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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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22
unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
View File

@@ -36,7 +36,7 @@
*
* The user must provide a subroutine which calculates the
* functions. The Jacobian is either provided by the user, or approximated
* using a forward-difference method.
* using a forward-difference method.
*
*/
template<typename FunctorType, typename Scalar=double>
@@ -50,7 +50,7 @@ public:
Running = -1,
ImproperInputParameters = 0,
RelativeErrorTooSmall = 1,
TooManyFunctionEvaluation = 2,
TooManyFunctionEvaluation = 2,
TolTooSmall = 3,
NotMakingProgressJacobian = 4,
NotMakingProgressIterations = 5,
@@ -156,7 +156,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::hybrj1(
parameters.xtol = tol;
diag.setConstant(n, 1.);
return solve(
x,
x,
2
);
}
@@ -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;

26
unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
View File

@@ -37,7 +37,7 @@
* http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm
*/
template<typename FunctorType, typename Scalar=double>
class LevenbergMarquardt
class LevenbergMarquardt
{
public:
LevenbergMarquardt(FunctorType &_functor)
@@ -50,7 +50,7 @@ public:
RelativeErrorTooSmall = 2,
RelativeErrorAndReductionTooSmall = 3,
CosinusTooSmall = 4,
TooManyFunctionEvaluation = 5,
TooManyFunctionEvaluation = 5,
FtolTooSmall = 6,
XtolTooSmall = 7,
GtolTooSmall = 8,
@@ -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
View File

@@ -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.;

8
unsupported/Eigen/src/NonLinearOptimization/lmpar.h
View File

@@ -36,7 +36,7 @@ void ei_lmpar(
for (j = 0; j < n; ++j) {
if (r(j,j) == 0. && nsing == n-1)
nsing = j - 1;
if (nsing < n-1)
if (nsing < n-1)
wa1[j] = 0.;
}
for (j = nsing; j>=0; --j) {
@@ -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) {
@@ -76,7 +76,7 @@ void ei_lmpar(
// way:
for (j = 0; j < n; ++j) {
Scalar sum = 0.;
for (i = 0; i < j; ++i)
for (i = 0; i < j; ++i)
sum += r(i,j) * wa1[i];
wa1[j] = (wa1[j] - sum) / r(j,j);
}
@@ -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;