{
printf("This is letter recognition sample.\n"
"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] # to use boost/mlp classifier instead of default Random Trees\n" );
help();
}
return 0;
}