opencv/modules/ml/test/test_mltests.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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// Intel License Agreement
// For Open Source Computer Vision Library
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#include "test_precomp.hpp"
using namespace cv;
using namespace std;
CV_AMLTest::CV_AMLTest( const char* _modelName ) : CV_MLBaseTest( _modelName )
{
validationFN = "avalidation.xml";
}
int CV_AMLTest::run_test_case( int testCaseIdx )
{
int code = cvtest::TS::OK;
code = prepare_test_case( testCaseIdx );
if (code == cvtest::TS::OK)
{
//#define GET_STAT
#ifdef GET_STAT
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const char* data_name = ((CvFileNode*)cvGetSeqElem( dataSetNames, testCaseIdx ))->data.str.ptr;
printf("%s, %s ", name, data_name);
const int icount = 100;
float res[icount];
for (int k = 0; k < icount; k++)
{
#endif
data->shuffleTrainTest();
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code = train( testCaseIdx );
#ifdef GET_STAT
float case_result = get_error();
res[k] = case_result;
}
float mean = 0, sigma = 0;
for (int k = 0; k < icount; k++)
{
mean += res[k];
}
mean = mean /icount;
for (int k = 0; k < icount; k++)
{
sigma += (res[k] - mean)*(res[k] - mean);
}
sigma = sqrt(sigma/icount);
printf("%f, %f\n", mean, sigma);
#endif
}
return code;
}
int CV_AMLTest::validate_test_results( int testCaseIdx )
{
int iters;
float mean, sigma;
// read validation params
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FileNode resultNode =
validationFS.getFirstTopLevelNode()["validation"][modelName][dataSetNames[testCaseIdx]]["result"];
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resultNode["iter_count"] >> iters;
if ( iters > 0)
{
resultNode["mean"] >> mean;
resultNode["sigma"] >> sigma;
model->save(format("/Users/vp/tmp/dtree/testcase_%02d.cur.yml", testCaseIdx));
float curErr = get_test_error( testCaseIdx );
const int coeff = 4;
ts->printf( cvtest::TS::LOG, "Test case = %d; test error = %f; mean error = %f (diff=%f), %d*sigma = %f\n",
testCaseIdx, curErr, mean, abs( curErr - mean), coeff, coeff*sigma );
if ( abs( curErr - mean) > coeff*sigma )
{
ts->printf( cvtest::TS::LOG, "abs(%f - %f) > %f - OUT OF RANGE!\n", curErr, mean, coeff*sigma, coeff );
return cvtest::TS::FAIL_BAD_ACCURACY;
}
else
ts->printf( cvtest::TS::LOG, ".\n" );
}
else
{
ts->printf( cvtest::TS::LOG, "validation info is not suitable" );
return cvtest::TS::FAIL_INVALID_TEST_DATA;
}
return cvtest::TS::OK;
}
TEST(ML_DTree, regression) { CV_AMLTest test( CV_DTREE ); test.safe_run(); }
TEST(ML_Boost, regression) { CV_AMLTest test( CV_BOOST ); test.safe_run(); }
TEST(ML_RTrees, regression) { CV_AMLTest test( CV_RTREES ); test.safe_run(); }
TEST(DISABLED_ML_ERTrees, regression) { CV_AMLTest test( CV_ERTREES ); test.safe_run(); }
TEST(ML_NBAYES, regression_5911)
{
int N=12;
Ptr<ml::NormalBayesClassifier> nb = cv::ml::NormalBayesClassifier::create();
// data:
Mat_<float> X(N,4);
X << 1,2,3,4, 1,2,3,4, 1,2,3,4, 1,2,3,4,
5,5,5,5, 5,5,5,5, 5,5,5,5, 5,5,5,5,
4,3,2,1, 4,3,2,1, 4,3,2,1, 4,3,2,1;
// labels:
Mat_<int> Y(N,1);
Y << 0,0,0,0, 1,1,1,1, 2,2,2,2;
nb->train(X, ml::ROW_SAMPLE, Y);
// single prediction:
Mat R1,P1;
for (int i=0; i<N; i++)
{
Mat r,p;
nb->predictProb(X.row(i), r, p);
R1.push_back(r);
P1.push_back(p);
}
// bulk prediction (continuous memory):
Mat R2,P2;
nb->predictProb(X, R2, P2);
EXPECT_EQ(sum(R1 == R2)[0], 255 * R2.total());
EXPECT_EQ(sum(P1 == P2)[0], 255 * P2.total());
// bulk prediction, with non-continuous memory storage
Mat R3_(N, 1+1, CV_32S),
P3_(N, 3+1, CV_32F);
nb->predictProb(X, R3_.col(0), P3_.colRange(0,3));
Mat R3 = R3_.col(0).clone(),
P3 = P3_.colRange(0,3).clone();
EXPECT_EQ(sum(R1 == R3)[0], 255 * R3.total());
EXPECT_EQ(sum(P1 == P3)[0], 255 * P3.total());
}
/* End of file. */