the Index types change.
As discussed on the list (too long to explain here).
diff --git a/test/array.cpp b/test/array.cpp
index 8006531..df1e1b4 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -127,9 +127,12 @@
// count
VERIFY(((m1.abs()+1)>RealScalar(0.1)).count() == rows*cols);
+
+ typedef Array<typename ArrayType::Index, Dynamic, 1> ArrayOfIndices;
+
// TODO allows colwise/rowwise for array
- VERIFY_IS_APPROX(((m1.abs()+1)>RealScalar(0.1)).colwise().count(), ArrayXi::Constant(cols,rows).transpose());
- VERIFY_IS_APPROX(((m1.abs()+1)>RealScalar(0.1)).rowwise().count(), ArrayXi::Constant(rows, cols));
+ VERIFY_IS_APPROX(((m1.abs()+1)>RealScalar(0.1)).colwise().count(), ArrayOfIndices::Constant(cols,rows).transpose());
+ VERIFY_IS_APPROX(((m1.abs()+1)>RealScalar(0.1)).rowwise().count(), ArrayOfIndices::Constant(rows, cols));
}
template<typename ArrayType> void array_real(const ArrayType& m)
diff --git a/test/array_for_matrix.cpp b/test/array_for_matrix.cpp
index 516c040..477d178 100644
--- a/test/array_for_matrix.cpp
+++ b/test/array_for_matrix.cpp
@@ -124,9 +124,12 @@
// count
VERIFY(((m1.array().abs()+1)>RealScalar(0.1)).count() == rows*cols);
+
+ typedef Matrix<typename MatrixType::Index, Dynamic, 1> VectorOfIndices;
+
// TODO allows colwise/rowwise for array
- VERIFY_IS_APPROX(((m1.array().abs()+1)>RealScalar(0.1)).matrix().colwise().count(), RowVectorXi::Constant(cols,rows));
- VERIFY_IS_APPROX(((m1.array().abs()+1)>RealScalar(0.1)).matrix().rowwise().count(), VectorXi::Constant(rows, cols));
+ VERIFY_IS_APPROX(((m1.array().abs()+1)>RealScalar(0.1)).matrix().colwise().count(), VectorOfIndices::Constant(cols,rows).transpose());
+ VERIFY_IS_APPROX(((m1.array().abs()+1)>RealScalar(0.1)).matrix().rowwise().count(), VectorOfIndices::Constant(rows, cols));
}
template<typename VectorType> void lpNorm(const VectorType& v)
diff --git a/test/nomalloc.cpp b/test/nomalloc.cpp
index a80145f..9eb8d13 100644
--- a/test/nomalloc.cpp
+++ b/test/nomalloc.cpp
@@ -133,7 +133,7 @@
void test_nomalloc()
{
// check that our operator new is indeed called:
- VERIFY_RAISES_ASSERT(MatrixXd dummy = MatrixXd::Random(3,3));
+ VERIFY_RAISES_ASSERT(MatrixXd dummy(MatrixXd::Random(3,3)));
CALL_SUBTEST_1(nomalloc(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2(nomalloc(Matrix4d()) );
CALL_SUBTEST_3(nomalloc(Matrix<float,32,32>()) );
diff --git a/test/qr_colpivoting.cpp b/test/qr_colpivoting.cpp
index a34feed..7064bc2 100644
--- a/test/qr_colpivoting.cpp
+++ b/test/qr_colpivoting.cpp
@@ -38,7 +38,7 @@
MatrixType m1;
createRandomPIMatrixOfRank(rank,rows,cols,m1);
ColPivHouseholderQR<MatrixType> qr(m1);
- VERIFY_IS_APPROX(rank, qr.rank());
+ VERIFY(rank == qr.rank());
VERIFY(cols - qr.rank() == qr.dimensionOfKernel());
VERIFY(!qr.isInjective());
VERIFY(!qr.isInvertible());
@@ -66,7 +66,7 @@
Matrix<Scalar,Rows,Cols> m1;
createRandomPIMatrixOfRank(rank,Rows,Cols,m1);
ColPivHouseholderQR<Matrix<Scalar,Rows,Cols> > qr(m1);
- VERIFY_IS_APPROX(rank, qr.rank());
+ VERIFY(rank == qr.rank());
VERIFY(Cols - qr.rank() == qr.dimensionOfKernel());
VERIFY(qr.isInjective() == (rank == Rows));
VERIFY(qr.isSurjective() == (rank == Cols));
diff --git a/test/qr_fullpivoting.cpp b/test/qr_fullpivoting.cpp
index 82c42c7..33350ce 100644
--- a/test/qr_fullpivoting.cpp
+++ b/test/qr_fullpivoting.cpp
@@ -37,7 +37,7 @@
MatrixType m1;
createRandomPIMatrixOfRank(rank,rows,cols,m1);
FullPivHouseholderQR<MatrixType> qr(m1);
- VERIFY_IS_APPROX(rank, qr.rank());
+ VERIFY(rank == qr.rank());
VERIFY(cols - qr.rank() == qr.dimensionOfKernel());
VERIFY(!qr.isInjective());
VERIFY(!qr.isInvertible());
diff --git a/test/sizeof.cpp b/test/sizeof.cpp
index a724359..779f3b5 100644
--- a/test/sizeof.cpp
+++ b/test/sizeof.cpp
@@ -30,7 +30,7 @@
if (MatrixType::RowsAtCompileTime!=Dynamic && MatrixType::ColsAtCompileTime!=Dynamic)
VERIFY(sizeof(MatrixType)==sizeof(Scalar)*MatrixType::SizeAtCompileTime);
else
- VERIFY(sizeof(MatrixType)==sizeof(Scalar*) + 2 * sizeof(int));
+ VERIFY(sizeof(MatrixType)==sizeof(Scalar*) + 2 * sizeof(typename MatrixType::Index));
}
void test_sizeof()
diff --git a/test/visitor.cpp b/test/visitor.cpp
index 65ee60b..1ddabc6 100644
--- a/test/visitor.cpp
+++ b/test/visitor.cpp
@@ -27,22 +27,23 @@
template<typename MatrixType> void matrixVisitor(const MatrixType& p)
{
typedef typename MatrixType::Scalar Scalar;
+ typedef typename MatrixType::Index Index;
- int rows = p.rows();
- int cols = p.cols();
+ Index rows = p.rows();
+ Index cols = p.cols();
// construct a random matrix where all coefficients are different
MatrixType m;
m = MatrixType::Random(rows, cols);
- for(int i = 0; i < m.size(); i++)
- for(int i2 = 0; i2 < i; i2++)
+ for(Index i = 0; i < m.size(); i++)
+ for(Index i2 = 0; i2 < i; i2++)
while(m(i) == m(i2)) // yes, ==
m(i) = ei_random<Scalar>();
Scalar minc = Scalar(1000), maxc = Scalar(-1000);
- int minrow=0,mincol=0,maxrow=0,maxcol=0;
- for(int j = 0; j < cols; j++)
- for(int i = 0; i < rows; i++)
+ Index minrow=0,mincol=0,maxrow=0,maxcol=0;
+ for(Index j = 0; j < cols; j++)
+ for(Index i = 0; i < rows; i++)
{
if(m(i,j) < minc)
{
@@ -57,7 +58,7 @@
maxcol = j;
}
}
- int eigen_minrow, eigen_mincol, eigen_maxrow, eigen_maxcol;
+ Index eigen_minrow, eigen_mincol, eigen_maxrow, eigen_maxcol;
Scalar eigen_minc, eigen_maxc;
eigen_minc = m.minCoeff(&eigen_minrow,&eigen_mincol);
eigen_maxc = m.maxCoeff(&eigen_maxrow,&eigen_maxcol);
@@ -74,20 +75,21 @@
template<typename VectorType> void vectorVisitor(const VectorType& w)
{
typedef typename VectorType::Scalar Scalar;
+ typedef typename VectorType::Index Index;
- int size = w.size();
+ Index size = w.size();
// construct a random vector where all coefficients are different
VectorType v;
v = VectorType::Random(size);
- for(int i = 0; i < size; i++)
- for(int i2 = 0; i2 < i; i2++)
+ for(Index i = 0; i < size; i++)
+ for(Index i2 = 0; i2 < i; i2++)
while(v(i) == v(i2)) // yes, ==
v(i) = ei_random<Scalar>();
Scalar minc = Scalar(1000), maxc = Scalar(-1000);
- int minidx=0,maxidx=0;
- for(int i = 0; i < size; i++)
+ Index minidx=0,maxidx=0;
+ for(Index i = 0; i < size; i++)
{
if(v(i) < minc)
{
@@ -100,7 +102,7 @@
maxidx = i;
}
}
- int eigen_minidx, eigen_maxidx;
+ Index eigen_minidx, eigen_maxidx;
Scalar eigen_minc, eigen_maxc;
eigen_minc = v.minCoeff(&eigen_minidx);
eigen_maxc = v.maxCoeff(&eigen_maxidx);
