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Fixed mingw build warnings

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This commit is contained in:
Andrey Kamaev
2012-06-20 17:57:26 +00:00
parent 988c405f79
commit e94e5866a1
11 changed files with 166 additions and 131 deletions
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39
modules/ml/test/test_mltests2.cpp
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@@ -52,7 +52,7 @@ void nbayes_check_data( CvMLData* _data )
CV_Error( CV_StsBadArg, "missing values are not supported" );
const CvMat* var_types = _data->get_var_types();
bool is_classifier = var_types->data.ptr[var_types->cols-1] == CV_VAR_CATEGORICAL;
if( ( fabs( cvNorm( var_types, 0, CV_L1 ) -
if( ( fabs( cvNorm( var_types, 0, CV_L1 ) -
(var_types->rows + var_types->cols - 2)*CV_VAR_ORDERED - CV_VAR_CATEGORICAL ) > FLT_EPSILON ) ||
!is_classifier )
CV_Error( CV_StsBadArg, "incorrect types of predictors or responses" );
@@ -89,7 +89,7 @@ float nbayes_calc_error( CvNormalBayesClassifier* nbayes, CvMLData* _data, int t
{
CvMat sample;
int si = sidx ? sidx[i] : i;
cvGetRow( values, &sample, si );
cvGetRow( values, &sample, si );
float r = (float)nbayes->predict( &sample, 0 );
if( pred_resp )
pred_resp[i] = r;
@@ -151,7 +151,7 @@ float knearest_calc_error( CvKNearest* knearest, CvMLData* _data, int k, int typ
{
CvMat sample;
int si = sidx ? sidx[i] : i;
cvGetRow( &predictors, &sample, si );
cvGetRow( &predictors, &sample, si );
float r = knearest->find_nearest( &sample, k );
if( pred_resp )
pred_resp[i] = r;
@@ -166,14 +166,14 @@ float knearest_calc_error( CvKNearest* knearest, CvMLData* _data, int k, int typ
{
CvMat sample;
int si = sidx ? sidx[i] : i;
cvGetRow( &predictors, &sample, si );
cvGetRow( &predictors, &sample, si );
float r = knearest->find_nearest( &sample, k );
if( pred_resp )
pred_resp[i] = r;
float d = r - response->data.fl[si*r_step];
err += d*d;
}
err = sample_count ? err / (float)sample_count : -FLT_MAX;
err = sample_count ? err / (float)sample_count : -FLT_MAX;
}
return err;
}
@@ -239,7 +239,7 @@ bool svm_train_auto( CvSVM* svm, CvMLData* _data, CvSVMParams _params,
const CvMat* _responses = _data->get_responses();
const CvMat* _var_idx = _data->get_var_idx();
const CvMat* _sample_idx = _data->get_train_sample_idx();
return svm->train_auto( _train_data, _responses, _var_idx,
return svm->train_auto( _train_data, _responses, _var_idx,
_sample_idx, _params, k_fold, C_grid, gamma_grid, p_grid, nu_grid, coef_grid, degree_grid );
}
float svm_calc_error( CvSVM* svm, CvMLData* _data, int type, vector<float> *resp )
@@ -268,7 +268,7 @@ float svm_calc_error( CvSVM* svm, CvMLData* _data, int type, vector<float> *resp
{
CvMat sample;
int si = sidx ? sidx[i] : i;
cvGetRow( values, &sample, si );
cvGetRow( values, &sample, si );
float r = svm->predict( &sample );
if( pred_resp )
pred_resp[i] = r;
@@ -290,7 +290,7 @@ float svm_calc_error( CvSVM* svm, CvMLData* _data, int type, vector<float> *resp
float d = r - response->data.fl[si*r_step];
err += d*d;
}
err = sample_count ? err / (float)sample_count : -FLT_MAX;
err = sample_count ? err / (float)sample_count : -FLT_MAX;
}
return err;
}
@@ -395,7 +395,7 @@ float ann_calc_error( CvANN_MLP* ann, CvMLData* _data, map<int, int>& cls_map, i
{
CvMat sample;
int si = sidx ? sidx[i] : i;
cvGetRow( &predictors, &sample, si );
cvGetRow( &predictors, &sample, si );
ann->predict( &sample, &_output );
CvPoint best_cls = {0,0};
cvMinMaxLoc( &_output, 0, 0, 0, &best_cls, 0 );
@@ -417,7 +417,7 @@ int str_to_boost_type( string& str )
if ( !str.compare("DISCRETE") )
return CvBoost::DISCRETE;
if ( !str.compare("REAL") )
return CvBoost::REAL;
return CvBoost::REAL;
if ( !str.compare("LOGIT") )
return CvBoost::LOGIT;
if ( !str.compare("GENTLE") )
@@ -480,7 +480,7 @@ CV_MLBaseTest::~CV_MLBaseTest()
validationFS.release();
if( nbayes )
delete nbayes;
if( knearest )
if( knearest )
delete knearest;
if( svm )
delete svm;
@@ -519,15 +519,14 @@ int CV_MLBaseTest::read_params( CvFileStorage* _fs )
return cvtest::TS::OK;;
}
void CV_MLBaseTest::run( int start_from )
void CV_MLBaseTest::run( int )
{
string filename = ts->get_data_path();
filename += get_validation_filename();
validationFS.open( filename, FileStorage::READ );
read_params( *validationFS );
int code = cvtest::TS::OK;
start_from = 0;
for (int i = 0; i < test_case_count; i++)
{
int temp_code = run_test_case( i );
@@ -594,7 +593,7 @@ string& CV_MLBaseTest::get_validation_filename()
int CV_MLBaseTest::train( int testCaseIdx )
{
bool is_trained = false;
FileNode modelParamsNode =
FileNode modelParamsNode =
validationFS.getFirstTopLevelNode()["validation"][modelName][dataSetNames[testCaseIdx]]["model_params"];
if( !modelName.compare(CV_NBAYES) )
@@ -651,7 +650,7 @@ int CV_MLBaseTest::train( int testCaseIdx )
modelParamsNode["max_categories"] >> MAX_CATEGORIES;
modelParamsNode["cv_folds"] >> CV_FOLDS;
modelParamsNode["is_pruned"] >> IS_PRUNED;
is_trained = dtree->train( &data,
is_trained = dtree->train( &data,
CvDTreeParams(MAX_DEPTH, MIN_SAMPLE_COUNT, REG_ACCURACY, USE_SURROGATE,
MAX_CATEGORIES, CV_FOLDS, false, IS_PRUNED, 0 )) != 0;
}
@@ -683,7 +682,7 @@ int CV_MLBaseTest::train( int testCaseIdx )
modelParamsNode["is_pruned"] >> IS_PRUNED;
modelParamsNode["nactive_vars"] >> NACTIVE_VARS;
modelParamsNode["max_trees_num"] >> MAX_TREES_NUM;
is_trained = rtrees->train( &data, CvRTParams( MAX_DEPTH, MIN_SAMPLE_COUNT, REG_ACCURACY,
is_trained = rtrees->train( &data, CvRTParams( MAX_DEPTH, MIN_SAMPLE_COUNT, REG_ACCURACY,
USE_SURROGATE, MAX_CATEGORIES, 0, true, // (calc_var_importance == true) <=> RF processes variable importance
NACTIVE_VARS, MAX_TREES_NUM, OOB_EPS, CV_TERMCRIT_ITER)) != 0;
}
@@ -713,7 +712,7 @@ int CV_MLBaseTest::train( int testCaseIdx )
return cvtest::TS::OK;
}
float CV_MLBaseTest::get_error( int testCaseIdx, int type, vector<float> *resp )
float CV_MLBaseTest::get_error( int /*testCaseIdx*/, int type, vector<float> *resp )
{
float err = 0;
if( !modelName.compare(CV_NBAYES) )
@@ -721,8 +720,8 @@ float CV_MLBaseTest::get_error( int testCaseIdx, int type, vector<float> *resp )
else if( !modelName.compare(CV_KNEAREST) )
{
assert( 0 );
testCaseIdx = 0;
/*int k = 2;
/*testCaseIdx = 0;
int k = 2;
validationFS.getFirstTopLevelNode()["validation"][modelName][dataSetNames[testCaseIdx]]["model_params"]["k"] >> k;
err = knearest->calc_error( &data, k, type, resp );*/
}