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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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34
Eigen/src/SVD/SVD.h
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@@ -115,7 +115,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
// in s and the super-diagonal elements in e.
int nct = std::min(m-1,n);
int nrt = std::max(0,std::min(n-2,m));
for (k = 0; k < std::max(nct,nrt); k++)
for (k = 0; k < std::max(nct,nrt); ++k)
{
if (k < nct)
{
@@ -132,7 +132,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
m_sigma[k] = -m_sigma[k];
}
for (j = k+1; j < n; j++)
for (j = k+1; j < n; ++j)
{
if ((k < nct) && (m_sigma[k] != 0.0))
{
@@ -168,7 +168,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
{
// Apply the transformation.
work.end(m-k-1) = matA.corner(BottomRight,m-k-1,n-k-1) * e.end(n-k-1);
for (j = k+1; j < n; j++)
for (j = k+1; j < n; ++j)
matA.col(j).end(m-k-1) += (-e[j]/e[k+1]) * work.end(m-k-1);
}
@@ -192,7 +192,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
// If required, generate U.
if (wantu)
{
for (j = nct; j < nu; j++)
for (j = nct; j < nu; ++j)
{
m_matU.col(j).setZero();
m_matU(j,j) = 1.0;
@@ -201,7 +201,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
{
if (m_sigma[k] != 0.0)
{
for (j = k+1; j < nu; j++)
for (j = k+1; j < nu; ++j)
{
Scalar t = m_matU.col(k).end(m-k).dot(m_matU.col(j).end(m-k)); // FIXME is it really a dot product we want ?
t = -t/m_matU(k,k);
@@ -227,7 +227,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
{
if ((k < nrt) & (e[k] != 0.0))
{
for (j = k+1; j < nu; j++)
for (j = k+1; j < nu; ++j)
{
Scalar t = m_matV.col(k).end(n-k-1).dot(m_matV.col(j).end(n-k-1)); // FIXME is it really a dot product we want ?
t = -t/m_matV(k+1,k);
@@ -302,7 +302,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
k = ks;
}
}
k++;
++k;
// Perform the task indicated by kase.
switch (kase)
@@ -326,7 +326,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
}
if (wantv)
{
for (i = 0; i < n; i++)
for (i = 0; i < n; ++i)
{
t = cs*m_matV(i,j) + sn*m_matV(i,p-1);
m_matV(i,p-1) = -sn*m_matV(i,j) + cs*m_matV(i,p-1);
@@ -342,7 +342,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
{
Scalar f(e[k-1]);
e[k-1] = 0.0;
for (j = k; j < p; j++)
for (j = k; j < p; ++j)
{
Scalar t(hypot(m_sigma[j],f));
Scalar cs( m_sigma[j]/t);
@@ -352,7 +352,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
e[j] = cs*e[j];
if (wantu)
{
for (i = 0; i < m; i++)
for (i = 0; i < m; ++i)
{
t = cs*m_matU(i,j) + sn*m_matU(i,k-1);
m_matU(i,k-1) = -sn*m_matU(i,j) + cs*m_matU(i,k-1);
@@ -390,7 +390,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
// Chase zeros.
for (j = k; j < p-1; j++)
for (j = k; j < p-1; ++j)
{
Scalar t = hypot(f,g);
Scalar cs = f/t;
@@ -403,7 +403,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
m_sigma[j+1] = cs*m_sigma[j+1];
if (wantv)
{
for (i = 0; i < n; i++)
for (i = 0; i < n; ++i)
{
t = cs*m_matV(i,j) + sn*m_matV(i,j+1);
m_matV(i,j+1) = -sn*m_matV(i,j) + cs*m_matV(i,j+1);
@@ -420,7 +420,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
e[j+1] = cs*e[j+1];
if (wantu && (j < m-1))
{
for (i = 0; i < m; i++)
for (i = 0; i < m; ++i)
{
t = cs*m_matU(i,j) + sn*m_matU(i,j+1);
m_matU(i,j+1) = -sn*m_matU(i,j) + cs*m_matU(i,j+1);
@@ -456,7 +456,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
m_matV.col(k).swap(m_matV.col(k+1));
if (wantu && (k < m-1))
m_matU.col(k).swap(m_matU.col(k+1));
k++;
++k;
}
iter = 0;
p--;
@@ -473,12 +473,12 @@ SVD<MatrixType>& SVD<MatrixType>::sort()
int mv = m_matV.rows();
int n = m_matU.cols();
for (int i=0; i<n; i++)
for (int i=0; i<n; ++i)
{
int k = i;
Scalar p = m_sigma.coeff(i);
for (int j=i+1; j<n; j++)
for (int j=i+1; j<n; ++j)
{
if (m_sigma.coeff(j) > p)
{
@@ -520,7 +520,7 @@ bool SVD<MatrixType>::solve(const MatrixBase<OtherDerived> &b, ResultType* resul
{
Matrix<Scalar,MatrixUType::RowsAtCompileTime,1> aux = m_matU.transpose() * b.col(j);
for (int i = 0; i <m_matU.cols(); i++)
for (int i = 0; i <m_matU.cols(); ++i)
{
Scalar si = m_sigma.coeff(i);
if (ei_isMuchSmallerThan(ei_abs(si),maxVal))