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* suppressed some minor warnings

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This commit is contained in:
Kenneth Frank Riddile
2009-01-06 04:38:00 +00:00
parent 1c29d70312
commit 6736e52d25
8 changed files with 28 additions and 28 deletions
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24
Eigen/src/SVD/SVD.h
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@@ -208,7 +208,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
m_matU.col(j).end(m-k) += t * m_matU.col(k).end(m-k);
}
m_matU.col(k).end(m-k) = - m_matU.col(k).end(m-k);
m_matU(k,k) = 1.0 + m_matU(k,k);
m_matU(k,k) = Scalar(1) + m_matU(k,k);
if (k-1>0)
m_matU.col(k).start(k-1).setZero();
}
@@ -242,7 +242,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
// Main iteration loop for the singular values.
int pp = p-1;
int iter = 0;
Scalar eps(pow(2.0,-52.0));
Scalar eps(Scalar(pow(2.0,-52.0)));
while (p > 0)
{
int k=0;
@@ -260,7 +260,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
// s(k), ..., s(p) are not negligible (qr step).
// kase = 4 if e(p-1) is negligible (convergence).
for (k = p-2; k >= -1; k--)
for (k = p-2; k >= -1; --k)
{
if (k == -1)
break;
@@ -277,11 +277,11 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
else
{
int ks;
for (ks = p-1; ks >= k; ks--)
for (ks = p-1; ks >= k; --ks)
{
if (ks == k)
break;
Scalar t( (ks != p ? ei_abs(e[ks]) : 0.) + (ks != k+1 ? ei_abs(e[ks-1]) : 0.));
Scalar t( Scalar((ks != p ? ei_abs(e[ks]) : 0.) + (ks != k+1 ? ei_abs(e[ks-1]) : 0.)) );
if (ei_abs(m_sigma[ks]) <= eps*t)
{
m_sigma[ks] = 0.0;
@@ -313,9 +313,9 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
{
Scalar f(e[p-2]);
e[p-2] = 0.0;
for (j = p-2; j >= k; j--)
for (j = p-2; j >= k; --j)
{
Scalar t(hypot(m_sigma[j],f));
Scalar t(Scalar(hypot(m_sigma[j],f)));
Scalar cs(m_sigma[j]/t);
Scalar sn(f/t);
m_sigma[j] = t;
@@ -344,7 +344,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
e[k-1] = 0.0;
for (j = k; j < p; ++j)
{
Scalar t(hypot(m_sigma[j],f));
Scalar t(Scalar(hypot(m_sigma[j],f)));
Scalar cs( m_sigma[j]/t);
Scalar sn(f/t);
m_sigma[j] = t;
@@ -375,7 +375,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
Scalar epm1 = e[p-2]/scale;
Scalar sk = m_sigma[k]/scale;
Scalar ek = e[k]/scale;
Scalar b = ((spm1 + sp)*(spm1 - sp) + epm1*epm1)/2.0;
Scalar b = Scalar(((spm1 + sp)*(spm1 - sp) + epm1*epm1)/2.0);
Scalar c = (sp*epm1)*(sp*epm1);
Scalar shift = 0.0;
if ((b != 0.0) || (c != 0.0))
@@ -392,7 +392,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
for (j = k; j < p-1; ++j)
{
Scalar t = hypot(f,g);
Scalar t = Scalar(hypot(f,g));
Scalar cs = f/t;
Scalar sn = g/t;
if (j != k)
@@ -410,7 +410,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
m_matV(i,j) = t;
}
}
t = hypot(f,g);
t = Scalar(hypot(f,g));
cs = f/t;
sn = g/t;
m_sigma[j] = t;
@@ -439,7 +439,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
// Make the singular values positive.
if (m_sigma[k] <= 0.0)
{
m_sigma[k] = (m_sigma[k] < 0.0 ? -m_sigma[k] : 0.0);
m_sigma[k] = Scalar((m_sigma[k] < 0.0 ? -m_sigma[k] : 0.0));
if (wantv)
m_matV.col(k).start(pp+1) = -m_matV.col(k).start(pp+1);
}