ContinuousDistribution (Java Statistical Analysis Tool 0.0.8 API)

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

All Implemented Interfaces:

Serializable, Cloneable

Direct Known Subclasses:

Beta, Cauchy, ChiSquared, Exponential, FisherSendor, Gamma, KernelDensityEstimator, Kolmogorov, Laplace, Levy, Logistic, LogNormal, LogUniform, MaxwellBoltzmann, Normal, Pareto, Rayleigh, StudentT, TruncatedDistribution, Uniform, Weibull
```
public abstract class ContinuousDistribution
extends Distribution
```
The ContinuousDistribution represents the contract for a continuous in one dimension.

Many of the functions of a Continuous Distribution are implemented by default using numerical calculation and integration. For this reason, the base implementations may be slower or less accurate than desired - and could produce incorrect results for poorly behaved functions or large magnitude inputs. These base implementations are provided for easy completeness, but may not be appropriate for all methods. If needed, the implementer should check if these methods provide the needed level of accuracy and speed.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description

ContinuousDistribution()

Constructors
Constructor and Description
`ContinuousDistribution()`

Method Summary

All Methods Static Methods Instance Methods Abstract 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.
`abstract ContinuousDistribution`	`clone()`
`abstract double[]`	`getCurrentVariableValues()` Returns an array, where each value contains the value of a parameter in the distribution.
`String`	`getDescriptiveName()` The descriptive name of a distribution returns the name of the distribution, followed by the parameters of the distribution and their values.
`abstract String`	`getDistributionName()` Return the name of the distribution.
`static Function`	`getFunctionPDF(ContinuousDistribution dist)` Wraps the `pdf(double)` function of the given distribution in a function object for use.
`protected double`	`getIntegrationMax()`
`protected double`	`getIntegrationMin()`
`abstract String[]`	`getVariables()` Returns an array, where each value contains the name of a parameter in the distribution.
`double`	`invCdf(double p)` Computes the inverse Cumulative Density Function (CDF^-1) at the given point.
`double`	`logPdf(double x)` Computes the log of the Probability Density Function.
`double`	`mean()` Computes the mean value of the distribution
`double`	`mode()` Computes the mode of the distribution.
`abstract double`	`pdf(double x)` Computes the value of the Probability Density Function (PDF) at the given point
`abstract 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.
`abstract 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.
`String`	`toString()`
`double`	`variance()` Computes the variance of the distribution.

Methods inherited from class jsat.distributions.Distribution
getFunctionCDF, invCdf, max, median, min, sample, sampleVec, standardDeviation

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

- Constructor Detail
  - ContinuousDistribution
```
public ContinuousDistribution()
```
- Method Detail
  - logPdf
```
public double logPdf(double x)
```
    Computes the log of the Probability Density Function. Note, that then the probability is zero, Double.NEGATIVE_INFINITY would be the true value. Instead, this method will always return the negative of Double.MAX_VALUE. This is to avoid propagating bad values through computation.
    
    Parameters:
    
    x - the value to get the log(PDF) of
    
    Returns:
    
    the value of log(PDF(x))
  - pdf
```
public abstract double pdf(double x)
```
    Computes the value of the Probability Density Function (PDF) at the given point
    
    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.
    
    Specified by:
    
    cdf in class Distribution
    
    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 Distribution
    
    Parameters:
    
    p - the probability value
    
    Returns:
    
    the value such that the CDF would return p
  - mean
```
public double mean()
```
    Description copied from class: Distribution
    
    Computes the mean value of the distribution
    
    Specified by:
    
    mean in class Distribution
    
    Returns:
    
    the mean value of the distribution
  - variance
```
public double variance()
```
    Description copied from class: Distribution
    
    Computes the variance of the distribution. Not all distributions have a finite variance for all parameter values. NaN may be returned if the variance is not defined for the current values of the distribution. Infinity is a possible value to be returned by some distributions.
    
    Specified by:
    
    variance in class Distribution
    
    Returns:
    
    the variance of the distribution.
  - 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.
    
    Specified by:
    
    skewness in class Distribution
    
    Returns:
    
    the skewness 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.
    
    Specified by:
    
    mode in class Distribution
    
    Returns:
    
    the mode of the distribution
  - getIntegrationMin
```
protected double getIntegrationMin()
```
  - getIntegrationMax
```
protected double getIntegrationMax()
```
  - getDescriptiveName
```
public String getDescriptiveName()
```
    The descriptive name of a distribution returns the name of the distribution, followed by the parameters of the distribution and their values.
    
    Returns:
    
    the name of the distribution that includes parameter values
  - getDistributionName
```
public abstract String getDistributionName()
```
    Return the name of the distribution.
    
    Returns:
    
    the name of the distribution.
  - getVariables
```
public abstract String[] getVariables()
```
    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 getCurrentVariableValues()
    
    Returns:
    
    a string of the variable names this distribution uses
  - getCurrentVariableValues
```
public abstract double[] getCurrentVariableValues()
```
    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 getVariables()
    
    Returns:
    
    the current values of the parameters used by this distribution, in the same order as their names are returned by getVariables()
  - setVariable
```
public abstract void setVariable(String var,
                                 double value)
```
    Sets one of the variables of this distribution by the name.
    
    Parameters:
    
    var - the variable to set
    
    value - the value to set
  - clone
```
public abstract ContinuousDistribution clone()
```
    Specified by:
    
    clone in class Distribution
  - setUsingData
```
public abstract 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.
    
    Parameters:
    
    data - the data to use to attempt to fit against
  - toString
```
public String toString()
```
    Overrides:
    
    toString in class Object
  - getFunctionPDF
```
public static Function getFunctionPDF(ContinuousDistribution dist)
```
    Wraps the pdf(double) function of the given distribution in a function object for use.
    
    Parameters:
    
    dist - the distribution to wrap the pdf of
    
    Returns:
    
    a function for evaluating the pdf of the given distribution

Class ContinuousDistribution

Constructor Summary

Method Summary

Methods inherited from class jsat.distributions.Distribution

Methods inherited from class java.lang.Object

Constructor Detail

ContinuousDistribution

Method Detail

logPdf

pdf

cdf

invCdf

mean

variance

skewness

mode

getIntegrationMin

getIntegrationMax

getDescriptiveName

getDistributionName

getVariables

getCurrentVariableValues

setVariable

clone

setUsingData

toString

getFunctionPDF