devtools/eigen
1
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2026-04-10 11:34:33 +08:00
Code Issues Packages Projects Releases Wiki Activity

Fix bug #314:

Browse Source 
- remove most of the metaprogramming kung fu in MathFunctions.h (only keep functions that differs from the std)
- remove the overloads for array expression that were in the std namespace
This commit is contained in:
Gael Guennebaud
2012-11-06 15:25:50 +01:00
parent 959ef37006
commit a76fbbf397
88 changed files with 496 additions and 468 deletions
Download Patch File Download Diff File Expand all files Collapse all files

15
unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
View File

@@ -52,7 +52,7 @@ public:
Parameters()
: factor(Scalar(100.))
, maxfev(1000)
, xtol(internal::sqrt(NumTraits<Scalar>::epsilon()))
, xtol(std::sqrt(NumTraits<Scalar>::epsilon()))
, nb_of_subdiagonals(-1)
, nb_of_superdiagonals(-1)
, epsfcn(Scalar(0.)) {}
@@ -70,7 +70,7 @@ public:
HybridNonLinearSolverSpace::Status hybrj1(
FVectorType &x,
const Scalar tol = internal::sqrt(NumTraits<Scalar>::epsilon())
const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon())
);
HybridNonLinearSolverSpace::Status solveInit(FVectorType &x);
@@ -79,7 +79,7 @@ public:
HybridNonLinearSolverSpace::Status hybrd1(
FVectorType &x,
const Scalar tol = internal::sqrt(NumTraits<Scalar>::epsilon())
const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon())
);
HybridNonLinearSolverSpace::Status solveNumericalDiffInit(FVectorType &x);
@@ -185,6 +185,8 @@ template<typename FunctorType, typename Scalar>
HybridNonLinearSolverSpace::Status
HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(FVectorType &x)
{
using std::abs;
assert(x.size()==n); // check the caller is not cheating us
Index j;
@@ -276,7 +278,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(FVectorType &x)
++ncsuc;
if (ratio >= Scalar(.5) || ncsuc > 1)
delta = (std::max)(delta, pnorm / Scalar(.5));
if (internal::abs(ratio - 1.) <= Scalar(.1)) {
if (abs(ratio - 1.) <= Scalar(.1)) {
delta = pnorm / Scalar(.5);
}
}
@@ -423,6 +425,9 @@ template<typename FunctorType, typename Scalar>
HybridNonLinearSolverSpace::Status
HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(FVectorType &x)
{
using std::sqrt;
using std::abs;
assert(x.size()==n); // check the caller is not cheating us
Index j;
@@ -516,7 +521,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(FVectorType
++ncsuc;
if (ratio >= Scalar(.5) || ncsuc > 1)
delta = (std::max)(delta, pnorm / Scalar(.5));
if (internal::abs(ratio - 1.) <= Scalar(.1)) {
if (abs(ratio - 1.) <= Scalar(.1)) {
delta = pnorm / Scalar(.5);
}
}

36
unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
View File

@@ -55,8 +55,8 @@ public:
Parameters()
: factor(Scalar(100.))
, maxfev(400)
, ftol(internal::sqrt(NumTraits<Scalar>::epsilon()))
, xtol(internal::sqrt(NumTraits<Scalar>::epsilon()))
, ftol(std::sqrt(NumTraits<Scalar>::epsilon()))
, xtol(std::sqrt(NumTraits<Scalar>::epsilon()))
, gtol(Scalar(0.))
, epsfcn(Scalar(0.)) {}
Scalar factor;
@@ -72,7 +72,7 @@ public:
LevenbergMarquardtSpace::Status lmder1(
FVectorType &x,
const Scalar tol = internal::sqrt(NumTraits<Scalar>::epsilon())
const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon())
);
LevenbergMarquardtSpace::Status minimize(FVectorType &x);
@@ -83,12 +83,12 @@ public:
FunctorType &functor,
FVectorType &x,
Index *nfev,
const Scalar tol = internal::sqrt(NumTraits<Scalar>::epsilon())
const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon())
);
LevenbergMarquardtSpace::Status lmstr1(
FVectorType &x,
const Scalar tol = internal::sqrt(NumTraits<Scalar>::epsilon())
const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon())
);
LevenbergMarquardtSpace::Status minimizeOptimumStorage(FVectorType &x);
@@ -206,6 +206,9 @@ template<typename FunctorType, typename Scalar>
LevenbergMarquardtSpace::Status
LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType &x)
{
using std::abs;
using std::sqrt;
assert(x.size()==n); // check the caller is not cheating us
/* calculate the jacobian matrix. */
@@ -249,7 +252,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType &x)
if (fnorm != 0.)
for (Index j = 0; j < n; ++j)
if (wa2[permutation.indices()[j]] != 0.)
gnorm = (std::max)(gnorm, internal::abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
gnorm = (std::max)(gnorm, abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
/* test for convergence of the gradient norm. */
if (gnorm <= parameters.gtol)
@@ -288,7 +291,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType &x)
/* the scaled directional derivative. */
wa3 = fjac.template triangularView<Upper>() * (qrfac.colsPermutation().inverse() *wa1);
temp1 = internal::abs2(wa3.stableNorm() / fnorm);
temp2 = internal::abs2(internal::sqrt(par) * pnorm / fnorm);
temp2 = internal::abs2(sqrt(par) * pnorm / fnorm);
prered = temp1 + temp2 / Scalar(.5);
dirder = -(temp1 + temp2);
@@ -326,9 +329,9 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType &x)
}
/* tests for convergence. */
if (internal::abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm)
if (abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm)
return LevenbergMarquardtSpace::RelativeErrorAndReductionTooSmall;
if (internal::abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
if (abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
return LevenbergMarquardtSpace::RelativeReductionTooSmall;
if (delta <= parameters.xtol * xnorm)
return LevenbergMarquardtSpace::RelativeErrorTooSmall;
@@ -336,7 +339,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType &x)
/* tests for termination and stringent tolerances. */
if (nfev >= parameters.maxfev)
return LevenbergMarquardtSpace::TooManyFunctionEvaluation;
if (internal::abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() && Scalar(.5) * ratio <= 1.)
if (abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() && Scalar(.5) * ratio <= 1.)
return LevenbergMarquardtSpace::FtolTooSmall;
if (delta <= NumTraits<Scalar>::epsilon() * xnorm)
return LevenbergMarquardtSpace::XtolTooSmall;
@@ -423,6 +426,9 @@ template<typename FunctorType, typename Scalar>
LevenbergMarquardtSpace::Status
LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorType &x)
{
using std::abs;
using std::sqrt;
assert(x.size()==n); // check the caller is not cheating us
Index i, j;
@@ -496,7 +502,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorTyp
if (fnorm != 0.)
for (j = 0; j < n; ++j)
if (wa2[permutation.indices()[j]] != 0.)
gnorm = (std::max)(gnorm, internal::abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
gnorm = (std::max)(gnorm, abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
/* test for convergence of the gradient norm. */
if (gnorm <= parameters.gtol)
@@ -535,7 +541,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorTyp
/* the scaled directional derivative. */
wa3 = fjac.topLeftCorner(n,n).template triangularView<Upper>() * (permutation.inverse() * wa1);
temp1 = internal::abs2(wa3.stableNorm() / fnorm);
temp2 = internal::abs2(internal::sqrt(par) * pnorm / fnorm);
temp2 = internal::abs2(sqrt(par) * pnorm / fnorm);
prered = temp1 + temp2 / Scalar(.5);
dirder = -(temp1 + temp2);
@@ -573,9 +579,9 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorTyp
}
/* tests for convergence. */
if (internal::abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm)
if (abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm)
return LevenbergMarquardtSpace::RelativeErrorAndReductionTooSmall;
if (internal::abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
if (abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
return LevenbergMarquardtSpace::RelativeReductionTooSmall;
if (delta <= parameters.xtol * xnorm)
return LevenbergMarquardtSpace::RelativeErrorTooSmall;
@@ -583,7 +589,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorTyp
/* tests for termination and stringent tolerances. */
if (nfev >= parameters.maxfev)
return LevenbergMarquardtSpace::TooManyFunctionEvaluation;
if (internal::abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() && Scalar(.5) * ratio <= 1.)
if (abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() && Scalar(.5) * ratio <= 1.)
return LevenbergMarquardtSpace::FtolTooSmall;
if (delta <= NumTraits<Scalar>::epsilon() * xnorm)
return LevenbergMarquardtSpace::XtolTooSmall;

4
unsupported/Eigen/src/NonLinearOptimization/chkder.h
View File

@@ -16,6 +16,10 @@ void chkder(
Matrix< Scalar, Dynamic, 1 > &err
)
{
using std::sqrt;
using std::abs;
using std::log;
typedef DenseIndex Index;
const Scalar eps = sqrt(NumTraits<Scalar>::epsilon());

3
unsupported/Eigen/src/NonLinearOptimization/covar.h
View File

@@ -6,8 +6,9 @@ template <typename Scalar>
void covar(
Matrix< Scalar, Dynamic, Dynamic > &r,
const VectorXi &ipvt,
Scalar tol = sqrt(NumTraits<Scalar>::epsilon()) )
Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon()) )
{
using std::abs;
typedef DenseIndex Index;
/* Local variables */

3
unsupported/Eigen/src/NonLinearOptimization/dogleg.h
View File

@@ -10,6 +10,9 @@ void dogleg(
Scalar delta,
Matrix< Scalar, Dynamic, 1 > &x)
{
using std::abs;
using std::sqrt;
typedef DenseIndex Index;
/* Local variables */

3
unsupported/Eigen/src/NonLinearOptimization/fdjac1.h
View File

@@ -11,6 +11,9 @@ DenseIndex fdjac1(
DenseIndex ml, DenseIndex mu,
Scalar epsfcn)
{
using std::sqrt;
using std::abs;
typedef DenseIndex Index;
/* Local variables */

4
unsupported/Eigen/src/NonLinearOptimization/lmpar.h
View File

@@ -12,6 +12,8 @@ void lmpar(
Scalar &par,
Matrix< Scalar, Dynamic, 1 > &x)
{
using std::abs;
using std::sqrt;
typedef DenseIndex Index;
/* Local variables */
@@ -168,6 +170,8 @@ void lmpar2(
Matrix< Scalar, Dynamic, 1 > &x)
{
using std::sqrt;
using std::abs;
typedef DenseIndex Index;
/* Local variables */