Cauchy (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.distributions.Distribution
- - jsat.distributions.ContinuousDistribution
  - - jsat.distributions.Cauchy

All Implemented Interfaces:

Serializable, Cloneable
```
public final class Cauchy
extends ContinuousDistribution
```
Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description

Cauchy()

Cauchy(double x0, double y)

Constructors
Constructor and Description
`Cauchy()`
`Cauchy(double x0, double y)`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`double`	`cdf(double x)` Computes the value of the Cumulative Density Function (CDF) at the given point.
`ContinuousDistribution`	`clone()`
`boolean`	`equals(Object obj)`
`double[]`	`getCurrentVariableValues()` Returns an array, where each value contains the value of a parameter in the distribution.
`String`	`getDistributionName()` Return the name of the distribution.
`double`	`getLocation()`
`double`	`getScale()`
`String[]`	`getVariables()` Returns an array, where each value contains the name of a parameter in the distribution.
`int`	`hashCode()`
`double`	`invCdf(double p)` Computes the inverse Cumulative Density Function (CDF^-1) at the given point.
`double`	`max()` The maximum value for which the `#pdf(double)` is meant to return a value.
`double`	`mean()` The Cauchy distribution is unique in that it does not have a mean value (undefined).
`double`	`median()` Computes the median value of the distribution
`double`	`min()` The minimum value for which the `#pdf(double)` is meant to return a value.
`double`	`mode()` Computes the mode of the distribution.
`double`	`pdf(double x)` Computes the value of the Probability Density Function (PDF) at the given point
`void`	`setLocation(double x0)`
`void`	`setScale(double y)`
`void`	`setUsingData(Vec data)` Attempts to set the variables used by this distribution based on population sample data, assuming the sample data is from this type of distribution.
`void`	`setVariable(String var, double value)` Sets one of the variables of this distribution by the name.
`double`	`skewness()` Computes the skewness of the distribution.
`double`	`standardDeviation()` The Cauchy distribution is unique in that it does not have a standard deviation value (undefined).
`double`	`variance()` The Cauchy distribution is unique in that it does not have a variance value (undefined).

Methods inherited from class jsat.distributions.ContinuousDistribution
getDescriptiveName, getFunctionPDF, getIntegrationMax, getIntegrationMin, logPdf, toString

Methods inherited from class jsat.distributions.Distribution
getFunctionCDF, invCdf, sample, sampleVec

Methods inherited from class java.lang.Object
finalize, getClass, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - Cauchy
```
public Cauchy(double x0,
              double y)
```
  - Cauchy
```
public Cauchy()
```
- Method Detail
  - setLocation
```
public void setLocation(double x0)
```
  - setScale
```
public void setScale(double y)
```
  - getScale
```
public double getScale()
```
  - getLocation
```
public double getLocation()
```
  - pdf
```
public double pdf(double x)
```
    Description copied from class: ContinuousDistribution
    
    Computes the value of the Probability Density Function (PDF) at the given point
    
    Specified by:
    
    pdf in class ContinuousDistribution
    
    Parameters:
    
    x - the value to get the PDF
    
    Returns:
    
    the PDF(x)
  - cdf
```
public double cdf(double x)
```
    Description copied from class: Distribution
    
    Computes the value of the Cumulative Density Function (CDF) at the given point. The CDF returns a value in the range [0, 1], indicating what portion of values occur at or below that point.
    
    Overrides:
    
    cdf in class ContinuousDistribution
    
    Parameters:
    
    x - the value to get the CDF of
    
    Returns:
    
    the CDF(x)
  - invCdf
```
public double invCdf(double p)
```
    Description copied from class: Distribution
    
    Computes the inverse Cumulative Density Function (CDF^-1) at the given point. It takes in a value in the range of [0, 1] and returns the value x, such that CDF(x) = p
    
    Overrides:
    
    invCdf in class ContinuousDistribution
    
    Parameters:
    
    p - the probability value
    
    Returns:
    
    the value such that the CDF would return p
  - min
```
public double min()
```
    Description copied from class: Distribution
    
    The minimum value for which the #pdf(double) is meant to return a value. Note that Double.NEGATIVE_INFINITY is a valid return value.
    
    Specified by:
    
    min in class Distribution
    
    Returns:
    
    the minimum value for which the #pdf(double) is meant to return a value.
  - max
```
public double max()
```
    Description copied from class: Distribution
    
    The maximum value for which the #pdf(double) is meant to return a value. Note that Double.POSITIVE_INFINITY is a valid return value.
    
    Specified by:
    
    max in class Distribution
    
    Returns:
    
    the maximum value for which the #pdf(double) is meant to return a value.
  - getDistributionName
```
public String getDistributionName()
```
    Description copied from class: ContinuousDistribution
    
    Return the name of the distribution.
    
    Specified by:
    
    getDistributionName in class ContinuousDistribution
    
    Returns:
    
    the name of the distribution.
  - getVariables
```
public String[] getVariables()
```
    Description copied from class: ContinuousDistribution
    
    Returns an array, where each value contains the name of a parameter in the distribution. The order must always be the same, and match up with the values returned by ContinuousDistribution.getCurrentVariableValues()
    
    Specified by:
    
    getVariables in class ContinuousDistribution
    
    Returns:
    
    a string of the variable names this distribution uses
  - getCurrentVariableValues
```
public double[] getCurrentVariableValues()
```
    Description copied from class: ContinuousDistribution
    
    Returns an array, where each value contains the value of a parameter in the distribution. The order must always be the same, and match up with the values returned by ContinuousDistribution.getVariables()
    
    Specified by:
    
    getCurrentVariableValues in class ContinuousDistribution
    
    Returns:
    
    the current values of the parameters used by this distribution, in the same order as their names are returned by ContinuousDistribution.getVariables()
  - setVariable
```
public void setVariable(String var,
                        double value)
```
    Description copied from class: ContinuousDistribution
    
    Sets one of the variables of this distribution by the name.
    
    Specified by:
    
    setVariable in class ContinuousDistribution
    
    Parameters:
    
    var - the variable to set
    
    value - the value to set
  - clone
```
public ContinuousDistribution clone()
```
    Specified by:
    
    clone in class ContinuousDistribution
  - setUsingData
```
public void setUsingData(Vec data)
```
    Description copied from class: ContinuousDistribution
    
    Attempts to set the variables used by this distribution based on population sample data, assuming the sample data is from this type of distribution.
    
    Specified by:
    
    setUsingData in class ContinuousDistribution
    
    Parameters:
    
    data - the data to use to attempt to fit against
  - mean
```
public double mean()
```
    The Cauchy distribution is unique in that it does not have a mean value (undefined).
    
    Overrides:
    
    mean in class ContinuousDistribution
    
    Returns:
    
    Double.NaN since there is no mean value
  - median
```
public double median()
```
    Description copied from class: Distribution
    
    Computes the median value of the distribution
    
    Overrides:
    
    median in class Distribution
    
    Returns:
    
    the median value of the distribution
  - mode
```
public double mode()
```
    Description copied from class: Distribution
    
    Computes the mode of the distribution. Not all distributions have a mode for all parameter values. NaN may be returned if the mode is not defined for the current values of the distribution.
    
    Overrides:
    
    mode in class ContinuousDistribution
    
    Returns:
    
    the mode of the distribution
  - variance
```
public double variance()
```
    The Cauchy distribution is unique in that it does not have a variance value (undefined).
    
    Overrides:
    
    variance in class ContinuousDistribution
    
    Returns:
    
    Double.NaN since there is no variance value
  - standardDeviation
```
public double standardDeviation()
```
    The Cauchy distribution is unique in that it does not have a standard deviation value (undefined).
    
    Overrides:
    
    standardDeviation in class Distribution
    
    Returns:
    
    Double.NaN since there is no standard deviation value
  - skewness
```
public double skewness()
```
    Description copied from class: Distribution
    
    Computes the skewness of the distribution. Not all distributions have a finite skewness for all parameter values. NaN may be returned if the skewness is not defined for the current values of the distribution.
    
    Overrides:
    
    skewness in class ContinuousDistribution
    
    Returns:
    
    the skewness of the distribution.
  - hashCode
```
public int hashCode()
```
    Overrides:
    
    hashCode in class Object
  - equals
```
public boolean equals(Object obj)
```
    Overrides:
    
    equals in class Object

Class Cauchy

Constructor Summary

Method Summary

Methods inherited from class jsat.distributions.ContinuousDistribution

Methods inherited from class jsat.distributions.Distribution

Methods inherited from class java.lang.Object

Constructor Detail

Cauchy

Cauchy

Method Detail

setLocation

setScale

getScale

getLocation

pdf

cdf

invCdf

min

max

getDistributionName

getVariables

getCurrentVariableValues

setVariable

clone

setUsingData

mean

median

mode

variance

standardDeviation

skewness

hashCode

equals