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Silenced type conversion warnings.

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This commit is contained in:
Hauke Heibel 2010-02-03 19:20:25 +01:00
parent 05837be8fb
commit 1a77334d54
6 changed files with 23 additions and 15 deletions
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4
test/nullary.cpp
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@ -50,8 +50,8 @@ template<typename VectorType>
void testVectorType(const VectorType& base)
{
typedef typename ei_traits<VectorType>::Scalar Scalar;
Scalar low = ei_random(-500,500);
Scalar high = ei_random(-500,500);
Scalar low = ei_random<Scalar>(-500,500);
Scalar high = ei_random<Scalar>(-500,500);
if (low>high) std::swap(low,high);
const int size = base.size();
const Scalar step = (high-low)/(size-1);

3
test/redux.cpp
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@ -27,6 +27,7 @@
template<typename MatrixType> void matrixRedux(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
int rows = m.rows();
int cols = m.cols();
@ -44,7 +45,7 @@ template<typename MatrixType> void matrixRedux(const MatrixType& m)
minc = std::min(ei_real(minc), ei_real(m1(i,j)));
maxc = std::max(ei_real(maxc), ei_real(m1(i,j)));
}
const Scalar mean = s/Scalar(rows*cols);
const Scalar mean = s/Scalar(RealScalar(rows*cols));
VERIFY_IS_APPROX(m1.sum(), s);
VERIFY_IS_APPROX(m1.mean(), mean);

12
unsupported/Eigen/FFT
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@ -187,7 +187,7 @@ class FFT
{
m_impl.inv( dst,src,nfft );
if ( HasFlag( Unscaled ) == false)
scale(dst,1./nfft,nfft);
scale(dst,_Scalar(1./nfft),nfft);
}
inline
@ -237,8 +237,14 @@ class FFT
private:
template <typename _It,typename _Val>
inline
void scale(_It x,_Val s,int nx)
inline void scale(_It x,_Val s,int nx)
{
for (int k=0;k<nx;++k)
*x++ *= _Scalar(s);
}
template <typename _Val>
inline void scale(std::complex<_Val>* x,_Val s,int nx)
{
for (int k=0;k<nx;++k)
*x++ *= s;

10
unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h
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@ -85,7 +85,7 @@ MatrixType MatrixFunctionAtomic<MatrixType>::compute(const MatrixType& A)
{
// TODO: Use that A is upper triangular
m_Arows = A.rows();
m_avgEival = A.trace() / Scalar(m_Arows);
m_avgEival = A.trace() / Scalar(RealScalar(m_Arows));
m_Ashifted = A - m_avgEival * MatrixType::Identity(m_Arows, m_Arows);
computeMu();
MatrixType F = m_f(m_avgEival, 0) * MatrixType::Identity(m_Arows, m_Arows);
@ -94,7 +94,7 @@ MatrixType MatrixFunctionAtomic<MatrixType>::compute(const MatrixType& A)
for (int s = 1; s < 1.1 * m_Arows + 10; s++) { // upper limit is fairly arbitrary
Fincr = m_f(m_avgEival, s) * P;
F += Fincr;
P = (1/(s + 1.0)) * P * m_Ashifted;
P = Scalar(RealScalar(1.0/(s + 1))) * P * m_Ashifted;
if (taylorConverged(s, F, Fincr, P)) {
return F;
}
@ -127,9 +127,9 @@ bool MatrixFunctionAtomic<MatrixType>::taylorConverged(int s, const MatrixType&
for (int r = 0; r < n; r++) {
RealScalar mx = 0;
for (int i = 0; i < n; i++)
mx = std::max(mx, std::abs(m_f(m_Ashifted(i, i) + m_avgEival, s+r)));
if (r != 0)
rfactorial *= r;
mx = std::max(mx, std::abs(m_f(m_Ashifted(i, i) + m_avgEival, s+r)));
if (r != 0)
rfactorial *= RealScalar(r);
delta = std::max(delta, mx / rfactorial);
}
const RealScalar P_norm = P.cwiseAbs().rowwise().sum().maxCoeff();

4
unsupported/test/FFT.cpp
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@ -46,10 +46,10 @@ complex<long double> promote(long double x) { return complex<long double>( x);
long double difpower=0;
cerr <<"idx\ttruth\t\tvalue\t|dif|=\n";
long double pi = acos((long double)-1);
for (int k0=0;k0<fftbuf.size();++k0) {
for (int k0=0;k0<static_cast<int>(fftbuf.size());++k0) {
complex<long double> acc = 0;
long double phinc = -2.*k0* pi / timebuf.size();
for (int k1=0;k1<timebuf.size();++k1) {
for (int k1=0;k1<static_cast<int>(timebuf.size());++k1) {
acc += promote( timebuf[k1] ) * exp( complex<long double>(0,k1*phinc) );
}
totalpower += norm(acc);

5
unsupported/test/matrix_function.cpp
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@ -33,14 +33,15 @@ template<typename MatrixType>
MatrixType createRandomMatrix(const int size)
{
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
MatrixType result;
if (ei_random<int>(0,1) == 0) {
result = MatrixType::Random(size, size);
} else {
MatrixType diag = MatrixType::Zero(size, size);
for (int i = 0; i < size; ++i) {
diag(i, i) = static_cast<Scalar>(ei_random<int>(0,2))
+ ei_random<Scalar>() * static_cast<Scalar>(0.01);
diag(i, i) = Scalar(RealScalar(ei_random<int>(0,2)))
+ ei_random<Scalar>() * Scalar(RealScalar(0.01));
}
MatrixType A = MatrixType::Random(size, size);
result = A.inverse() * diag * A;