gov.sandia.cognition.statistics.distribution
Class UnivariateGaussian.ErrorFunction.Inverse

java.lang.Object
  extended by gov.sandia.cognition.util.AbstractCloneableSerializable
      extended by gov.sandia.cognition.math.AbstractScalarFunction<Double>
          extended by gov.sandia.cognition.math.AbstractUnivariateScalarFunction
              extended by gov.sandia.cognition.statistics.distribution.UnivariateGaussian.ErrorFunction.Inverse
All Implemented Interfaces:
Evaluator<Double,Double>, ScalarFunction<Double>, UnivariateScalarFunction, CloneableSerializable, Serializable, Cloneable
Enclosing class:
UnivariateGaussian.ErrorFunction

public static class UnivariateGaussian.ErrorFunction.Inverse
extends AbstractUnivariateScalarFunction

Inverse of the ErrorFunction

See Also:
Serialized Form

Field Summary
static UnivariateGaussian.ErrorFunction.Inverse INSTANCE
          Default instance.
 
Constructor Summary
UnivariateGaussian.ErrorFunction.Inverse()
          Default constructor
 
Method Summary
 double evaluate(double y)
          Inverse of the error function.
 
Methods inherited from class gov.sandia.cognition.math.AbstractUnivariateScalarFunction
evaluate, evaluateAsDouble
 
Methods inherited from class gov.sandia.cognition.util.AbstractCloneableSerializable
clone
 
Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 
Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable
clone
 

Field Detail

INSTANCE

public static final UnivariateGaussian.ErrorFunction.Inverse INSTANCE
Default instance.

Constructor Detail

UnivariateGaussian.ErrorFunction.Inverse

public UnivariateGaussian.ErrorFunction.Inverse()
Default constructor

Method Detail

evaluate

public double evaluate(double y)
Inverse of the error function. x = erfinv(y) satisfies y = erf(x), y is [-1,1) and x is any double.

Parameters:
y - Computes the value of the error function inverse such that y = erf(x)
Returns:
Returns the value "x" such that y = erf(x)