optimize

package
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 Go to latest Published: Sep 13, 2021 License: GPL-2.0 Imports: 21 Imported by: 3

Documentation

Overview

Package optimize is a collection of optimizers and MCMC samplers.

Index

Constants

View Source 
const (
	// Constrained Optimization BY Linear Approximations
	// (local, gradient-free)
	NLOPT_COBYLA = iota
	// BOBYQA optimizer (local, gradient-free)
	NLOPT_BOBYQA
	// Downhill simplex (local, gradient-free)
	NLOPT_SIMPLEX
	// LBFGS: local Broyden–Fletcher–Goldfarb–Shanno
	// (local, gradient-based)
	NLOPT_LBFGS
	// SQP: sequential quadratic programming
	// (local, gradient-based)
	NLOPT_SQP
	// DIviding RECTangles algorithm (global)
	NLOPT_DIRECT
	// Controlled Random Search (global)
	NLOPT_CRS
	// Multi-Level Single-Linkage (global)
	NLOPT_MLSL
)

NLopt optimization methods.

View Source 
const (
	// RandomizeMin is the minimum value for randomization.
	RandomizeMin = -1e9
	// RandomizeMax is the maximum value for randomization.
	RandomizeMax = +1e9
)
View Source 
const (
	// TINY is ftol for the downhill simplex.
	TINY = 1e-10
	// SMALL is atol for downhill simplex.
	SMALL = 1e-6
)

Variables

This section is empty.

Functions

func DiscreteProposal 

func DiscreteProposal(state int, nstates int) (newstate int)

DiscreteProposal returns function returning a random integer converted to float64.

func DiscreteUniformPrior 

func DiscreteUniformPrior(nstates int) func(float64) float64

DiscreteUniformPrior returns function returning 1/nstates.

func ExponentialPrior 

func ExponentialPrior(rate float64, inczero bool) func(float64) float64

ExponentialPrior returns a function returning a exponential distribution.

func GammaPrior 

func GammaPrior(shape, scale float64, inczero bool) func(float64) float64

GammaPrior returns a function returning a gamma distribution.

func NormalProposal 

func NormalProposal(sd float64) func(float64) float64

NormalProposal returns normal proposal function.

func ProductPrior 

func ProductPrior(f, g func(float64) float64) func(float64) float64

ProductPrior returns a function which a multiplication of two functions.

func Rand 

func Rand() float64

Rand returns a random value in the range [0, 1], including 1.

func ReadFloats 

func ReadFloats(s string) ([]float64, error)

ReadFloats converts string of floats into slice of float64.

func UniformGlobalProposal 

func UniformGlobalProposal(min, max float64) func(float64) float64

UniformGlobalProposal returns uniform proposal function given max and min.

func UniformPrior 

func UniformPrior(min, max float64, incmin, incmax bool) func(float64) float64

UniformPrior returns a uniform prior function.

func UniformProposal 

func UniformProposal(width float64) func(float64) float64

UniformProposal returns uniform proposal function.

Types

type AdaptiveParameter 

type AdaptiveParameter struct {
	*BasicFloatParameter

	*AdaptiveSettings
	// contains filtered or unexported fields
}

AdaptiveParameter is an adaptive parameter for adaptive MCMC.

func NewAdaptiveParameter 

func NewAdaptiveParameter(par *float64, name string, ap *AdaptiveSettings) (a *AdaptiveParameter)

NewAdaptiveParameter creates a new adaptive MCMC parameter.

func (*AdaptiveParameter) Accept 

func (a *AdaptiveParameter) Accept(iter int)

Accept is called if value is accepted.

func (*AdaptiveParameter) AdaptiveProposal 

func (a *AdaptiveParameter) AdaptiveProposal() func(float64) float64

AdaptiveProposal proposes a new point using adaptive MCMC.

func (*AdaptiveParameter) CheckConvergenceMu 

func (a *AdaptiveParameter) CheckConvergenceMu()

CheckConvergenceMu checks if Mu converged.

func (*AdaptiveParameter) RobbinsMonro 

func (a *AdaptiveParameter) RobbinsMonro() (gamma float64)

RobbinsMonro implements Robbins-Monro algorithm for learning mean and variance.

func (*AdaptiveParameter) UpdateMu 

func (a *AdaptiveParameter) UpdateMu()

UpdateMu updates Mu value.

type AdaptiveSettings 

type AdaptiveSettings struct {
	// WSize window size to compute mean and variance.
	WSize int
	// K specifies how often Mu should be updated.
	K int
	// Skip is the number of iterations to skip before starting
	// adaptation.
	Skip int
	// MaxAdapt is the number of iterations to adapt.
	MaxAdapt int
	// MaxUpdate maximum number of update for a parameter.
	MaxUpdate int
	// Epsilon is part of stopping criteria for stopping
	// adaptation.
	Epsilon float64
	// C is a Robbins-Monro algorithm parameter
	C float64
	// Nu is a Robbins-Monro algorithm parameter
	Nu float64
	// Lambda is the proposal multiplier.
	Lambda float64
	// SD is initial standard deviation.
	SD float64
}

AdaptiveSettings are settings for an adaptive MCMC.

func NewAdaptiveSettings 

func NewAdaptiveSettings() *AdaptiveSettings

NewAdaptiveSettings creates new settings for adaptive MCMC.

func (*AdaptiveSettings) ParameterGenerator 

func (as *AdaptiveSettings) ParameterGenerator(par *float64, name string) FloatParameter

ParameterGenerator generates an adaptive MCMC parameter.

type BaseOptimizer 

type BaseOptimizer struct {
	Optimizable

	// Quiet controls whether output should be printed.
	Quiet bool
	// contains filtered or unexported fields
}

BaseOptimizer contains basic data for an optimizer.

func (*BaseOptimizer) GetMaxL 

func (o *BaseOptimizer) GetMaxL() float64

GetMaxL returns the maximum likelihood value. Can me larger than current likelihood for MCMC, simulated annealing, etc.

func (*BaseOptimizer) GetMaxLParameters 

func (o *BaseOptimizer) GetMaxLParameters() (s string)

GetMaxLParameters returns parameter values for the maximum likelihood value as a string.

func (*BaseOptimizer) GetNCalls 

func (o *BaseOptimizer) GetNCalls() int

GetNCalls return total number of likelihood function calls.

func (*BaseOptimizer) GetNIter 

func (o *BaseOptimizer) GetNIter() int

GetNIter returns total number of iterations. Can be smaller than number of likelihood function calls for the gradient-based methods.

func (*BaseOptimizer) GetOptimizable 

func (o *BaseOptimizer) GetOptimizable() Optimizable

GetOptimizable get the model.

func (*BaseOptimizer) GetParametersMap 

func (o *BaseOptimizer) GetParametersMap(par []float64) (m map[string]float64)

GetParametersMap returns parameter values as a map.

func (*BaseOptimizer) LoadFromOptimizer 

func (o *BaseOptimizer) LoadFromOptimizer(opt Optimizer)

LoadFromOptimizer can load a model from another optimizer.

func (*BaseOptimizer) PrintHeader 

func (o *BaseOptimizer) PrintHeader()

PrintHeader prints the report header.

func (*BaseOptimizer) PrintLine 

func (o *BaseOptimizer) PrintLine(par FloatParameters, l float64, repPeriod int)

PrintLine prints one line of report (iteration, parameter values, likelihood).

func (*BaseOptimizer) PrintResults 

func (o *BaseOptimizer) PrintResults(quiet bool)

PrintResults prints the optimization results.

func (*BaseOptimizer) SaveCheckpoint 

func (o *BaseOptimizer) SaveCheckpoint(final bool)

SaveCheckpoint saves current optimization point.

func (*BaseOptimizer) SaveStart 

func (o *BaseOptimizer) SaveStart()

SaveStart saves starting point and likelihood before optimization.

func (*BaseOptimizer) SetCheckpointIO 

func (o *BaseOptimizer) SetCheckpointIO(cio *checkpoint.CheckpointIO)

SetCheckpointIO sets object for checkpoint I/O.

func (*BaseOptimizer) SetOptimizable 

func (o *BaseOptimizer) SetOptimizable(opt Optimizable)

SetOptimizable sets a model for the optimization.

func (*BaseOptimizer) SetReportPeriod 

func (o *BaseOptimizer) SetReportPeriod(period int)

SetReportPeriod specifies how often report line should be printed.

func (*BaseOptimizer) SetTrajectoryOutput 

func (o *BaseOptimizer) SetTrajectoryOutput(output io.Writer)

SetTrajectoryOutput specifies an output writer for the trajectory.

func (*BaseOptimizer) Summary 

func (o *BaseOptimizer) Summary() Summary

Summary returns optimization summary.

func (*BaseOptimizer) WatchSignals 

func (o *BaseOptimizer) WatchSignals(sigs ...os.Signal)

WatchSignals installs OS hooks to react to signals.

type BasicFloatParameter 

type BasicFloatParameter struct {
	// contains filtered or unexported fields
}

BasicFloatParameter implementation of FloatParameter interface.

func NewBasicFloatParameter 

func NewBasicFloatParameter(par *float64, name string) *BasicFloatParameter

NewBasicFloatParameter creates a new BasicFloatParameter.

func (*BasicFloatParameter) Accept 

func (p *BasicFloatParameter) Accept(iter int)

Accept accepts the proposal.

func (*BasicFloatParameter) Get 

func (p *BasicFloatParameter) Get() float64

Get returns the parameter value.

func (*BasicFloatParameter) GetMax 

func (p *BasicFloatParameter) GetMax() float64

GetMax returns a maximum value.

func (*BasicFloatParameter) GetMin 

func (p *BasicFloatParameter) GetMin() float64

GetMin returns a minimal value.

func (*BasicFloatParameter) InRange 

func (p *BasicFloatParameter) InRange() bool

InRange returns true if parameter value is between min and max.

func (*BasicFloatParameter) Name 

func (p *BasicFloatParameter) Name() string

Name returns the parameter name.

func (*BasicFloatParameter) OldPrior 

func (p *BasicFloatParameter) OldPrior() float64

OldPrior returns prior value for the old x.

func (*BasicFloatParameter) Prior 

func (p *BasicFloatParameter) Prior() float64

Prior returns prior value for the current x.

func (*BasicFloatParameter) Propose 

func (p *BasicFloatParameter) Propose()

Propose set x to a new proposed value

func (*BasicFloatParameter) Randomize 

func (p *BasicFloatParameter) Randomize()

Randomize initializes parameter with a random value. The random value is uniformly distributed in the range [max(RMIN, min);min(RMAX, max)].

func (*BasicFloatParameter) Reject 

func (p *BasicFloatParameter) Reject()

Reject rejects the proposal (and set x to an old value).

func (*BasicFloatParameter) Set 

func (p *BasicFloatParameter) Set(v float64)

Set sets the parameter.

func (*BasicFloatParameter) SetMax 

func (p *BasicFloatParameter) SetMax(max float64)

SetMax sets a maximal value.

func (*BasicFloatParameter) SetMin 

func (p *BasicFloatParameter) SetMin(min float64)

SetMin sets a minimal value.

func (*BasicFloatParameter) SetOnChange 

func (p *BasicFloatParameter) SetOnChange(f func())

SetOnChange sets a callback which should be called when the value is changed.

func (*BasicFloatParameter) SetPriorFunc 

func (p *BasicFloatParameter) SetPriorFunc(f func(float64) float64)

SetPriorFunc sets a prior for the parameter.

func (*BasicFloatParameter) SetProposalFunc 

func (p *BasicFloatParameter) SetProposalFunc(f func(float64) float64)

SetProposalFunc sets a proposal for the parameter.

func (*BasicFloatParameter) String 

func (p *BasicFloatParameter) String() string

String returns a string representation of a value.

func (*BasicFloatParameter) ValueInRange 

func (p *BasicFloatParameter) ValueInRange(v float64) bool

ValueInRange returns true if value is between min and max.

type DS 

type DS struct {
	BaseOptimizer
	// contains filtered or unexported fields
}

DS is a downhill simplex optimizer.

func NewDS 

func NewDS() (ds *DS)

NewDS creates a new DH optimizer.

func (*DS) Run 

func (ds *DS) Run(iterations int)

Run starts the optimization.

type DiscreteParameter 

type DiscreteParameter struct {
	*BasicFloatParameter
	// NStates number of discrete states of the parameter.
	NStates int
}

DiscreteParameter is a discrete parameter. It is internally based on a BasicFloatParameter. The state is stored as an integer number stored converted to float (e.g. 3.0 instead of 3).

func NewDiscreteParameter 

func NewDiscreteParameter(par *float64, name string, nstates int) (dpar *DiscreteParameter)

NewDiscreteParameter creates a new DiscreteParameter.

func (*DiscreteParameter) Propose 

func (p *DiscreteParameter) Propose()

Propose set x to a new proposed value

func (*DiscreteParameter) String 

func (p *DiscreteParameter) String() string

String returns a string representation of a value.

type FloatParameter 

type FloatParameter interface {
	// Name returns the parameter name.
	Name() string
	// Prior returns prior value for the current x.
	Prior() float64
	// OldPrior returns prior value for the old x.
	OldPrior() float64
	// Propose set x to a new proposed value
	Propose()
	// Accept accepts the proposal.
	Accept(int)
	// Reject rejects the proposal (and set x to an old value).
	Reject()
	// String returns a string representation of a value.
	String() string
	// SetMin sets a minimal value.
	SetMin(float64)
	// SetMax sets a maximal value.
	SetMax(float64)
	// GetMin returns a minimal value.
	GetMin() float64
	// GetMax returns a maximal value.
	GetMax() float64
	// SetOnChange sets a callback which should be called when the
	// value is changed.
	SetOnChange(func())
	// SetProposalFunc sets a proposal for the parameter.
	SetProposalFunc(func(float64) float64)
	// SetPriorFunc sets a prior for the parameter.
	SetPriorFunc(func(float64) float64)
	// Get returns the parameter value.
	Get() float64
	// Set sets the parameter.
	Set(float64)
	// Randomize initializes a parameter with a random value.
	// The random value is uniformly distributed in the range
	// [max(RadnomizeMin, min);min(RandomizeMax, max)].
	Randomize()
	// InRange returns true if parameter value is between min and
	// max.
	InRange() bool
	// ValueInRange returns true if value is between min and max.
	ValueInRange(float64) bool
}

FloatParameter is a floating point parameter.

func BasicFloatParameterGenerator 

func BasicFloatParameterGenerator(par *float64, name string) FloatParameter

BasicFloatParameterGenerator is a function which returns a FloatParameter which is a BasicFloatParameter.

type FloatParameterGenerator 

type FloatParameterGenerator func(*float64, string) FloatParameter

FloatParameterGenerator is a function which returns a FloatParameter.

type FloatParameters 

type FloatParameters []FloatParameter

FloatParameters is an array (slice) of FloatParameters.

func (*FloatParameters) Append 

func (p *FloatParameters) Append(par FloatParameter)

Append appends a new float parameter.

func (FloatParameters) GetMap 

func (p FloatParameters) GetMap() map[string]float64

GetMap get parameters from FloatParameters in a map.

func (*FloatParameters) InRange 

func (p *FloatParameters) InRange() bool

InRange returns true if parameter value is between min and max.

func (FloatParameters) MarshalJSON 

func (p FloatParameters) MarshalJSON() ([]byte, error)

MarshalJSON creates a JSON representation of FlatParameters.

func (*FloatParameters) Names 

func (p *FloatParameters) Names(is []string) (s []string)

Names returns a slice of parameter names.

func (*FloatParameters) NamesString 

func (p *FloatParameters) NamesString() (s string)

NamesString returns a string with tab-separated parameter names.

func (*FloatParameters) Randomize 

func (p *FloatParameters) Randomize()

Randomize sets random values to all the parameters. If no minimum and maximum are specified, RandomizeMin and RandomizeMax are used.

func (*FloatParameters) ReadFromJSON 

func (p *FloatParameters) ReadFromJSON(filename string) error

ReadFromJSON sets parameter values from JSON map.

func (*FloatParameters) ReadLine 

func (p *FloatParameters) ReadLine(l string) error

ReadLine sets values from a string.

func (*FloatParameters) SetByName 

func (p *FloatParameters) SetByName(name string, v float64) error

SetByName sets parameter value by its' name.

func (*FloatParameters) SetFromMap 

func (p *FloatParameters) SetFromMap(m map[string]float64) error

SetFromMap sets parameters from a map. This map is produced by JSON parser usually.

func (*FloatParameters) SetValues 

func (p *FloatParameters) SetValues(v []float64) error

SetValues sets values from a slice.

func (*FloatParameters) UnmarshalJSON 

func (p *FloatParameters) UnmarshalJSON(b []byte) error

UnmarshalJSON sets parameters using JSON bytes.

func (*FloatParameters) Update 

func (p *FloatParameters) Update(pSrc *FloatParameters)

Update updates values from another FloatParameters object.

func (*FloatParameters) Values 

func (p *FloatParameters) Values(iv []float64) (v []float64)

Values returns a slice of parameter values.

func (*FloatParameters) ValuesInRange 

func (p *FloatParameters) ValuesInRange(vals []float64) bool

ValuesInRange returns true if all parameters are in range.

func (*FloatParameters) ValuesString 

func (p *FloatParameters) ValuesString() (s string)

ValuesString returns a tab-separated string of values.

type LBFGSB 

type LBFGSB struct {
	BaseOptimizer
	// contains filtered or unexported fields
}

LBFGSB is wrapper around go-lbfgsb library. It uses L-BFGS-B algorithm of hill climbing.

func NewLBFGSB 

func NewLBFGSB() (lbfgsb *LBFGSB)

NewLBFGSB creates a new LBFGSB optimizer.

func (*LBFGSB) EvaluateFunction 

func (l *LBFGSB) EvaluateFunction(x []float64) float64

EvaluateFunction evaluates likelihood function for point x.

func (*LBFGSB) EvaluateGradient 

func (l *LBFGSB) EvaluateGradient(x []float64) (grad []float64)

EvaluateGradient evaluates gradient for point x.

func (*LBFGSB) Logger 

func (l *LBFGSB) Logger(info *lbfgsb.OptimizationIterationInformation)

Logger is a function which is called back on every iteration.

func (*LBFGSB) Run 

func (l *LBFGSB) Run(iterations int)

Run starts the optimization.

func (*LBFGSB) Summary 

func (l *LBFGSB) Summary() Summary

Summary returns optimization summary (i.e. success/error, etc).

type MH 

type MH struct {
	BaseOptimizer
	AccPeriod int

	SD float64
	// contains filtered or unexported fields
}

MH is a Metropolis-Hastings sampler.

func NewMH 

func NewMH(annealing bool, annealingSkip int) (mcmc *MH)

NewMH creates a new MH sampler.

func (*MH) Run 

func (m *MH) Run(iterations int)

Run starts sampling.

type NLOPT 

type NLOPT struct {
	BaseOptimizer
	// contains filtered or unexported fields
}

NLOPT is nlopt optimizer.

func NewNLOPT 

func NewNLOPT(algorithm int, seed int64) (nlopt *NLOPT)

NewNLOPT creates a new NLOPT optimizer.

func (*NLOPT) Run 

func (n *NLOPT) Run(iterations int)

Run runs the optimizer.

func (*NLOPT) Summary 

func (n *NLOPT) Summary() Summary

Summary returns optimization summary (i.e. success/error, etc).

type None 

type None struct {
	BaseOptimizer
}

None is an optimizer which computes initial value and exits.

func NewNone 

func NewNone() *None

NewNone creates an optimizer which computes initial likelihood only.

func (*None) Run 

func (i *None) Run(iterations int)

Run starts sampling.

type Optimizable 

type Optimizable interface {
	// GetFloatParameters returns array (slice) of float
	// parameters.
	GetFloatParameters() FloatParameters
	// Copy creates a copy of optimizable.
	Copy() Optimizable
	// Likelihood returns likelihood.
	Likelihood() float64
}

Optimizable is something which can be optimized using the optimizer.

type Optimizer 

type Optimizer interface {
	// SetOptimizable sets model for the optimization.
	SetOptimizable(Optimizable)
	// GetOptimizable returns the underlying model.
	GetOptimizable() Optimizable
	// WatchSignals installs OS hooks to react to signals.
	WatchSignals(...os.Signal)
	// SetReportPeriod specifies how often report line should be printed.
	SetReportPeriod(period int)
	// SetTrajectoryOutput specifies an output writer for the trajectory.
	SetTrajectoryOutput(io.Writer)
	// SetCheckpointIO sets CheckpointIO class.
	SetCheckpointIO(*checkpoint.CheckpointIO)
	// Starts the optimization or sampling.
	Run(iterations int)
	// GetMaxL returns the maximum likelihood value.
	GetMaxL() float64
	// GetMaxLParameters returns parameter values for the maximum
	// likelihood value.
	GetMaxLParameters() string
	// GetNCalls return total number of likelihood function calls.
	GetNCalls() int
	// GetNIter returns total number of iterations. Can be smaller than
	// number of likelihood function calls for the gradient-based methods.
	GetNIter() int
	// LoadFromOptimizer can load a model from another optimizer.
	LoadFromOptimizer(Optimizer)
	// PrintResults prints the results of optimization; quiet=true reduces
	// log levels of estimated parameter values.
	PrintResults(quiet bool)
	// Summary returns optimization summary for JSON output.
	Summary() Summary
}

Optimizer an optimizer interface.

type Summary 

type Summary interface {
	GetMaxLikelihood() float64
	GetMaxLikelihoodParameters() map[string]float64
}

Summary allows quering of maximum likelihood estimates.

Source Files

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