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Warning fixes continued

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This commit is contained in:
Andrey Kamaev
2012-06-09 15:00:04 +00:00
parent f6b451c607
commit f2d3b9b4a1
127 changed files with 6298 additions and 6277 deletions
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modules/ml/test/test_emknearestkmeans.cpp
+14 -14
View File
@@ -80,7 +80,7 @@ void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const Mat&
CV_Assert( _means.rows == (int)sizes.size() && covs.size() == sizes.size() );
CV_Assert( !data.empty() && data.rows == total );
CV_Assert( data.type() == dataType );
labels.create( data.rows, 1, labelType );
randn( data, Scalar::all(-1.0), Scalar::all(1.0) );
@@ -99,7 +99,7 @@ void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const Mat&
for( int i = bi; i < ei; i++, p++ )
{
Mat r = data.row(i);
r = r * (*cit) + *mit;
r = r * (*cit) + *mit;
if( labelType == CV_32FC1 )
labels.at<float>(p, 0) = (float)l;
else if( labelType == CV_32SC1 )
@@ -224,14 +224,14 @@ void CV_KMeansTest::run( int /*start_from*/ )
const int iters = 100;
int sizesArr[] = { 5000, 7000, 8000 };
int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2];
Mat data( pointsCount, 2, CV_32FC1 ), labels;
vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) );
Mat means;
vector<Mat> covs;
defaultDistribs( means, covs );
generateData( data, labels, sizes, means, covs, CV_32FC1, CV_32SC1 );
int code = cvtest::TS::OK;
float err;
Mat bestLabels;
@@ -327,24 +327,24 @@ void CV_KNearestTest::run( int /*start_from*/ )
class EM_Params
{
public:
EM_Params(int nclusters=10, int covMatType=EM::COV_MAT_DIAGONAL, int startStep=EM::START_AUTO_STEP,
const cv::TermCriteria& termCrit=cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 100, FLT_EPSILON),
const cv::Mat* probs=0, const cv::Mat* weights=0,
const cv::Mat* means=0, const std::vector<cv::Mat>* covs=0)
: nclusters(nclusters), covMatType(covMatType), startStep(startStep),
probs(probs), weights(weights), means(means), covs(covs), termCrit(termCrit)
EM_Params(int _nclusters=10, int _covMatType=EM::COV_MAT_DIAGONAL, int _startStep=EM::START_AUTO_STEP,
const cv::TermCriteria& _termCrit=cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 100, FLT_EPSILON),
const cv::Mat* _probs=0, const cv::Mat* _weights=0,
const cv::Mat* _means=0, const std::vector<cv::Mat>* _covs=0)
: nclusters(_nclusters), covMatType(_covMatType), startStep(_startStep),
probs(_probs), weights(_weights), means(_means), covs(_covs), termCrit(_termCrit)
{}
int nclusters;
int covMatType;
int startStep;
// all 4 following matrices should have type CV_32FC1
const cv::Mat* probs;
const cv::Mat* weights;
const cv::Mat* means;
const std::vector<cv::Mat>* covs;
cv::TermCriteria termCrit;
};
@@ -497,7 +497,7 @@ void CV_EMTest::run( int /*start_from*/ )
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
ts->set_failed_test_info( code );
}
modules/ml/test/test_gbttest.cpp
+35 -35
View File
@@ -12,8 +12,8 @@ class CV_GBTreesTest : public cvtest::BaseTest
{
public:
CV_GBTreesTest();
~CV_GBTreesTest();
~CV_GBTreesTest();
protected:
void run(int);
@@ -21,21 +21,21 @@ protected:
int TestSaveLoad();
int checkPredictError(int test_num);
int checkLoadSave();
int checkLoadSave();
string model_file_name1;
string model_file_name2;
string* datasets;
string data_path;
CvMLData* data;
CvGBTrees* gtb;
vector<float> test_resps1;
vector<float> test_resps2;
int64 initSeed;
int64 initSeed;
};
@@ -47,7 +47,7 @@ int _get_len(const CvMat* mat)
CV_GBTreesTest::CV_GBTreesTest()
{
int64 seeds[] = { CV_BIG_INT(0x00009fff4f9c8d52),
int64 seeds[] = { CV_BIG_INT(0x00009fff4f9c8d52),
CV_BIG_INT(0x0000a17166072c7c),
CV_BIG_INT(0x0201b32115cd1f9a),
CV_BIG_INT(0x0513cb37abcd1234),
@@ -55,7 +55,7 @@ CV_GBTreesTest::CV_GBTreesTest()
};
int seedCount = sizeof(seeds)/sizeof(seeds[0]);
cv::RNG& rng = cv::theRNG();
cv::RNG& rng = cv::theRNG();
initSeed = rng.state;
rng.state = seeds[rng(seedCount)];
@@ -69,14 +69,14 @@ CV_GBTreesTest::~CV_GBTreesTest()
if (data)
delete data;
delete[] datasets;
cv::theRNG().state = initSeed;
cv::theRNG().state = initSeed;
}
int CV_GBTreesTest::TestTrainPredict(int test_num)
{
int code = cvtest::TS::OK;
int weak_count = 200;
float shrinkage = 0.1f;
float subsample_portion = 0.5f;
@@ -89,7 +89,7 @@ int CV_GBTreesTest::TestTrainPredict(int test_num)
case (2) : loss_function_type = CvGBTrees::ABSOLUTE_LOSS; break;
case (3) : loss_function_type = CvGBTrees::HUBER_LOSS; break;
case (0) : loss_function_type = CvGBTrees::DEVIANCE_LOSS; break;
default :
default :
{
ts->printf( cvtest::TS::LOG, "Bad test_num value in CV_GBTreesTest::TestTrainPredict(..) function." );
return cvtest::TS::FAIL_BAD_ARG_CHECK;
@@ -101,7 +101,7 @@ int CV_GBTreesTest::TestTrainPredict(int test_num)
{
data = new CvMLData();
data->set_delimiter(',');
if (data->read_csv(datasets[dataset_num].c_str()))
{
ts->printf( cvtest::TS::LOG, "File reading error." );
@@ -124,25 +124,25 @@ int CV_GBTreesTest::TestTrainPredict(int test_num)
CvTrainTestSplit spl( train_sample_count );
data->set_train_test_split( &spl );
}
data->mix_train_and_test_idx();
data->mix_train_and_test_idx();
if (gtb) delete gtb;
gtb = new CvGBTrees();
bool tmp_code = true;
tmp_code = gtb->train(data, CvGBTreesParams(loss_function_type, weak_count,
shrinkage, subsample_portion,
max_depth, use_surrogates));
if (!tmp_code)
{
ts->printf( cvtest::TS::LOG, "Model training was failed.");
return cvtest::TS::FAIL_INVALID_OUTPUT;
}
code = checkPredictError(test_num);
return code;
}
@@ -152,14 +152,14 @@ int CV_GBTreesTest::checkPredictError(int test_num)
{
if (!gtb)
return cvtest::TS::FAIL_GENERIC;
//float mean[] = {5.430247f, 13.5654f, 12.6569f, 13.1661f};
//float sigma[] = {0.4162694f, 3.21161f, 3.43297f, 3.00624f};
float mean[] = {5.80226f, 12.68689f, 13.49095f, 13.19628f};
float mean[] = {5.80226f, 12.68689f, 13.49095f, 13.19628f};
float sigma[] = {0.4764534f, 3.166919f, 3.022405f, 2.868722f};
float current_error = gtb->calc_error(data, CV_TEST_ERROR);
if ( abs( current_error - mean[test_num]) > 6*sigma[test_num] )
{
ts->printf( cvtest::TS::LOG, "Test error is out of range:\n"
@@ -177,7 +177,7 @@ int CV_GBTreesTest::TestSaveLoad()
{
if (!gtb)
return cvtest::TS::FAIL_GENERIC;
model_file_name1 = cv::tempfile();
model_file_name2 = cv::tempfile();
@@ -186,9 +186,9 @@ int CV_GBTreesTest::TestSaveLoad()
gtb->load(model_file_name1.c_str());
gtb->calc_error(data, CV_TEST_ERROR, &test_resps2);
gtb->save(model_file_name2.c_str());
return checkLoadSave();
}
@@ -200,7 +200,7 @@ int CV_GBTreesTest::checkLoadSave()
// 1. compare files
ifstream f1( model_file_name1.c_str() ), f2( model_file_name2.c_str() );
string s1, s2;
int lineIdx = 0;
int lineIdx = 0;
CV_Assert( f1.is_open() && f2.is_open() );
for( ; !f1.eof() && !f2.eof(); lineIdx++ )
{
@@ -244,23 +244,23 @@ int CV_GBTreesTest::checkLoadSave()
void CV_GBTreesTest::run(int)
{
string data_path = string(ts->get_data_path());
string dataPath = string(ts->get_data_path());
datasets = new string[2];
datasets[0] = data_path + string("spambase.data"); /*string("dataset_classification.csv");*/
datasets[1] = data_path + string("housing_.data"); /*string("dataset_regression.csv");*/
datasets[0] = dataPath + string("spambase.data"); /*string("dataset_classification.csv");*/
datasets[1] = dataPath + string("housing_.data"); /*string("dataset_regression.csv");*/
int code = cvtest::TS::OK;
for (int i = 0; i < 4; i++)
{
int temp_code = TestTrainPredict(i);
if (temp_code != cvtest::TS::OK)
{
code = temp_code;
break;
}
else if (i==0)
{
temp_code = TestSaveLoad();
@@ -269,13 +269,13 @@ void CV_GBTreesTest::run(int)
delete data;
data = 0;
}
delete gtb;
gtb = 0;
}
delete data;
data = 0;
ts->set_failed_test_info( code );
}