gov.sandia.cognition.statistics.distribution
Class CauchyDistribution.CDF

java.lang.Object
  extended by gov.sandia.cognition.util.AbstractCloneableSerializable
      extended by gov.sandia.cognition.statistics.AbstractDistribution<NumberType>
          extended by gov.sandia.cognition.statistics.AbstractClosedFormUnivariateDistribution<Double>
              extended by gov.sandia.cognition.statistics.AbstractClosedFormSmoothUnivariateDistribution
                  extended by gov.sandia.cognition.statistics.distribution.CauchyDistribution
                      extended by gov.sandia.cognition.statistics.distribution.CauchyDistribution.CDF
All Implemented Interfaces:
Evaluator<Double,Double>, ClosedFormDifferentiableEvaluator<Double,Double,Double>, DifferentiableEvaluator<Double,Double,Double>, Vectorizable, ScalarFunction<Double>, UnivariateScalarFunction, ClosedFormComputableDistribution<Double>, ClosedFormCumulativeDistributionFunction<Double>, ClosedFormDistribution<Double>, ClosedFormUnivariateDistribution<Double>, ComputableDistribution<Double>, CumulativeDistributionFunction<Double>, Distribution<Double>, DistributionWithMean<Double>, SmoothCumulativeDistributionFunction, SmoothUnivariateDistribution, UnivariateDistribution<Double>, CloneableSerializable, Serializable, Cloneable
Enclosing class:
CauchyDistribution

public static class CauchyDistribution.CDF
extends CauchyDistribution
implements SmoothCumulativeDistributionFunction

CDF of the CauchyDistribution.

See Also:
Serialized Form

Nested Class Summary
 
Nested classes/interfaces inherited from class gov.sandia.cognition.statistics.distribution.CauchyDistribution
CauchyDistribution.CDF, CauchyDistribution.PDF
 
Field Summary
 
Fields inherited from class gov.sandia.cognition.statistics.distribution.CauchyDistribution
DEFAULT_LOCATION, DEFAULT_SCALE, location, scale
 
Constructor Summary
CauchyDistribution.CDF()
          Creates a new instance of CauchyDistribution
CauchyDistribution.CDF(CauchyDistribution other)
          Copy constructor
CauchyDistribution.CDF(double location, double scale)
          Creates a new instance of CauchyDistribution
 
Method Summary
 Double differentiate(Double input)
          Differentiates the output with respect to the input
 double evaluate(double input)
          Produces a double output for the given double input
 Double evaluate(Double input)
          Evaluates the function on the given input and returns the output.
 double evaluateAsDouble(Double input)
          Evaluates the scalar function as a double.
 CauchyDistribution.CDF getCDF()
          Gets the CDF of a scalar distribution.
 CauchyDistribution.PDF getDerivative()
          Gets the closed-form derivative of the function.
 
Methods inherited from class gov.sandia.cognition.statistics.distribution.CauchyDistribution
clone, convertFromVector, convertToVector, getLocation, getMaxSupport, getMean, getMinSupport, getProbabilityFunction, getScale, getVariance, sample, setLocation, setScale
 
Methods inherited from class gov.sandia.cognition.statistics.AbstractDistribution
sample
 
Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 
Methods inherited from interface gov.sandia.cognition.statistics.SmoothUnivariateDistribution
getMean, getProbabilityFunction
 
Methods inherited from interface gov.sandia.cognition.statistics.UnivariateDistribution
getMaxSupport, getMinSupport, getVariance
 
Methods inherited from interface gov.sandia.cognition.statistics.Distribution
sample, sample
 
Methods inherited from interface gov.sandia.cognition.math.matrix.Vectorizable
clone, convertFromVector, convertToVector
 

Constructor Detail

CauchyDistribution.CDF

public CauchyDistribution.CDF()
Creates a new instance of CauchyDistribution


CauchyDistribution.CDF

public CauchyDistribution.CDF(double location,
                              double scale)
Creates a new instance of CauchyDistribution

Parameters:
location - Central location (also the median and mode) of the distribution.
scale - Scale of the distribution, must be greater than zero.

CauchyDistribution.CDF

public CauchyDistribution.CDF(CauchyDistribution other)
Copy constructor

Parameters:
other - CauchyDistribution to copy.
Method Detail

evaluate

public Double evaluate(Double input)
Description copied from interface: Evaluator
Evaluates the function on the given input and returns the output.

Specified by:
evaluate in interface Evaluator<Double,Double>
Parameters:
input - The input to evaluate.
Returns:
The output produced by evaluating the input.

evaluateAsDouble

public double evaluateAsDouble(Double input)
Description copied from interface: ScalarFunction
Evaluates the scalar function as a double.

Specified by:
evaluateAsDouble in interface ScalarFunction<Double>
Parameters:
input - The input value.
Returns:
The scalar output calculated from the given input.

evaluate

public double evaluate(double input)
Description copied from interface: UnivariateScalarFunction
Produces a double output for the given double input

Specified by:
evaluate in interface UnivariateScalarFunction
Parameters:
input - Input to the Evaluator
Returns:
output at the given input

getCDF

public CauchyDistribution.CDF getCDF()
Description copied from interface: UnivariateDistribution
Gets the CDF of a scalar distribution.

Specified by:
getCDF in interface ClosedFormUnivariateDistribution<Double>
Specified by:
getCDF in interface SmoothUnivariateDistribution
Specified by:
getCDF in interface UnivariateDistribution<Double>
Overrides:
getCDF in class CauchyDistribution
Returns:
CDF of the scalar distribution.

getDerivative

public CauchyDistribution.PDF getDerivative()
Description copied from interface: ClosedFormDifferentiableEvaluator
Gets the closed-form derivative of the function.

Specified by:
getDerivative in interface ClosedFormDifferentiableEvaluator<Double,Double,Double>
Specified by:
getDerivative in interface SmoothCumulativeDistributionFunction
Returns:
Closed-form derivative of the function.

differentiate

public Double differentiate(Double input)
Description copied from interface: DifferentiableEvaluator
Differentiates the output with respect to the input

Specified by:
differentiate in interface DifferentiableEvaluator<Double,Double,Double>
Parameters:
input - Input about which to compute the derivative
Returns:
Derivative of the output with respect to the given input