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port unsupported modules to new API

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This commit is contained in:
Gael Guennebaud 2010-01-05 15:38:20 +01:00
parent cab85218db
commit 39209edd71
9 changed files with 189 additions and 189 deletions
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2
unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h
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@ -207,7 +207,7 @@ MatrixExponential<MatrixType>::MatrixExponential(const MatrixType &M, MatrixType
m_tmp2(M.rows(),M.cols()),
m_Id(MatrixType::Identity(M.rows(), M.cols())),
m_squarings(0),
m_l1norm(static_cast<float>(M.cwise().abs().colwise().sum().maxCoeff()))
m_l1norm(static_cast<float>(M.cwiseAbs().colwise().sum().maxCoeff()))
{
computeUV(RealScalar());
m_tmp1 = m_U + m_V; // numerator of Pade approximant

8
unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h
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@ -110,7 +110,7 @@ void MatrixFunctionAtomic<MatrixType>::computeMu()
const MatrixType N = MatrixType::Identity(m_Arows, m_Arows) - m_Ashifted;
VectorType e = VectorType::Ones(m_Arows);
N.template triangularView<UpperTriangular>().solveInPlace(e);
m_mu = e.cwise().abs().maxCoeff();
m_mu = e.cwiseAbs().maxCoeff();
}
/** \brief Determine whether Taylor series has converged */
@ -119,8 +119,8 @@ bool MatrixFunctionAtomic<MatrixType>::taylorConverged(int s, const MatrixType&
const MatrixType& Fincr, const MatrixType& P)
{
const int n = F.rows();
const RealScalar F_norm = F.cwise().abs().rowwise().sum().maxCoeff();
const RealScalar Fincr_norm = Fincr.cwise().abs().rowwise().sum().maxCoeff();
const RealScalar F_norm = F.cwiseAbs().rowwise().sum().maxCoeff();
const RealScalar Fincr_norm = Fincr.cwiseAbs().rowwise().sum().maxCoeff();
if (Fincr_norm < epsilon<Scalar>() * F_norm) {
RealScalar delta = 0;
RealScalar rfactorial = 1;
@ -132,7 +132,7 @@ bool MatrixFunctionAtomic<MatrixType>::taylorConverged(int s, const MatrixType&
rfactorial *= r;
delta = std::max(delta, mx / rfactorial);
}
const RealScalar P_norm = P.cwise().abs().rowwise().sum().maxCoeff();
const RealScalar P_norm = P.cwiseAbs().rowwise().sum().maxCoeff();
if (m_mu * delta * P_norm < epsilon<Scalar>() * F_norm)
return true;
}

16
unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
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@ -241,7 +241,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
/* on the first iteration, calculate the norm of the scaled x */
/* and initialize the step bound delta. */
wa3 = diag.cwise() * x;
wa3 = diag.cwiseProduct(x);
xnorm = wa3.stableNorm();
delta = parameters.factor * xnorm;
if (delta == 0.)
@ -285,7 +285,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
/* Computing MAX */
if (mode != 2)
diag = diag.cwise().max(wa2);
diag = diag.cwiseMax(wa2);
/* beginning of the inner loop. */
@ -299,7 +299,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
wa1 = -wa1;
wa2 = x + wa1;
wa3 = diag.cwise() * wa1;
wa3 = diag.cwiseProduct(wa1);
pnorm = wa3.stableNorm();
/* on the first iteration, adjust the initial step bound. */
@ -364,7 +364,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
if (ratio >= Scalar(1e-4)) {
/* successful iteration. update x, fvec, and their norms. */
x = wa2;
wa2 = diag.cwise() * x;
wa2 = diag.cwiseProduct(x);
fvec = wa4;
xnorm = wa2.stableNorm();
fnorm = fnorm1;
@ -555,7 +555,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
/* on the first iteration, calculate the norm of the scaled x */
/* and initialize the step bound delta. */
wa3 = diag.cwise() * x;
wa3 = diag.cwiseProduct(x);
xnorm = wa3.stableNorm();
delta = parameters.factor * xnorm;
if (delta == 0.)
@ -599,7 +599,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
/* Computing MAX */
if (mode != 2)
diag = diag.cwise().max(wa2);
diag = diag.cwiseMax(wa2);
/* beginning of the inner loop. */
@ -613,7 +613,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
wa1 = -wa1;
wa2 = x + wa1;
wa3 = diag.cwise() * wa1;
wa3 = diag.cwiseProduct(wa1);
pnorm = wa3.stableNorm();
/* on the first iteration, adjust the initial step bound. */
@ -678,7 +678,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
if (ratio >= Scalar(1e-4)) {
/* successful iteration. update x, fvec, and their norms. */
x = wa2;
wa2 = diag.cwise() * x;
wa2 = diag.cwiseProduct(x);
fvec = wa4;
xnorm = wa2.stableNorm();
fnorm = fnorm1;

22
unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
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@ -253,7 +253,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
wa2 = fjac.colwise().blueNorm();
ei_qrfac<Scalar>(m, n, fjac.data(), fjac.rows(), true, ipvt.data(), wa1.data());
ipvt.cwise()-=1; // qrfac() creates ipvt with fortran convention (1->n), convert it to c (0->n-1)
ipvt.array() -= 1; // qrfac() creates ipvt with fortran convention (1->n), convert it to c (0->n-1)
/* on the first iteration and if mode is 1, scale according */
/* to the norms of the columns of the initial jacobian. */
@ -269,7 +269,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
/* on the first iteration, calculate the norm of the scaled x */
/* and initialize the step bound delta. */
wa3 = diag.cwise() * x;
wa3 = diag.cwiseProduct(x);
xnorm = wa3.stableNorm();
delta = parameters.factor * xnorm;
if (delta == 0.)
@ -316,7 +316,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
/* rescale if necessary. */
if (mode != 2) /* Computing MAX */
diag = diag.cwise().max(wa2);
diag = diag.cwiseMax(wa2);
/* beginning of the inner loop. */
do {
@ -329,7 +329,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
wa1 = -wa1;
wa2 = x + wa1;
wa3 = diag.cwise() * wa1;
wa3 = diag.cwiseProduct(wa1);
pnorm = wa3.stableNorm();
/* on the first iteration, adjust the initial step bound. */
@ -395,7 +395,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
if (ratio >= Scalar(1e-4)) {
/* successful iteration. update x, fvec, and their norms. */
x = wa2;
wa2 = diag.cwise() * x;
wa2 = diag.cwiseProduct(x);
fvec = wa4;
xnorm = wa2.stableNorm();
fnorm = fnorm1;
@ -538,10 +538,10 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
wa2[j] = fjac.col(j).head(j).stableNorm();
}
if (sing) {
ipvt.cwise()+=1;
ipvt.array() += 1;
wa2 = fjac.colwise().blueNorm();
ei_qrfac<Scalar>(n, n, fjac.data(), fjac.rows(), true, ipvt.data(), wa1.data());
ipvt.cwise()-=1; // qrfac() creates ipvt with fortran convention (1->n), convert it to c (0->n-1)
ipvt.array() -= 1; // qrfac() creates ipvt with fortran convention (1->n), convert it to c (0->n-1)
for (j = 0; j < n; ++j) {
if (fjac(j,j) != 0.) {
sum = 0.;
@ -569,7 +569,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
/* on the first iteration, calculate the norm of the scaled x */
/* and initialize the step bound delta. */
wa3 = diag.cwise() * x;
wa3 = diag.cwiseProduct(x);
xnorm = wa3.stableNorm();
delta = parameters.factor * xnorm;
if (delta == 0.)
@ -599,7 +599,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
/* rescale if necessary. */
if (mode != 2) /* Computing MAX */
diag = diag.cwise().max(wa2);
diag = diag.cwiseMax(wa2);
/* beginning of the inner loop. */
do {
@ -612,7 +612,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
wa1 = -wa1;
wa2 = x + wa1;
wa3 = diag.cwise() * wa1;
wa3 = diag.cwiseProduct(wa1);
pnorm = wa3.stableNorm();
/* on the first iteration, adjust the initial step bound. */
@ -678,7 +678,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
if (ratio >= Scalar(1e-4)) {
/* successful iteration. update x, fvec, and their norms. */
x = wa2;
wa2 = diag.cwise() * x;
wa2 = diag.cwiseProduct(x);
fvec = wa4;
xnorm = wa2.stableNorm();
fnorm = fnorm1;

4
unsupported/Eigen/src/NonLinearOptimization/dogleg.h
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@ -50,7 +50,7 @@ void ei_dogleg(
/* test whether the gauss-newton direction is acceptable. */
wa1.fill(0.);
wa2 = diag.cwise() * x;
wa2 = diag.cwiseProduct(x);
qnorm = wa2.stableNorm();
if (qnorm <= delta)
return;
@ -80,7 +80,7 @@ void ei_dogleg(
/* calculate the point along the scaled gradient */
/* at which the quadratic is minimized. */
wa1.cwise() /= diag*gnorm;
wa1.array() /= (diag*gnorm).array();
l = 0;
for (j = 0; j < n; ++j) {
sum = 0.;

4
unsupported/Eigen/src/NonLinearOptimization/lmpar.h
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@ -54,7 +54,7 @@ void ei_lmpar(
/* for acceptance of the gauss-newton direction. */
iter = 0;
wa2 = diag.cwise() * x;
wa2 = diag.cwiseProduct(x);
dxnorm = wa2.blueNorm();
fp = dxnorm - delta;
if (fp <= Scalar(0.1) * delta) {
@ -117,7 +117,7 @@ void ei_lmpar(
Matrix< Scalar, Dynamic, 1 > sdiag(n);
ei_qrsolv<Scalar>(r, ipvt, wa1, qtb, x, sdiag);
wa2 = diag.cwise() * x;
wa2 = diag.cwiseProduct(x);
dxnorm = wa2.blueNorm();
temp = fp;
fp = dxnorm - delta;

2
unsupported/test/BVH.cpp
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@ -45,7 +45,7 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(double, Dim)
template<typename Scalar, int Dim> AlignedBox<Scalar, Dim> ei_bounding_box(const Matrix<Scalar, Dim, 1> &v) { return AlignedBox<Scalar, Dim>(v); }
template<int Dim> AlignedBox<double, Dim> ei_bounding_box(const Ball<Dim> &b)
{ return AlignedBox<double, Dim>(b.center.cwise() - b.radius, b.center.cwise() + b.radius); }
{ return AlignedBox<double, Dim>(b.center.array() - b.radius, b.center.array() + b.radius); }
template<int Dim>

2
unsupported/test/matrix_exponential.cpp
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@ -36,7 +36,7 @@ double binom(int n, int k)
template <typename Derived, typename OtherDerived>
double relerr(const MatrixBase<Derived>& A, const MatrixBase<OtherDerived>& B)
{
return std::sqrt((A - B).cwise().abs2().sum() / std::min(A.cwise().abs2().sum(), B.cwise().abs2().sum()));
return std::sqrt((A - B).cwiseAbs2().sum() / std::min(A.cwiseAbs2().sum(), B.cwiseAbs2().sum()));
}
template <typename T>