void testVectorType(const VectorType& base)
{
typedef typename internal::traits<VectorType>::Index Index;
typedef typename internal::traits<VectorType>::Scalar Scalar;
const Index size = base.size();
Scalar high = internal::random<Scalar>(-500,500);
Scalar low = (size == 1 ? high : internal::random<Scalar>(-500,500));
if (low>high) std::swap(low,high);
const Scalar step = ((size == 1) ? 1 : (high-low)/(size-1));
// check whether the result yields what we expect it to do
VectorType m(base);
m.setLinSpaced(size,low,high);
VectorType n(size);
for (int i=0; i<size; ++i)
n(i) = low+i*step;
VERIFY_IS_APPROX(m,n);
// random access version
m = VectorType::LinSpaced(size,low,high);
VERIFY_IS_APPROX(m,n);
// Assignment of a RowVectorXd to a MatrixXd (regression test for bug #79).
VERIFY( (MatrixXd(RowVectorXd::LinSpaced(3, 0, 1)) - RowVector3d(0, 0.5, 1)).norm() < std::numeric_limits<Scalar>::epsilon() );
// These guys sometimes fail! This is not good. Any ideas how to fix them!?
//VERIFY( m(m.size()-1) == high );
//VERIFY( m(0) == low );
// sequential access version
m = VectorType::LinSpaced(Sequential,size,low,high);
VERIFY_IS_APPROX(m,n);
// These guys sometimes fail! This is not good. Any ideas how to fix them!?
//VERIFY( m(m.size()-1) == high );
//VERIFY( m(0) == low );
// check whether everything works with row and col major vectors
Matrix<Scalar,Dynamic,1> row_vector(size);
Matrix<Scalar,1,Dynamic> col_vector(size);
row_vector.setLinSpaced(size,low,high);
col_vector.setLinSpaced(size,low,high);
VERIFY( row_vector.isApprox(col_vector.transpose(), NumTraits<Scalar>::epsilon()));
Matrix<Scalar,Dynamic,1> size_changer(size+50);
size_changer.setLinSpaced(size,low,high);
VERIFY( size_changer.size() == size );
typedef Matrix<Scalar,1,1> ScalarMatrix;
ScalarMatrix scalar;
scalar.setLinSpaced(1,low,high);
VERIFY_IS_APPROX( scalar, ScalarMatrix::Constant(high) );
VERIFY_IS_APPROX( ScalarMatrix::LinSpaced(1,low,high), ScalarMatrix::Constant(high) );
}
void testVectorType(const VectorType& base)
{
typedef typename VectorType::Scalar Scalar;
const Index size = base.size();
Scalar high = internal::random<Scalar>(-500,500);
Scalar low = (size == 1 ? high : internal::random<Scalar>(-500,500));
if (low>high) std::swap(low,high);
const Scalar step = ((size == 1) ? 1 : (high-low)/(size-1));
// check whether the result yields what we expect it to do
VectorType m(base);
m.setLinSpaced(size,low,high);
if(!NumTraits<Scalar>::IsInteger)
{
VectorType n(size);
for (int i=0; i<size; ++i)
n(i) = low+i*step;
VERIFY_IS_APPROX(m,n);
}
VectorType n(size);
for (int i=0; i<size; ++i)
n(i) = size==1 ? low : (low + ((high-low)*Scalar(i))/(size-1));
VERIFY_IS_APPROX(m,n);
// random access version
m = VectorType::LinSpaced(size,low,high);
VERIFY_IS_APPROX(m,n);
VERIFY( internal::isApprox(m(m.size()-1),high) );
VERIFY( size==1 || internal::isApprox(m(0),low) );
// sequential access version
m = VectorType::LinSpaced(Sequential,size,low,high);
VERIFY_IS_APPROX(m,n);
VERIFY( internal::isApprox(m(m.size()-1),high) );
VERIFY( size==1 || internal::isApprox(m(0),low) );
// check whether everything works with row and col major vectors
Matrix<Scalar,Dynamic,1> row_vector(size);
Matrix<Scalar,1,Dynamic> col_vector(size);
row_vector.setLinSpaced(size,low,high);
col_vector.setLinSpaced(size,low,high);
// when using the extended precision (e.g., FPU) the relative error might exceed 1 bit
// when computing the squared sum in isApprox, thus the 2x factor.
VERIFY( row_vector.isApprox(col_vector.transpose(), Scalar(2)*NumTraits<Scalar>::epsilon()));
Matrix<Scalar,Dynamic,1> size_changer(size+50);
size_changer.setLinSpaced(size,low,high);
VERIFY( size_changer.size() == size );
typedef Matrix<Scalar,1,1> ScalarMatrix;
ScalarMatrix scalar;
scalar.setLinSpaced(1,low,high);
VERIFY_IS_APPROX( scalar, ScalarMatrix::Constant(high) );
VERIFY_IS_APPROX( ScalarMatrix::LinSpaced(1,low,high), ScalarMatrix::Constant(high) );
// regression test for bug 526 (linear vectorized transversal)
if (size > 1) {
m.tail(size-1).setLinSpaced(low, high);
VERIFY_IS_APPROX(m(size-1), high);
}
}