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Merge branch 'master' into retraction_name

release/4.3a0
Alex Cunningham 2011-11-06 19:40:48 +00:00
parent 5798868ab7
commit 42a3963c7e
6 changed files with 162 additions and 17 deletions
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2
gtsam/base/Makefile.am
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@ -37,7 +37,7 @@ sources += DSFVector.cpp
check_PROGRAMS += tests/testBTree tests/testDSF tests/testDSFVector
# Timing tests
noinst_PROGRAMS = tests/timeMatrix tests/timeVirtual tests/timeTest
noinst_PROGRAMS = tests/timeMatrix tests/timeVirtual tests/timeVirtual2 tests/timeTest
noinst_PROGRAMS += tests/timeMatrixOps
#----------------------------------------------------------------------------------------------------

11
gtsam/base/tests/timeVirtual.cpp
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@ -1,3 +1,14 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file timeVirtual.cpp
* @brief Time the overhead of using virtual destructors and methods

135
gtsam/base/tests/timeVirtual2.cpp Normal file
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@ -0,0 +1,135 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file timeVirtual.cpp
* @brief Time the overhead of using virtual destructors and methods
* @author Richard Roberts
* @date Nov 6, 2011
*/
#include <gtsam/base/timing.h>
#include <boost/shared_ptr.hpp>
#include <boost/intrusive_ptr.hpp>
#include <iostream>
using namespace std;
using namespace boost;
struct DtorTestBase {
DtorTestBase() { cout << " DtorTestBase" << endl; }
virtual ~DtorTestBase() { cout << " ~DtorTestBase" << endl; }
};
struct DtorTestDerived : public DtorTestBase {
DtorTestDerived() { cout << " DtorTestDerived" << endl; }
virtual ~DtorTestDerived() { cout << " ~DtorTestDerived" << endl; }
};
struct VirtualBase {
VirtualBase() { }
virtual void method() = 0;
virtual ~VirtualBase() { }
};
struct VirtualDerived : public VirtualBase {
double data;
VirtualDerived() { data = rand(); }
virtual void method() { data = rand(); }
virtual ~VirtualDerived() { }
};
struct NonVirtualBase {
NonVirtualBase() { }
~NonVirtualBase() { }
};
struct NonVirtualDerived : public NonVirtualBase {
double data;
NonVirtualDerived() { data = rand(); }
void method() { data = rand(); }
~NonVirtualDerived() { }
};
int main(int argc, char *argv[]) {
// Virtual destructor test
cout << "Stack objects:" << endl;
cout << "Base:" << endl;
{ DtorTestBase b; }
cout << "Derived:" << endl;
{ DtorTestDerived d; }
cout << "Heap objects:" << endl;
cout << "Base:" << endl;
{ DtorTestBase *b = new DtorTestBase(); delete b; }
cout << "Derived:" << endl;
{ DtorTestDerived *d = new DtorTestDerived(); delete d; }
cout << "Derived with base pointer:" << endl;
{ DtorTestBase *b = new DtorTestDerived(); delete b; }
int n = 10000000;
VirtualBase** b = new VirtualBase*[n];
tic_(0, "Virtual");
tic_(0, "new");
for(int i=0; i<n; ++i)
b[i] = new VirtualDerived();
toc_(0, "new");
tic_(1, "method");
for(int i=0; i<n; ++i)
b[i]->method();
toc_(1, "method");
tic_(2, "dynamic_cast");
for(int i=0; i<n; ++i) {
VirtualDerived* d = dynamic_cast<VirtualDerived*>(b[i]);
if(d)
d->method();
}
toc_(2, "dynamic_cast");
tic_(3, "delete");
for(int i=0; i<n; ++i)
delete b[i];
toc_(3, "delete");
toc_(0, "Virtual");
delete[] b;
NonVirtualDerived** d = new NonVirtualDerived*[n];
tic_(1, "NonVirtual");
tic_(0, "new");
for(int i=0; i<n; ++i)
d[i] = new NonVirtualDerived();
toc_(0, "new");
tic_(1, "method");
for(int i=0; i<n; ++i)
d[i]->method();
toc_(1, "method");
tic_(2, "dynamic_cast (does nothing)");
for(int i=0; i<n; ++i)
d[i]->method();
toc_(2, "dynamic_cast (does nothing)");
tic_(3, "delete");
for(int i=0; i<n; ++i)
delete d[i];
toc_(3, "delete");
toc_(1, "NonVirtual");
delete[] d;
tictoc_finishedIteration_();
tictoc_print_();
return 0;
}

12
gtsam/nonlinear/DoglegOptimizerImpl.cpp
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@ -9,33 +9,33 @@
namespace gtsam {
/* ************************************************************************* */
VectorValues DoglegOptimizerImpl::ComputeDoglegPoint(
double Delta, const VectorValues& x_u, const VectorValues& x_n) {
double Delta, const VectorValues& x_u, const VectorValues& x_n, const bool verbose) {
// Get magnitude of each update and find out which segment Delta falls in
assert(Delta >= 0.0);
double DeltaSq = Delta*Delta;
double x_u_norm_sq = x_u.vector().squaredNorm();
double x_n_norm_sq = x_n.vector().squaredNorm();
cout << "Steepest descent magnitude " << sqrt(x_u_norm_sq) << ", Newton's method magnitude " << sqrt(x_n_norm_sq) << endl;
if(verbose) cout << "Steepest descent magnitude " << sqrt(x_u_norm_sq) << ", Newton's method magnitude " << sqrt(x_n_norm_sq) << endl;
if(DeltaSq < x_u_norm_sq) {
// Trust region is smaller than steepest descent update
VectorValues x_d = VectorValues::SameStructure(x_u);
x_d.vector() = x_u.vector() * sqrt(DeltaSq / x_u_norm_sq);
cout << "In steepest descent region with fraction " << sqrt(DeltaSq / x_u_norm_sq) << " of steepest descent magnitude" << endl;
if(verbose) cout << "In steepest descent region with fraction " << sqrt(DeltaSq / x_u_norm_sq) << " of steepest descent magnitude" << endl;
return x_d;
} else if(DeltaSq < x_n_norm_sq) {
// Trust region boundary is between steepest descent point and Newton's method point
return ComputeBlend(Delta, x_u, x_n);
} else {
assert(DeltaSq >= x_n_norm_sq);
cout << "In pure Newton's method region" << endl;
if(verbose) cout << "In pure Newton's method region" << endl;
// Trust region is larger than Newton's method point
return x_n;
}
}
/* ************************************************************************* */
VectorValues DoglegOptimizerImpl::ComputeBlend(double Delta, const VectorValues& x_u, const VectorValues& x_n) {
VectorValues DoglegOptimizerImpl::ComputeBlend(double Delta, const VectorValues& x_u, const VectorValues& x_n, const bool verbose) {
// See doc/trustregion.lyx or doc/trustregion.pdf
@ -64,7 +64,7 @@ VectorValues DoglegOptimizerImpl::ComputeBlend(double Delta, const VectorValues&
}
// Compute blended point
cout << "In blend region with fraction " << tau << " of Newton's method point" << endl;
if(verbose) cout << "In blend region with fraction " << tau << " of Newton's method point" << endl;
VectorValues blend = VectorValues::SameStructure(x_u);
blend.vector() = (1. - tau) * x_u.vector() + tau * x_n.vector();
return blend;

16
gtsam/nonlinear/DoglegOptimizerImpl.h
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@ -75,7 +75,7 @@ struct DoglegOptimizerImpl {
template<class M, class F, class VALUES>
static IterationResult Iterate(
double Delta, TrustRegionAdaptationMode mode, const M& Rd,
const F& f, const VALUES& x0, const Ordering& ordering, double f_error);
const F& f, const VALUES& x0, const Ordering& ordering, const double f_error, const bool verbose=false);
/**
* Compute the dogleg point given a trust region radius \f$ \Delta \f$. The
@ -98,7 +98,7 @@ struct DoglegOptimizerImpl {
* @param bayesNet The Bayes' net \f$ (R,d) \f$ as described above.
* @return The dogleg point \f$ \delta x_d \f$
*/
static VectorValues ComputeDoglegPoint(double Delta, const VectorValues& x_u, const VectorValues& x_n);
static VectorValues ComputeDoglegPoint(double Delta, const VectorValues& x_u, const VectorValues& x_n, const bool verbose=false);
/** Compute the minimizer \f$ \delta x_u \f$ of the line search along the gradient direction \f$ g \f$ of
* the function
@ -132,7 +132,7 @@ struct DoglegOptimizerImpl {
* @param xu Steepest descent minimizer
* @param xn Newton's method minimizer
*/
static VectorValues ComputeBlend(double Delta, const VectorValues& x_u, const VectorValues& x_n);
static VectorValues ComputeBlend(double Delta, const VectorValues& x_u, const VectorValues& x_n, const bool verbose=false);
};
@ -140,7 +140,7 @@ struct DoglegOptimizerImpl {
template<class M, class F, class VALUES>
typename DoglegOptimizerImpl::IterationResult DoglegOptimizerImpl::Iterate(
double Delta, TrustRegionAdaptationMode mode, const M& Rd,
const F& f, const VALUES& x0, const Ordering& ordering, double f_error) {
const F& f, const VALUES& x0, const Ordering& ordering, const double f_error, const bool verbose) {
// Compute steepest descent and Newton's method points
VectorValues dx_u = ComputeSteepestDescentPoint(Rd);
@ -156,7 +156,7 @@ typename DoglegOptimizerImpl::IterationResult DoglegOptimizerImpl::Iterate(
// Compute dog leg point
result.dx_d = ComputeDoglegPoint(Delta, dx_u, dx_n);
cout << "Delta = " << Delta << ", dx_d_norm = " << result.dx_d.vector().norm() << endl;
if(verbose) cout << "Delta = " << Delta << ", dx_d_norm = " << result.dx_d.vector().norm() << endl;
// Compute expmapped solution
const VALUES x_d(x0.retract(result.dx_d, ordering));
@ -167,8 +167,8 @@ typename DoglegOptimizerImpl::IterationResult DoglegOptimizerImpl::Iterate(
// Compute decrease in M
const double new_M_error = jfg.error(result.dx_d);
cout << "f error: " << f_error << " -> " << result.f_error << endl;
cout << "M error: " << M_error << " -> " << new_M_error << endl;
if(verbose) cout << "f error: " << f_error << " -> " << result.f_error << endl;
if(verbose) cout << "M error: " << M_error << " -> " << new_M_error << endl;
// Compute gain ratio. Here we take advantage of the invariant that the
// Bayes' net error at zero is equal to the nonlinear error
@ -176,7 +176,7 @@ typename DoglegOptimizerImpl::IterationResult DoglegOptimizerImpl::Iterate(
0.5 :
(f_error - result.f_error) / (M_error - new_M_error);
cout << "rho = " << rho << endl;
if(verbose) cout << "rho = " << rho << endl;
if(rho >= 0.75) {
// M agrees very well with f, so try to increase lambda

3
tests/Makefile.am
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@ -14,8 +14,7 @@ check_PROGRAMS += testGraph
check_PROGRAMS += testInference
check_PROGRAMS += testGaussianJunctionTree
check_PROGRAMS += testNonlinearEquality testNonlinearFactor testNonlinearFactorGraph
check_PROGRAMS += testNonlinearOptimizer
# check_PROGRAMS += testDoglegOptimizer # Uses debugging prints so commented out in SVN
check_PROGRAMS += testNonlinearOptimizer testDoglegOptimizer
check_PROGRAMS += testSymbolicBayesNet testSymbolicFactorGraph
check_PROGRAMS += testTupleValues
check_PROGRAMS += testNonlinearISAM