Functions
array_for_matrix.cpp File Reference
#include "main.h"

Functions

template<typename MatrixType >
void array_for_matrix (const MatrixType &m)
 
template<typename MatrixType >
void comparisons (const MatrixType &m)
 
template<typename VectorType >
void lpNorm (const VectorType &v)
 
template<typename MatrixType >
void cwise_min_max (const MatrixType &m)
 
template<typename MatrixTraits >
void resize (const MatrixTraits &t)
 
template<int >
void regression_bug_654 ()
 
template<int >
void regrrssion_bug_1410 ()
 
 EIGEN_DECLARE_TEST (array_for_matrix)
 

Function Documentation

◆ array_for_matrix()

template<typename MatrixType >
void array_for_matrix ( const MatrixType &  m)
13  {
14  typedef typename MatrixType::Scalar Scalar;
15  typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> ColVectorType;
16  typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime> RowVectorType;
17 
18  Index rows = m.rows();
19  Index cols = m.cols();
20 
21  MatrixType m1 = MatrixType::Random(rows, cols), m2 = MatrixType::Random(rows, cols), m3(rows, cols);
22  ColVectorType cv1 = ColVectorType::Random(rows);
23  RowVectorType rv1 = RowVectorType::Random(cols);
24 
25  Scalar s1 = internal::random<Scalar>(), s2 = internal::random<Scalar>();
26 
27  // Prevent overflows for integer types.
28  if (Eigen::NumTraits<Scalar>::IsInteger) {
29  Scalar kMaxVal = Scalar(1000);
30  m1.array() = m1.array() - kMaxVal * (m1.array() / kMaxVal);
31  m2.array() = m2.array() - kMaxVal * (m2.array() / kMaxVal);
32  }
33 
34  // scalar addition
35  VERIFY_IS_APPROX(m1.array() + s1, s1 + m1.array());
36  VERIFY_IS_APPROX((m1.array() + s1).matrix(), MatrixType::Constant(rows, cols, s1) + m1);
37  VERIFY_IS_APPROX(((m1 * Scalar(2)).array() - s2).matrix(), (m1 + m1) - MatrixType::Constant(rows, cols, s2));
38  m3 = m1;
39  m3.array() += s2;
40  VERIFY_IS_APPROX(m3, (m1.array() + s2).matrix());
41  m3 = m1;
42  m3.array() -= s1;
43  VERIFY_IS_APPROX(m3, (m1.array() - s1).matrix());
44 
45  // reductions
46  VERIFY_IS_MUCH_SMALLER_THAN(m1.colwise().sum().sum() - m1.sum(), m1.squaredNorm());
47  VERIFY_IS_MUCH_SMALLER_THAN(m1.rowwise().sum().sum() - m1.sum(), m1.squaredNorm());
48  VERIFY_IS_MUCH_SMALLER_THAN(m1.colwise().sum() + m2.colwise().sum() - (m1 + m2).colwise().sum(),
49  (m1 + m2).squaredNorm());
50  VERIFY_IS_MUCH_SMALLER_THAN(m1.rowwise().sum() - m2.rowwise().sum() - (m1 - m2).rowwise().sum(),
51  (m1 - m2).squaredNorm());
52  VERIFY_IS_APPROX(m1.colwise().sum(), m1.colwise().redux(internal::scalar_sum_op<Scalar, Scalar>()));
53 
54  // vector-wise ops
55  m3 = m1;
56  VERIFY_IS_APPROX(m3.colwise() += cv1, m1.colwise() + cv1);
57  m3 = m1;
58  VERIFY_IS_APPROX(m3.colwise() -= cv1, m1.colwise() - cv1);
59  m3 = m1;
60  VERIFY_IS_APPROX(m3.rowwise() += rv1, m1.rowwise() + rv1);
61  m3 = m1;
62  VERIFY_IS_APPROX(m3.rowwise() -= rv1, m1.rowwise() - rv1);
63 
64  // empty objects
65  VERIFY_IS_EQUAL((m1.template block<0, Dynamic>(0, 0, 0, cols).colwise().sum()), RowVectorType::Zero(cols));
66  VERIFY_IS_EQUAL((m1.template block<Dynamic, 0>(0, 0, rows, 0).rowwise().sum()), ColVectorType::Zero(rows));
67  VERIFY_IS_EQUAL((m1.template block<0, Dynamic>(0, 0, 0, cols).colwise().prod()), RowVectorType::Ones(cols));
68  VERIFY_IS_EQUAL((m1.template block<Dynamic, 0>(0, 0, rows, 0).rowwise().prod()), ColVectorType::Ones(rows));
69 
70  VERIFY_IS_EQUAL(m1.block(0, 0, 0, cols).colwise().sum(), RowVectorType::Zero(cols));
71  VERIFY_IS_EQUAL(m1.block(0, 0, rows, 0).rowwise().sum(), ColVectorType::Zero(rows));
72  VERIFY_IS_EQUAL(m1.block(0, 0, 0, cols).colwise().prod(), RowVectorType::Ones(cols));
73  VERIFY_IS_EQUAL(m1.block(0, 0, rows, 0).rowwise().prod(), ColVectorType::Ones(rows));
74 
75  // verify the const accessors exist
76  const Scalar& ref_m1 = m.matrix().array().coeffRef(0);
77  const Scalar& ref_m2 = m.matrix().array().coeffRef(0, 0);
78  const Scalar& ref_a1 = m.array().matrix().coeffRef(0);
79  const Scalar& ref_a2 = m.array().matrix().coeffRef(0, 0);
80  VERIFY(&ref_a1 == &ref_m1);
81  VERIFY(&ref_a2 == &ref_m2);
82 
83  // Check write accessors:
84  m1.array().coeffRef(0, 0) = 1;
85  VERIFY_IS_APPROX(m1(0, 0), Scalar(1));
86  m1.array()(0, 0) = 2;
87  VERIFY_IS_APPROX(m1(0, 0), Scalar(2));
88  m1.array().matrix().coeffRef(0, 0) = 3;
89  VERIFY_IS_APPROX(m1(0, 0), Scalar(3));
90  m1.array().matrix()(0, 0) = 4;
91  VERIFY_IS_APPROX(m1(0, 0), Scalar(4));
92 }
m1
Matrix3d m1
Definition: IOFormat.cpp:2
m2
MatrixType m2(n_dims)
rows
int rows
Definition: Tutorial_commainit_02.cpp:1
cols
int cols
Definition: Tutorial_commainit_02.cpp:1
Scalar
SCALAR Scalar
Definition: bench_gemm.cpp:45
MatrixType
MatrixXf MatrixType
Definition: benchmark-blocking-sizes.cpp:52
Eigen::Matrix
The matrix class, also used for vectors and row-vectors.
Definition: Eigen/Eigen/src/Core/Matrix.h:186
matrix
Eigen::Map< Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic, Eigen::ColMajor >, 0, Eigen::OuterStride<> > matrix(T *data, int rows, int cols, int stride)
Definition: common.h:85
VERIFY_IS_APPROX
#define VERIFY_IS_APPROX(a, b)
Definition: integer_types.cpp:13
m
int * m
Definition: level2_cplx_impl.h:294
VERIFY
#define VERIFY(a)
Definition: main.h:362
VERIFY_IS_EQUAL
#define VERIFY_IS_EQUAL(a, b)
Definition: main.h:367
VERIFY_IS_MUCH_SMALLER_THAN
#define VERIFY_IS_MUCH_SMALLER_THAN(a, b)
Definition: main.h:371
Eigen::array
std::array< T, N > array
Definition: EmulateArray.h:231
Eigen::Index
EIGEN_DEFAULT_DENSE_INDEX_TYPE Index
The Index type as used for the API.
Definition: Meta.h:83
oomph::PseudoSolidHelper::Zero
double Zero
Definition: pseudosolid_node_update_elements.cc:35
Eigen::NumTraits
Holds information about the various numeric (i.e. scalar) types allowed by Eigen.
Definition: NumTraits.h:217

References cols, m, m1, m2(), matrix(), rows, VERIFY, VERIFY_IS_APPROX, VERIFY_IS_EQUAL, VERIFY_IS_MUCH_SMALLER_THAN, and oomph::PseudoSolidHelper::Zero.

Referenced by EIGEN_DECLARE_TEST().

◆ comparisons()

template<typename MatrixType >
void comparisons ( const MatrixType &  m)
95  {
96  using std::abs;
97  typedef typename MatrixType::Scalar Scalar;
98  typedef typename NumTraits<Scalar>::Real RealScalar;
99 
100  Index rows = m.rows();
101  Index cols = m.cols();
102 
103  Index r = internal::random<Index>(0, rows - 1), c = internal::random<Index>(0, cols - 1);
104 
105  MatrixType m1 = MatrixType::Random(rows, cols), m2 = MatrixType::Random(rows, cols), m3(rows, cols);
106 
107  VERIFY(((m1.array() + Scalar(1)) > m1.array()).all());
108  VERIFY(((m1.array() - Scalar(1)) < m1.array()).all());
109  if (rows * cols > 1) {
110  m3 = m1;
111  m3(r, c) += 1;
112  VERIFY(!(m1.array() < m3.array()).all());
113  VERIFY(!(m1.array() > m3.array()).all());
114  }
115 
116  // comparisons to scalar
117  VERIFY((m1.array() != (m1(r, c) + 1)).any());
118  VERIFY((m1.array() > (m1(r, c) - 1)).any());
119  VERIFY((m1.array() < (m1(r, c) + 1)).any());
120  VERIFY((m1.array() == m1(r, c)).any());
121  VERIFY(m1.cwiseEqual(m1(r, c)).any());
122 
123  // test Select
124  VERIFY_IS_APPROX((m1.array() < m2.array()).select(m1, m2), m1.cwiseMin(m2));
125  VERIFY_IS_APPROX((m1.array() > m2.array()).select(m1, m2), m1.cwiseMax(m2));
126  Scalar mid = m1.cwiseAbs().minCoeff() / Scalar(2) + m1.cwiseAbs().maxCoeff() / Scalar(2);
127  for (int j = 0; j < cols; ++j)
128  for (int i = 0; i < rows; ++i) m3(i, j) = abs(m1(i, j)) < mid ? 0 : m1(i, j);
129  VERIFY_IS_APPROX(
130  (m1.array().abs() < MatrixType::Constant(rows, cols, mid).array()).select(MatrixType::Zero(rows, cols), m1), m3);
131  // shorter versions:
132  VERIFY_IS_APPROX((m1.array().abs() < MatrixType::Constant(rows, cols, mid).array()).select(0, m1), m3);
133  VERIFY_IS_APPROX((m1.array().abs() >= MatrixType::Constant(rows, cols, mid).array()).select(m1, 0), m3);
134  // even shorter version:
135  VERIFY_IS_APPROX((m1.array().abs() < mid).select(0, m1), m3);
136 
137  // count
138  VERIFY(((m1.array().abs() + 1) > RealScalar(0.1)).count() == rows * cols);
139 
140  // and/or
141  VERIFY(((m1.array() < RealScalar(0)).matrix() && (m1.array() > RealScalar(0)).matrix()).count() == 0);
142  VERIFY(((m1.array() < RealScalar(0)).matrix() || (m1.array() >= RealScalar(0)).matrix()).count() == rows * cols);
143  VERIFY(((m1.array() < -mid).matrix() || (m1.array() > mid).matrix()).count() ==
144  (m1.cwiseAbs().array() > mid).count());
145 
146  typedef Matrix<Index, Dynamic, 1> VectorOfIndices;
147 
148  // TODO allows colwise/rowwise for array
149  VERIFY_IS_APPROX(((m1.array().abs() + 1) > RealScalar(0.1)).matrix().colwise().count(),
150  VectorOfIndices::Constant(cols, rows).transpose());
151  VERIFY_IS_APPROX(((m1.array().abs() + 1) > RealScalar(0.1)).matrix().rowwise().count(),
152  VectorOfIndices::Constant(rows, cols));
153 }
abs
AnnoyingScalar abs(const AnnoyingScalar &x)
Definition: AnnoyingScalar.h:135
i
int i
Definition: BiCGSTAB_step_by_step.cpp:9
RealScalar
NumTraits< Scalar >::Real RealScalar
Definition: bench_gemm.cpp:46
UniformPSDSelfTest.r
r
Definition: UniformPSDSelfTest.py:20
anonymous_namespace{skew_symmetric_matrix3.cpp}::transpose
void transpose()
Definition: skew_symmetric_matrix3.cpp:135
calibrate.c
int c
Definition: calibrate.py:100
j
std::ptrdiff_t j
Definition: tut_arithmetic_redux_minmax.cpp:2

References abs(), calibrate::c, cols, i, j, m, m1, m2(), matrix(), UniformPSDSelfTest::r, rows, anonymous_namespace{skew_symmetric_matrix3.cpp}::transpose(), VERIFY, VERIFY_IS_APPROX, and oomph::PseudoSolidHelper::Zero.

Referenced by EIGEN_DECLARE_TEST().

◆ cwise_min_max()

template<typename MatrixType >
void cwise_min_max ( const MatrixType &  m)
177  {
178  typedef typename MatrixType::Scalar Scalar;
179 
180  Index rows = m.rows();
181  Index cols = m.cols();
182 
183  MatrixType m1 = MatrixType::Random(rows, cols);
184 
185  // min/max with array
186  Scalar maxM1 = m1.maxCoeff();
187  Scalar minM1 = m1.minCoeff();
188 
189  VERIFY_IS_APPROX(MatrixType::Constant(rows, cols, minM1), m1.cwiseMin(MatrixType::Constant(rows, cols, minM1)));
190  VERIFY_IS_APPROX(m1, m1.cwiseMin(MatrixType::Constant(rows, cols, maxM1)));
191 
192  VERIFY_IS_APPROX(MatrixType::Constant(rows, cols, maxM1), m1.cwiseMax(MatrixType::Constant(rows, cols, maxM1)));
193  VERIFY_IS_APPROX(m1, m1.cwiseMax(MatrixType::Constant(rows, cols, minM1)));
194 
195  // min/max with scalar input
196  VERIFY_IS_APPROX(MatrixType::Constant(rows, cols, minM1), m1.cwiseMin(minM1));
197  VERIFY_IS_APPROX(m1, m1.cwiseMin(maxM1));
198  VERIFY_IS_APPROX(-m1, (-m1).cwiseMin(-minM1));
199  VERIFY_IS_APPROX(-m1.array(), ((-m1).array().min)(-minM1));
200 
201  VERIFY_IS_APPROX(MatrixType::Constant(rows, cols, maxM1), m1.cwiseMax(maxM1));
202  VERIFY_IS_APPROX(m1, m1.cwiseMax(minM1));
203  VERIFY_IS_APPROX(-m1, (-m1).cwiseMax(-maxM1));
204  VERIFY_IS_APPROX(-m1.array(), ((-m1).array().max)(-maxM1));
205 
206  VERIFY_IS_APPROX(MatrixType::Constant(rows, cols, minM1).array(), (m1.array().min)(minM1));
207  VERIFY_IS_APPROX(m1.array(), (m1.array().min)(maxM1));
208 
209  VERIFY_IS_APPROX(MatrixType::Constant(rows, cols, maxM1).array(), (m1.array().max)(maxM1));
210  VERIFY_IS_APPROX(m1.array(), (m1.array().max)(minM1));
211 
212  // Test NaN propagation for min/max.
213  if (!NumTraits<Scalar>::IsInteger) {
214  m1(0, 0) = NumTraits<Scalar>::quiet_NaN();
215  // Elementwise.
216  VERIFY((numext::isnan)(m1.template cwiseMax<PropagateNaN>(MatrixType::Constant(rows, cols, Scalar(1)))(0, 0)));
217  VERIFY((numext::isnan)(m1.template cwiseMin<PropagateNaN>(MatrixType::Constant(rows, cols, Scalar(1)))(0, 0)));
218  VERIFY(!(numext::isnan)(m1.template cwiseMax<PropagateNumbers>(MatrixType::Constant(rows, cols, Scalar(1)))(0, 0)));
219  VERIFY(!(numext::isnan)(m1.template cwiseMin<PropagateNumbers>(MatrixType::Constant(rows, cols, Scalar(1)))(0, 0)));
220  VERIFY((numext::isnan)(m1.template cwiseMax<PropagateNaN>(Scalar(1))(0, 0)));
221  VERIFY((numext::isnan)(m1.template cwiseMin<PropagateNaN>(Scalar(1))(0, 0)));
222  VERIFY(!(numext::isnan)(m1.template cwiseMax<PropagateNumbers>(Scalar(1))(0, 0)));
223  VERIFY(!(numext::isnan)(m1.template cwiseMin<PropagateNumbers>(Scalar(1))(0, 0)));
224 
225  VERIFY((numext::isnan)(
226  m1.array().template max<PropagateNaN>(MatrixType::Constant(rows, cols, Scalar(1)).array())(0, 0)));
227  VERIFY((numext::isnan)(
228  m1.array().template min<PropagateNaN>(MatrixType::Constant(rows, cols, Scalar(1)).array())(0, 0)));
229  VERIFY(!(numext::isnan)(
230  m1.array().template max<PropagateNumbers>(MatrixType::Constant(rows, cols, Scalar(1)).array())(0, 0)));
231  VERIFY(!(numext::isnan)(
232  m1.array().template min<PropagateNumbers>(MatrixType::Constant(rows, cols, Scalar(1)).array())(0, 0)));
233  VERIFY((numext::isnan)(m1.array().template max<PropagateNaN>(Scalar(1))(0, 0)));
234  VERIFY((numext::isnan)(m1.array().template min<PropagateNaN>(Scalar(1))(0, 0)));
235  VERIFY(!(numext::isnan)(m1.array().template max<PropagateNumbers>(Scalar(1))(0, 0)));
236  VERIFY(!(numext::isnan)(m1.array().template min<PropagateNumbers>(Scalar(1))(0, 0)));
237 
238  // Reductions.
239  VERIFY((numext::isnan)(m1.template maxCoeff<PropagateNaN>()));
240  VERIFY((numext::isnan)(m1.template minCoeff<PropagateNaN>()));
241  if (m1.size() > 1) {
242  VERIFY(!(numext::isnan)(m1.template maxCoeff<PropagateNumbers>()));
243  VERIFY(!(numext::isnan)(m1.template minCoeff<PropagateNumbers>()));
244  } else {
245  VERIFY((numext::isnan)(m1.template maxCoeff<PropagateNumbers>()));
246  VERIFY((numext::isnan)(m1.template minCoeff<PropagateNumbers>()));
247  }
248  }
249 }
isnan
#define isnan(X)
Definition: main.h:109
SYCL::cwiseMin
T cwiseMin(T x, T y)
Definition: cxx11_tensor_builtins_sycl.cpp:73
SYCL::cwiseMax
T cwiseMax(T x, T y)
Definition: cxx11_tensor_builtins_sycl.cpp:64

References cols, SYCL::cwiseMax(), SYCL::cwiseMin(), isnan, m, m1, rows, VERIFY, and VERIFY_IS_APPROX.

Referenced by EIGEN_DECLARE_TEST().

◆ EIGEN_DECLARE_TEST()

EIGEN_DECLARE_TEST ( array_for_matrix  )
299  {
300  for (int i = 0; i < g_repeat; i++) {
301  CALL_SUBTEST_1(array_for_matrix(Matrix<float, 1, 1>()));
302  CALL_SUBTEST_2(array_for_matrix(Matrix2f()));
303  CALL_SUBTEST_3(array_for_matrix(Matrix4d()));
304  CALL_SUBTEST_4(array_for_matrix(
305  MatrixXcf(internal::random<int>(1, EIGEN_TEST_MAX_SIZE), internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
306  CALL_SUBTEST_5(array_for_matrix(
307  MatrixXf(internal::random<int>(1, EIGEN_TEST_MAX_SIZE), internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
308  CALL_SUBTEST_6(array_for_matrix(
309  MatrixXi(internal::random<int>(1, EIGEN_TEST_MAX_SIZE), internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
310  }
311  for (int i = 0; i < g_repeat; i++) {
312  CALL_SUBTEST_1(comparisons(Matrix<float, 1, 1>()));
313  CALL_SUBTEST_2(comparisons(Matrix2f()));
314  CALL_SUBTEST_3(comparisons(Matrix4d()));
315  CALL_SUBTEST_5(comparisons(
316  MatrixXf(internal::random<int>(1, EIGEN_TEST_MAX_SIZE), internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
317  CALL_SUBTEST_6(comparisons(
318  MatrixXi(internal::random<int>(1, EIGEN_TEST_MAX_SIZE), internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
319  }
320  for (int i = 0; i < g_repeat; i++) {
321  CALL_SUBTEST_1(cwise_min_max(Matrix<float, 1, 1>()));
322  CALL_SUBTEST_2(cwise_min_max(Matrix2f()));
323  CALL_SUBTEST_3(cwise_min_max(Matrix4d()));
324  CALL_SUBTEST_5(cwise_min_max(
325  MatrixXf(internal::random<int>(1, EIGEN_TEST_MAX_SIZE), internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
326  CALL_SUBTEST_6(cwise_min_max(
327  MatrixXi(internal::random<int>(1, EIGEN_TEST_MAX_SIZE), internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
328  }
329  for (int i = 0; i < g_repeat; i++) {
330  CALL_SUBTEST_1(lpNorm(Matrix<float, 1, 1>()));
331  CALL_SUBTEST_2(lpNorm(Vector2f()));
332  CALL_SUBTEST_7(lpNorm(Vector3d()));
333  CALL_SUBTEST_8(lpNorm(Vector4f()));
334  CALL_SUBTEST_5(lpNorm(VectorXf(internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
335  CALL_SUBTEST_4(lpNorm(VectorXcf(internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
336  }
337  CALL_SUBTEST_5(lpNorm(VectorXf(0)));
338  CALL_SUBTEST_4(lpNorm(VectorXcf(0)));
339  for (int i = 0; i < g_repeat; i++) {
340  CALL_SUBTEST_4(resize(
341  MatrixXcf(internal::random<int>(1, EIGEN_TEST_MAX_SIZE), internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
342  CALL_SUBTEST_5(
343  resize(MatrixXf(internal::random<int>(1, EIGEN_TEST_MAX_SIZE), internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
344  CALL_SUBTEST_6(
345  resize(MatrixXi(internal::random<int>(1, EIGEN_TEST_MAX_SIZE), internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
346  }
347  CALL_SUBTEST_6(regression_bug_654<0>());
348  CALL_SUBTEST_6(regrrssion_bug_1410<0>());
349 }
comparisons
void comparisons(const MatrixType &m)
Definition: array_for_matrix.cpp:95
cwise_min_max
void cwise_min_max(const MatrixType &m)
Definition: array_for_matrix.cpp:177
lpNorm
void lpNorm(const VectorType &v)
Definition: array_for_matrix.cpp:156
resize
void resize(const MatrixTraits &t)
Definition: array_for_matrix.cpp:252
array_for_matrix
void array_for_matrix(const MatrixType &m)
Definition: array_for_matrix.cpp:13
EIGEN_TEST_MAX_SIZE
#define EIGEN_TEST_MAX_SIZE
Definition: boostmultiprec.cpp:16
Eigen::g_repeat
static int g_repeat
Definition: main.h:191
CALL_SUBTEST_6
#define CALL_SUBTEST_6(FUNC)
Definition: split_test_helper.h:34
CALL_SUBTEST_3
#define CALL_SUBTEST_3(FUNC)
Definition: split_test_helper.h:16
CALL_SUBTEST_1
#define CALL_SUBTEST_1(FUNC)
Definition: split_test_helper.h:4
CALL_SUBTEST_8
#define CALL_SUBTEST_8(FUNC)
Definition: split_test_helper.h:46
CALL_SUBTEST_5
#define CALL_SUBTEST_5(FUNC)
Definition: split_test_helper.h:28
CALL_SUBTEST_2
#define CALL_SUBTEST_2(FUNC)
Definition: split_test_helper.h:10
CALL_SUBTEST_7
#define CALL_SUBTEST_7(FUNC)
Definition: split_test_helper.h:40
CALL_SUBTEST_4
#define CALL_SUBTEST_4(FUNC)
Definition: split_test_helper.h:22

References array_for_matrix(), CALL_SUBTEST_1, CALL_SUBTEST_2, CALL_SUBTEST_3, CALL_SUBTEST_4, CALL_SUBTEST_5, CALL_SUBTEST_6, CALL_SUBTEST_7, CALL_SUBTEST_8, comparisons(), cwise_min_max(), EIGEN_TEST_MAX_SIZE, Eigen::g_repeat, i, lpNorm(), and resize().

◆ lpNorm()

template<typename VectorType >
void lpNorm ( const VectorType &  v)
156  {
157  using std::sqrt;
158  typedef typename VectorType::RealScalar RealScalar;
159  VectorType u = VectorType::Random(v.size());
160 
161  if (v.size() == 0) {
162  VERIFY_IS_APPROX(u.template lpNorm<Infinity>(), RealScalar(0));
163  VERIFY_IS_APPROX(u.template lpNorm<1>(), RealScalar(0));
164  VERIFY_IS_APPROX(u.template lpNorm<2>(), RealScalar(0));
165  VERIFY_IS_APPROX(u.template lpNorm<5>(), RealScalar(0));
166  } else {
167  VERIFY_IS_APPROX(u.template lpNorm<Infinity>(), u.cwiseAbs().maxCoeff());
168  }
169 
170  VERIFY_IS_APPROX(u.template lpNorm<1>(), u.cwiseAbs().sum());
171  VERIFY_IS_APPROX(u.template lpNorm<2>(), sqrt(u.array().abs().square().sum()));
172  VERIFY_IS_APPROX(numext::pow(u.template lpNorm<5>(), typename VectorType::RealScalar(5)),
173  u.array().abs().pow(5).sum());
174 }
sqrt
AnnoyingScalar sqrt(const AnnoyingScalar &x)
Definition: AnnoyingScalar.h:134
v
Array< int, Dynamic, 1 > v
Definition: Array_initializer_list_vector_cxx11.cpp:1
Eigen::bfloat16_impl::pow
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 pow(const bfloat16 &a, const bfloat16 &b)
Definition: BFloat16.h:625
VectorType
Definition: fft_test_shared.h:66

References Eigen::bfloat16_impl::pow(), sqrt(), v, and VERIFY_IS_APPROX.

Referenced by EIGEN_DECLARE_TEST().

◆ regression_bug_654()

template<int >
void regression_bug_654 ( )
277  {
278  ArrayXf a = RowVectorXf(3);
279  VectorXf v = Array<float, 1, Dynamic>(3);
280 }
Eigen::Array
General-purpose arrays with easy API for coefficient-wise operations.
Definition: Array.h:48
a
const Scalar * a
Definition: level2_cplx_impl.h:32

References a, and v.

◆ regrrssion_bug_1410()

template<int >
void regrrssion_bug_1410 ( )
284  {
285  const Matrix4i M;
286  const Array4i A;
287  ArrayWrapper<const Matrix4i> MA = M.array();
288  MA.row(0);
289  MatrixWrapper<const Array4i> AM = A.matrix();
290  AM.row(0);
291 
292  VERIFY((internal::traits<ArrayWrapper<const Matrix4i> >::Flags & LvalueBit) == 0);
293  VERIFY((internal::traits<MatrixWrapper<const Array4i> >::Flags & LvalueBit) == 0);
294 
295  VERIFY((internal::traits<ArrayWrapper<Matrix4i> >::Flags & LvalueBit) == LvalueBit);
296  VERIFY((internal::traits<MatrixWrapper<Array4i> >::Flags & LvalueBit) == LvalueBit);
297 }
A
Matrix< SCALARA, Dynamic, Dynamic, opt_A > A
Definition: bench_gemm.cpp:47
M
Matrix< RealScalar, Dynamic, Dynamic > M
Definition: bench_gemm.cpp:50
Eigen::ArrayWrapper
Expression of a mathematical vector or matrix as an array object.
Definition: ArrayWrapper.h:43
Eigen::MatrixWrapper
Expression of an array as a mathematical vector or matrix.
Definition: ArrayWrapper.h:122
Eigen::LvalueBit
const unsigned int LvalueBit
Definition: Constants.h:148
Flags
Extend namespace for flags.
Definition: fsi_chan_precond_driver.cc:56

References Eigen::LvalueBit, and VERIFY.

◆ resize()

template<typename MatrixTraits >
void resize ( const MatrixTraits &  t)
252  {
253  typedef typename MatrixTraits::Scalar Scalar;
254  typedef Matrix<Scalar, Dynamic, Dynamic> MatrixType;
255  typedef Array<Scalar, Dynamic, Dynamic> Array2DType;
256  typedef Matrix<Scalar, Dynamic, 1> VectorType;
257  typedef Array<Scalar, Dynamic, 1> Array1DType;
258 
259  Index rows = t.rows(), cols = t.cols();
260 
261  MatrixType m(rows, cols);
262  VectorType v(rows);
263  Array2DType a2(rows, cols);
264  Array1DType a1(rows);
265 
266  m.array().resize(rows + 1, cols + 1);
267  VERIFY(m.rows() == rows + 1 && m.cols() == cols + 1);
268  a2.matrix().resize(rows + 1, cols + 1);
269  VERIFY(a2.rows() == rows + 1 && a2.cols() == cols + 1);
270  v.array().resize(cols);
271  VERIFY(v.size() == cols);
272  a1.matrix().resize(cols);
273  VERIFY(a1.size() == cols);
274 }
plotPSD.t
t
Definition: plotPSD.py:36

References cols, m, rows, plotPSD::t, v, and VERIFY.

Referenced by EIGEN_DECLARE_TEST().