mewin/glm
1
0
Fork 0
Code Releases Activity

Added further details on the comparison issue with covariance matrices on some VMs.

Browse Source 
Also corrected some code style guide, and changed `nullptr` to `GLM_NULLPTR` for better compatibility.
Tests are now executed in blocks of related tests, and only inbetween blocks the tests will exit.
This commit is contained in:
SGrottel
2021-05-10 21:32:01 +02:00
parent d0d7945141
commit a0ccbcc63d
2 changed files with 119 additions and 74 deletions
Download Patch File Download Diff File Expand all files Collapse all files

74
test/gtx/gtx_pca.cpp
View File

@@ -32,8 +32,7 @@ bool matrixEpsilonEqual(glm::mat<D, D, T, Q> const& a, glm::mat<D, D, T, Q> cons
template<typename T>
T failReport(T line)
{
printf("I:Failed in line %d\n", static_cast<int>(line));
fprintf(stderr, "E:Failed in line %d\n", static_cast<int>(line));
fprintf(stderr, "Failed in line %d\n", static_cast<int>(line));
return line;
}
@@ -211,12 +210,19 @@ namespace _1aga
template<glm::length_t D, typename T, glm::qualifier Q>
int checkCovarMat(glm::mat<D, D, T, Q> const& covarMat)
{
const T* expectedCovarData = nullptr;
const T* expectedCovarData = GLM_NULLPTR;
getExpectedCovarDataPtr(expectedCovarData);
for(glm::length_t x = 0; x < D; ++x)
for(glm::length_t y = 0; y < D; ++y)
if(!glm::equal(covarMat[y][x], expectedCovarData[x * 4 + y], static_cast<T>(0.000001)))
{
fprintf(stderr, "E: %.15lf != %.15lf ; diff: %.20lf\n",
static_cast<double>(covarMat[y][x]),
static_cast<double>(expectedCovarData[x * 4 + y]),
static_cast<double>(covarMat[y][x] - expectedCovarData[x * 4 + y])
);
return failReport(__LINE__);
}
return 0;
}
@@ -305,8 +311,8 @@ namespace _1aga
glm::vec<D, T, Q> const& evals,
glm::mat<D, D, T, Q> const& evecs)
{
const T* expectedEvals = nullptr;
const T* expectedEvecs = nullptr;
const T* expectedEvals = GLM_NULLPTR;
const T* expectedEvecs = GLM_NULLPTR;
getExpectedEigenvaluesEigenvectorsDataPtr<D, T>(expectedEvals, expectedEvecs);
for(int i = 0; i < D; ++i)
@@ -441,8 +447,7 @@ int testCovar(glm::length_t dataSize, unsigned int randomEngineSeed)
mat covarMat = glm::computeCovarianceMatrix(testData.data(), testData.size(), center);
if(_1aga::checkCovarMat(covarMat))
{
fprintf(stdout, "I:Reconstructed covarMat:\n%s\n", glm::to_string(covarMat).c_str());
fprintf(stderr, "E:Reconstructed covarMat:\n%s\n", glm::to_string(covarMat).c_str());
fprintf(stderr, "Reconstructed covarMat:\n%s\n", glm::to_string(covarMat).c_str());
return failReport(__LINE__);
}
@@ -636,6 +641,7 @@ int rndTest(unsigned int randomEngineSeed)
int main()
{
int error(0);
// A small smoke test to fail early with most problems
if(smokeTest())
@@ -643,54 +649,62 @@ int main()
// test sorting utility.
if(testEigenvalueSort<2, float, glm::defaultp>() != 0)
return failReport(__LINE__);
error = failReport(__LINE__);
if(testEigenvalueSort<2, double, glm::defaultp>() != 0)
return failReport(__LINE__);
error = failReport(__LINE__);
if(testEigenvalueSort<3, float, glm::defaultp>() != 0)
return failReport(__LINE__);
error = failReport(__LINE__);
if(testEigenvalueSort<3, double, glm::defaultp>() != 0)
return failReport(__LINE__);
error = failReport(__LINE__);
if(testEigenvalueSort<4, float, glm::defaultp>() != 0)
return failReport(__LINE__);
error = failReport(__LINE__);
if(testEigenvalueSort<4, double, glm::defaultp>() != 0)
return failReport(__LINE__);
error = failReport(__LINE__);
if (error != 0)
return error;
// Note: the random engine uses a fixed seed to create consistent and reproducible test data
// test covariance matrix computation from different data sources
if(testCovar<2, float, glm::defaultp>(100, 12345) != 0)
return failReport(__LINE__);
error = failReport(__LINE__);
if(testCovar<2, double, glm::defaultp>(100, 42) != 0)
return failReport(__LINE__);
error = failReport(__LINE__);
if(testCovar<3, float, glm::defaultp>(100, 2021) != 0)
return failReport(__LINE__);
error = failReport(__LINE__);
if(testCovar<3, double, glm::defaultp>(100, 815) != 0)
return failReport(__LINE__);
error = failReport(__LINE__);
if(testCovar<4, float, glm::defaultp>(100, 3141) != 0)
return failReport(__LINE__);
error = failReport(__LINE__);
if(testCovar<4, double, glm::defaultp>(100, 174) != 0)
return failReport(__LINE__);
error = failReport(__LINE__);
if (error != 0)
return error;
// test PCA eigen vector reconstruction
if(testEigenvectors<2, float, glm::defaultp>() != 0)
return failReport(__LINE__);
error = failReport(__LINE__);
if(testEigenvectors<2, double, glm::defaultp>() != 0)
return failReport(__LINE__);
error = failReport(__LINE__);
if(testEigenvectors<3, float, glm::defaultp>() != 0)
return failReport(__LINE__);
error = failReport(__LINE__);
if(testEigenvectors<3, double, glm::defaultp>() != 0)
return failReport(__LINE__);
if (testEigenvectors<4, float, glm::defaultp>() != 0)
return failReport(__LINE__);
if (testEigenvectors<4, double, glm::defaultp>() != 0)
return failReport(__LINE__);
error = failReport(__LINE__);
if(testEigenvectors<4, float, glm::defaultp>() != 0)
error = failReport(__LINE__);
if(testEigenvectors<4, double, glm::defaultp>() != 0)
error = failReport(__LINE__);
if(error != 0)
return error;
// Final tests with randomized data
#if GLM_HAS_CXX11_STL == 1
if(rndTest(12345) != 0)
return failReport(__LINE__);
error = failReport(__LINE__);
if(rndTest(42) != 0)
return failReport(__LINE__);
error = failReport(__LINE__);
if (error != 0)
return error;
#endif // GLM_HAS_CXX11_STL == 1
return 0;
return error;
}