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.misc |
Classes that don't fit elsewhere ;^)
|
Modifier and Type | Class | Description |
---|---|---|
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 | Description |
---|---|---|
interface |
MFWithGradient |
interface for a function of several variables with a gradient
|
Modifier and Type | Class | Description |
---|---|---|
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)
|
class |
EvaluationCounter |
A utiltity class that can be used to track the number of evaluations of a
general function
|
Modifier and Type | Method | 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
|
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)
|
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) |
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static double[] |
NumericalDerivative.gradient(MultivariateFunction f,
double[] x) |
determine gradient
|
static void |
NumericalDerivative.gradient(MultivariateFunction f,
double[] x,
double[] grad) |
determine gradient
|
void |
MinimiserMonitor.newMinimum(double value,
double[] parameterValues,
MultivariateFunction beingOptimized) |
Inform monitor of a new minimum, along with the current arguments.
|
void |
ConjugateDirectionSearch.optimize(MultivariateFunction f,
double[] xvector,
double tolfx,
double tolx) |
|
void |
ConjugateDirectionSearch.optimize(MultivariateFunction f,
double[] xvector,
double tolfx,
double tolx,
MinimiserMonitor monitor) |
|
void |
ConjugateGradientSearch.optimize(MultivariateFunction f,
double[] x,
double tolfx,
double tolx) |
|
void |
ConjugateGradientSearch.optimize(MultivariateFunction f,
double[] x,
double tolfx,
double tolx,
MinimiserMonitor monitor) |
|
void |
DifferentialEvolution.optimize(MultivariateFunction func,
double[] xvec,
double tolfx,
double tolx) |
|
void |
DifferentialEvolution.optimize(MultivariateFunction func,
double[] xvec,
double tolfx,
double tolx,
MinimiserMonitor monitor) |
|
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.
|
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.
|
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 |
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) |
|
void |
OrthogonalSearch.optimize(MultivariateFunction f,
double[] xvec,
double tolfx,
double tolx,
MinimiserMonitor monitor) |
Constructor | Description |
---|---|
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)
|
BoundsCheckedFunction(MultivariateFunction func,
double largeNumber) |
construct constrained multivariate function
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EvaluationCounter(MultivariateFunction base) |
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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
|
Modifier and Type | Method | 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.
|
Modifier and Type | Method | 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.
|