org.apache.commons.math3.analysis.function
Class Gaussian.Parametric

java.lang.Object
  extended by org.apache.commons.math3.analysis.function.Gaussian.Parametric
All Implemented Interfaces:
ParametricUnivariateFunction
Enclosing class:
Gaussian

public static class Gaussian.Parametric
extends Object
implements ParametricUnivariateFunction

Parametric function where the input array contains the parameters of the Gaussian, ordered as follows:


Constructor Summary
Gaussian.Parametric()
           
 
Method Summary
 double[] gradient(double x, double... param)
          Computes the value of the gradient at x.
 double value(double x, double... param)
          Computes the value of the Gaussian at x.
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Constructor Detail

Gaussian.Parametric

public Gaussian.Parametric()
Method Detail

value

public double value(double x,
                    double... param)
             throws NullArgumentException,
                    DimensionMismatchException,
                    NotStrictlyPositiveException
Computes the value of the Gaussian at x.

Specified by:
value in interface ParametricUnivariateFunction
Parameters:
x - Value for which the function must be computed.
param - Values of norm, mean and standard deviation.
Returns:
the value of the function.
Throws:
NullArgumentException - if param is null.
DimensionMismatchException - if the size of param is not 3.
NotStrictlyPositiveException - if param[2] is negative.

gradient

public double[] gradient(double x,
                         double... param)
                  throws NullArgumentException,
                         DimensionMismatchException,
                         NotStrictlyPositiveException
Computes the value of the gradient at x. The components of the gradient vector are the partial derivatives of the function with respect to each of the parameters (norm, mean and standard deviation).

Specified by:
gradient in interface ParametricUnivariateFunction
Parameters:
x - Value at which the gradient must be computed.
param - Values of norm, mean and standard deviation.
Returns:
the gradient vector at x.
Throws:
NullArgumentException - if param is null.
DimensionMismatchException - if the size of param is not 3.
NotStrictlyPositiveException - if param[2] is negative.


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