Homec4science
Diffusion akantu (features/ci_on_macos) Browse

rAKA/test/test_common/test_tensors.cc
b4c0dfe20c45features/ci_on_macos

Last Change
Raw File

test_tensors.cc
View Options

/**
* @file test_tensors.cc
*
* @author Nicolas Richart <nicolas.richart@epfl.ch>
*
* @date creation: Tue Nov 14 2017
* @date last modification: Tue Feb 05 2019
*
* @brief test the tensors types
*
*
* @section LICENSE
*
* Copyright (©) 2016-2021 EPFL (Ecole Polytechnique Fédérale de Lausanne)
* Laboratory (LSMS - Laboratoire de Simulation en Mécanique des Solides)
*
* Akantu is free software: you can redistribute it and/or modify it under the
* terms of the GNU Lesser General Public License as published by the Free
* Software Foundation, either version 3 of the License, or (at your option) any
* later version.
*
* Akantu is distributed in the hope that it will be useful, but WITHOUT ANY
* WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
* A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
* details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with Akantu. If not, see <http://www.gnu.org/licenses/>.
*
*/
/* -------------------------------------------------------------------------- */
#include "aka_array.hh"
#include "aka_iterators.hh"
#include "aka_types.hh"
/* -------------------------------------------------------------------------- */
#include <cstdlib>
#include <gtest/gtest.h>
#include <memory>
/* -------------------------------------------------------------------------- */
using namespace akantu;
namespace {
/* -------------------------------------------------------------------------- */
class TensorConstructorFixture : public ::testing::Test {
public:
void SetUp() override {
for (auto &r : reference) {
r = rand(); // google-test seeds srand()
}
}
void TearDown() override {}
template <typename V> void compareToRef(const V &v) {
for (int i = 0; i < size_; ++i) {
EXPECT_DOUBLE_EQ(reference[i], v.data()[i]);
}
}
protected:
const int size_{24};
const std::array<int, 2> mat_size{{4, 6}};
// const std::array<int, 3> tens3_size{{4, 2, 3}};
std::array<double, 24> reference;
};
/* -------------------------------------------------------------------------- */
class TensorFixture : public TensorConstructorFixture {
public:
TensorFixture()
: vref(reference.data(), size_),
mref(reference.data(), mat_size[0], mat_size[1]) {}
protected:
VectorProxy<double> vref;
MatrixProxy<double> mref;
};
/* -------------------------------------------------------------------------- */
// Vector ----------------------------------------------------------------------
TEST_F(TensorConstructorFixture, VectorDefaultConstruct) {
Vector<double> v;
EXPECT_EQ(0, v.size());
EXPECT_EQ(nullptr, v.data());
}
TEST_F(TensorConstructorFixture, VectorConstruct1) {
Vector<double> v(size_);
EXPECT_EQ(size_, v.size());
}
TEST_F(TensorConstructorFixture, VectorConstructWrapped) {
VectorProxy<double> v(reference.data(), size_);
EXPECT_EQ(size_, v.size());
EXPECT_EQ(v.data(), reference.data());
for (int i = 0; i < size_; ++i) {
EXPECT_DOUBLE_EQ(reference[i], v(i));
EXPECT_DOUBLE_EQ(reference[i], v[i]);
}
}
TEST_F(TensorConstructorFixture, VectorConstructInitializer) {
Vector<double> v{0., 1., 2., 3., 4., 5.};
EXPECT_EQ(6, v.size());
for (int i = 0; i < 6; ++i) {
EXPECT_DOUBLE_EQ(i, v(i));
}
}
TEST_F(TensorConstructorFixture, VectorConstructCopy1) {
VectorProxy<double> vref(reference.data(), reference.size());
Vector<double> v(vref);
EXPECT_EQ(size_, v.size());
compareToRef(v);
}
TEST_F(TensorConstructorFixture, VectorConstructProxy1) {
VectorProxy<double> vref(reference.data(), reference.size());
EXPECT_EQ(size_, vref.size());
compareToRef(vref);
Vector<double> v(vref);
EXPECT_EQ(size_, v.size());
compareToRef(v);
}
/* -------------------------------------------------------------------------- */
TEST_F(TensorFixture, VectorEqual) {
Vector<double> v;
v = vref;
compareToRef(v);
EXPECT_EQ(size_, v.size());
}
TEST_F(TensorFixture, VectorEqualProxy) {
VectorProxy<double> vref_proxy(vref);
Vector<double> v;
v = vref;
compareToRef(v);
EXPECT_EQ(size_, v.size());
}
TEST_F(TensorFixture, VectorEqualProxy2) {
Vector<double> v_store(size_);
v_store.zero();
VectorProxy<double> v(v_store.data(), size_);
v = vref;
compareToRef(v);
compareToRef(v_store);
}
/* -------------------------------------------------------------------------- */
TEST_F(TensorFixture, VectorSet) {
Vector<double> v(vref);
compareToRef(v);
double r = rand();
v.set(r);
for (int i = 0; i < size_; ++i)
EXPECT_DOUBLE_EQ(r, v[i]);
}
TEST_F(TensorFixture, VectorClear) {
Vector<double> v(vref);
compareToRef(v);
v.zero();
for (int i = 0; i < size_; ++i)
EXPECT_DOUBLE_EQ(0, v[i]);
}
/* -------------------------------------------------------------------------- */
TEST_F(TensorFixture, VectorDivide) {
Vector<double> v;
double r = rand();
v = vref / r;
for (int i = 0; i < size_; ++i) {
EXPECT_DOUBLE_EQ(reference[i] / r, v[i]);
}
}
TEST_F(TensorFixture, VectorMultiply1) {
Vector<double> v;
double r = rand();
v = vref * r;
for (int i = 0; i < size_; ++i) {
EXPECT_DOUBLE_EQ(reference[i] * r, v[i]);
}
}
TEST_F(TensorFixture, VectorMultiply2) {
Vector<double> v;
double r = rand();
v = r * vref;
for (int i = 0; i < size_; ++i) {
EXPECT_DOUBLE_EQ(reference[i] * r, v[i]);
}
}
TEST_F(TensorFixture, VectorAddition) {
Vector<double> v;
v = vref + vref;
for (int i = 0; i < size_; ++i) {
EXPECT_DOUBLE_EQ(reference[i] * 2., v[i]);
}
}
TEST_F(TensorFixture, VectorSubstract) {
Vector<double> v;
v = vref - vref;
for (int i = 0; i < size_; ++i) {
EXPECT_DOUBLE_EQ(0., v[i]);
}
}
TEST_F(TensorFixture, VectorDivideEqual) {
Vector<double> v(vref);
double r = rand();
v /= r;
for (int i = 0; i < size_; ++i) {
EXPECT_DOUBLE_EQ(reference[i] / r, v[i]);
}
}
TEST_F(TensorFixture, VectorMultiplyEqual1) {
Vector<double> v(vref);
double r = rand();
v *= r;
for (int i = 0; i < size_; ++i) {
EXPECT_DOUBLE_EQ(reference[i] * r, v[i]);
}
}
TEST_F(TensorFixture, VectorMultiplyEqual2) {
Vector<double> v(vref);
v.array() *= v.array();
for (int i = 0; i < size_; ++i) {
EXPECT_DOUBLE_EQ(reference[i] * reference[i], v[i]);
}
}
TEST_F(TensorFixture, VectorAdditionEqual) {
Vector<double> v(vref);
v += vref;
for (int i = 0; i < size_; ++i) {
EXPECT_DOUBLE_EQ(reference[i] * 2., v[i]);
}
}
TEST_F(TensorFixture, VectorSubstractEqual) {
Vector<double> v(vref);
v -= vref;
for (int i = 0; i < size_; ++i) {
EXPECT_DOUBLE_EQ(0., v[i]);
}
}
/* -------------------------------------------------------------------------- */
// Matrix ----------------------------------------------------------------------
TEST_F(TensorConstructorFixture, MatrixDefaultConstruct) {
Matrix<double> m;
EXPECT_EQ(0, m.size());
EXPECT_EQ(0, m.rows());
EXPECT_EQ(0, m.cols());
EXPECT_EQ(nullptr, m.data());
}
TEST_F(TensorConstructorFixture, MatrixConstruct1) {
Matrix<double> m(mat_size[0], mat_size[1]);
EXPECT_EQ(size_, m.size());
EXPECT_EQ(mat_size[0], m.rows());
EXPECT_EQ(mat_size[1], m.cols());
}
TEST_F(TensorConstructorFixture, MatrixConstructWrapped) {
MatrixProxy<double> m(reference.data(), mat_size[0], mat_size[1]);
EXPECT_EQ(size_, m.size());
EXPECT_EQ(mat_size[0], m.rows());
EXPECT_EQ(mat_size[1], m.cols());
for (int i = 0; i < mat_size[0]; ++i) {
for (int j = 0; j < mat_size[1]; ++j) {
EXPECT_DOUBLE_EQ(reference[i + j * mat_size[0]], m(i, j));
}
}
compareToRef(m);
}
TEST_F(TensorConstructorFixture, MatrixConstructInitializer) {
Matrix<double> m{{0., 1., 2.}, {3., 4., 5.}};
EXPECT_EQ(6, m.size());
EXPECT_EQ(2, m.rows());
EXPECT_EQ(3, m.cols());
int c = 0;
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 3; ++j, ++c) {
EXPECT_DOUBLE_EQ(c, m(i, j));
}
}
}
TEST_F(TensorConstructorFixture, MatrixConstructCopy1) {
MatrixProxy<double> mref(reference.data(), mat_size[0], mat_size[1]);
Matrix<double> m(mref);
EXPECT_EQ(size_, m.size());
EXPECT_EQ(mat_size[0], m.rows());
EXPECT_EQ(mat_size[1], m.cols());
compareToRef(m);
}
TEST_F(TensorConstructorFixture, MatrixConstructProxy1) {
MatrixProxy<double> mref(reference.data(), mat_size[0], mat_size[1]);
EXPECT_EQ(size_, mref.size());
EXPECT_EQ(mat_size[0], mref.size(0));
EXPECT_EQ(mat_size[1], mref.size(1));
compareToRef(mref);
Matrix<double> m(mref);
EXPECT_EQ(size_, m.size());
EXPECT_EQ(mat_size[0], m.rows());
EXPECT_EQ(mat_size[1], m.cols());
compareToRef(m);
}
/* -------------------------------------------------------------------------- */
TEST_F(TensorFixture, MatrixEqual) {
Matrix<double> m;
m = mref;
compareToRef(m);
EXPECT_EQ(size_, m.size());
EXPECT_EQ(mat_size[0], m.rows());
EXPECT_EQ(mat_size[1], m.cols());
}
TEST_F(TensorFixture, MatrixEqualProxy1) {
MatrixProxy<double> mref_proxy(mref.data(), mref.rows(), mref.cols());
Matrix<double> m;
m = mref;
compareToRef(m);
EXPECT_EQ(size_, m.size());
EXPECT_EQ(mat_size[0], m.rows());
EXPECT_EQ(mat_size[1], m.cols());
}
TEST_F(TensorFixture, MatrixEqualProxy2) {
Matrix<double> m_store(mat_size[0], mat_size[1]);
m_store.zero();
MatrixProxy<double> m(m_store.data(), mat_size[0], mat_size[1]);
m = mref;
compareToRef(m);
compareToRef(m_store);
}
TEST_F(TensorFixture, MatrixEqualSlice) {
Matrix<double> m(mat_size[0], mat_size[1]);
m.zero();
for (Int i = 0; i < m.cols(); ++i) {
m(i) = mref(i);
}
compareToRef(m);
}
/* -------------------------------------------------------------------------- */
TEST_F(TensorFixture, MatrixSet) {
Matrix<double> m(mref);
compareToRef(m);
double r = rand();
m.set(r);
for (int i = 0; i < size_; ++i) {
EXPECT_DOUBLE_EQ(r, m.array()(i));
}
}
TEST_F(TensorFixture, MatrixClear) {
Matrix<double> m(mref);
compareToRef(m);
m.zero();
for (int i = 0; i < size_; ++i) {
EXPECT_DOUBLE_EQ(0, m.array()(i));
}
}
/* -------------------------------------------------------------------------- */
TEST_F(TensorFixture, MatrixDivide) {
Matrix<double> m;
double r = rand();
m = mref / r;
for (int i = 0; i < size_; ++i) {
EXPECT_DOUBLE_EQ(reference[i] / r, m.array()(i));
}
}
TEST_F(TensorFixture, MatrixMultiply1) {
Matrix<double> m;
double r = rand();
m = mref * r;
for (int i = 0; i < size_; ++i) {
EXPECT_DOUBLE_EQ(reference[i] * r, m.array()(i));
}
}
TEST_F(TensorFixture, MatrixMultiply2) {
Matrix<double> m;
double r = rand();
m = r * mref;
for (int i = 0; i < size_; ++i) {
EXPECT_DOUBLE_EQ(reference[i] * r, m.array()(i));
}
}
TEST_F(TensorFixture, MatrixAddition) {
Matrix<double> m;
m = mref + mref;
for (int i = 0; i < size_; ++i) {
EXPECT_DOUBLE_EQ(reference[i] * 2., m.array()(i));
}
}
TEST_F(TensorFixture, MatrixSubstract) {
Matrix<double> m;
m = mref - mref;
for (int i = 0; i < size_; ++i) {
EXPECT_DOUBLE_EQ(0., m.array()(i));
}
}
TEST_F(TensorFixture, MatrixDivideEqual) {
Matrix<double> m(mref);
double r = rand();
m /= r;
for (int i = 0; i < size_; ++i) {
EXPECT_DOUBLE_EQ(reference[i] / r, m.array()(i));
}
}
TEST_F(TensorFixture, MatrixMultiplyEqual1) {
Matrix<double> m(mref);
double r = rand();
m *= r;
for (int i = 0; i < size_; ++i) {
EXPECT_DOUBLE_EQ(reference[i] * r, m.array()(i));
}
}
TEST_F(TensorFixture, MatrixAdditionEqual) {
Matrix<double> m(mref);
m += mref;
for (int i = 0; i < size_; ++i) {
EXPECT_DOUBLE_EQ(reference[i] * 2., m.array()(i));
}
}
TEST_F(TensorFixture, MatrixSubstractEqual) {
Matrix<double> m(mref);
m -= mref;
for (int i = 0; i < size_; ++i) {
EXPECT_DOUBLE_EQ(0., m.array()(i));
}
}
TEST_F(TensorFixture, MatrixIterator) {
Matrix<double> m(mref);
UInt col_count = 0;
for (auto &&col : m) {
VectorProxy<Real> col_hand(m.data() + col_count * m.rows(), m.rows());
Vector<Real> col_wrap(col);
auto comp = (col_wrap - col_hand).lpNorm<Eigen::Infinity>();
EXPECT_DOUBLE_EQ(0., comp);
++col_count;
}
}
TEST_F(TensorFixture, MatrixIteratorZip) {
Matrix<double> m1(mref);
Matrix<double> m2(mref);
UInt col_count = 0;
for (auto &&col : zip(m1, m2)) {
Vector<Real> col1(std::get<0>(col));
Vector<Real> col2(std::get<1>(col));
auto comp = (col1 - col2).lpNorm<Eigen::Infinity>();
EXPECT_DOUBLE_EQ(0., comp);
++col_count;
}
}
#if defined(AKANTU_USE_LAPACK)
TEST_F(TensorFixture, MatrixEigs) {
Matrix<double, 4, 4> A{
{0, 1., 0, 0}, {1., 0, 0, 0}, {0, 1., 0, 1.}, {0, 0, 4., 0}};
Matrix<double, 4, 4> v;
Vector<double, 4> lambda;
lambda.zero();
v.zero();
A.eig(lambda, v);
Vector<double> eigs_ref{2, 1., -1., -2};
auto Av = (A * v).eval();
// Eigen::PermutationMatrix<Eigen::Dynamic, Eigen::Dynamic>
// perm(lambda.size()); perm.setIdentity();
// std::sort(perm.indices().data(),
// perm.indices().data() + perm.indices().size(),
// [&lambda](const Eigen::Index & a, const Eigen::Index & b) {
// return (lambda(a) - lambda(b)) > 0;
// });
// std::cout << v << std::endl;
// std::cout << lambda << std::endl;
// std::cout << v * perm << std::endl;
// std::cout << perm.transpose() * lambda << std::endl;
// std::cout << (Av(0) - lambda(0) * v(0)).eval() << std::endl;
for (int i = 0; i < 4; ++i) {
EXPECT_NEAR(eigs_ref(i), lambda(i), 1e-14);
}
for (int i = 0; i < 4; ++i) {
auto lambda_v_minus_a_v =
(lambda(i) * v(i) - Av(i)).template lpNorm<Eigen::Infinity>();
EXPECT_NEAR(lambda_v_minus_a_v, 0., 1e-14);
}
}
#endif
/* -------------------------------------------------------------------------- */
} // namespace
c4science · Help