mirror of
https://github.com/g-truc/glm.git
synced 2024-11-10 04:31:47 +00:00
Corrected errors on Xcode C++98 pure related to language extensions accidentially used.
This commit is contained in:
SGrottel
parent
b8adc27808
commit
0f5b544d5d
@ -70,7 +70,7 @@ namespace glm {
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GLM_INLINE T transferSign(T const& v, T const& s)
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{
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return ((s) >= 0 ? glm::abs(v) : -glm::abs(v));
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};
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}
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template<typename T>
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GLM_INLINE T pythag(T const& a, T const& b) {
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@ -86,7 +86,7 @@ namespace glm {
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absa /= absb;
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absa *= absa;
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return absb * glm::sqrt(static_cast<T>(1) + absa);
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};
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}
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}
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@ -6,6 +6,33 @@
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#include <vector>
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#include <random>
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template<glm::length_t D, typename T, glm::qualifier Q>
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bool vectorEpsilonEqual(glm::vec<D, T, Q> const& a, glm::vec<D, T, Q> const& b)
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{
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for (int c = 0; c < D; ++c)
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if (!glm::epsilonEqual(a[c], b[c], static_cast<T>(0.000001)))
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return false;
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return true;
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}
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template<glm::length_t D, typename T, glm::qualifier Q>
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bool matrixEpsilonEqual(glm::mat<D, D, T, Q> const& a, glm::mat<D, D, T, Q> const& b)
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{
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for (int c = 0; c < D; ++c)
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for (int r = 0; r < D; ++r)
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if (!glm::epsilonEqual(a[c][r], b[c][r], static_cast<T>(0.000001)))
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return false;
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return true;
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}
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template<typename T>
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T failReport(T line)
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{
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printf("Failed in line %d\n", static_cast<int>(line));
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fprintf(stderr, "Failed in line %d\n", static_cast<int>(line));
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return line;
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}
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// Test data: 1AGA 'agarose double helix'
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// https://www.rcsb.org/structure/1aga
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// The fourth coordinate is randomized
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@ -147,11 +174,11 @@ namespace _1aga
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3.830, 3.522, 5.367, -0.302,
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5.150, 4.461, 2.116, -1.615
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};
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static const size_t _1agaSize = sizeof(_1aga) / (4 * sizeof(double));
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static const glm::length_t _1agaSize = sizeof(_1aga) / (4 * sizeof(double));
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outTestData.resize(_1agaSize);
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for(size_t i = 0; i < _1agaSize; ++i)
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for(size_t d = 0; d < static_cast<size_t>(vec::length()); ++d)
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for(glm::length_t i = 0; i < _1agaSize; ++i)
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for(glm::length_t d = 0; d < static_cast<glm::length_t>(vec::length()); ++d)
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outTestData[i][d] = static_cast<typename vec::value_type>(_1aga[i * 4 + d]);
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}
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@ -182,10 +209,10 @@ namespace _1aga
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{
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const T* expectedCovarData = nullptr;
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getExpectedCovarDataPtr(expectedCovarData);
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for(size_t x = 0; x < D; ++x)
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for(size_t y = 0; y < D; ++y)
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for(glm::length_t x = 0; x < D; ++x)
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for(glm::length_t y = 0; y < D; ++y)
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if(!glm::equal(covarMat[y][x], expectedCovarData[x * 4 + y], static_cast<T>(0.000001)))
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return 1;
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return failReport(__LINE__);
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return 0;
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}
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@ -280,12 +307,12 @@ namespace _1aga
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for(int i = 0; i < D; ++i)
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if(!glm::equal(evals[i], expectedEvals[i], static_cast<T>(0.000001)))
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return 1;
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return failReport(__LINE__);
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for (int i = 0; i < D; ++i)
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for (int d = 0; d < D; ++d)
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if (!glm::equal(evecs[i][d], expectedEvecs[i * D + d], static_cast<T>(0.000001)))
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return 1;
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return failReport(__LINE__);
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return 0;
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}
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@ -296,29 +323,20 @@ namespace _1aga
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template<typename vec>
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vec computeCenter(const std::vector<vec>& testData)
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{
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double c[vec::length()];
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double c[4];
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std::fill(c, c + vec::length(), 0.0);
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for(vec const& v : testData)
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for(size_t d = 0; d < static_cast<size_t>(vec::length()); ++d)
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c[d] += static_cast<double>(v[d]);
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typename std::vector<vec>::const_iterator e = testData.end();
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for(typename std::vector<vec>::const_iterator i = testData.begin(); i != e; ++i)
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for(glm::length_t d = 0; d < static_cast<glm::length_t>(vec::length()); ++d)
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c[d] += static_cast<double>((*i)[d]);
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vec cVec;
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for(size_t d = 0; d < static_cast<size_t>(vec::length()); ++d)
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vec cVec(0);
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for(glm::length_t d = 0; d < static_cast<glm::length_t>(vec::length()); ++d)
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cVec[d] = static_cast<typename vec::value_type>(c[d] / static_cast<double>(testData.size()));
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return cVec;
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}
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template<glm::length_t D, typename T, glm::qualifier Q>
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bool matrixEpsilonEqual(glm::mat<D, D, T, Q> const& a, glm::mat<D, D, T, Q> const& b)
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{
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for (int c = 0; c < D; ++c)
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for (int r = 0; r < D; ++r)
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if (!glm::epsilonEqual(a[c][r], b[c][r], static_cast<T>(0.000001)))
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return false;
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return true;
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}
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// Test sorting of Eigenvalue&Eigenvector lists. Use exhaustive search.
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template<glm::length_t D, typename T, glm::qualifier Q>
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int testEigenvalueSort()
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@ -339,8 +357,7 @@ int testEigenvalueSort()
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)
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);
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// Permutations of test input data for exhaustive check, based on `D` (1 <= D <= 4)
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static const int permutationCount[]
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{
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static const int permutationCount[] = {
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0,
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1,
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2,
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@ -348,8 +365,7 @@ int testEigenvalueSort()
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24
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};
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// The permutations t perform, based on `D` (1 <= D <= 4)
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static const glm::ivec4 permutation[]
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{
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static const glm::ivec4 permutation[] = {
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{ 0, 1, 2, 3 },
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{ 1, 0, 2, 3 }, // last for D = 2
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{ 0, 2, 1, 3 },
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@ -377,10 +393,10 @@ int testEigenvalueSort()
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};
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// initial sanity check
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if(!glm::all(glm::epsilonEqual(refVal, refVal, static_cast<T>(0.000001))))
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return 1;
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if(!vectorEpsilonEqual(refVal, refVal))
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return failReport(__LINE__);
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if(!matrixEpsilonEqual(refVec, refVec))
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return 1;
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return failReport(__LINE__);
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// Exhaustive search through all permutations
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for(int p = 0; p < permutationCount[D]; ++p)
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@ -395,10 +411,10 @@ int testEigenvalueSort()
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glm::sortEigenvalues(testVal, testVec);
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if (!glm::all(glm::epsilonEqual(testVal, refVal, static_cast<T>(0.000001))))
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return 2 + p * 2;
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if (!vectorEpsilonEqual(testVal, refVal))
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return failReport(__LINE__);
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if (!matrixEpsilonEqual(testVec, refVec))
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return 2 + 1 + p * 2;
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return failReport(__LINE__);
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}
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return 0;
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@ -406,7 +422,7 @@ int testEigenvalueSort()
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// Test covariance matrix creation functions
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template<glm::length_t D, typename T, glm::qualifier Q>
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int testCovar(unsigned int dataSize, unsigned int randomEngineSeed)
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int testCovar(glm::length_t dataSize, unsigned int randomEngineSeed)
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{
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typedef glm::vec<D, T, Q> vec;
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typedef glm::mat<D, D, T, Q> mat;
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@ -420,7 +436,7 @@ int testCovar(unsigned int dataSize, unsigned int randomEngineSeed)
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mat covarMat = glm::computeCovarianceMatrix(testData.data(), testData.size(), center);
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if(_1aga::checkCovarMat(covarMat))
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return 1;
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return failReport(__LINE__);
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// #2: test function variant consitency with random data
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std::default_random_engine rndEng(randomEngineSeed);
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@ -428,18 +444,19 @@ int testCovar(unsigned int dataSize, unsigned int randomEngineSeed)
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testData.resize(dataSize);
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// some common offset of all data
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T offset[D];
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for(size_t d = 0; d < D; ++d)
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for(glm::length_t d = 0; d < D; ++d)
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offset[d] = normalDist(rndEng);
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// init data
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for(size_t i = 0; i < dataSize; ++i)
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for(size_t d = 0; d < D; ++d)
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for(glm::length_t i = 0; i < dataSize; ++i)
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for(glm::length_t d = 0; d < D; ++d)
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testData[i][d] = offset[d] + normalDist(rndEng);
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center = computeCenter(testData);
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std::vector<vec> centeredTestData;
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centeredTestData.reserve(testData.size());
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for(vec const& v : testData)
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centeredTestData.push_back(v - center);
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std::vector<vec>::const_iterator e = testData.end();
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for(std::vector<vec>::const_iterator i = testData.begin(); i != e; ++i)
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centeredTestData.push_back((*i) - center);
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mat c1 = glm::computeCovarianceMatrix(centeredTestData.data(), centeredTestData.size());
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mat c2 = glm::computeCovarianceMatrix<D, T, Q>(centeredTestData.begin(), centeredTestData.end());
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@ -447,11 +464,11 @@ int testCovar(unsigned int dataSize, unsigned int randomEngineSeed)
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mat c4 = glm::computeCovarianceMatrix<D, T, Q>(testData.rbegin(), testData.rend(), center);
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if(!matrixEpsilonEqual(c1, c2))
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return 1;
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return failReport(__LINE__);
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if(!matrixEpsilonEqual(c1, c3))
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return 1;
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return failReport(__LINE__);
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if(!matrixEpsilonEqual(c1, c4))
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return 1;
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return failReport(__LINE__);
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return 0;
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}
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@ -471,11 +488,11 @@ int testEigenvectors()
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mat eigenvectors;
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unsigned int c = glm::findEigenvaluesSymReal(covarMat, eigenvalues, eigenvectors);
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if(c != D)
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return 1;
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return failReport(__LINE__);
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glm::sortEigenvalues(eigenvalues, eigenvectors);
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if(_1aga::checkEigenvaluesEigenvectors(eigenvalues, eigenvectors) != 0)
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return 1;
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return failReport(__LINE__);
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return 0;
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}
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@ -501,7 +518,7 @@ int smokeTest()
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vec3 eVal;
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int eCnt = glm::findEigenvaluesSymReal(covar, eVal, eVec);
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if(eCnt != 3)
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return 1;
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return failReport(__LINE__);
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// sort eVec by decending eVal
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if(eVal[0] < eVal[1])
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@ -520,12 +537,12 @@ int smokeTest()
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std::swap(eVec[1], eVec[2]);
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}
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if(!glm::all(glm::equal(glm::abs(eVec[0]), vec3(0, 1, 0))))
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return 2;
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if(!glm::all(glm::equal(glm::abs(eVec[1]), vec3(1, 0, 0))))
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return 3;
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if(!glm::all(glm::equal(glm::abs(eVec[2]), vec3(0, 0, 1))))
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return 4;
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if(!vectorEpsilonEqual(glm::abs(eVec[0]), vec3(0, 1, 0)))
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return failReport(__LINE__);
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if(!vectorEpsilonEqual(glm::abs(eVec[1]), vec3(1, 0, 0)))
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return failReport(__LINE__);
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if(!vectorEpsilonEqual(glm::abs(eVec[2]), vec3(0, 0, 1)))
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return failReport(__LINE__);
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return 0;
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}
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@ -586,7 +603,7 @@ int rndTest(unsigned int randomEngineSeed)
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glm::dmat3 evecs;
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int evcnt = glm::findEigenvaluesSymReal(covarMat, evals, evecs);
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if(evcnt != 3)
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return 1;
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return failReport(__LINE__);
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glm::sortEigenvalues(evals, evecs);
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//printf("\n");
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@ -595,11 +612,11 @@ int rndTest(unsigned int randomEngineSeed)
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//printf("evec1: %.10lf, %.10lf, %.10lf\n", evecs[1].x, evecs[1].y, evecs[1].z);
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if(glm::length(glm::abs(x) - glm::abs(evecs[0])) > 0.000001)
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return 1;
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return failReport(__LINE__);
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if(glm::length(glm::abs(y) - glm::abs(evecs[2])) > 0.000001)
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return 1;
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return failReport(__LINE__);
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if(glm::length(glm::abs(z) - glm::abs(evecs[1])) > 0.000001)
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return 1;
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return failReport(__LINE__);
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return 0;
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}
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@ -609,56 +626,56 @@ int main()
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// A small smoke test to fail early with most problems
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if(smokeTest())
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return __LINE__;
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return failReport(__LINE__);
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// test sorting utility.
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if(testEigenvalueSort<2, float, glm::defaultp>() != 0)
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return __LINE__;
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return failReport(__LINE__);
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if(testEigenvalueSort<2, double, glm::defaultp>() != 0)
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return __LINE__;
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return failReport(__LINE__);
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if(testEigenvalueSort<3, float, glm::defaultp>() != 0)
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return __LINE__;
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return failReport(__LINE__);
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if(testEigenvalueSort<3, double, glm::defaultp>() != 0)
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return __LINE__;
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return failReport(__LINE__);
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if(testEigenvalueSort<4, float, glm::defaultp>() != 0)
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return __LINE__;
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return failReport(__LINE__);
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if(testEigenvalueSort<4, double, glm::defaultp>() != 0)
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return __LINE__;
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return failReport(__LINE__);
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// Note: the random engine uses a fixed seed to create consistent and reproducible test data
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// test covariance matrix computation from different data sources
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if(testCovar<2, float, glm::defaultp>(100, 12345) != 0)
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return __LINE__;
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return failReport(__LINE__);
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if(testCovar<2, double, glm::defaultp>(100, 42) != 0)
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return __LINE__;
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return failReport(__LINE__);
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if(testCovar<3, float, glm::defaultp>(100, 2021) != 0)
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return __LINE__;
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return failReport(__LINE__);
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if(testCovar<3, double, glm::defaultp>(100, 815) != 0)
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return __LINE__;
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return failReport(__LINE__);
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if(testCovar<4, float, glm::defaultp>(100, 3141) != 0)
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return __LINE__;
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return failReport(__LINE__);
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if(testCovar<4, double, glm::defaultp>(100, 174) != 0)
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return __LINE__;
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return failReport(__LINE__);
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// test PCA eigen vector reconstruction
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if(testEigenvectors<2, float, glm::defaultp>() != 0)
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return __LINE__;
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return failReport(__LINE__);
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if(testEigenvectors<2, double, glm::defaultp>() != 0)
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return __LINE__;
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return failReport(__LINE__);
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if(testEigenvectors<3, float, glm::defaultp>() != 0)
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return __LINE__;
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return failReport(__LINE__);
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if(testEigenvectors<3, double, glm::defaultp>() != 0)
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return __LINE__;
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return failReport(__LINE__);
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if (testEigenvectors<4, float, glm::defaultp>() != 0)
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return __LINE__;
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return failReport(__LINE__);
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if (testEigenvectors<4, double, glm::defaultp>() != 0)
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return __LINE__;
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return failReport(__LINE__);
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// Final tests with randomized data
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if(rndTest(12345) != 0)
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return __LINE__;
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return failReport(__LINE__);
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if(rndTest(42) != 0)
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return __LINE__;
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return failReport(__LINE__);
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return 0;
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}
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