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* replace postfix ++ by prefix ++ wherever that makes sense in Eigen/

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* fix some "unused variable" warnings in the tests; there remains a libstdc++ "deprecated"
warning which I haven't looked much into
This commit is contained in:
Benoit Jacob
2008-12-17 14:30:01 +00:00
parent 2110cca431
commit 89f468671d
28 changed files with 164 additions and 162 deletions
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36
Eigen/src/QR/EigenSolver.h
View File

@@ -122,7 +122,7 @@ MatrixType EigenSolver<MatrixType>::pseudoEigenvalueMatrix() const
{
int n = m_eivec.cols();
MatrixType matD = MatrixType::Zero(n,n);
for (int i=0; i<n; i++)
for (int i=0; i<n; ++i)
{
if (ei_isMuchSmallerThan(ei_imag(m_eivalues.coeff(i)), ei_real(m_eivalues.coeff(i))))
matD.coeffRef(i,i) = ei_real(m_eivalues.coeff(i));
@@ -130,7 +130,7 @@ MatrixType EigenSolver<MatrixType>::pseudoEigenvalueMatrix() const
{
matD.template block<2,2>(i,i) << ei_real(m_eivalues.coeff(i)), ei_imag(m_eivalues.coeff(i)),
-ei_imag(m_eivalues.coeff(i)), ei_real(m_eivalues.coeff(i));
i++;
++i;
}
}
return matD;
@@ -145,7 +145,7 @@ typename EigenSolver<MatrixType>::EigenvectorType EigenSolver<MatrixType>::eigen
{
int n = m_eivec.cols();
EigenvectorType matV(n,n);
for (int j=0; j<n; j++)
for (int j=0; j<n; ++j)
{
if (ei_isMuchSmallerThan(ei_abs(ei_imag(m_eivalues.coeff(j))), ei_abs(ei_real(m_eivalues.coeff(j)))))
{
@@ -155,14 +155,14 @@ typename EigenSolver<MatrixType>::EigenvectorType EigenSolver<MatrixType>::eigen
else
{
// we have a pair of complex eigen values
for (int i=0; i<n; i++)
for (int i=0; i<n; ++i)
{
matV.coeffRef(i,j) = Complex(m_eivec.coeff(i,j), m_eivec.coeff(i,j+1));
matV.coeffRef(i,j+1) = Complex(m_eivec.coeff(i,j), -m_eivec.coeff(i,j+1));
}
matV.col(j).normalize();
matV.col(j+1).normalize();
j++;
++j;
}
}
return matV;
@@ -198,7 +198,7 @@ void EigenSolver<MatrixType>::orthes(MatrixType& matH, RealVectorType& ort)
int low = 0;
int high = n-1;
for (int m = low+1; m <= high-1; m++)
for (int m = low+1; m <= high-1; ++m)
{
// Scale column.
RealScalar scale = matH.block(m, m-1, high-m+1, 1).cwise().abs().sum();
@@ -290,7 +290,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
// FIXME to be efficient the following would requires a triangular reduxion code
// Scalar norm = matH.upper().cwise().abs().sum() + matH.corner(BottomLeft,n,n).diagonal().cwise().abs().sum();
Scalar norm = 0.0;
for (int j = 0; j < nn; j++)
for (int j = 0; j < nn; ++j)
{
// FIXME what's the purpose of the following since the condition is always false
if ((j < low) || (j > high))
@@ -361,7 +361,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
q = q / r;
// Row modification
for (int j = n-1; j < nn; j++)
for (int j = n-1; j < nn; ++j)
{
z = matH.coeff(n-1,j);
matH.coeffRef(n-1,j) = q * z + p * matH.coeff(n,j);
@@ -369,7 +369,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
}
// Column modification
for (int i = 0; i <= n; i++)
for (int i = 0; i <= n; ++i)
{
z = matH.coeff(i,n-1);
matH.coeffRef(i,n-1) = q * z + p * matH.coeff(i,n);
@@ -377,7 +377,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
}
// Accumulate transformations
for (int i = low; i <= high; i++)
for (int i = low; i <= high; ++i)
{
z = m_eivec.coeff(i,n-1);
m_eivec.coeffRef(i,n-1) = q * z + p * m_eivec.coeff(i,n);
@@ -410,7 +410,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
if (iter == 10)
{
exshift += x;
for (int i = low; i <= n; i++)
for (int i = low; i <= n; ++i)
matH.coeffRef(i,i) -= x;
s = ei_abs(matH.coeff(n,n-1)) + ei_abs(matH.coeff(n-1,n-2));
x = y = 0.75 * s;
@@ -428,7 +428,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
if (y < x)
s = -s;
s = x - w / ((y - x) / 2.0 + s);
for (int i = low; i <= n; i++)
for (int i = low; i <= n; ++i)
matH.coeffRef(i,i) -= s;
exshift += s;
x = y = w = 0.964;
@@ -463,7 +463,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
m--;
}
for (int i = m+2; i <= n; i++)
for (int i = m+2; i <= n; ++i)
{
matH.coeffRef(i,i-2) = 0.0;
if (i > m+2)
@@ -471,7 +471,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
}
// Double QR step involving rows l:n and columns m:n
for (int k = m; k <= n-1; k++)
for (int k = m; k <= n-1; ++k)
{
int notlast = (k != n-1);
if (k != m) {
@@ -510,7 +510,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
r = r / p;
// Row modification
for (int j = k; j < nn; j++)
for (int j = k; j < nn; ++j)
{
p = matH.coeff(k,j) + q * matH.coeff(k+1,j);
if (notlast)
@@ -523,7 +523,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
}
// Column modification
for (int i = 0; i <= std::min(n,k+3); i++)
for (int i = 0; i <= std::min(n,k+3); ++i)
{
p = x * matH.coeff(i,k) + y * matH.coeff(i,k+1);
if (notlast)
@@ -536,7 +536,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
}
// Accumulate transformations
for (int i = low; i <= high; i++)
for (int i = low; i <= high; ++i)
{
p = x * m_eivec.coeff(i,k) + y * m_eivec.coeff(i,k+1);
if (notlast)
@@ -686,7 +686,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
}
// Vectors of isolated roots
for (int i = 0; i < nn; i++)
for (int i = 0; i < nn; ++i)
{
// FIXME again what's the purpose of this test ?
// in this algo low==0 and high==nn-1 !!

2
Eigen/src/QR/QR.h
View File

@@ -87,7 +87,7 @@ void QR<MatrixType>::_compute(const MatrixType& matrix)
int rows = matrix.rows();
int cols = matrix.cols();
for (int k = 0; k < cols; k++)
for (int k = 0; k < cols; ++k)
{
int remainingSize = rows-k;

2
Eigen/src/QR/SelfAdjointEigenSolver.h
View File

@@ -202,7 +202,7 @@ void SelfAdjointEigenSolver<MatrixType>::compute(const MatrixType& matrix, bool
// Sort eigenvalues and corresponding vectors.
// TODO make the sort optional ?
// TODO use a better sort algorithm !!
for (int i = 0; i < n-1; i++)
for (int i = 0; i < n-1; ++i)
{
int k;
m_eivalues.segment(i,n-i).minCoeff(&k);

2
Eigen/src/QR/Tridiagonalization.h
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@@ -268,7 +268,7 @@ void Tridiagonalization<MatrixType>::_compute(MatrixType& matA, CoeffVectorType&
* if we remove the specialization of Block for Matrix then it is even worse, much worse ! */
#ifdef EIGEN_NEVER_DEFINED
for (int j1=i+1; j1<n; ++j1)
for (int i1=j1; i1<n; i1++)
for (int i1=j1; i1<n; ++i1)
matA.coeffRef(i1,j1) -= matA.coeff(i1,i)*ei_conj(hCoeffs.coeff(j1-1))
+ hCoeffs.coeff(i1-1)*ei_conj(matA.coeff(j1,i));
#endif