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normal bayes classifier has been parallelized using TBB; letter_recog sample updated to demosntrate knearest & bayes classifiers (thanks to Konstantin Krivakin for the patches)

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Vadim Pisarevsky 2011-04-14 17:04:39 +00:00
parent 56b206dc7b
commit b9fa21d011
2 changed files with 233 additions and 47 deletions
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123
modules/ml/src/nbayes.cpp
View File

@ -277,63 +277,74 @@ bool CvNormalBayesClassifier::train( const CvMat* _train_data, const CvMat* _res
return result;
}
struct predict_body {
predict_body(CvMat* _c, CvMat** _cov_rotate_mats, CvMat** _inv_eigen_values, CvMat** _avg,
const CvMat* _samples, const int* _vidx, CvMat* _cls_labels,
CvMat* _results, float* _value, int _var_count1
)
{
c = _c;
cov_rotate_mats = _cov_rotate_mats;
inv_eigen_values = _inv_eigen_values;
avg = _avg;
samples = _samples;
vidx = _vidx;
cls_labels = _cls_labels;
results = _results;
value = _value;
var_count1 = _var_count1;
}
CvMat* c;
CvMat** cov_rotate_mats;
CvMat** inv_eigen_values;
CvMat** avg;
const CvMat* samples;
const int* vidx;
CvMat* cls_labels;
float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results ) const
{
float value = 0;
CvMat* results;
float* value;
int var_count1;
void operator()( const cv::BlockedRange& range ) const
{
int i, j, cls = -1;
double opt = FLT_MAX;
int rtype = 0, rstep = 0;
int cls = -1;
int rtype = 0, rstep = 0;
int nclasses = cls_labels->cols;
int _var_count = avg[0]->cols;
if( !CV_IS_MAT(samples) || CV_MAT_TYPE(samples->type) != CV_32FC1 || samples->cols != var_all )
CV_Error( CV_StsBadArg,
"The input samples must be 32f matrix with the number of columns = var_all" );
if( samples->rows > 1 && !results )
CV_Error( CV_StsNullPtr,
"When the number of input samples is >1, the output vector of results must be passed" );
if( results )
if (results)
{
if( !CV_IS_MAT(results) || (CV_MAT_TYPE(results->type) != CV_32FC1 &&
CV_MAT_TYPE(results->type) != CV_32SC1) ||
(results->cols != 1 && results->rows != 1) ||
results->cols + results->rows - 1 != samples->rows )
CV_Error( CV_StsBadArg, "The output array must be integer or floating-point vector "
"with the number of elements = number of rows in the input matrix" );
rtype = CV_MAT_TYPE(results->type);
rstep = CV_IS_MAT_CONT(results->type) ? 1 : results->step/CV_ELEM_SIZE(rtype);
}
const int* vidx = var_idx ? var_idx->data.i : 0;
// allocate memory and initializing headers for calculating
cv::AutoBuffer<double> buffer(nclasses + var_count);
CvMat diff = cvMat( 1, var_count, CV_64FC1, &buffer[0] );
for( int k = 0; k < samples->rows; k++ )
// allocate memory and initializing headers for calculating
cv::AutoBuffer<double> buffer(nclasses + var_count1);
CvMat diff = cvMat( 1, var_count1, CV_64FC1, &buffer[0] );
for(int k = range.begin(); k < range.end(); k += 1 )
{
int ival;
double opt = FLT_MAX;
for( i = 0; i < nclasses; i++ )
for(int i = 0; i < nclasses; i++ )
{
double cur = c->data.db[i];
CvMat* u = cov_rotate_mats[i];
CvMat* w = inv_eigen_values[i];
const double* avg_data = avg[i]->data.db;
const float* x = (const float*)(samples->data.ptr + samples->step*k);
// cov = u w u' --> cov^(-1) = u w^(-1) u'
for( j = 0; j < _var_count; j++ )
for(int j = 0; j < _var_count; j++ )
diff.data.db[j] = avg_data[j] - x[vidx ? vidx[j] : j];
cvGEMM( &diff, u, 1, 0, 0, &diff, CV_GEMM_B_T );
for( j = 0; j < _var_count; j++ )
for(int j = 0; j < _var_count; j++ )
{
double d = diff.data.db[j];
cur += d*d*w->data.db[j];
@ -356,17 +367,39 @@ float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results ) c
results->data.fl[k*rstep] = (float)ival;
}
if( k == 0 )
value = (float)ival;
/*if( _probs )
{
CV_CALL( cvConvertScale( &expo, &expo, -0.5 ));
CV_CALL( cvExp( &expo, &expo ));
if( _probs->cols == 1 )
CV_CALL( cvReshape( &expo, &expo, 1, nclasses ));
CV_CALL( cvConvertScale( &expo, _probs, 1./cvSum( &expo ).val[0] ));
}*/
*value = (float)ival;
}
}
};
float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results ) const
{
float value = 0;
if( !CV_IS_MAT(samples) || CV_MAT_TYPE(samples->type) != CV_32FC1 || samples->cols != var_all )
CV_Error( CV_StsBadArg,
"The input samples must be 32f matrix with the number of columns = var_all" );
if( samples->rows > 1 && !results )
CV_Error( CV_StsNullPtr,
"When the number of input samples is >1, the output vector of results must be passed" );
if( results )
{
if( !CV_IS_MAT(results) || (CV_MAT_TYPE(results->type) != CV_32FC1 &&
CV_MAT_TYPE(results->type) != CV_32SC1) ||
(results->cols != 1 && results->rows != 1) ||
results->cols + results->rows - 1 != samples->rows )
CV_Error( CV_StsBadArg, "The output array must be integer or floating-point vector "
"with the number of elements = number of rows in the input matrix" );
}
const int* vidx = var_idx ? var_idx->data.i : 0;
cv::parallel_for(cv::BlockedRange(0, samples->rows), predict_body(c, cov_rotate_mats, inv_eigen_values, avg, samples,
vidx, cls_labels, results, &value, var_count
));
return value;
}

157
samples/cpp/letter_recog.cpp
View File

@ -9,7 +9,7 @@
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"
"(or Boosting classifier, or MLP, or Knearest, or Nbayes - 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] # to use boost/mlp classifier instead of default Random Trees\n" );
" [-boost|-mlp|-knearest|-nbayes] # to use boost/mlp/knearest classifier instead of default Random Trees\n" );
}
// This function reads data and responses from the file <filename>
@ -484,6 +484,147 @@ int build_mlp_classifier( char* data_filename,
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[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);
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);
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[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);
(int)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);
cvReleaseMat( &train_resp );
cvReleaseMat( &result );
cvReleaseMat( &data );
cvReleaseMat( &responses );
return 0;
}
int main( int argc, char *argv[] )
{
@ -519,6 +660,14 @@ int main( int argc, char *argv[] )
{
method = 2;
}
else if ( strcmp(argv[i], "-knearest") == 0)
{
method = 3;
}
else if ( strcmp(argv[i], "-nbayes") == 0)
{
method = 4;
}
else
break;
}
@ -530,6 +679,10 @@ int main( int argc, char *argv[] )
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) :
-1) < 0)
{
help();