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Packages that use MultivariateFunction | |
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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.misc | Classes that don't fit elsewhere ;^) |
Uses of MultivariateFunction in pal.eval |
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Classes in pal.eval that implement MultivariateFunction | |
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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. |
class |
ModelParameters
estimates substitution model parameters from the data |
Uses of MultivariateFunction in pal.math |
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Subinterfaces of MultivariateFunction in pal.math | |
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interface |
MFWithGradient
interface for a function of several variables with a gradient |
Classes in pal.math that implement MultivariateFunction | |
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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) |
class |
EvaluationCounter
A utiltity class that can be used to track the number of evaluations of a general function |
Methods in pal.math with parameters of type MultivariateFunction | |
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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)
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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 |
ConjugateGradientSearch.optimize(MultivariateFunction f,
double[] x,
double tolfx,
double tolx)
|
void |
OrthogonalSearch.optimize(MultivariateFunction f,
double[] xvec,
double tolfx,
double tolx)
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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 |
ConjugateDirectionSearch.optimize(MultivariateFunction f,
double[] xvector,
double tolfx,
double tolx)
|
void |
DifferentialEvolution.optimize(MultivariateFunction func,
double[] xvec,
double tolfx,
double tolx)
|
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 |
ConjugateGradientSearch.optimize(MultivariateFunction f,
double[] x,
double tolfx,
double tolx,
MinimiserMonitor monitor)
|
void |
OrthogonalSearch.optimize(MultivariateFunction f,
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 |
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 |
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. |
Constructors in pal.math with parameters of type MultivariateFunction | |
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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)
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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 |
Uses of MultivariateFunction in pal.misc |
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Methods in pal.misc that return MultivariateFunction | |
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static MultivariateFunction |
Utils.combineMultivariateFunction(MultivariateFunction base,
Parameterized[] additionalParameters)
Creates an interface between a parameterised object to allow it to act as a multivariate minimum. |
Methods in pal.misc with parameters of type MultivariateFunction | |
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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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