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hops Namespace Reference

Namespaces

 CSmMALAProposalDetails
 
 internal
 

Classes

class  AcceptanceRateRecorder
 
class  AcceptanceRateTuner
 
class  AdaptiveMetropolisProposal
 
class  BallWalkProposal
 
class  BinarySearchAcceptanceRateTuner
 Deprecated as there are issues due to the uncertainty in estimating acceptance rates. More...
 
class  ChainData
 
class  Coldness
 Mixin that adds coldness to model evaluations. More...
 
class  CoordinateHitAndRunProposal
 
class  CSmMALAProposal
 
class  CsvReader
 
class  CsvWriter
 
class  Data
 
class  DegenerateMultivariateGaussian
 
class  DikinEllipsoidCalculator
 
class  DikinProposal
 
class  DNest4Adapter
 
class  DNest4EnvironmentSingleton
 
struct  EmptyChainDataException
 
class  Exception
 
class  ExpectedSquaredJumpDistanceTuner
 
class  FileWriter
 
class  FileWriterFactory
 
class  FullGammaModel
 
class  GammaModel1
 
class  GammaModel2
 
class  GaussianProcess
 
class  GaussianProposal
 
class  GaussianStepDistribution
 
class  GurobiEnvironmentSingleton
 
class  Hdf5Reader
 
class  Hdf5Writer
 Warning: This writer can not append to existing datasets. More...
 
class  HitAndRunProposal
 
struct  IsAcceptProposalAvailable
 
struct  IsAcceptProposalAvailable< T, std::void_t< decltype(std::declval< T >().acceptProposal())> >
 
struct  IsAddMessageAvailable
 
struct  IsAddMessageAvailable< T, std::void_t< decltype(std::declval< T >().addMessage(std::declval< const std::string & >()))> >
 
struct  IsCalculateLogAcceptanceProbabilityAvailable
 
struct  IsCalculateLogAcceptanceProbabilityAvailable< T, std::void_t< decltype(std::declval< T >().computeLogAcceptanceProbability())> >
 
struct  IsClearRecordsAvailable
 
struct  IsClearRecordsAvailable< T, std::void_t< decltype(std::declval< T >().clearRecords())> >
 
struct  IsGetColdnessAvailable
 
struct  IsGetColdnessAvailable< T, std::void_t< decltype(std::declval< T >().getColdness())> >
 
struct  IsGetExchangeAttemptProbabilityAvailable
 
struct  IsGetExchangeAttemptProbabilityAvailable< T, std::void_t< decltype(std::declval< T >().getExchangeAttemptProbability())> >
 
struct  IsGetStepSizeAvailable
 
struct  IsGetStepSizeAvailable< T, std::void_t< decltype(std::declval< T >().getStepSize())> >
 
struct  IsInstallDataObjectAvailable
 
struct  IsInstallDataObjectAvailable< T, std::void_t< decltype(std::declval< T >().installDataObject(std::declval< ChainData & >()))> >
 
struct  IsResetAcceptanceRateAvailable
 
struct  IsResetAcceptanceRateAvailable< T, std::void_t< decltype(std::declval< T >().resetAcceptanceRate())> >
 
struct  IsSetColdnessAvailable
 
struct  IsSetColdnessAvailable< T, std::void_t< decltype(std::declval< T >().setColdness(std::declval< double >()))> >
 
struct  IsSetExchangeAttemptProbabilityAvailable
 
struct  IsSetExchangeAttemptProbabilityAvailable< T, std::void_t< decltype(std::declval< T >().setExchangeAttemptProbability(std::declval< double >()))> >
 
struct  IsSetFisherWeightAvailable
 
struct  IsSetFisherWeightAvailable< T, std::void_t< decltype(std::declval< T >().setFisherWeight(std::declval< double >()))> >
 
struct  IsSetStepSizeAvailable
 
struct  IsSetStepSizeAvailable< T, std::void_t< decltype(std::declval< T >().setStepSize(std::declval< double >()))> >
 
struct  IsStoreRecordAvailable
 
struct  IsStoreRecordAvailable< T, std::void_t< decltype(std::declval< T >().storeRecord())> >
 
struct  IsWriteRecordsToFileAvailable
 
struct  IsWriteRecordsToFileAvailable< T, std::void_t< decltype(std::declval< T >().writeRecordsToFile(std::declval< const FileWriter * >()))> >
 
class  LinearProgram
 
class  LinearProgramClpImpl
 
class  LinearProgramFactory
 
class  LinearProgramGurobiImpl
 
struct  LinearProgramSolution
 
class  MarkovChainAdapter
 
class  MarkovChainFactory
 
class  MarkovChainInterface
 
class  MaximumVolumeEllipsoid
 [Deprectated] Use PolyRound (https://gitlab.com/csb.ethz/PolyRound) for effective and efficient rounding. More...
 
class  MessageRecorder
 
class  MetropolisHastingsFilter
 
class  MinimalGammaModel
 
struct  MissingStartingPointsException
 
class  Mixture
 
class  Model
 
class  ModelMixin
 ModelMixin Mixin to add model likelihood to computeLogAcceptanceRate(). More...
 
class  ModelWrapper
 
class  MpiInitializerFinalizer
 
class  MultivariateGaussian
 
class  NegativeLogLikelihoodRecorder
 
class  NoOpDrawAdapter
 
struct  NoProblemProvidedException
 
struct  NoProposal
 
class  ParallelTempering
 Mixin for adding parallel tempering to Markov chains. Requires MPI. More...
 
class  Problem
 
class  ReversibleJumpProposal
 
class  Rosenbrock
 multi-dimensional extension of rosenbrock function to N dimensions. Only defined on spaces of even N! More...
 
class  RunBase
 
class  SbmlModel
 
class  SbmlReader
 
class  SimpleExpectedSquaredJumpDistanceTuner
 
class  SimplexFactory
 Factory for creating simplices. More...
 
class  SquaredExponentialKernel
 
class  StateRecorder
 
class  StateTransformation
 Mixin for undoing transformations to the Markov chain state. More...
 
class  ThompsonSampling
 
class  TimestampRecorder
 
class  Transformation
 
class  TruncatedNormalDistribution
 Truncated normal distribution with mean 0. More...
 
class  UniformBallKernel
 
class  UniformStepDistribution
 
struct  UninitializedDataFieldException
 
class  ZeroKernel
 

Typedefs

typedef MarkovChainInterface< Eigen::VectorXd > MarkovChain
 
using RandomNumberGenerator = pcg64
 
using MatrixType = Eigen::MatrixXd
 
template<typename Model >
using Run = RunBase< Model, NoProposal >
 
using VectorType = Eigen::VectorXd
 

Enumerations

enum  FileWriterType { FileWriterType::CSV, FileWriterType::HDF5 }
 
enum  LinearProgramSolver { LinearProgramSolver::CLP, LinearProgramSolver::GUROBI }
 
enum  LinearProgramStatus {
  LinearProgramStatus::UNDEFINED, LinearProgramStatus::ERROR, LinearProgramStatus::OPTIMAL, LinearProgramStatus::INFEASIBLE,
  LinearProgramStatus::UNBOUNDED
}
 
enum  MarkovChainAttribute {
  MarkovChainAttribute::FISHER_WEIGHT, MarkovChainAttribute::PARALLEL_TEMPERING_COLDNESS, MarkovChainAttribute::PARALLEL_TEMPERING_EXCHANGE_PROBABILITY, MarkovChainAttribute::PREVIOUS_STEP_ACCEPTANCE_PROBABILITY,
  MarkovChainAttribute::STEP_SIZE
}
 
enum  MarkovChainType {
  MarkovChainType::AdaptiveMetropolis, MarkovChainType::BallWalk, MarkovChainType::CoordinateHitAndRun, MarkovChainType::CSmMALA,
  MarkovChainType::DikinWalk, MarkovChainType::Gaussian, MarkovChainType::HitAndRun
}
 

Functions

std::string MarkovChainTypeToFullString (MarkovChainType markovChainType)
 
std::string MarkovChainTypeToShortcutString (MarkovChainType markovChainType)
 
template<typename Derived1 , typename Derived2 >
void normalizePolytope (Eigen::MatrixBase< Derived1 > &A, Eigen::MatrixBase< Derived2 > &b)
 Normalizes polytope defined by Ax < b. More...
 
size_t nextGoodSizeFFT (size_t N)
 
template<typename StateType >
void computeAutocorrelations (const std::vector< StateType > &draws, Eigen::VectorXd &autocorrelations, unsigned long dimension)
 
template<typename StateType >
void computeAutocorrelations (const std::vector< StateType > *draws, Eigen::VectorXd &autocorrelations, unsigned long dimension)
 
template<typename StateType , typename MatrixType >
MatrixType computeCovariance (const std::vector< const std::vector< StateType > * > &draws, const MatrixType &covarianceSeen, StateType &meanSeen, unsigned long numberOfSeenDraws)
 
template<typename StateType , typename MatrixType >
MatrixType computeCovariance (const std::vector< StateType > *draws, const MatrixType &covarianceSeen, StateType &meanSeen, unsigned long numberOfSeenDraws)
 
template<typename StateType , typename MatrixType >
MatrixType computeCovariance (const std::vector< std::vector< StateType >> &draws, const MatrixType &covarianceSeen, StateType &meanSeen, unsigned long numberOfSeenDraws)
 
template<typename StateType , typename MatrixType >
MatrixType computeCovariance (const std::vector< StateType > &draws, const MatrixType &covarianceSeen, StateType &meanSeen, unsigned long numberOfSeenDraws)
 
template<typename StateType , typename MatrixType >
MatrixType computeCovariance (const std::vector< const std::vector< StateType > * > &draws)
 
template<typename StateType , typename MatrixType >
MatrixType computeCovariance (const std::vector< StateType > *draws)
 
template<typename StateType , typename MatrixType >
MatrixType computeCovariance (const std::vector< std::vector< StateType >> &draws)
 
template<typename StateType , typename MatrixType >
MatrixType computeCovariance (const std::vector< StateType > &draws)
 
template<typename StateType >
double computeEffectiveSampleSize (const std::vector< const std::vector< StateType > * > &chains, unsigned long dimension)
 
template<typename StateType >
std::vector< double > computeEffectiveSampleSize (const std::vector< const std::vector< StateType > * > &chains)
 
template<typename StateType >
double computeEffectiveSampleSize (const std::vector< std::vector< StateType >> &chains, unsigned long dimension)
 
template<typename StateType >
std::vector< double > computeEffectiveSampleSize (const std::vector< std::vector< StateType >> &chains)
 
template<typename StateType , typename MatrixType >
double computeExpectedSquaredJumpDistance (const std::vector< StateType > &draws, unsigned long numUnseen, double esjdSeen, unsigned long numSeen, const MatrixType &sqrtCovariance)
 
template<typename StateType , typename MatrixType >
double computeExpectedSquaredJumpDistance (const std::vector< StateType > &draws, const MatrixType &sqrtCovariance)
 
template<typename StateType , typename MatrixType >
double computeExpectedSquaredJumpDistance (const std::vector< StateType > &draws)
 
template<typename StateType , typename MatrixType >
std::vector< double > computeExpectedSquaredJumpDistance (const std::vector< std::vector< StateType >> &chains, unsigned long numUnseen, std::vector< double > esjdSeen, unsigned long numSeen, const MatrixType &sqrtCovariance)
 
template<typename StateType , typename MatrixType >
std::vector< double > computeExpectedSquaredJumpDistance (const std::vector< std::vector< StateType >> &chains, const MatrixType &sqrtCovariance)
 
template<typename StateType , typename MatrixType >
std::vector< double > computeExpectedSquaredJumpDistance (const std::vector< std::vector< StateType >> &chains)
 
template<typename StateType , typename MatrixType >
std::vector< double > computeExpectedSquaredJumpDistance (const std::vector< const std::vector< StateType > * > &chains, unsigned long numUnseen, std::vector< double > esjdSeen, unsigned long numSeen, const MatrixType &sqrtCovariance)
 
template<typename StateType , typename MatrixType >
std::vector< double > computeExpectedSquaredJumpDistance (const std::vector< const std::vector< StateType > * > &chains, const MatrixType &sqrtCovariance)
 
template<typename StateType , typename MatrixType >
std::vector< double > computeExpectedSquaredJumpDistance (const std::vector< const std::vector< StateType > * > &chains)
 
template<class T >
std::enable_if<!std::numeric_limits< T >::is_integer, bool >::type almost_equal (T x, T y, int ulp=2)
 
template<typename StateType >
bool isConstantChain (const std::vector< const std::vector< StateType > * > &chains, long dimension)
 
template<typename StateType >
bool isConstantChain (const std::vector< std::vector< StateType >> &chains, long dimension)
 
template<typename StateType >
bool isConstantChain (const std::vector< const std::vector< StateType > * > &chains)
 
template<typename StateType >
bool isConstantChain (const std::vector< std::vector< StateType >> &chains)
 
template<typename StateType >
double computePotentialScaleReductionFactor (const std::vector< const std::vector< StateType > * > &chains, unsigned long dimension, unsigned long numUnseen, std::vector< double > &sampleVariancesSeen, std::vector< double > &intraChainExpectationsSeen, double &interChainExpectationSeen, unsigned long &numSeen)
 
template<typename StateType >
double computePotentialScaleReductionFactor (const std::vector< std::vector< StateType >> &chains, unsigned long dimension, unsigned long numUnseen, std::vector< double > &sampleVariancesSeen, std::vector< double > &intraChainExpectationsSeen, double &interChainExpectationSeen, unsigned long &numSeen)
 
template<typename StateType >
std::vector< double > computePotentialScaleReductionFactor (const std::vector< const std::vector< StateType > * > &chains, unsigned long numUnseen, std::vector< std::vector< double >> &sampleVariancesSeen, std::vector< std::vector< double >> &intraChainExpectationsSeen, std::vector< double > &interChainExpectationSeen, unsigned long &numSeen)
 
template<typename StateType >
std::vector< double > computePotentialScaleReductionFactor (const std::vector< std::vector< StateType >> &chains, unsigned long numUnseen, std::vector< std::vector< double >> &sampleVariancesSeen, std::vector< std::vector< double >> &intraChainExpectationsSeen, std::vector< double > &interChainExpectationSeen, unsigned long &numSeen)
 
template<typename StateType >
double computePotentialScaleReductionFactor (const std::vector< const std::vector< StateType > * > &chains, unsigned long dimension)
 
template<typename StateType >
double computePotentialScaleReductionFactor (const std::vector< std::vector< StateType >> &chains, unsigned long dimension)
 
template<typename StateType >
std::vector< double > computePotentialScaleReductionFactor (const std::vector< const std::vector< StateType > * > &chains)
 
template<typename StateType >
std::vector< double > computePotentialScaleReductionFactor (const std::vector< std::vector< StateType >> &chains)
 
Eigen::VectorXd computeAcceptanceRate (Data &data)
 
Eigen::VectorXd computeExpectedSquaredJumpDistance (Data &data)
 
Eigen::VectorXd computeEffectiveSampleSize (Data &data)
 
Eigen::VectorXd computePotentialScaleReductionFactor (Data &data)
 
double computeTotalNumberOfSamples (Data &data)
 
Eigen::VectorXd computeTotalTimeTaken (Data &data)
 
template<typename Model , typename Proposal >
void tune (RunBase< Model, Proposal > &run, AcceptanceRateTuner::param_type &parameters)
 
template<typename Model , typename Proposal >
void tune (RunBase< Model, Proposal > &run, ExpectedSquaredJumpDistanceTuner::param_type &parameters)
 
template<typename Model , typename Proposal >
void tune (RunBase< Model, Proposal > &run, SimpleExpectedSquaredJumpDistanceTuner::param_type &parameters)
 

Typedef Documentation

◆ MarkovChain

typedef MarkovChainInterface<Eigen::VectorXd> hops::MarkovChain

◆ MatrixType

using hops::MatrixType = typedef Eigen::MatrixXd

◆ RandomNumberGenerator

using hops::RandomNumberGenerator = typedef pcg64

◆ Run

template<typename Model >
using hops::Run = typedef RunBase<Model, NoProposal>

◆ VectorType

using hops::VectorType = typedef Eigen::VectorXd

Enumeration Type Documentation

◆ FileWriterType

enum hops::FileWriterType
strong
Enumerator
CSV 
HDF5 

◆ LinearProgramSolver

enum hops::LinearProgramSolver
strong
Enumerator
CLP 
GUROBI 

◆ LinearProgramStatus

enum hops::LinearProgramStatus
strong
Enumerator
UNDEFINED 
ERROR 
OPTIMAL 
INFEASIBLE 
UNBOUNDED 

◆ MarkovChainAttribute

enum hops::MarkovChainAttribute
strong
Enumerator
FISHER_WEIGHT 
PARALLEL_TEMPERING_COLDNESS 
PARALLEL_TEMPERING_EXCHANGE_PROBABILITY 
PREVIOUS_STEP_ACCEPTANCE_PROBABILITY 
STEP_SIZE 

◆ MarkovChainType

enum hops::MarkovChainType
strong
Enumerator
AdaptiveMetropolis 
BallWalk 
CoordinateHitAndRun 
CSmMALA 
DikinWalk 
Gaussian 
HitAndRun 

Function Documentation

◆ almost_equal()

template<class T >
std::enable_if<!std::numeric_limits<T>::is_integer, bool>::type hops::almost_equal ( T  x,
T  y,
int  ulp = 2 
)

◆ computeAcceptanceRate()

Eigen::VectorXd hops::computeAcceptanceRate ( Data &  data)
inline

◆ computeAutocorrelations() [1/2]

template<typename StateType >
void hops::computeAutocorrelations ( const std::vector< StateType > &  draws,
Eigen::VectorXd &  autocorrelations,
unsigned long  dimension 
)

◆ computeAutocorrelations() [2/2]

template<typename StateType >
void hops::computeAutocorrelations ( const std::vector< StateType > *  draws,
Eigen::VectorXd &  autocorrelations,
unsigned long  dimension 
)

◆ computeCovariance() [1/8]

template<typename StateType , typename MatrixType >
MatrixType hops::computeCovariance ( const std::vector< const std::vector< StateType > * > &  draws)

◆ computeCovariance() [2/8]

template<typename StateType , typename MatrixType >
MatrixType hops::computeCovariance ( const std::vector< const std::vector< StateType > * > &  draws,
const MatrixType &  covarianceSeen,
StateType &  meanSeen,
unsigned long  numberOfSeenDraws 
)

◆ computeCovariance() [3/8]

template<typename StateType , typename MatrixType >
MatrixType hops::computeCovariance ( const std::vector< StateType > &  draws)

◆ computeCovariance() [4/8]

template<typename StateType , typename MatrixType >
MatrixType hops::computeCovariance ( const std::vector< StateType > &  draws,
const MatrixType &  covarianceSeen,
StateType &  meanSeen,
unsigned long  numberOfSeenDraws 
)

◆ computeCovariance() [5/8]

template<typename StateType , typename MatrixType >
MatrixType hops::computeCovariance ( const std::vector< StateType > *  draws)

◆ computeCovariance() [6/8]

template<typename StateType , typename MatrixType >
MatrixType hops::computeCovariance ( const std::vector< StateType > *  draws,
const MatrixType &  covarianceSeen,
StateType &  meanSeen,
unsigned long  numberOfSeenDraws 
)

◆ computeCovariance() [7/8]

template<typename StateType , typename MatrixType >
MatrixType hops::computeCovariance ( const std::vector< std::vector< StateType >> &  draws)

◆ computeCovariance() [8/8]

template<typename StateType , typename MatrixType >
MatrixType hops::computeCovariance ( const std::vector< std::vector< StateType >> &  draws,
const MatrixType &  covarianceSeen,
StateType &  meanSeen,
unsigned long  numberOfSeenDraws 
)

◆ computeEffectiveSampleSize() [1/5]

template<typename StateType >
std::vector<double> hops::computeEffectiveSampleSize ( const std::vector< const std::vector< StateType > * > &  chains)

◆ computeEffectiveSampleSize() [2/5]

template<typename StateType >
double hops::computeEffectiveSampleSize ( const std::vector< const std::vector< StateType > * > &  chains,
unsigned long  dimension 
)

◆ computeEffectiveSampleSize() [3/5]

template<typename StateType >
std::vector<double> hops::computeEffectiveSampleSize ( const std::vector< std::vector< StateType >> &  chains)

◆ computeEffectiveSampleSize() [4/5]

template<typename StateType >
double hops::computeEffectiveSampleSize ( const std::vector< std::vector< StateType >> &  chains,
unsigned long  dimension 
)

◆ computeEffectiveSampleSize() [5/5]

Eigen::VectorXd hops::computeEffectiveSampleSize ( Data &  data)
inline

◆ computeExpectedSquaredJumpDistance() [1/10]

template<typename StateType , typename MatrixType >
std::vector<double> hops::computeExpectedSquaredJumpDistance ( const std::vector< const std::vector< StateType > * > &  chains)

◆ computeExpectedSquaredJumpDistance() [2/10]

template<typename StateType , typename MatrixType >
std::vector<double> hops::computeExpectedSquaredJumpDistance ( const std::vector< const std::vector< StateType > * > &  chains,
const MatrixType &  sqrtCovariance 
)

◆ computeExpectedSquaredJumpDistance() [3/10]

template<typename StateType , typename MatrixType >
std::vector<double> hops::computeExpectedSquaredJumpDistance ( const std::vector< const std::vector< StateType > * > &  chains,
unsigned long  numUnseen,
std::vector< double >  esjdSeen,
unsigned long  numSeen,
const MatrixType &  sqrtCovariance 
)

◆ computeExpectedSquaredJumpDistance() [4/10]

template<typename StateType , typename MatrixType >
double hops::computeExpectedSquaredJumpDistance ( const std::vector< StateType > &  draws)

◆ computeExpectedSquaredJumpDistance() [5/10]

template<typename StateType , typename MatrixType >
double hops::computeExpectedSquaredJumpDistance ( const std::vector< StateType > &  draws,
const MatrixType &  sqrtCovariance 
)

◆ computeExpectedSquaredJumpDistance() [6/10]

template<typename StateType , typename MatrixType >
double hops::computeExpectedSquaredJumpDistance ( const std::vector< StateType > &  draws,
unsigned long  numUnseen,
double  esjdSeen,
unsigned long  numSeen,
const MatrixType &  sqrtCovariance 
)

◆ computeExpectedSquaredJumpDistance() [7/10]

template<typename StateType , typename MatrixType >
std::vector<double> hops::computeExpectedSquaredJumpDistance ( const std::vector< std::vector< StateType >> &  chains)

◆ computeExpectedSquaredJumpDistance() [8/10]

template<typename StateType , typename MatrixType >
std::vector<double> hops::computeExpectedSquaredJumpDistance ( const std::vector< std::vector< StateType >> &  chains,
const MatrixType &  sqrtCovariance 
)

◆ computeExpectedSquaredJumpDistance() [9/10]

template<typename StateType , typename MatrixType >
std::vector<double> hops::computeExpectedSquaredJumpDistance ( const std::vector< std::vector< StateType >> &  chains,
unsigned long  numUnseen,
std::vector< double >  esjdSeen,
unsigned long  numSeen,
const MatrixType &  sqrtCovariance 
)

◆ computeExpectedSquaredJumpDistance() [10/10]

Eigen::VectorXd hops::computeExpectedSquaredJumpDistance ( Data &  data)
inline

◆ computePotentialScaleReductionFactor() [1/9]

template<typename StateType >
std::vector<double> hops::computePotentialScaleReductionFactor ( const std::vector< const std::vector< StateType > * > &  chains)

◆ computePotentialScaleReductionFactor() [2/9]

template<typename StateType >
double hops::computePotentialScaleReductionFactor ( const std::vector< const std::vector< StateType > * > &  chains,
unsigned long  dimension 
)

◆ computePotentialScaleReductionFactor() [3/9]

template<typename StateType >
double hops::computePotentialScaleReductionFactor ( const std::vector< const std::vector< StateType > * > &  chains,
unsigned long  dimension,
unsigned long  numUnseen,
std::vector< double > &  sampleVariancesSeen,
std::vector< double > &  intraChainExpectationsSeen,
double &  interChainExpectationSeen,
unsigned long &  numSeen 
)

◆ computePotentialScaleReductionFactor() [4/9]

template<typename StateType >
std::vector<double> hops::computePotentialScaleReductionFactor ( const std::vector< const std::vector< StateType > * > &  chains,
unsigned long  numUnseen,
std::vector< std::vector< double >> &  sampleVariancesSeen,
std::vector< std::vector< double >> &  intraChainExpectationsSeen,
std::vector< double > &  interChainExpectationSeen,
unsigned long &  numSeen 
)

◆ computePotentialScaleReductionFactor() [5/9]

template<typename StateType >
std::vector<double> hops::computePotentialScaleReductionFactor ( const std::vector< std::vector< StateType >> &  chains)

◆ computePotentialScaleReductionFactor() [6/9]

template<typename StateType >
double hops::computePotentialScaleReductionFactor ( const std::vector< std::vector< StateType >> &  chains,
unsigned long  dimension 
)

◆ computePotentialScaleReductionFactor() [7/9]

template<typename StateType >
double hops::computePotentialScaleReductionFactor ( const std::vector< std::vector< StateType >> &  chains,
unsigned long  dimension,
unsigned long  numUnseen,
std::vector< double > &  sampleVariancesSeen,
std::vector< double > &  intraChainExpectationsSeen,
double &  interChainExpectationSeen,
unsigned long &  numSeen 
)

◆ computePotentialScaleReductionFactor() [8/9]

template<typename StateType >
std::vector<double> hops::computePotentialScaleReductionFactor ( const std::vector< std::vector< StateType >> &  chains,
unsigned long  numUnseen,
std::vector< std::vector< double >> &  sampleVariancesSeen,
std::vector< std::vector< double >> &  intraChainExpectationsSeen,
std::vector< double > &  interChainExpectationSeen,
unsigned long &  numSeen 
)

◆ computePotentialScaleReductionFactor() [9/9]

Eigen::VectorXd hops::computePotentialScaleReductionFactor ( Data &  data)
inline

◆ computeTotalNumberOfSamples()

double hops::computeTotalNumberOfSamples ( Data &  data)
inline

◆ computeTotalTimeTaken()

Eigen::VectorXd hops::computeTotalTimeTaken ( Data &  data)
inline

◆ isConstantChain() [1/4]

template<typename StateType >
bool hops::isConstantChain ( const std::vector< const std::vector< StateType > * > &  chains)

◆ isConstantChain() [2/4]

template<typename StateType >
bool hops::isConstantChain ( const std::vector< const std::vector< StateType > * > &  chains,
long  dimension 
)

◆ isConstantChain() [3/4]

template<typename StateType >
bool hops::isConstantChain ( const std::vector< std::vector< StateType >> &  chains)

◆ isConstantChain() [4/4]

template<typename StateType >
bool hops::isConstantChain ( const std::vector< std::vector< StateType >> &  chains,
long  dimension 
)

◆ MarkovChainTypeToFullString()

std::string hops::MarkovChainTypeToFullString ( MarkovChainType  markovChainType)

◆ MarkovChainTypeToShortcutString()

std::string hops::MarkovChainTypeToShortcutString ( MarkovChainType  markovChainType)

◆ nextGoodSizeFFT()

size_t hops::nextGoodSizeFFT ( size_t  N)
inline

◆ normalizePolytope()

template<typename Derived1 , typename Derived2 >
void hops::normalizePolytope ( Eigen::MatrixBase< Derived1 > &  A,
Eigen::MatrixBase< Derived2 > &  b 
)

Normalizes polytope defined by Ax < b.

Template Parameters
Derived1
Derived2
Parameters
ADense representation of A
b

◆ tune() [1/3]

template<typename Model , typename Proposal >
void hops::tune ( RunBase< Model, Proposal > &  run,
AcceptanceRateTuner::param_type &  parameters 
)

◆ tune() [2/3]

template<typename Model , typename Proposal >
void hops::tune ( RunBase< Model, Proposal > &  run,
ExpectedSquaredJumpDistanceTuner::param_type &  parameters 
)

◆ tune() [3/3]

template<typename Model , typename Proposal >
void hops::tune ( RunBase< Model, Proposal > &  run,
SimpleExpectedSquaredJumpDistanceTuner::param_type &  parameters 
)
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