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Packages that use MultivariateMinimum | |
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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 |
Uses of MultivariateMinimum in pal.eval |
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Methods in pal.eval with parameters of type MultivariateMinimum | |
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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)
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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 |
ChiSquareValue.optimiseParameters(MultivariateMinimum mm)
optimise parameters of a tree by minimising its chi-square value (tree must be a ParameterizedTree) |
double |
LikelihoodValue.optimiseParameters(MultivariateMinimum mm)
optimise parameters of tree by maximising its likelihood (this assumes that tree is 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 |
Uses of MultivariateMinimum in pal.math |
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Subclasses of MultivariateMinimum in pal.math | |
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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. |
Methods in pal.math that return MultivariateMinimum | |
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MultivariateMinimum |
MultivariateMinimum.Factory.generateNewMinimiser()
Generate a new Multivariate Minimum |
Uses of MultivariateMinimum in pal.treesearch |
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Methods in pal.treesearch with parameters of type MultivariateMinimum | |
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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)
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