void test_eigen2_array()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( array(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( array(Matrix2f()) );
CALL_SUBTEST_3( array(Matrix4d()) );
CALL_SUBTEST_4( array(MatrixXcf(3, 3)) );
CALL_SUBTEST_5( array(MatrixXf(8, 12)) );
CALL_SUBTEST_6( array(MatrixXi(8, 12)) );
}
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( comparisons(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( comparisons(Matrix2f()) );
CALL_SUBTEST_3( comparisons(Matrix4d()) );
CALL_SUBTEST_5( comparisons(MatrixXf(8, 12)) );
CALL_SUBTEST_6( comparisons(MatrixXi(8, 12)) );
}
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( lpNorm(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( lpNorm(Vector2f()) );
CALL_SUBTEST_3( lpNorm(Vector3d()) );
CALL_SUBTEST_4( lpNorm(Vector4f()) );
CALL_SUBTEST_5( lpNorm(VectorXf(16)) );
CALL_SUBTEST_7( lpNorm(VectorXcd(10)) );
}
}
void test_mapstride()
{
for(int i = 0; i < g_repeat; i++) {
EIGEN_UNUSED int maxn = 30;
CALL_SUBTEST_1( map_class_vector<Aligned>(Matrix<float, 1, 1>()) );
CALL_SUBTEST_1( map_class_vector<Unaligned>(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( map_class_vector<Aligned>(Vector4d()) );
CALL_SUBTEST_2( map_class_vector<Unaligned>(Vector4d()) );
CALL_SUBTEST_3( map_class_vector<Aligned>(RowVector4f()) );
CALL_SUBTEST_3( map_class_vector<Unaligned>(RowVector4f()) );
CALL_SUBTEST_4( map_class_vector<Aligned>(VectorXcf(internal::random<int>(1,maxn))) );
CALL_SUBTEST_4( map_class_vector<Unaligned>(VectorXcf(internal::random<int>(1,maxn))) );
CALL_SUBTEST_5( map_class_vector<Aligned>(VectorXi(internal::random<int>(1,maxn))) );
CALL_SUBTEST_5( map_class_vector<Unaligned>(VectorXi(internal::random<int>(1,maxn))) );
CALL_SUBTEST_1( map_class_matrix<Aligned>(Matrix<float, 1, 1>()) );
CALL_SUBTEST_1( map_class_matrix<Unaligned>(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( map_class_matrix<Aligned>(Matrix4d()) );
CALL_SUBTEST_2( map_class_matrix<Unaligned>(Matrix4d()) );
CALL_SUBTEST_3( map_class_matrix<Aligned>(Matrix<float,3,5>()) );
CALL_SUBTEST_3( map_class_matrix<Unaligned>(Matrix<float,3,5>()) );
CALL_SUBTEST_3( map_class_matrix<Aligned>(Matrix<float,4,8>()) );
CALL_SUBTEST_3( map_class_matrix<Unaligned>(Matrix<float,4,8>()) );
CALL_SUBTEST_4( map_class_matrix<Aligned>(MatrixXcf(internal::random<int>(1,maxn),internal::random<int>(1,maxn))) );
CALL_SUBTEST_4( map_class_matrix<Unaligned>(MatrixXcf(internal::random<int>(1,maxn),internal::random<int>(1,maxn))) );
CALL_SUBTEST_5( map_class_matrix<Aligned>(MatrixXi(internal::random<int>(1,maxn),internal::random<int>(1,maxn))) );
CALL_SUBTEST_5( map_class_matrix<Unaligned>(MatrixXi(internal::random<int>(1,maxn),internal::random<int>(1,maxn))) );
CALL_SUBTEST_6( map_class_matrix<Aligned>(MatrixXcd(internal::random<int>(1,maxn),internal::random<int>(1,maxn))) );
CALL_SUBTEST_6( map_class_matrix<Unaligned>(MatrixXcd(internal::random<int>(1,maxn),internal::random<int>(1,maxn))) );
}
}
void test_array_for_matrix()
{
int maxsize = 40;
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( array_for_matrix(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( array_for_matrix(Matrix2f()) );
CALL_SUBTEST_3( array_for_matrix(Matrix4d()) );
CALL_SUBTEST_4( array_for_matrix(MatrixXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
CALL_SUBTEST_5( array_for_matrix(MatrixXf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
CALL_SUBTEST_6( array_for_matrix(MatrixXi(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
}
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( comparisons(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( comparisons(Matrix2f()) );
CALL_SUBTEST_3( comparisons(Matrix4d()) );
CALL_SUBTEST_5( comparisons(MatrixXf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
CALL_SUBTEST_6( comparisons(MatrixXi(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
}
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( lpNorm(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( lpNorm(Vector2f()) );
CALL_SUBTEST_7( lpNorm(Vector3d()) );
CALL_SUBTEST_8( lpNorm(Vector4f()) );
CALL_SUBTEST_5( lpNorm(VectorXf(internal::random<int>(1,maxsize))) );
CALL_SUBTEST_4( lpNorm(VectorXcf(internal::random<int>(1,maxsize))) );
}
}
void test_redux()
{
// the max size cannot be too large, otherwise reduxion operations obviously generate large errors.
int maxsize = (std::min)(100,EIGEN_TEST_MAX_SIZE);
EIGEN_UNUSED_VARIABLE(maxsize);
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( matrixRedux(Matrix<float, 1, 1>()) );
CALL_SUBTEST_1( matrixRedux(Array<float, 1, 1>()) );
CALL_SUBTEST_2( matrixRedux(Matrix2f()) );
CALL_SUBTEST_2( matrixRedux(Array2f()) );
CALL_SUBTEST_3( matrixRedux(Matrix4d()) );
CALL_SUBTEST_3( matrixRedux(Array4d()) );
CALL_SUBTEST_4( matrixRedux(MatrixXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
CALL_SUBTEST_4( matrixRedux(ArrayXXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
CALL_SUBTEST_5( matrixRedux(MatrixXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
CALL_SUBTEST_5( matrixRedux(ArrayXXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
CALL_SUBTEST_6( matrixRedux(MatrixXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
CALL_SUBTEST_6( matrixRedux(ArrayXXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
}
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_7( vectorRedux(Vector4f()) );
CALL_SUBTEST_7( vectorRedux(Array4f()) );
CALL_SUBTEST_5( vectorRedux(VectorXd(internal::random<int>(1,maxsize))) );
CALL_SUBTEST_5( vectorRedux(ArrayXd(internal::random<int>(1,maxsize))) );
CALL_SUBTEST_8( vectorRedux(VectorXf(internal::random<int>(1,maxsize))) );
CALL_SUBTEST_8( vectorRedux(ArrayXf(internal::random<int>(1,maxsize))) );
}
}
void test_adjoint()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( adjoint(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( adjoint(Matrix3d()) );
CALL_SUBTEST_3( adjoint(Matrix4f()) );
CALL_SUBTEST_4( adjoint(MatrixXcf(internal::random<int>(1,50), internal::random<int>(1,50))) );
CALL_SUBTEST_5( adjoint(MatrixXi(internal::random<int>(1,50), internal::random<int>(1,50))) );
CALL_SUBTEST_6( adjoint(MatrixXf(internal::random<int>(1,50), internal::random<int>(1,50))) );
}
// test a large matrix only once
CALL_SUBTEST_7( adjoint(Matrix<float, 100, 100>()) );
#ifdef EIGEN_TEST_PART_4
{
MatrixXcf a(10,10), b(10,10);
VERIFY_RAISES_ASSERT(a = a.transpose());
VERIFY_RAISES_ASSERT(a = a.transpose() + b);
VERIFY_RAISES_ASSERT(a = b + a.transpose());
VERIFY_RAISES_ASSERT(a = a.conjugate().transpose());
VERIFY_RAISES_ASSERT(a = a.adjoint());
VERIFY_RAISES_ASSERT(a = a.adjoint() + b);
VERIFY_RAISES_ASSERT(a = b + a.adjoint());
// no assertion should be triggered for these cases:
a.transpose() = a.transpose();
a.transpose() += a.transpose();
a.transpose() += a.transpose() + b;
a.transpose() = a.adjoint();
a.transpose() += a.adjoint();
a.transpose() += a.adjoint() + b;
}
#endif
}
void test_eigen2_product_large()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( product(MatrixXf(ei_random<int>(1,320), ei_random<int>(1,320))) );
CALL_SUBTEST_2( product(MatrixXd(ei_random<int>(1,320), ei_random<int>(1,320))) );
CALL_SUBTEST_3( product(MatrixXi(ei_random<int>(1,320), ei_random<int>(1,320))) );
CALL_SUBTEST_4( product(MatrixXcf(ei_random<int>(1,50), ei_random<int>(1,50))) );
CALL_SUBTEST_5( product(Matrix<float,Dynamic,Dynamic,RowMajor>(ei_random<int>(1,320), ei_random<int>(1,320))) );
}
#ifdef EIGEN_TEST_PART_6
{
// test a specific issue in DiagonalProduct
int N = 1000000;
VectorXf v = VectorXf::Ones(N);
MatrixXf m = MatrixXf::Ones(N,3);
m = (v+v).asDiagonal() * m;
VERIFY_IS_APPROX(m, MatrixXf::Constant(N,3,2));
}
{
// test deferred resizing in Matrix::operator=
MatrixXf a = MatrixXf::Random(10,4), b = MatrixXf::Random(4,10), c = a;
VERIFY_IS_APPROX((a = a * b), (c * b).eval());
}
{
MatrixXf mat1(10,10); mat1.setRandom();
MatrixXf mat2(32,10); mat2.setRandom();
MatrixXf result = mat1.row(2)*mat2.transpose();
VERIFY_IS_APPROX(result, (mat1.row(2)*mat2.transpose()).eval());
}
#endif
}
void test_mapped_matrix()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( map_class_vector(Matrix<float, 1, 1>()) );
CALL_SUBTEST_1( check_const_correctness(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( map_class_vector(Vector4d()) );
CALL_SUBTEST_2( map_class_vector(VectorXd(13)) );
CALL_SUBTEST_2( check_const_correctness(Matrix4d()) );
CALL_SUBTEST_3( map_class_vector(RowVector4f()) );
CALL_SUBTEST_4( map_class_vector(VectorXcf(8)) );
CALL_SUBTEST_5( map_class_vector(VectorXi(12)) );
CALL_SUBTEST_5( check_const_correctness(VectorXi(12)) );
CALL_SUBTEST_1( map_class_matrix(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( map_class_matrix(Matrix4d()) );
CALL_SUBTEST_11( map_class_matrix(Matrix<float,3,5>()) );
CALL_SUBTEST_4( map_class_matrix(MatrixXcf(internal::random<int>(1,10),internal::random<int>(1,10))) );
CALL_SUBTEST_5( map_class_matrix(MatrixXi(internal::random<int>(1,10),internal::random<int>(1,10))) );
CALL_SUBTEST_6( map_static_methods(Matrix<double, 1, 1>()) );
CALL_SUBTEST_7( map_static_methods(Vector3f()) );
CALL_SUBTEST_8( map_static_methods(RowVector3d()) );
CALL_SUBTEST_9( map_static_methods(VectorXcd(8)) );
CALL_SUBTEST_10( map_static_methods(VectorXf(12)) );
CALL_SUBTEST_11( map_not_aligned_on_scalar<double>() );
}
}
void test_product_large()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST( product(MatrixXf(ei_random<int>(1,320), ei_random<int>(1,320))) );
CALL_SUBTEST( product(MatrixXd(ei_random<int>(1,320), ei_random<int>(1,320))) );
CALL_SUBTEST( product(MatrixXi(ei_random<int>(1,320), ei_random<int>(1,320))) );
CALL_SUBTEST( product(MatrixXcf(ei_random<int>(1,50), ei_random<int>(1,50))) );
CALL_SUBTEST( product(Matrix<float,Dynamic,Dynamic,RowMajor>(ei_random<int>(1,320), ei_random<int>(1,320))) );
}
{
// test a specific issue in DiagonalProduct
int N = 1000000;
VectorXf v = VectorXf::Ones(N);
MatrixXf m = MatrixXf::Ones(N,3);
m = (v+v).asDiagonal() * m;
VERIFY_IS_APPROX(m, MatrixXf::Constant(N,3,2));
}
{
// test deferred resizing in Matrix::operator=
MatrixXf a = MatrixXf::Random(10,4), b = MatrixXf::Random(4,10), c = a;
VERIFY_IS_APPROX((a = a * b), (c * b).eval());
}
}
void test_sizeof()
{
CALL_SUBTEST(verifySizeOf(Matrix<float, 1, 1>()) );
CALL_SUBTEST(verifySizeOf(Array<float, 2, 1>()) );
CALL_SUBTEST(verifySizeOf(Array<float, 3, 1>()) );
CALL_SUBTEST(verifySizeOf(Array<float, 4, 1>()) );
CALL_SUBTEST(verifySizeOf(Array<float, 5, 1>()) );
CALL_SUBTEST(verifySizeOf(Array<float, 6, 1>()) );
CALL_SUBTEST(verifySizeOf(Array<float, 7, 1>()) );
CALL_SUBTEST(verifySizeOf(Array<float, 8, 1>()) );
CALL_SUBTEST(verifySizeOf(Array<float, 9, 1>()) );
CALL_SUBTEST(verifySizeOf(Array<float, 10, 1>()) );
CALL_SUBTEST(verifySizeOf(Array<float, 11, 1>()) );
CALL_SUBTEST(verifySizeOf(Array<float, 12, 1>()) );
CALL_SUBTEST(verifySizeOf(Vector2d()) );
CALL_SUBTEST(verifySizeOf(Vector4f()) );
CALL_SUBTEST(verifySizeOf(Matrix4d()) );
CALL_SUBTEST(verifySizeOf(Matrix<double, 4, 2>()) );
CALL_SUBTEST(verifySizeOf(Matrix<bool, 7, 5>()) );
CALL_SUBTEST(verifySizeOf(MatrixXcf(3, 3)) );
CALL_SUBTEST(verifySizeOf(MatrixXi(8, 12)) );
CALL_SUBTEST(verifySizeOf(MatrixXcd(20, 20)) );
CALL_SUBTEST(verifySizeOf(Matrix<float, 100, 100>()) );
VERIFY(sizeof(std::complex<float>) == 2*sizeof(float));
VERIFY(sizeof(std::complex<double>) == 2*sizeof(double));
}
/*
* Here we have another version of the method run.
* In contrast of the above one, here we run the algorithm with a fixed dimensions among all iterations.
* @param dimFixed vector of integer with length the number of cluster.
* Each component contains the number of dimensions of the corresponding cluster.
* @param nbIter The maximum number of iterations.
*/
void SimpleStrategy::run(VectorXi dimFixed, int nbIter){
// iter will count the number of iterations of the algorithm.
int iter=1;
//as its name indicates, logliklihood_prev will store the previous liklihood; ie logliklihood befor runnig alg
double logliklihood_prev=m_algo->getModel()->getLoglik();
//logliklihood_max stores the maximum liklihood among the iterations of the algorithm.
double logliklihood_max=m_algo->getModel()->getLoglik();
// m_logliktotal and m_Rtotal stores respectively loglik and dimensions among the iteration of the algorithm
m_loglikTotal=VectorXd(nbIter+1);
m_loglikTotal(0)=m_algo->getModel()->getLoglik();
m_Rtotal=MatrixXi(m_algo->getModel()->getNbClust(),nbIter+1);
m_Rtotal.col(0)=m_algo->getModel()->getParamobj().r;
//criterion stop: small increase in consecutive loglikelihood or max iterations
do {
// storing the loglik befor running algo.
logliklihood_prev=m_algo->getModel()->getLoglik();
/*
run algo. here we use the version of running algo with a fixed dimensions among all iterations.
*/
m_algo -> run(dimFixed);
//update clusters and chck if there is an empty class. here is the only place that the clusters membership is calculate
m_algo->getModel()->updateClusters();
//stores the new loglikelihood and the news dimensions
m_loglikTotal(iter)=m_algo->getModel()->getLoglik();
m_Rtotal.col(iter)=m_algo->getModel()->getParamobj().r;
//if we have increased the loglikelihood, this models is copied in bestModels
if (m_algo->getModel()->getLoglik() > logliklihood_max){
//update the logliklihood_max.
logliklihood_max=m_algo->getModel()->getLoglik();
//update clusters. here is the only place that the clusters membership is calculate
m_algo->getModel()->updateClusters();
// we stores the current model which make increasing the logliklihood in the bestModel.
*m_bestModel=*(m_algo->getModel());
}
iter+=1;
} while (fabs(m_algo->getModel()->getLoglik()-logliklihood_prev) > m_epsilon && iter <= nbIter
&& m_algo->getModel()->getEmpty()==false);
// we stores only the liklihood and dimension which have been calculated.
m_loglikTotal.conservativeResize(iter);
m_Rtotal.conservativeResize(m_algo->getModel()->getNbClust(),iter);
//we update the actual model with the best model
*(m_algo->getModel())=*m_bestModel;
//compute the new criterion
m_algo->getModel()->aic();
m_algo->getModel()->bic();
m_algo->getModel()->icl();
}
void test_miscmatrices()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( miscMatrices(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( miscMatrices(Matrix4d()) );
CALL_SUBTEST_3( miscMatrices(MatrixXcf(3, 3)) );
CALL_SUBTEST_4( miscMatrices(MatrixXi(8, 12)) );
CALL_SUBTEST_5( miscMatrices(MatrixXcd(20, 20)) );
}
}
void test_eigen2_basicstuff()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( basicStuff(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( basicStuff(Matrix4d()) );
CALL_SUBTEST_3( basicStuff(MatrixXcf(3, 3)) );
CALL_SUBTEST_4( basicStuff(MatrixXi(8, 12)) );
CALL_SUBTEST_5( basicStuff(MatrixXcd(20, 20)) );
CALL_SUBTEST_6( basicStuff(Matrix<float, 100, 100>()) );
CALL_SUBTEST_7( basicStuff(Matrix<long double,Dynamic,Dynamic>(10,10)) );
}
}
void test_adjoint()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST( adjoint(Matrix<float, 1, 1>()) );
CALL_SUBTEST( adjoint(Matrix3d()) );
CALL_SUBTEST( adjoint(Matrix4f()) );
CALL_SUBTEST( adjoint(MatrixXcf(4, 4)) );
CALL_SUBTEST( adjoint(MatrixXi(8, 12)) );
CALL_SUBTEST( adjoint(MatrixXf(21, 21)) );
}
// test a large matrix only once
CALL_SUBTEST( adjoint(Matrix<float, 100, 100>()) );
}
void test_eigen2_linearstructure()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( linearStructure(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( linearStructure(Matrix2f()) );
CALL_SUBTEST_3( linearStructure(Vector3d()) );
CALL_SUBTEST_4( linearStructure(Matrix4d()) );
CALL_SUBTEST_5( linearStructure(MatrixXcf(3, 3)) );
CALL_SUBTEST_6( linearStructure(MatrixXf(8, 12)) );
CALL_SUBTEST_7( linearStructure(MatrixXi(8, 12)) );
CALL_SUBTEST_8( linearStructure(MatrixXcd(20, 20)) );
}
}
void test_diagonal()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( diagonal(Matrix<float, 1, 1>()) );
CALL_SUBTEST_1( diagonal(Matrix<float, 4, 9>()) );
CALL_SUBTEST_1( diagonal(Matrix<float, 7, 3>()) );
CALL_SUBTEST_2( diagonal(Matrix4d()) );
CALL_SUBTEST_2( diagonal(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_2( diagonal(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_2( diagonal(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_1( diagonal(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_1( diagonal(Matrix<float,Dynamic,4>(3, 4)) );
}
}
void test_linearstructure()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( linearStructure(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( linearStructure(Matrix2f()) );
CALL_SUBTEST_3( linearStructure(Vector3d()) );
CALL_SUBTEST_4( linearStructure(Matrix4d()) );
CALL_SUBTEST_5( linearStructure(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) );
CALL_SUBTEST_6( linearStructure(MatrixXf (internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_7( linearStructure(MatrixXi (internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_8( linearStructure(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) );
CALL_SUBTEST_9( linearStructure(ArrayXXf (internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
}
}
void test_sizeof()
{
CALL_SUBTEST(verifySizeOf(Matrix<float, 1, 1>()) );
CALL_SUBTEST(verifySizeOf(Matrix4d()) );
CALL_SUBTEST(verifySizeOf(Matrix<double, 4, 2>()) );
CALL_SUBTEST(verifySizeOf(Matrix<bool, 7, 5>()) );
CALL_SUBTEST(verifySizeOf(MatrixXcf(3, 3)) );
CALL_SUBTEST(verifySizeOf(MatrixXi(8, 12)) );
CALL_SUBTEST(verifySizeOf(MatrixXcd(20, 20)) );
CALL_SUBTEST(verifySizeOf(Matrix<float, 100, 100>()) );
VERIFY(sizeof(std::complex<float>) == 2*sizeof(float));
VERIFY(sizeof(std::complex<double>) == 2*sizeof(double));
}
void test_eigen2_sum()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( matrixSum(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( matrixSum(Matrix2f()) );
CALL_SUBTEST_3( matrixSum(Matrix4d()) );
CALL_SUBTEST_4( matrixSum(MatrixXcf(3, 3)) );
CALL_SUBTEST_5( matrixSum(MatrixXf(8, 12)) );
CALL_SUBTEST_6( matrixSum(MatrixXi(8, 12)) );
}
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_5( vectorSum(VectorXf(5)) );
CALL_SUBTEST_7( vectorSum(VectorXd(10)) );
CALL_SUBTEST_5( vectorSum(VectorXf(33)) );
}
}
void test_basicstuff()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( basicStuff(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( basicStuff(Matrix4d()) );
CALL_SUBTEST_3( basicStuff(MatrixXcf(ei_random<int>(1,100), ei_random<int>(1,100))) );
CALL_SUBTEST_4( basicStuff(MatrixXi(ei_random<int>(1,100), ei_random<int>(1,100))) );
CALL_SUBTEST_5( basicStuff(MatrixXcd(ei_random<int>(1,100), ei_random<int>(1,100))) );
CALL_SUBTEST_6( basicStuff(Matrix<float, 100, 100>()) );
CALL_SUBTEST_7( basicStuff(Matrix<long double,Dynamic,Dynamic>(ei_random<int>(1,100),ei_random<int>(1,100))) );
CALL_SUBTEST_3( basicStuffComplex(MatrixXcf(ei_random<int>(1,100), ei_random<int>(1,100))) );
CALL_SUBTEST_5( basicStuffComplex(MatrixXcd(ei_random<int>(1,100), ei_random<int>(1,100))) );
}
CALL_SUBTEST_2(casting());
}
void test_eigen2_visitor()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( matrixVisitor(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( matrixVisitor(Matrix2f()) );
CALL_SUBTEST_3( matrixVisitor(Matrix4d()) );
CALL_SUBTEST_4( matrixVisitor(MatrixXd(8, 12)) );
CALL_SUBTEST_5( matrixVisitor(Matrix<double,Dynamic,Dynamic,RowMajor>(20, 20)) );
CALL_SUBTEST_6( matrixVisitor(MatrixXi(8, 12)) );
}
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_7( vectorVisitor(Vector4f()) );
CALL_SUBTEST_4( vectorVisitor(VectorXd(10)) );
CALL_SUBTEST_4( vectorVisitor(RowVectorXd(10)) );
CALL_SUBTEST_8( vectorVisitor(VectorXf(33)) );
}
}
void test_block()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( block(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( block(Matrix4d()) );
CALL_SUBTEST_3( block(MatrixXcf(3, 3)) );
CALL_SUBTEST_4( block(MatrixXi(8, 12)) );
CALL_SUBTEST_5( block(MatrixXcd(20, 20)) );
CALL_SUBTEST_6( block(MatrixXf(20, 20)) );
CALL_SUBTEST_8( block(Matrix<float,Dynamic,4>(3, 4)) );
#ifndef EIGEN_DEFAULT_TO_ROW_MAJOR
CALL_SUBTEST_6( data_and_stride(MatrixXf(internal::random(5,50), internal::random(5,50))) );
CALL_SUBTEST_7( data_and_stride(Matrix<int,Dynamic,Dynamic,RowMajor>(internal::random(5,50), internal::random(5,50))) );
#endif
}
}
void test_product_large()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST( product(MatrixXf(ei_random<int>(1,320), ei_random<int>(1,320))) );
CALL_SUBTEST( product(MatrixXd(ei_random<int>(1,320), ei_random<int>(1,320))) );
CALL_SUBTEST( product(MatrixXi(ei_random<int>(1,320), ei_random<int>(1,320))) );
CALL_SUBTEST( product(MatrixXcf(ei_random<int>(1,50), ei_random<int>(1,50))) );
CALL_SUBTEST( product(Matrix<float,Dynamic,Dynamic,RowMajor>(ei_random<int>(1,320), ei_random<int>(1,320))) );
}
{
// test a specific issue in DiagonalProduct
int N = 1000000;
VectorXf v = VectorXf::Ones(N);
MatrixXf m = MatrixXf::Ones(N,3);
m = (v+v).asDiagonal() * m;
VERIFY_IS_APPROX(m, MatrixXf::Constant(N,3,2));
}
}
void test_ref()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( ref_vector(Matrix<float, 1, 1>()) );
CALL_SUBTEST_1( check_const_correctness(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( ref_vector(Vector4d()) );
CALL_SUBTEST_2( check_const_correctness(Matrix4d()) );
CALL_SUBTEST_3( ref_vector(Vector4cf()) );
CALL_SUBTEST_4( ref_vector(VectorXcf(8)) );
CALL_SUBTEST_5( ref_vector(VectorXi(12)) );
CALL_SUBTEST_5( check_const_correctness(VectorXi(12)) );
CALL_SUBTEST_1( ref_matrix(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( ref_matrix(Matrix4d()) );
CALL_SUBTEST_1( ref_matrix(Matrix<float,3,5>()) );
CALL_SUBTEST_4( ref_matrix(MatrixXcf(internal::random<int>(1,10),internal::random<int>(1,10))) );
CALL_SUBTEST_4( ref_matrix(Matrix<std::complex<double>,10,15>()) );
CALL_SUBTEST_5( ref_matrix(MatrixXi(internal::random<int>(1,10),internal::random<int>(1,10))) );
CALL_SUBTEST_6( call_ref() );
}
}
void test_basicstuff()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( basicStuff(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( basicStuff(Matrix4d()) );
CALL_SUBTEST_3( basicStuff(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_4( basicStuff(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_5( basicStuff(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_6( basicStuff(Matrix<float, 100, 100>()) );
CALL_SUBTEST_7( basicStuff(Matrix<long double,Dynamic,Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE),internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_3( basicStuffComplex(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_5( basicStuffComplex(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
}
CALL_SUBTEST_1(fixedSizeMatrixConstruction<unsigned char>());
CALL_SUBTEST_1(fixedSizeMatrixConstruction<double>());
CALL_SUBTEST_1(fixedSizeMatrixConstruction<double>());
CALL_SUBTEST_2(casting());
}
void test_eigen2_map()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( map_class_vector(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( map_class_vector(Vector4d()) );
CALL_SUBTEST_3( map_class_vector(RowVector4f()) );
CALL_SUBTEST_4( map_class_vector(VectorXcf(8)) );
CALL_SUBTEST_5( map_class_vector(VectorXi(12)) );
CALL_SUBTEST_1( map_class_matrix(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( map_class_matrix(Matrix4d()) );
CALL_SUBTEST_6( map_class_matrix(Matrix<float,3,5>()) );
CALL_SUBTEST_4( map_class_matrix(MatrixXcf(ei_random<int>(1,10),ei_random<int>(1,10))) );
CALL_SUBTEST_5( map_class_matrix(MatrixXi(ei_random<int>(1,10),ei_random<int>(1,10))) );
CALL_SUBTEST_1( map_static_methods(Matrix<double, 1, 1>()) );
CALL_SUBTEST_2( map_static_methods(Vector3f()) );
CALL_SUBTEST_7( map_static_methods(RowVector3d()) );
CALL_SUBTEST_4( map_static_methods(VectorXcd(8)) );
CALL_SUBTEST_5( map_static_methods(VectorXf(12)) );
}
}
CKroneckerCF::CKroneckerCF(PCovarianceFunction col,
PCovarianceFunction row): AMultiCF(2,2) {
kroneckerIndicator = MatrixXi(0,0);
setRowCovariance(row);
setColCovariance(col);
}
CKroneckerCF::CKroneckerCF() : AMultiCF(2,2) {
vecCovariances.resize(2);
kroneckerIndicator = MatrixXi(0,0);
}
void test_product_large()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( product(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_2( product(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_3( product(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_4( product(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) );
CALL_SUBTEST_5( product(Matrix<float,Dynamic,Dynamic,RowMajor>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_1( test_aliasing<float>() );
}
#if defined EIGEN_TEST_PART_6
{
// test a specific issue in DiagonalProduct
int N = 1000000;
VectorXf v = VectorXf::Ones(N);
MatrixXf m = MatrixXf::Ones(N,3);
m = (v+v).asDiagonal() * m;
VERIFY_IS_APPROX(m, MatrixXf::Constant(N,3,2));
}
{
// test deferred resizing in Matrix::operator=
MatrixXf a = MatrixXf::Random(10,4), b = MatrixXf::Random(4,10), c = a;
VERIFY_IS_APPROX((a = a * b), (c * b).eval());
}
{
// check the functions to setup blocking sizes compile and do not segfault
// FIXME check they do what they are supposed to do !!
std::ptrdiff_t l1 = internal::random<int>(10000,20000);
std::ptrdiff_t l2 = internal::random<int>(100000,200000);
std::ptrdiff_t l3 = internal::random<int>(1000000,2000000);
setCpuCacheSizes(l1,l2,l3);
VERIFY(l1==l1CacheSize());
VERIFY(l2==l2CacheSize());
std::ptrdiff_t k1 = internal::random<int>(10,100)*16;
std::ptrdiff_t m1 = internal::random<int>(10,100)*16;
std::ptrdiff_t n1 = internal::random<int>(10,100)*16;
// only makes sure it compiles fine
internal::computeProductBlockingSizes<float,float,std::ptrdiff_t>(k1,m1,n1,1);
}
{
// test regression in row-vector by matrix (bad Map type)
MatrixXf mat1(10,32); mat1.setRandom();
MatrixXf mat2(32,32); mat2.setRandom();
MatrixXf r1 = mat1.row(2)*mat2.transpose();
VERIFY_IS_APPROX(r1, (mat1.row(2)*mat2.transpose()).eval());
MatrixXf r2 = mat1.row(2)*mat2;
VERIFY_IS_APPROX(r2, (mat1.row(2)*mat2).eval());
}
{
Eigen::MatrixXd A(10,10), B, C;
A.setRandom();
C = A;
for(int k=0; k<79; ++k)
C = C * A;
B.noalias() = (((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A)) * ((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A)))
* (((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A)) * ((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A)));
VERIFY_IS_APPROX(B,C);
}
#endif
// Regression test for bug 714:
#if defined EIGEN_HAS_OPENMP
omp_set_dynamic(1);
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_6( product(Matrix<float,Dynamic,Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
}
#endif
}