Package | Description |
---|---|
pal.eval |
Classes for evaluating evolutionary hypothesis (chi-square and likelihood
criteria) and estimating model parameters.
|
pal.math |
Classes for math stuff such as optimisation, numerical derivatives, matrix exponentials,
random numbers, special function etc.
|
pal.treesearch |
Modifier and Type | Method and Description |
---|---|
static double |
LikelihoodOptimiser.optimiseAlternate(ParameterizedTree tree,
Alignment alignment,
SubstitutionModel model,
MultivariateMinimum minimiser,
int fxFracDigits,
int xFracDigits)
Optimise parameters to acheive maximum likelihood using an alternating stategy.
|
static double |
LikelihoodOptimiser.optimiseAlternate(ParameterizedTree tree,
Alignment alignment,
SubstitutionModel model,
MultivariateMinimum minimiser,
int fxFracDigits,
int xFracDigits,
MinimiserMonitor monitor)
Optimise parameters to acheive maximum likelihood using an alternating stategy.
|
static double |
LikelihoodOptimiser.optimiseCombined(ParameterizedTree tree,
Alignment alignment,
SubstitutionModel model,
MultivariateMinimum minimiser,
int fxFracDigits,
int xFracDigits)
Optimise parameters to acheive maximum likelihood using a combined stategy.
|
static double |
LikelihoodOptimiser.optimiseCombined(ParameterizedTree tree,
Alignment alignment,
SubstitutionModel model,
MultivariateMinimum minimiser,
int fxFracDigits,
int xFracDigits,
MinimiserMonitor monitor)
Optimise parameters to acheive maximum likelihood using a combined stategy.
|
double |
LikelihoodOptimiser.optimiseLogLikelihood(Parameterized parameters,
MultivariateMinimum minimiser,
int fxFracDigits,
int xFracDigits) |
double |
LikelihoodOptimiser.optimiseLogLikelihood(Parameterized parameters,
MultivariateMinimum minimiser,
int fxFracDigits,
int xFracDigits,
MinimiserMonitor monitor) |
static double |
LikelihoodOptimiser.optimiseModel(Tree tree,
Alignment alignment,
SubstitutionModel model,
MultivariateMinimum minimiser,
int fxFracDigits,
int xFracDigits,
MinimiserMonitor monitor)
Optimise model parameters only to acheive maximum likelihood using a combined stategy.
|
double |
LikelihoodValue.optimiseParameters(MultivariateMinimum mm)
optimise parameters of tree by maximising its likelihood
(this assumes that tree is a ParameterizedTree)
|
double |
ChiSquareValue.optimiseParameters(MultivariateMinimum mm)
optimise parameters of a tree by minimising its chi-square value
(tree must be a ParameterizedTree)
|
static double |
LikelihoodOptimiser.optimiseTree(ParameterizedTree tree,
Alignment alignment,
SubstitutionModel model,
MultivariateMinimum minimiser,
int fxFracDigits,
int xFracDigits)
Optimise tree branchlengths only to acheive maximum likelihood using a combined stategy.
|
static double |
LikelihoodOptimiser.optimiseTree(ParameterizedTree tree,
Alignment alignment,
SubstitutionModel model,
MultivariateMinimum minimiser,
int fxFracDigits,
int xFracDigits,
MinimiserMonitor monitor)
Optimise tree branchlengths only to acheive maximum likelihood using a combined stategy.
|
double |
DemographicValue.optimize(MultivariateMinimum givenMvm)
optimize log-likelihood value and compute corresponding SEs
given an optimizer
|
Modifier and Type | Class and Description |
---|---|
class |
ConjugateDirectionSearch
methods for minimization of a real-valued function of
several variables without using derivatives (Brent's modification
of a conjugate direction search method proposed by Powell)
|
class |
ConjugateGradientSearch
minimization of a real-valued function of
several variables using a the nonlinear
conjugate gradient method where several variants of the direction
update are available (Fletcher-Reeves, Polak-Ribiere,
Beale-Sorenson, Hestenes-Stiefel) and bounds are respected.
|
class |
DifferentialEvolution
global minimization of a real-valued function of several
variables without using derivatives using a genetic algorithm
(Differential Evolution)
|
class |
GeneralizedDEOptimizer
Provides an general interface to the DifferentialEvolution class that is not
tied to a certain number of parameters (as DifferentialEvolution is).
|
class |
OrthogonalSearch
minimization of a real-valued function of
several variables without using derivatives, using the simple
strategy of optimizing variables one by one.
|
Modifier and Type | Method and Description |
---|---|
MultivariateMinimum |
MultivariateMinimum.Factory.generateNewMinimiser()
Generate a new Multivariate Minimum
|
Modifier and Type | Method and Description |
---|---|
UndoableAction |
UnrootedMLSearcher.getBranchLengthWithModelOptimiseAction(StoppingCriteria.Factory stopper,
MultivariateMinimum minimiser,
int fxFracDigits,
int xFracDigits) |
UndoableAction |
UnrootedMLSearcher.getModelOptimiseAction(MultivariateMinimum minimiser,
int fxFracDigits,
int xFracDigits) |
UndoableAction |
UnrootedMLSearcher.getModelOptimiseAction(MultivariateMinimum minimiser,
MinimiserMonitor monitor,
int fxFracDigits,
int xFracDigits) |
double |
GeneralLikelihoodSearcher.optimiseAllFullHeirarchy(StoppingCriteria mainStopper,
StoppingCriteria subStopper,
MultivariateMinimum rateMinimiser,
int fxFracDigits,
int xFracDigits,
AlgorithmCallback callback,
SearchMonitor monitor,
MinimiserMonitor rateMonitor) |
double |
GeneralConstraintGroupManager.optimiseAllGlobalClockConstraints(MultivariateMinimum minimiser,
GeneralConstraintGroupManager.LikelihoodScoreAccess scoreAccess,
int fxFracDigits,
int xFracDigits,
MinimiserMonitor rateMonitor)
Optimise all the global clock parameters related to this group
|
double |
GeneralLikelihoodSearcher.optimiseAllPlusSubstitutionModel(StoppingCriteria stopper,
MultivariateMinimum rateMinimiser,
MultivariateMinimum substitutionModelMinimiser,
int fxFracDigits,
int xFracDigits,
AlgorithmCallback callback,
SearchMonitor monitor,
int substitutionModelOptimiseFrequency,
MinimiserMonitor substitutionModelMonitor,
MinimiserMonitor rateMonitor) |
double |
GeneralLikelihoodSearcher.optimiseAllSimple(StoppingCriteria stopper,
MultivariateMinimum rateMinimiser,
int fxFracDigits,
int xFracDigits,
AlgorithmCallback callback) |
double |
GeneralLikelihoodSearcher.optimiseAllSimple(StoppingCriteria stopper,
MultivariateMinimum rateMinimiser,
int fxFracDigits,
int xFracDigits,
AlgorithmCallback callback,
SearchMonitor monitor,
MinimiserMonitor rateMonitor) |
double |
GeneralLikelihoodSearcher.optimiseAllSimple(StoppingCriteria stopper,
MultivariateMinimum rateMinimiser,
int fxFracDigits,
int xFracDigits,
AlgorithmCallback callback,
SearchMonitor monitor,
MinimiserMonitor rateMonitor,
int groupOptimistionType) |
double |
GeneralLikelihoodSearcher.optimiseAllSimpleHeirarchy(StoppingCriteria stopper,
MultivariateMinimum rateMinimiser,
int fxFracDigits,
int xFracDigits,
AlgorithmCallback callback,
SearchMonitor monitor,
MinimiserMonitor rateMonitor) |
double |
GeneralLikelihoodSearcher.optimiseConstraintRateModels(MultivariateMinimum minimiser,
int fxFracDigits,
int xFracDigits,
MinimiserMonitor rateMonitor) |
double |
GeneralConstraintGroupManager.optimisePrimaryGlobalClockConstraints(MultivariateMinimum minimiser,
GeneralConstraintGroupManager.LikelihoodScoreAccess scoreAccess,
int fxFracDigits,
int xFracDigits,
MinimiserMonitor rateMonitor)
Optimise the global clock parameters marked as primary related to this group
|
double |
GeneralConstraintGroupManager.optimiseSecondaryGlobalClockConstraints(MultivariateMinimum minimiser,
GeneralConstraintGroupManager.LikelihoodScoreAccess scoreAccess,
int fxFracDigits,
int xFracDigits,
MinimiserMonitor rateMonitor)
Optimise the global clock parameters marked as secondary related to this group
|
double |
GeneralLikelihoodSearcher.optimiseSubstitutionModels(MultivariateMinimum minimiser,
int fxFracDigits,
int xFracDigits,
MinimiserMonitor monitor) |