Package | Description |
---|---|
pal.eval |
Classes for evaluating evolutionary hypothesis (chi-square and likelihood
criteria) and estimating model parameters.
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pal.math |
Classes for math stuff such as optimisation, numerical derivatives, matrix exponentials,
random numbers, special function etc.
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pal.misc |
Classes that don't fit elsewhere ;^)
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Modifier and Type | Class and Description |
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class |
ChiSquareValue
computes chi-square value of a (parameterized) tree for
its set of parameters (e.g., branch lengths)
and a given distance matrix
|
class |
DemographicValue
estimates demographic parameters by maximising the coalescent
prior for a tree with given branch lengths.
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class |
ModelParameters
estimates substitution model parameters from the data
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Modifier and Type | Interface and Description |
---|---|
interface |
MFWithGradient
interface for a function of several variables with a gradient
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Modifier and Type | Class and Description |
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class |
BoundsCheckedFunction
returns a very large number instead of the function value
if arguments are out of bound (useful for minimization with
minimizers that don't check argument boundaries)
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class |
EvaluationCounter
A utiltity class that can be used to track the number of evaluations of a
general function
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Modifier and Type | Method and Description |
---|---|
static double[] |
NumericalDerivative.diagonalHessian(MultivariateFunction f,
double[] x)
determine diagonal of Hessian
|
double |
MultivariateMinimum.findMinimum(MultivariateFunction f,
double[] xvec)
Find minimum close to vector x
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double |
MultivariateMinimum.findMinimum(MultivariateFunction f,
double[] xvec,
int fxFracDigits,
int xFracDigits)
Find minimum close to vector x
(desired fractional digits for each parameter is specified)
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double |
MultivariateMinimum.findMinimum(MultivariateFunction f,
double[] xvec,
int fxFracDigits,
int xFracDigits,
MinimiserMonitor monitor)
Find minimum close to vector x
(desired fractional digits for each parameter is specified)
|
protected OrthogonalSearch.RoundOptimiser |
OrthogonalSearch.generateOrthogonalRoundOptimiser(MultivariateFunction mf) |
static double[] |
MathUtils.getRandomArguments(MultivariateFunction mf) |
static double[] |
NumericalDerivative.gradient(MultivariateFunction f,
double[] x)
determine gradient
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static void |
NumericalDerivative.gradient(MultivariateFunction f,
double[] x,
double[] grad)
determine gradient
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void |
MinimiserMonitor.newMinimum(double value,
double[] parameterValues,
MultivariateFunction beingOptimized)
Inform monitor of a new minimum, along with the current arguments.
|
abstract void |
MultivariateMinimum.optimize(MultivariateFunction f,
double[] xvec,
double tolfx,
double tolx)
The actual optimization routine
(needs to be implemented in a subclass of MultivariateMinimum).
|
void |
OrthogonalSearch.optimize(MultivariateFunction f,
double[] xvec,
double tolfx,
double tolx) |
void |
ConjugateDirectionSearch.optimize(MultivariateFunction f,
double[] xvector,
double tolfx,
double tolx) |
void |
DifferentialEvolution.optimize(MultivariateFunction func,
double[] xvec,
double tolfx,
double tolx) |
void |
GeneralizedDEOptimizer.optimize(MultivariateFunction f,
double[] xvec,
double tolfx,
double tolx)
The actual optimization routine
It finds a minimum close to vector x when the
absolute tolerance for each parameter is specified.
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void |
ConjugateGradientSearch.optimize(MultivariateFunction f,
double[] x,
double tolfx,
double tolx) |
void |
MultivariateMinimum.optimize(MultivariateFunction f,
double[] xvec,
double tolfx,
double tolx,
MinimiserMonitor monitor)
The actual optimization routine
It finds a minimum close to vector x when the
absolute tolerance for each parameter is specified.
|
void |
OrthogonalSearch.optimize(MultivariateFunction f,
double[] xvec,
double tolfx,
double tolx,
MinimiserMonitor monitor) |
void |
ConjugateDirectionSearch.optimize(MultivariateFunction f,
double[] xvector,
double tolfx,
double tolx,
MinimiserMonitor monitor) |
void |
DifferentialEvolution.optimize(MultivariateFunction func,
double[] xvec,
double tolfx,
double tolx,
MinimiserMonitor monitor) |
void |
GeneralizedDEOptimizer.optimize(MultivariateFunction f,
double[] xvec,
double tolfx,
double tolx,
MinimiserMonitor monitor)
The actual optimization routine
It finds a minimum close to vector x when the
absolute tolerance for each parameter is specified.
|
void |
ConjugateGradientSearch.optimize(MultivariateFunction f,
double[] x,
double tolfx,
double tolx,
MinimiserMonitor monitor) |
Constructor and Description |
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BoundsCheckedFunction(MultivariateFunction func)
construct bound-checked multivariate function
(a large number will be returned on function evaluation if argument
is out of bounds; default is 1000000)
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BoundsCheckedFunction(MultivariateFunction func,
double largeNumber)
construct constrained multivariate function
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EvaluationCounter(MultivariateFunction base) |
LineFunction(MultivariateFunction func)
construct univariate function from multivariate function
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OrthogonalLineFunction(MultivariateFunction func)
construct univariate function from multivariate function
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OrthogonalLineFunction(MultivariateFunction func,
int selectedDimension,
double[] initialArguments)
construct univariate function from multivariate function
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Modifier and Type | Method and Description |
---|---|
static MultivariateFunction |
Utils.combineMultivariateFunction(MultivariateFunction base,
Parameterized[] additionalParameters)
Creates an interface between a parameterised object to allow it to act as
a multivariate minimum.
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Modifier and Type | Method and Description |
---|---|
static MultivariateFunction |
Utils.combineMultivariateFunction(MultivariateFunction base,
Parameterized[] additionalParameters)
Creates an interface between a parameterised object to allow it to act as
a multivariate minimum.
|