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add set/get interface for LMParameters in c++ and matlab

release/4.3a0
Yong-Dian Jian 2012-06-23 20:07:03 +00:00
parent 188478e4ed
commit 5acc52bbae
6 changed files with 123 additions and 24 deletions
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9
gtsam.h
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@ -961,6 +961,15 @@ class LevenbergMarquardtParams {
LevenbergMarquardtParams();
LevenbergMarquardtParams(double initial, double factor, double bound, size_t verbose);
void print(string s) const;
double getlambdaInitial() const ;
double getlambdaFactor() const ;
double getlambdaUpperBound() const;
string getVerbosityLM() const ;
void setlambdaInitial(double value);
void setlambdaFactor(double value);
void setlambdaUpperBound(double value);
void setVerbosityLM(string s);
};
}///\namespace gtsam

41
gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp
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@ -25,11 +25,52 @@
#include <gtsam/linear/GaussianJunctionTree.h>
#include <gtsam/linear/SimpleSPCGSolver.h>
#include <gtsam/linear/SubgraphSolver.h>
#include <boost/algorithm/string.hpp>
#include <string>
using namespace std;
namespace gtsam {
/* ************************************************************************* */
LevenbergMarquardtParams::VerbosityLM LevenbergMarquardtParams::verbosityLMTranslator(const std::string &src) const {
std::string s = src; boost::algorithm::to_upper(s);
if (s == "SILENT") return LevenbergMarquardtParams::SILENT;
if (s == "LAMBDA") return LevenbergMarquardtParams::LAMBDA;
if (s == "TRYLAMBDA") return LevenbergMarquardtParams::TRYLAMBDA;
if (s == "TRYCONFIG") return LevenbergMarquardtParams::TRYCONFIG;
if (s == "TRYDELTA") return LevenbergMarquardtParams::TRYDELTA;
if (s == "DAMPED") return LevenbergMarquardtParams::DAMPED;
/* default is silent */
return LevenbergMarquardtParams::SILENT;
}
/* ************************************************************************* */
std::string LevenbergMarquardtParams::verbosityLMTranslator(VerbosityLM value) const {
std::string s;
switch (value) {
case LevenbergMarquardtParams::SILENT: s = "SILENT" ; break;
case LevenbergMarquardtParams::LAMBDA: s = "LAMBDA" ; break;
case LevenbergMarquardtParams::TRYLAMBDA: s = "TRYLAMBDA" ; break;
case LevenbergMarquardtParams::TRYCONFIG: s = "TRYCONFIG" ; break;
case LevenbergMarquardtParams::TRYDELTA: s = "TRYDELTA" ; break;
case LevenbergMarquardtParams::DAMPED: s = "DAMPED" ; break;
default: s = "UNDEFINED" ; break;
}
return s;
}
/* ************************************************************************* */
void LevenbergMarquardtParams::print(const std::string& str) const {
SuccessiveLinearizationParams::print(str);
std::cout << " lambdaInitial: " << lambdaInitial << "\n";
std::cout << " lambdaFactor: " << lambdaFactor << "\n";
std::cout << " lambdaUpperBound: " << lambdaUpperBound << "\n";
std::cout << " verbosityLM: " << verbosityLMTranslator(verbosityLM) << "\n";
std::cout.flush();
}
/* ************************************************************************* */
void LevenbergMarquardtOptimizer::iterate() {

21
gtsam/nonlinear/LevenbergMarquardtOptimizer.h
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@ -53,15 +53,20 @@ public:
lambdaInitial(initial), lambdaFactor(factor), lambdaUpperBound(bound), verbosityLM(VerbosityLM(verbose)) {}
virtual ~LevenbergMarquardtParams() {}
virtual void print(const std::string& str = "") const;
virtual void print(const std::string& str = "") const {
SuccessiveLinearizationParams::print(str);
std::cout << " lambdaInitial: " << lambdaInitial << "\n";
std::cout << " lambdaFactor: " << lambdaFactor << "\n";
std::cout << " lambdaUpperBound: " << lambdaUpperBound << "\n";
std::cout << " verbosityLM: " << verbosityLM << "\n";
std::cout.flush();
}
inline double getlambdaInitial() const { return lambdaInitial; }
inline double getlambdaFactor() const { return lambdaFactor; }
inline double getlambdaUpperBound() const { return lambdaUpperBound; }
inline std::string getVerbosityLM() const { return verbosityLMTranslator(verbosityLM); }
inline void setlambdaInitial(double value) { lambdaInitial = value; }
inline void setlambdaFactor(double value) { lambdaFactor = value; }
inline void setlambdaUpperBound(double value) { lambdaUpperBound = value; }
inline void setVerbosityLM(const std::string &s) { verbosityLM = verbosityLMTranslator(s); }
VerbosityLM verbosityLMTranslator(const std::string &s) const;
std::string verbosityLMTranslator(VerbosityLM value) const;
};
/**

40
gtsam/nonlinear/NonlinearOptimizer.cpp
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@ -19,11 +19,49 @@
#include <iostream>
#include <iomanip>
#include <gtsam/nonlinear/NonlinearOptimizer.h>
#include <boost/algorithm/string.hpp>
using namespace std;
namespace gtsam {
/* ************************************************************************* */
NonlinearOptimizerParams::Verbosity NonlinearOptimizerParams::verbosityTranslator(const std::string &src) const {
std::string s = src; boost::algorithm::to_upper(s);
if (s == "SILENT") return NonlinearOptimizerParams::SILENT;
if (s == "ERROR") return NonlinearOptimizerParams::ERROR;
if (s == "VALUES") return NonlinearOptimizerParams::VALUES;
if (s == "DELTA") return NonlinearOptimizerParams::DELTA;
if (s == "LINEAR") return NonlinearOptimizerParams::LINEAR;
/* default is silent */
return NonlinearOptimizerParams::SILENT;
}
/* ************************************************************************* */
std::string NonlinearOptimizerParams::verbosityTranslator(Verbosity value) const {
std::string s;
switch (value) {
case NonlinearOptimizerParams::SILENT: s = "SILENT"; break;
case NonlinearOptimizerParams::ERROR: s = "ERROR"; break;
case NonlinearOptimizerParams::VALUES: s = "VALUES"; break;
case NonlinearOptimizerParams::DELTA: s = "DELTA"; break;
case NonlinearOptimizerParams::LINEAR: s = "LINEAR"; break;
default: s = "UNDEFINED"; break;
}
return s;
}
/* ************************************************************************* */
void NonlinearOptimizerParams::print(const std::string& str) const {
std::cout << str << "\n";
std::cout << "relative decrease threshold: " << relativeErrorTol << "\n";
std::cout << "absolute decrease threshold: " << absoluteErrorTol << "\n";
std::cout << " total error threshold: " << errorTol << "\n";
std::cout << " maximum iterations: " << maxIterations << "\n";
std::cout << " verbosity: " << verbosityTranslator(verbosity) << "\n";
std::cout.flush();
}
/* ************************************************************************* */
void NonlinearOptimizer::defaultOptimize() {

26
gtsam/nonlinear/NonlinearOptimizer.h
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@ -48,18 +48,24 @@ public:
maxIterations(100.0), relativeErrorTol(1e-5), absoluteErrorTol(1e-5),
errorTol(0.0), verbosity(SILENT) {}
virtual void print(const std::string& str = "") const {
std::cout << str << "\n";
std::cout << "relative decrease threshold: " << relativeErrorTol << "\n";
std::cout << "absolute decrease threshold: " << absoluteErrorTol << "\n";
std::cout << " total error threshold: " << errorTol << "\n";
std::cout << " maximum iterations: " << maxIterations << "\n";
std::cout << " verbosity level: " << verbosity << std::endl;
}
virtual ~NonlinearOptimizerParams() {}
};
virtual void print(const std::string& str = "") const ;
inline double getMaxIterations() const { return maxIterations; }
inline double getRelativeErrorTol() const { return relativeErrorTol; }
inline double getAbsoluteErrorTol() const { return absoluteErrorTol; }
inline double getErrorTol() const { return errorTol; }
inline std::string getVerbosity() const { return verbosityTranslator(verbosity); }
inline void setMaxIterations(double value) { maxIterations = value; }
inline void setRelativeErrorTol(double value) { relativeErrorTol = value; }
inline void setAbsoluteErrorTol(double value) { absoluteErrorTol = value; }
inline void setErrorTol(double value) { errorTol = value ; }
inline void setVerbosity(const std::string &src) { verbosity = verbosityTranslator(src); }
Verbosity verbosityTranslator(const std::string &s) const;
std::string verbosityTranslator(Verbosity value) const;
};
/**
* Base class for a nonlinear optimization state, including the current estimate

10
matlab/examples/SFMExample.m
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@ -61,14 +61,14 @@ for j=1:size(truth.points,2)
end
initialEstimate.print(sprintf('\nInitial estimate:\n '));
%% One-shot Optimize using Levenberg-Marquardt optimization with an ordering from colamd
%result = graph.optimize(initialEstimate,1);
%result.print(sprintf('\nFinal result:\n '));
%% Fine grain optimization, allowing user to iterate step by step
parameters = gtsamLevenbergMarquardtParams(1e-5, 1e-5, 0, 2);
parameters = gtsamLevenbergMarquardtParams(1e-5, 1e-5, 0, 0);
parameters.setlambdaInitial(1.0);
parameters.setVerbosityLM('trylambda');
optimizer = graph.optimizer(initialEstimate, parameters);
for i=1:5
optimizer.iterate();
end