artisynth.core.femmodels.integration

Class MonteCarloIntegrator

java.lang.Object

artisynth.core.femmodels.integration.MonteCarloIntegrator

Direct Known Subclasses:

MonteCarloFemElementIntegrator

public class MonteCarloIntegrator
extends java.lang.Object

Integrates generic functions using Monte-Carlo sampling

Author:

Antonio

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static interface	MonteCarloIntegrator.FunctionNdSampler Samples a function, producing the value and probability (or 'importance') of selecting the sampled point
static interface	MonteCarloIntegrator.FunctionSampler Samples a function, producing the value and probability (or 'importance') of selecting the sampled point

Constructor Summary

Constructors

Constructor and Description
MonteCarloIntegrator()

Method Summary

Modifier and Type	Method and Description
void	integrate(MonteCarloIntegrator.FunctionNdSampler func, double volume, VectorNd out) Integrates the supplied random sampler function, normalizing the error estimate threshold by the provided volume
static void	integrate(MonteCarloIntegrator.FunctionNdSampler func, int minSamples, int maxSamples, double maxVariance, VectorNd out) Integrates the provided N-dimensional function using importance-sampling
void	integrate(MonteCarloIntegrator.FunctionNdSampler func, VectorNd out) Integrates the supplied random sampler function
double	integrate(MonteCarloIntegrator.FunctionSampler func) Integrates the supplied random sampler function
double	integrate(MonteCarloIntegrator.FunctionSampler func, double volume) Integrates the supplied random sampler function, normalizing the error estimate threshold by the provided volume
static double	integrate(MonteCarloIntegrator.FunctionSampler func, int minSamples, int maxSamples, double maxVariance) Integrates the provided function using importance-sampling
void	setLimits(int minSamples, int maxSamples, double maxVariance) Integrator will continue integrating until either the variance estimate falls below maxVariance, or maxIterations is reached
void	setMaxSamples(int max)
void	setMaxVariance(int max)
void	setMinSamples(int min)

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

MonteCarloIntegrator

public MonteCarloIntegrator()

Method Detail

setLimits

public void setLimits(int minSamples,
                      int maxSamples,
                      double maxVariance)

Integrator will continue integrating until either the variance estimate falls below maxVariance, or maxIterations is reached

Parameters:

minSamples - minimum number of samples

maxSamples - maximum number of samples

maxVariance - variance threshold

setMaxSamples

public void setMaxSamples(int max)

setMinSamples

public void setMinSamples(int min)

setMaxVariance

public void setMaxVariance(int max)

integrate

public static double integrate(MonteCarloIntegrator.FunctionSampler func,
                               int minSamples,
                               int maxSamples,
                               double maxVariance)

Integrates the provided function using importance-sampling

Parameters:

func - function to integrate

minSamples - minimum number of samples

maxSamples - maximum number of samples

maxVariance - variance threshold to stop integration

Returns:

integral estimate

integrate

public static void integrate(MonteCarloIntegrator.FunctionNdSampler func,
                             int minSamples,
                             int maxSamples,
                             double maxVariance,
                             VectorNd out)

Integrates the provided N-dimensional function using importance-sampling

Parameters:

func - function to integrate

minSamples - minimum number of samples

maxSamples - maximum number of samples

maxVariance - variance threshold for integration

out - output of integration

integrate

public double integrate(MonteCarloIntegrator.FunctionSampler func)

Integrates the supplied random sampler function

Parameters:

func - function to integrate

Returns:

estimated integral

integrate

public double integrate(MonteCarloIntegrator.FunctionSampler func,
                        double volume)

Integrates the supplied random sampler function, normalizing the error estimate threshold by the provided volume

Parameters:

func - function to integrate

volume - volume of integration region

Returns:

estimated integral

integrate

public void integrate(MonteCarloIntegrator.FunctionNdSampler func,
                      VectorNd out)

Integrates the supplied random sampler function

Parameters:

func - function to integrate

out - estimated integral

integrate

public void integrate(MonteCarloIntegrator.FunctionNdSampler func,
                      double volume,
                      VectorNd out)

Integrates the supplied random sampler function, normalizing the error estimate threshold by the provided volume

Parameters:

func - function to integrate

volume - volume of integration region

out - estimated integral