void optimize_sinusoid( BOOSTNS::random::mt19937 & rng,
const po::variables_map & options ) {
Numeric regionwidth = 1.0;
Numeric eps = 0.1, lipsch = 20;
RDISOptimizer * popt = NULL;
const string domain =
BOOSTNS::str( BOOSTNS::format( "%1%:%2%" ) %
( -regionwidth/2.0 ) % ( regionwidth/2.0 ) );
// ( regionwidth / 4.0 ) % ( 5.0*regionwidth/4.0 ) );
eps = options.count( "epsilon" ) ? options[ "epsilon" ].as< Numeric >() : eps;
lipsch = options.count( "lipschitz" ) ? options[ "lipschitz" ].as< Numeric >() : lipsch;
if ( options.count( "timeAsSeed" ) && options[ "timeAsSeed" ].as< bool >() ) {
rng.seed( std::time(NULL) + getpid() );
if ( options[ "sampleOffset"].as< size_t >() > 0 ) {
std::cout << "WARNING: sampleOffset and timeAsSeed should not both be set" << std::endl;
}
}
const bool isBCD = ( options.count( "opttype" ) &&
options[ "opttype" ].as< string >() == "bcd" );
size_t h = options[ "sinHeight" ].as< size_t >();
size_t bf = options[ "sinBF" ].as< size_t >();
size_t arity = options[ "sinArity" ].as< size_t >();
bool oddArity = options[ "sinOddArity" ].as< bool >();
OptimizableFunctionSP funcsp =
OptimizableFunctionGenerator::makeHighDimSinusoid( h, bf, arity, oddArity );
OptimizableFunction & fsinusoid = *funcsp;
std::cout << fsinusoid << std::endl;
CGDSubspaceOptimizer ssopt( fsinusoid );
ssopt.setParameters( options );
// create the optimizer
popt = new RDISOptimizer( fsinusoid, ssopt, options );
RDISOptimizer & opt = *popt;
setSignalHandler( &opt );
opt.setApproxFactors( options[ "approxfactors" ].as< bool >() );
opt.setUseCache( false );
// opt.setUseExactCaching( false );
const Numeric MaxVal = std::numeric_limits< Numeric >::max();
size_t sampleOffset = options[ "sampleOffset" ].as< size_t >();
size_t nsamples = options[ "nsamples" ].as< size_t >() + sampleOffset;
const NumericInterval dom = fsinusoid.getVariables()[0]->getDomain().interval();
BOOSTNS::random::uniform_real_distribution<> unif( dom.lower(), dom.upper() );
std::vector< State > initialStates( nsamples );
// create all the random initial states up front (so they're consistent
// across runs if the seed doesn't change)
for ( size_t i = 0; i < nsamples; ++i ) {
for ( VariableID vid = 0; vid < fsinusoid.getNumVars(); ++vid ) {
initialStates[i].push_back( unif( rng ) );
}
}
const Clock::time_point start = Clock::now();
Clock::time_point timeout = Clock::time_point::max();
if ( options.count( "timeout" ) ) {
size_t ms = 1000.0 * options[ "timeout" ].as< Numeric >();
timeout = Clock::now() + bc::milliseconds( ms );
}
Numeric bestfopt = NumericMAX;
State bestx;
size_t i = sampleOffset;
for ( ; i < nsamples; ) {
Numeric finit = MaxVal;
const State & xinit = initialStates[i];
std::cout << "[" << i << "] optimization starting -- bounds: " <<
fsinusoid.computeBounds() << std::endl;
std::cout << "eps: " << eps << ", lipschitz: " << lipsch <<
", step: " << eps / lipsch <<
", finit: " << finit << std::endl << "xinit: " << xinit << std::endl;
std::cout << "initial eval: " << eval( fsinusoid, xinit ) << std::endl;
++i;
if ( options.count( "useDA" ) ) {
throw "deterministic annealing not yet supported";
} else if ( options.count( "useCP" ) ) {
throw "cutting plane not yet supported";
}
State xopt;
Numeric result = MaxVal;
bool timedOut = false;
if ( !isBCD ) {
result = opt.optimize( &xinit, finit, timeout, true, start );
xopt = opt.getOptState();
opt.printStats();
timedOut = ( opt.wasStopped() || opt.timedOut() );
} else {
timedOut = doBCDOpt( fsinusoid, ssopt, options, xinit, timeout, start,
xopt, result );
}
const Duration elapsed = Clock::now() - start;
assert( !xopt.empty() );
std::cout << "sinusoid opt. result: " << result << std::endl <<
"\tat " << xopt << std::endl;
Numeric actual = eval( fsinusoid, xopt );
std::cout << "actual eval: " << actual << " after " << elapsed.count()
<< " seconds " << std::endl;
if ( actual < bestfopt ) {
bestfopt = actual;
bestx = xopt;
}
if ( timedOut ) {
std::cout << "timeout detected..." << std::endl;
break; break;
}
}
}
void optimize_BA( BOOSTNS::random::mt19937 & rng,
const po::variables_map & options, const string & bafile ) {
// get optimization type from command line
const string opttype = options[ "opttype" ].as< string >();
const bool isRDIS = ( opttype == "rdis" );
const bool isBCD = ( opttype == "bcd" );
// const bool isDisc = ( opttype == "discrete" );
// const bool isGrid = ( opttype == "grid" );
Numeric eps = 1.0, lipsch = 20;
RDISOptimizer * popt = NULL;
assert( isRDIS || isBCD /*|| isDisc || isGrid*/ );
eps = options.count( "epsilon" ) ? options[ "epsilon" ].as< Numeric >() : eps;
lipsch = options.count( "lipschitz" ) ? options[ "lipschitz" ].as< Numeric >() : lipsch;
if ( options.count( "timeAsSeed" ) && options[ "timeAsSeed" ].as< bool >() ) {
rng.seed( std::time(NULL) + getpid() );
if ( options[ "sampleOffset"].as< size_t >() > 0 ) {
std::cout << "WARNING: sampleOffset and timeAsSeed should not both be set" << std::endl;
}
}
VariableCount ncams = options.count( "ncams" ) ?
options["ncams"].as< size_t >() : 0;
VariableCount npts = options.count( "npts" ) ?
options["npts"].as< size_t >() : 0;
// load and initialize the BA problem we'll optimize
BundleAdjustmentFunction bafunc;
if ( !bafunc.load( bafile, ncams, npts ) ) {
std::cout << "loading file " << bafile << " failed -- exiting" << std::endl;
return;
}
SubspaceOptimizer * pssopt;
if ( options["useCGD"].as< bool >() ) {
pssopt = new CGDSubspaceOptimizer( bafunc );
} else {
pssopt = new LMSubspaceOptimizer( bafunc );
}
SubspaceOptimizer & ssopt = *pssopt;
//// CGDSubspaceOptimizer ssopt( bafunc );
// LMSubspaceOptimizer ssopt( bafunc );
ssopt.setParameters( options );
// create the optimizer and initialize it properly
// popt = ( isRDIS ?
// new OptimizerISGD( bafunc, ssopt, options ) :
// new OptimizerDG( bafunc, options ) );
popt = new RDISOptimizer( bafunc, ssopt, options );
RDISOptimizer & opt = *popt;
setSignalHandler( &opt );
// opt.setOptType( 3 );
// opt.setUseFiniteDiffs( true );
// opt.setUseLinearLB( false );
opt.setApproxFactors( options[ "approxfactors" ].as< bool >() );
opt.setUseCache( !isRDIS );
// opt.setUseExactCaching( isDisc );
const Numeric MaxVal = std::numeric_limits< Numeric >::max();
size_t sampleOffset = options[ "sampleOffset" ].as< size_t >();
size_t nsamples = options[ "nsamples" ].as< size_t >() + sampleOffset;
std::vector< State > initialStates( nsamples );
if ( options.count( "randinit" ) && !options["randinit"].as< bool >() ) {
sampleOffset = 0;
nsamples = 1;
initialStates.clear();
initialStates.push_back( bafunc.getInitialState() );
} else {
// create all the random initial states up front (so they're consistent
// across runs if the seed doesn't change)
for ( size_t i = 0; i < nsamples; ++i ) {
for ( VariableID vid = 0; vid < bafunc.getNumVars(); ++vid ) {
NumericInterval si =
bafunc.getVariables()[vid]->getDomain().getSamplingInterval();
BOOSTNS::random::uniform_real_distribution<> unif(
si.lower(), si.upper() );
initialStates[i].push_back( unif( rng ) );
}
// if ( i >= sampleOffset ) {
// std::cout << "state " << i << ": " << initialStates[i] << std::endl;
// }
}
}
const Clock::time_point start = Clock::now();
Clock::time_point timeout = Clock::time_point::max();
if ( options.count( "timeout" ) ) {
size_t ms = 1000.0 * options[ "timeout" ].as< Numeric >();
timeout = Clock::now() + bc::milliseconds( ms );
}
Numeric bestfopt = NumericMAX;
State bestx;
size_t i = sampleOffset;
for ( ; i < nsamples; ) {
Numeric finit = MaxVal;
const State & xinit = initialStates[i];
std::cout << "[" << i << "] optimization starting -- bounds: " <<
bafunc.computeBounds() << std::endl;
std::cout << "eps: " << eps << ", lipschitz: " << lipsch <<
", step: " << eps / lipsch <<
", finit: " << finit << std::endl << "xinit: " << xinit << std::endl;
std::cout << "initial eval: " << bafunc.eval( xinit ) << std::endl;
if ( options.count( "useDA" ) ) {
throw "deterministic annealing not yet supported";
} else if ( options.count( "useCP" ) ) {
throw "cutting plane not yet supported";
}
++i;
State xopt;
Numeric result = MaxVal;
bool timedOut = false;
if ( !isBCD ) {
if ( options["ignoreCamVars"].as< bool >() ) {
for ( Variable * v : bafunc.getVariables() ) {
if ( bafunc.getVarType( v->getID() ) == BundleAdjust::CAM_FOCAL ||
bafunc.getVarType( v->getID() ) == BundleAdjust::CAM_RDL_K1 ||
bafunc.getVarType( v->getID() ) == BundleAdjust::CAM_RDL_K2 ) {
bafunc.getVariables()[i]->assign( bafunc.getInitialState()[i] );
}
}
}
result = opt.optimize( &xinit, finit, timeout, true, start );
xopt = opt.getOptState();
opt.printStats();
timedOut = ( opt.wasStopped() || opt.timedOut() );
} else {
if ( options["ignoreCamVars"].as< bool >() ) {
std::cout << "IGNORECAMVARS OPTION NOT SUPPORTED FOR BCD" << std::endl;
}
timedOut = doBCDOpt( bafunc, ssopt, options, xinit, timeout, start,
xopt, result );
}
assert( !xopt.empty() );
const Duration elapsed = Clock::now() - start;
std::cout << "bundle adjustment opt. result: " << result << std::endl
<< "\tat " << xopt << std::endl;
Numeric actual = bafunc.eval( xopt );
std::cout << "actual eval: " << actual << " after "
<< elapsed.count() << " seconds" << std::endl;
if ( actual < bestfopt ) {
bestfopt = actual;
bestx = xopt;
}
if ( timedOut ) {
std::cout << "timeout detected..." << std::endl;
break;
}
}
const Duration elapsed = Clock::now() - start;
std::cout << "----------------" << std::endl;
std::cout << "best: " << bestfopt << " in " << ( i - sampleOffset ) <<
" samples after " << elapsed.count() << " seconds at state " <<
std::endl << bestx << std::endl;
}