mirror of
https://github.com/opencv/opencv.git
synced 2024-12-27 11:28:14 +08:00
805 lines
25 KiB
C++
805 lines
25 KiB
C++
#include "opencv2/core/core_c.h"
|
|
#include "opencv2/ml/ml.hpp"
|
|
|
|
#include <cstdio>
|
|
#include <vector>
|
|
/*
|
|
|
|
*/
|
|
|
|
static void help()
|
|
{
|
|
printf("\nThe sample demonstrates how to train Random Trees classifier\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"
|
|
"\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|-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>
|
|
static int
|
|
read_num_class_data( const char* filename, int var_count,
|
|
CvMat** data, CvMat** responses )
|
|
{
|
|
const int M = 1024;
|
|
FILE* f = fopen( filename, "rt" );
|
|
CvMemStorage* storage;
|
|
CvSeq* seq;
|
|
char buf[M+2];
|
|
float* el_ptr;
|
|
CvSeqReader reader;
|
|
int i, j;
|
|
|
|
if( !f )
|
|
return 0;
|
|
|
|
el_ptr = new float[var_count+1];
|
|
storage = cvCreateMemStorage();
|
|
seq = cvCreateSeq( 0, sizeof(*seq), (var_count+1)*sizeof(float), storage );
|
|
|
|
for(;;)
|
|
{
|
|
char* ptr;
|
|
if( !fgets( buf, M, f ) || !strchr( buf, ',' ) )
|
|
break;
|
|
el_ptr[0] = buf[0];
|
|
ptr = buf+2;
|
|
for( i = 1; i <= var_count; i++ )
|
|
{
|
|
int n = 0;
|
|
sscanf( ptr, "%f%n", el_ptr + i, &n );
|
|
ptr += n + 1;
|
|
}
|
|
if( i <= var_count )
|
|
break;
|
|
cvSeqPush( seq, el_ptr );
|
|
}
|
|
fclose(f);
|
|
|
|
*data = cvCreateMat( seq->total, var_count, CV_32F );
|
|
*responses = cvCreateMat( seq->total, 1, CV_32F );
|
|
|
|
cvStartReadSeq( seq, &reader );
|
|
|
|
for( i = 0; i < seq->total; i++ )
|
|
{
|
|
const float* sdata = (float*)reader.ptr + 1;
|
|
float* ddata = data[0]->data.fl + var_count*i;
|
|
float* dr = responses[0]->data.fl + i;
|
|
|
|
for( j = 0; j < var_count; j++ )
|
|
ddata[j] = sdata[j];
|
|
*dr = sdata[-1];
|
|
CV_NEXT_SEQ_ELEM( seq->elem_size, reader );
|
|
}
|
|
|
|
cvReleaseMemStorage( &storage );
|
|
delete[] el_ptr;
|
|
return 1;
|
|
}
|
|
|
|
static
|
|
int build_rtrees_classifier( char* data_filename,
|
|
char* filename_to_save, char* filename_to_load )
|
|
{
|
|
CvMat* data = 0;
|
|
CvMat* responses = 0;
|
|
CvMat* var_type = 0;
|
|
CvMat* sample_idx = 0;
|
|
|
|
int ok = read_num_class_data( data_filename, 16, &data, &responses );
|
|
int nsamples_all = 0, ntrain_samples = 0;
|
|
int i = 0;
|
|
double train_hr = 0, test_hr = 0;
|
|
CvRTrees forest;
|
|
CvMat* var_importance = 0;
|
|
|
|
if( !ok )
|
|
{
|
|
printf( "Could not read the database %s\n", data_filename );
|
|
return -1;
|
|
}
|
|
|
|
printf( "The database %s is loaded.\n", data_filename );
|
|
nsamples_all = data->rows;
|
|
ntrain_samples = (int)(nsamples_all*0.8);
|
|
|
|
// Create or load Random Trees classifier
|
|
if( filename_to_load )
|
|
{
|
|
// load classifier from the specified file
|
|
forest.load( filename_to_load );
|
|
ntrain_samples = 0;
|
|
if( forest.get_tree_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
|
|
{
|
|
// create classifier by using <data> and <responses>
|
|
printf( "Training the classifier ...\n");
|
|
|
|
// 1. create type mask
|
|
var_type = cvCreateMat( data->cols + 1, 1, CV_8U );
|
|
cvSet( var_type, cvScalarAll(CV_VAR_ORDERED) );
|
|
cvSetReal1D( var_type, data->cols, CV_VAR_CATEGORICAL );
|
|
|
|
// 2. create sample_idx
|
|
sample_idx = cvCreateMat( 1, nsamples_all, CV_8UC1 );
|
|
{
|
|
CvMat mat;
|
|
cvGetCols( sample_idx, &mat, 0, ntrain_samples );
|
|
cvSet( &mat, cvRealScalar(1) );
|
|
|
|
cvGetCols( sample_idx, &mat, ntrain_samples, nsamples_all );
|
|
cvSetZero( &mat );
|
|
}
|
|
|
|
// 3. train classifier
|
|
forest.train( data, CV_ROW_SAMPLE, responses, 0, sample_idx, var_type, 0,
|
|
CvRTParams(10,10,0,false,15,0,true,4,100,0.01f,CV_TERMCRIT_ITER));
|
|
printf( "\n");
|
|
}
|
|
|
|
// compute prediction error on train and test data
|
|
for( i = 0; i < nsamples_all; i++ )
|
|
{
|
|
double r;
|
|
CvMat sample;
|
|
cvGetRow( data, &sample, i );
|
|
|
|
r = forest.predict( &sample );
|
|
r = fabs((double)r - responses->data.fl[i]) <= FLT_EPSILON ? 1 : 0;
|
|
|
|
if( i < ntrain_samples )
|
|
train_hr += r;
|
|
else
|
|
test_hr += r;
|
|
}
|
|
|
|
test_hr /= (double)(nsamples_all-ntrain_samples);
|
|
train_hr /= (double)ntrain_samples;
|
|
printf( "Recognition rate: train = %.1f%%, test = %.1f%%\n",
|
|
train_hr*100., test_hr*100. );
|
|
|
|
printf( "Number of trees: %d\n", forest.get_tree_count() );
|
|
|
|
// Print variable importance
|
|
var_importance = (CvMat*)forest.get_var_importance();
|
|
if( var_importance )
|
|
{
|
|
double rt_imp_sum = cvSum( var_importance ).val[0];
|
|
printf("var#\timportance (in %%):\n");
|
|
for( i = 0; i < var_importance->cols; i++ )
|
|
printf( "%-2d\t%-4.1f\n", i,
|
|
100.f*var_importance->data.fl[i]/rt_imp_sum);
|
|
}
|
|
|
|
//Print some proximitites
|
|
printf( "Proximities between some samples corresponding to the letter 'T':\n" );
|
|
{
|
|
CvMat sample1, sample2;
|
|
const int pairs[][2] = {{0,103}, {0,106}, {106,103}, {-1,-1}};
|
|
|
|
for( i = 0; pairs[i][0] >= 0; i++ )
|
|
{
|
|
cvGetRow( data, &sample1, pairs[i][0] );
|
|
cvGetRow( data, &sample2, pairs[i][1] );
|
|
printf( "proximity(%d,%d) = %.1f%%\n", pairs[i][0], pairs[i][1],
|
|
forest.get_proximity( &sample1, &sample2 )*100. );
|
|
}
|
|
}
|
|
|
|
// Save Random Trees classifier to file if needed
|
|
if( filename_to_save )
|
|
forest.save( filename_to_save );
|
|
|
|
cvReleaseMat( &sample_idx );
|
|
cvReleaseMat( &var_type );
|
|
cvReleaseMat( &data );
|
|
cvReleaseMat( &responses );
|
|
|
|
return 0;
|
|
}
|
|
|
|
|
|
static
|
|
int build_boost_classifier( char* data_filename,
|
|
char* filename_to_save, char* filename_to_load )
|
|
{
|
|
const int class_count = 26;
|
|
CvMat* data = 0;
|
|
CvMat* responses = 0;
|
|
CvMat* var_type = 0;
|
|
CvMat* temp_sample = 0;
|
|
CvMat* weak_responses = 0;
|
|
|
|
int ok = read_num_class_data( data_filename, 16, &data, &responses );
|
|
int nsamples_all = 0, ntrain_samples = 0;
|
|
int var_count;
|
|
int i, j, k;
|
|
double train_hr = 0, test_hr = 0;
|
|
CvBoost boost;
|
|
|
|
if( !ok )
|
|
{
|
|
printf( "Could not read the database %s\n", data_filename );
|
|
return -1;
|
|
}
|
|
|
|
printf( "The database %s is loaded.\n", data_filename );
|
|
nsamples_all = data->rows;
|
|
ntrain_samples = (int)(nsamples_all*0.5);
|
|
var_count = data->cols;
|
|
|
|
// Create or load Boosted Tree classifier
|
|
if( filename_to_load )
|
|
{
|
|
// load classifier from the specified file
|
|
boost.load( filename_to_load );
|
|
ntrain_samples = 0;
|
|
if( !boost.get_weak_predictors() )
|
|
{
|
|
printf( "Could not read the classifier %s\n", filename_to_load );
|
|
return -1;
|
|
}
|
|
printf( "The classifier %s is loaded.\n", filename_to_load );
|
|
}
|
|
else
|
|
{
|
|
// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
|
//
|
|
// As currently boosted tree classifier in MLL can only be trained
|
|
// for 2-class problems, we transform the training database by
|
|
// "unrolling" each training sample as many times as the number of
|
|
// classes (26) that we have.
|
|
//
|
|
// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
|
|
|
CvMat* new_data = cvCreateMat( ntrain_samples*class_count, var_count + 1, CV_32F );
|
|
CvMat* new_responses = cvCreateMat( ntrain_samples*class_count, 1, CV_32S );
|
|
|
|
// 1. unroll the database type mask
|
|
printf( "Unrolling the database...\n");
|
|
for( i = 0; i < ntrain_samples; i++ )
|
|
{
|
|
float* data_row = (float*)(data->data.ptr + data->step*i);
|
|
for( j = 0; j < class_count; j++ )
|
|
{
|
|
float* new_data_row = (float*)(new_data->data.ptr +
|
|
new_data->step*(i*class_count+j));
|
|
for( k = 0; k < var_count; k++ )
|
|
new_data_row[k] = data_row[k];
|
|
new_data_row[var_count] = (float)j;
|
|
new_responses->data.i[i*class_count + j] = responses->data.fl[i] == j+'A';
|
|
}
|
|
}
|
|
|
|
// 2. create type mask
|
|
var_type = cvCreateMat( var_count + 2, 1, CV_8U );
|
|
cvSet( var_type, cvScalarAll(CV_VAR_ORDERED) );
|
|
// the last indicator variable, as well
|
|
// as the new (binary) response are categorical
|
|
cvSetReal1D( var_type, var_count, CV_VAR_CATEGORICAL );
|
|
cvSetReal1D( var_type, var_count+1, CV_VAR_CATEGORICAL );
|
|
|
|
// 3. train classifier
|
|
printf( "Training the classifier (may take a few minutes)...\n");
|
|
boost.train( new_data, CV_ROW_SAMPLE, new_responses, 0, 0, var_type, 0,
|
|
CvBoostParams(CvBoost::REAL, 100, 0.95, 5, false, 0 ));
|
|
cvReleaseMat( &new_data );
|
|
cvReleaseMat( &new_responses );
|
|
printf("\n");
|
|
}
|
|
|
|
temp_sample = cvCreateMat( 1, var_count + 1, CV_32F );
|
|
weak_responses = cvCreateMat( 1, boost.get_weak_predictors()->total, CV_32F );
|
|
|
|
// compute prediction error on train and test data
|
|
for( i = 0; i < nsamples_all; i++ )
|
|
{
|
|
int best_class = 0;
|
|
double max_sum = -DBL_MAX;
|
|
double r;
|
|
CvMat sample;
|
|
cvGetRow( data, &sample, i );
|
|
for( k = 0; k < var_count; k++ )
|
|
temp_sample->data.fl[k] = sample.data.fl[k];
|
|
|
|
for( j = 0; j < class_count; j++ )
|
|
{
|
|
temp_sample->data.fl[var_count] = (float)j;
|
|
boost.predict( temp_sample, 0, weak_responses );
|
|
double sum = cvSum( weak_responses ).val[0];
|
|
if( max_sum < sum )
|
|
{
|
|
max_sum = sum;
|
|
best_class = j + 'A';
|
|
}
|
|
}
|
|
|
|
r = fabs(best_class - responses->data.fl[i]) < FLT_EPSILON ? 1 : 0;
|
|
|
|
if( i < ntrain_samples )
|
|
train_hr += r;
|
|
else
|
|
test_hr += r;
|
|
}
|
|
|
|
test_hr /= (double)(nsamples_all-ntrain_samples);
|
|
train_hr /= (double)ntrain_samples;
|
|
printf( "Recognition rate: train = %.1f%%, test = %.1f%%\n",
|
|
train_hr*100., test_hr*100. );
|
|
|
|
printf( "Number of trees: %d\n", boost.get_weak_predictors()->total );
|
|
|
|
// Save classifier to file if needed
|
|
if( filename_to_save )
|
|
boost.save( filename_to_save );
|
|
|
|
cvReleaseMat( &temp_sample );
|
|
cvReleaseMat( &weak_responses );
|
|
cvReleaseMat( &var_type );
|
|
cvReleaseMat( &data );
|
|
cvReleaseMat( &responses );
|
|
|
|
return 0;
|
|
}
|
|
|
|
|
|
static
|
|
int build_mlp_classifier( char* data_filename,
|
|
char* filename_to_save, char* filename_to_load )
|
|
{
|
|
const int class_count = 26;
|
|
CvMat* data = 0;
|
|
CvMat train_data;
|
|
CvMat* responses = 0;
|
|
CvMat* mlp_response = 0;
|
|
|
|
int ok = read_num_class_data( data_filename, 16, &data, &responses );
|
|
int nsamples_all = 0, ntrain_samples = 0;
|
|
int i, j;
|
|
double train_hr = 0, test_hr = 0;
|
|
CvANN_MLP mlp;
|
|
|
|
if( !ok )
|
|
{
|
|
printf( "Could not read the database %s\n", data_filename );
|
|
return -1;
|
|
}
|
|
|
|
printf( "The database %s is loaded.\n", data_filename );
|
|
nsamples_all = data->rows;
|
|
ntrain_samples = (int)(nsamples_all*0.8);
|
|
|
|
// Create or load MLP classifier
|
|
if( filename_to_load )
|
|
{
|
|
// load classifier from the specified file
|
|
mlp.load( filename_to_load );
|
|
ntrain_samples = 0;
|
|
if( !mlp.get_layer_count() )
|
|
{
|
|
printf( "Could not read the classifier %s\n", filename_to_load );
|
|
return -1;
|
|
}
|
|
printf( "The classifier %s is loaded.\n", filename_to_load );
|
|
}
|
|
else
|
|
{
|
|
// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
|
//
|
|
// MLP does not support categorical variables by explicitly.
|
|
// So, instead of the output class label, we will use
|
|
// a binary vector of <class_count> components for training and,
|
|
// therefore, MLP will give us a vector of "probabilities" at the
|
|
// prediction stage
|
|
//
|
|
// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
|
|
|
CvMat* new_responses = cvCreateMat( ntrain_samples, class_count, CV_32F );
|
|
|
|
// 1. unroll the responses
|
|
printf( "Unrolling the responses...\n");
|
|
for( i = 0; i < ntrain_samples; i++ )
|
|
{
|
|
int cls_label = cvRound(responses->data.fl[i]) - 'A';
|
|
float* bit_vec = (float*)(new_responses->data.ptr + i*new_responses->step);
|
|
for( j = 0; j < class_count; j++ )
|
|
bit_vec[j] = 0.f;
|
|
bit_vec[cls_label] = 1.f;
|
|
}
|
|
cvGetRows( data, &train_data, 0, ntrain_samples );
|
|
|
|
// 2. train classifier
|
|
int layer_sz[] = { data->cols, 100, 100, class_count };
|
|
CvMat layer_sizes =
|
|
cvMat( 1, (int)(sizeof(layer_sz)/sizeof(layer_sz[0])), CV_32S, layer_sz );
|
|
mlp.create( &layer_sizes );
|
|
printf( "Training the classifier (may take a few minutes)...\n");
|
|
|
|
#if 1
|
|
int method = CvANN_MLP_TrainParams::BACKPROP;
|
|
double method_param = 0.001;
|
|
int max_iter = 300;
|
|
#else
|
|
int method = CvANN_MLP_TrainParams::RPROP;
|
|
double method_param = 0.1;
|
|
int max_iter = 1000;
|
|
#endif
|
|
|
|
mlp.train( &train_data, new_responses, 0, 0,
|
|
CvANN_MLP_TrainParams(cvTermCriteria(CV_TERMCRIT_ITER,max_iter,0.01),
|
|
method, method_param));
|
|
cvReleaseMat( &new_responses );
|
|
printf("\n");
|
|
}
|
|
|
|
mlp_response = cvCreateMat( 1, class_count, CV_32F );
|
|
|
|
// compute prediction error on train and test data
|
|
for( i = 0; i < nsamples_all; i++ )
|
|
{
|
|
int best_class;
|
|
CvMat sample;
|
|
cvGetRow( data, &sample, i );
|
|
CvPoint max_loc;
|
|
mlp.predict( &sample, mlp_response );
|
|
cvMinMaxLoc( mlp_response, 0, 0, 0, &max_loc, 0 );
|
|
best_class = max_loc.x + 'A';
|
|
|
|
int r = fabs((double)best_class - responses->data.fl[i]) < FLT_EPSILON ? 1 : 0;
|
|
|
|
if( i < ntrain_samples )
|
|
train_hr += r;
|
|
else
|
|
test_hr += r;
|
|
}
|
|
|
|
test_hr /= (double)(nsamples_all-ntrain_samples);
|
|
train_hr /= (double)ntrain_samples;
|
|
printf( "Recognition rate: train = %.1f%%, test = %.1f%%\n",
|
|
train_hr*100., test_hr*100. );
|
|
|
|
// Save classifier to file if needed
|
|
if( filename_to_save )
|
|
mlp.save( filename_to_save );
|
|
|
|
cvReleaseMat( &mlp_response );
|
|
cvReleaseMat( &data );
|
|
cvReleaseMat( &responses );
|
|
|
|
return 0;
|
|
}
|
|
|
|
static
|
|
int build_knearest_classifier( char* data_filename, int K )
|
|
{
|
|
const int var_count = 16;
|
|
CvMat* data = 0;
|
|
CvMat train_data;
|
|
CvMat* responses;
|
|
|
|
int ok = read_num_class_data( data_filename, 16, &data, &responses );
|
|
int nsamples_all = 0, ntrain_samples = 0;
|
|
//int i, j;
|
|
//double /*train_hr = 0,*/ test_hr = 0;
|
|
CvANN_MLP mlp;
|
|
|
|
if( !ok )
|
|
{
|
|
printf( "Could not read the database %s\n", data_filename );
|
|
return -1;
|
|
}
|
|
|
|
printf( "The database %s is loaded.\n", data_filename );
|
|
nsamples_all = data->rows;
|
|
ntrain_samples = (int)(nsamples_all*0.8);
|
|
|
|
// 1. unroll the responses
|
|
printf( "Unrolling the responses...\n");
|
|
cvGetRows( data, &train_data, 0, ntrain_samples );
|
|
|
|
// 2. train classifier
|
|
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];
|
|
CvKNearest knearest(&train_data, train_resp);
|
|
|
|
CvMat* nearests = cvCreateMat( (nsamples_all - ntrain_samples), K, CV_32FC1);
|
|
float* _sample = new float[var_count * (nsamples_all - ntrain_samples)];
|
|
CvMat sample = cvMat( nsamples_all - ntrain_samples, 16, CV_32FC1, _sample );
|
|
float* true_results = new float[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);
|
|
knearest.find_nearest(&sample, K, result, 0, nearests, 0);
|
|
int true_resp = 0;
|
|
int accuracy = 0;
|
|
for (int i = 0; i < nsamples_all - ntrain_samples; i++)
|
|
{
|
|
if (result->data.fl[i] == true_results[i])
|
|
true_resp++;
|
|
for(int k = 0; k < K; k++ )
|
|
{
|
|
if( nearests->data.fl[i * K + k] == true_results[i])
|
|
accuracy++;
|
|
}
|
|
}
|
|
|
|
printf("true_resp = %f%%\tavg accuracy = %f%%\n", (float)true_resp / (nsamples_all - ntrain_samples) * 100,
|
|
(float)accuracy / (nsamples_all - ntrain_samples) / K * 100);
|
|
|
|
delete[] true_results;
|
|
delete[] _sample;
|
|
cvReleaseMat( &train_resp );
|
|
cvReleaseMat( &nearests );
|
|
cvReleaseMat( &result );
|
|
cvReleaseMat( &data );
|
|
cvReleaseMat( &responses );
|
|
|
|
return 0;
|
|
}
|
|
|
|
static
|
|
int build_nbayes_classifier( char* data_filename )
|
|
{
|
|
const int var_count = 16;
|
|
CvMat* data = 0;
|
|
CvMat train_data;
|
|
CvMat* responses;
|
|
|
|
int ok = read_num_class_data( data_filename, 16, &data, &responses );
|
|
int nsamples_all = 0, ntrain_samples = 0;
|
|
//int i, j;
|
|
//double /*train_hr = 0, */test_hr = 0;
|
|
CvANN_MLP mlp;
|
|
|
|
if( !ok )
|
|
{
|
|
printf( "Could not read the database %s\n", data_filename );
|
|
return -1;
|
|
}
|
|
|
|
printf( "The database %s is loaded.\n", data_filename );
|
|
nsamples_all = data->rows;
|
|
ntrain_samples = (int)(nsamples_all*0.5);
|
|
|
|
// 1. unroll the responses
|
|
printf( "Unrolling the responses...\n");
|
|
cvGetRows( data, &train_data, 0, ntrain_samples );
|
|
|
|
// 2. train classifier
|
|
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];
|
|
CvNormalBayesClassifier nbayes(&train_data, train_resp);
|
|
|
|
float* _sample = new float[var_count * (nsamples_all - ntrain_samples)];
|
|
CvMat sample = cvMat( nsamples_all - ntrain_samples, 16, CV_32FC1, _sample );
|
|
float* true_results = new float[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);
|
|
nbayes.predict(&sample, result);
|
|
int true_resp = 0;
|
|
//int accuracy = 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);
|
|
|
|
delete[] true_results;
|
|
delete[] _sample;
|
|
cvReleaseMat( &train_resp );
|
|
cvReleaseMat( &result );
|
|
cvReleaseMat( &data );
|
|
cvReleaseMat( &responses );
|
|
|
|
return 0;
|
|
}
|
|
|
|
static
|
|
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;
|
|
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;
|
|
|
|
// 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));
|
|
CvMat sample = cvMat( nsamples_all - ntrain_samples, 16, CV_32FC1, &_sample[0] );
|
|
std::vector<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");
|
|
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++)
|
|
{
|
|
if (result->data.fl[i] == true_results[i])
|
|
true_resp++;
|
|
}
|
|
|
|
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 );
|
|
cvReleaseMat( &responses );
|
|
|
|
return 0;
|
|
}
|
|
|
|
int main( int argc, char *argv[] )
|
|
{
|
|
char* filename_to_save = 0;
|
|
char* filename_to_load = 0;
|
|
char default_data_filename[] = "./letter-recognition.data";
|
|
char* data_filename = default_data_filename;
|
|
int method = 0;
|
|
|
|
int i;
|
|
for( i = 1; i < argc; i++ )
|
|
{
|
|
if( strcmp(argv[i],"-data") == 0 ) // flag "-data letter_recognition.xml"
|
|
{
|
|
i++;
|
|
data_filename = argv[i];
|
|
}
|
|
else if( strcmp(argv[i],"-save") == 0 ) // flag "-save filename.xml"
|
|
{
|
|
i++;
|
|
filename_to_save = argv[i];
|
|
}
|
|
else if( strcmp(argv[i],"-load") == 0) // flag "-load filename.xml"
|
|
{
|
|
i++;
|
|
filename_to_load = argv[i];
|
|
}
|
|
else if( strcmp(argv[i],"-boost") == 0)
|
|
{
|
|
method = 1;
|
|
}
|
|
else if( strcmp(argv[i],"-mlp") == 0 )
|
|
{
|
|
method = 2;
|
|
}
|
|
else if ( strcmp(argv[i], "-knearest") == 0)
|
|
{
|
|
method = 3;
|
|
}
|
|
else if ( strcmp(argv[i], "-nbayes") == 0)
|
|
{
|
|
method = 4;
|
|
}
|
|
else if ( strcmp(argv[i], "-svm") == 0)
|
|
{
|
|
method = 5;
|
|
}
|
|
else
|
|
break;
|
|
}
|
|
|
|
if( i < argc ||
|
|
(method == 0 ?
|
|
build_rtrees_classifier( data_filename, filename_to_save, filename_to_load ) :
|
|
method == 1 ?
|
|
build_boost_classifier( data_filename, filename_to_save, filename_to_load ) :
|
|
method == 2 ?
|
|
build_mlp_classifier( data_filename, filename_to_save, filename_to_load ) :
|
|
method == 3 ?
|
|
build_knearest_classifier( data_filename, 10 ) :
|
|
method == 4 ?
|
|
build_nbayes_classifier( data_filename) :
|
|
method == 5 ?
|
|
build_svm_classifier( data_filename, filename_to_save, filename_to_load ):
|
|
-1) < 0)
|
|
{
|
|
help();
|
|
}
|
|
return 0;
|
|
}
|