Logistic.Parametric (nosqlbench)

java.lang.Object

org.apache.commons.math4.analysis.function.Logistic.Parametric

All Implemented Interfaces:: ParametricUnivariateFunction

Enclosing class:: Logistic

public static class Logistic.Parametric
extends java.lang.Object
implements ParametricUnivariateFunction

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

k
m
b
q
a
n

Constructor Summary

Constructors

Constructor Description

Parametric()

Method Summary

Modifier and Type	Method	Description
`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 sigmoid at `x`.

Methods inherited from class java.lang.Object

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

Constructor Details
- Parametric
  
  public Parametric()
Method Details
- value
  
  public double value(double x, double... param) throws NullArgumentException, DimensionMismatchException, NotStrictlyPositiveException
  
  Computes the value of the sigmoid at x.
  
  Specified by:
  
  value in interface ParametricUnivariateFunction
  
  Parameters:
  
  x - Value for which the function must be computed.
  
  param - Values for k, m, b, q, a and n.
  
  Returns:
  
  the value of the function.
  
  Throws:
  
  NullArgumentException - if param is null.
  
  DimensionMismatchException - if the size of param is not 6.
  
  NotStrictlyPositiveException - if param[5] <= 0.
- 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.
  
  Specified by:
  
  gradient in interface ParametricUnivariateFunction
  
  Parameters:
  
  x - Value at which the gradient must be computed.
  
  param - Values for k, m, b, q, a and n.
  
  Returns:
  
  the gradient vector at x.
  
  Throws:
  
  NullArgumentException - if param is null.
  
  DimensionMismatchException - if the size of param is not 6.
  
  NotStrictlyPositiveException - if param[5] <= 0.