Distribution (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.distributions.Distribution

All Implemented Interfaces:

Serializable, Cloneable

Direct Known Subclasses:

ContinuousDistribution, DiscreteDistribution
```
public abstract class Distribution
extends Object
implements Cloneable, Serializable
```
Base distribution class for distributions that have only one input.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description

Distribution()

Constructors
Constructor and Description
`Distribution()`

Method Summary

All Methods Static Methods Instance Methods Abstract Methods Concrete Methods
Modifier and Type	Method and Description
`abstract double`	`cdf(double x)` Computes the value of the Cumulative Density Function (CDF) at the given point.
`abstract Distribution`	`clone()`
`static Function`	`getFunctionCDF(Distribution dist)` Wraps the `cdf(double)` function of the given distribution in a function object for use.
`double`	`invCdf(double p)` Computes the inverse Cumulative Density Function (CDF^-1) at the given point.
`protected double`	`invCdf(double p, Function cdf)` This method is provided as a quick helper function, as any CDF has a 1 to 1 mapping with an inverse, CDF^.-1.
`abstract double`	`max()` The maximum value for which the `#pdf(double)` is meant to return a value.
`abstract double`	`mean()` Computes the mean value of the distribution
`double`	`median()` Computes the median value of the distribution
`abstract double`	`min()` The minimum value for which the `#pdf(double)` is meant to return a value.
`abstract double`	`mode()` Computes the mode of the distribution.
`double[]`	`sample(int numSamples, Random rand)` This method returns a double array containing the values of random samples from this distribution.
`DenseVector`	`sampleVec(int numSamples, Random rand)` This method returns a double array containing the values of random samples from this distribution.
`abstract double`	`skewness()` Computes the skewness of the distribution.
`double`	`standardDeviation()` Computes the standard deviation of the distribution.
`abstract double`	`variance()` Computes the variance of the distribution.

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

- Constructor Detail
  - Distribution
```
public Distribution()
```
- Method Detail
  - cdf
```
public abstract double cdf(double x)
```
    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.
    
    Parameters:
    
    x - the value to get the CDF of
    
    Returns:
    
    the CDF(x)
  - invCdf
```
public double invCdf(double p)
```
    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
    
    Parameters:
    
    p - the probability value
    
    Returns:
    
    the value such that the CDF would return p
  - invCdf
```
protected double invCdf(double p,
                        Function cdf)
```
    This method is provided as a quick helper function, as any CDF has a 1 to 1 mapping with an inverse, CDF^.-1. This does a search for that value, and should only be used if the quantile function will be used infrequently or no alternative is available.
    
    Parameters:
    
    p - the [0,1] probability value
    
    cdf - a function that provides the CDF we want to emulate the inverse of
    
    Returns:
    
    the quantile function, CDF^-1(p) = x
  - mean
```
public abstract double mean()
```
    Computes the mean value of the distribution
    
    Returns:
    
    the mean value of the distribution
  - median
```
public double median()
```
    Computes the median value of the distribution
    
    Returns:
    
    the median value of the distribution
  - mode
```
public abstract double mode()
```
    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.
    
    Returns:
    
    the mode of the distribution
  - variance
```
public abstract double variance()
```
    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.
    
    Returns:
    
    the variance of the distribution.
  - skewness
```
public abstract double skewness()
```
    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.
    
    Returns:
    
    the skewness of the distribution.
  - standardDeviation
```
public double standardDeviation()
```
    Computes the standard deviation of the distribution. Not all distributions have a finite standard deviation 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.
    
    Returns:
    
    the standard deviation of the distribution
  - min
```
public abstract double min()
```
    The minimum value for which the #pdf(double) is meant to return a value. Note that Double.NEGATIVE_INFINITY is a valid return value.
    
    Returns:
    
    the minimum value for which the #pdf(double) is meant to return a value.
  - max
```
public abstract double max()
```
    The maximum value for which the #pdf(double) is meant to return a value. Note that Double.POSITIVE_INFINITY is a valid return value.
    
    Returns:
    
    the maximum value for which the #pdf(double) is meant to return a value.
  - sample
```
public double[] sample(int numSamples,
                       Random rand)
```
    This method returns a double array containing the values of random samples from this distribution.
    
    Parameters:
    
    numSamples - the number of random samples to take
    
    rand - the source of randomness
    
    Returns:
    
    an array of the random sample values
  - sampleVec
```
public DenseVector sampleVec(int numSamples,
                             Random rand)
```
    This method returns a double array containing the values of random samples from this distribution.
    
    Parameters:
    
    numSamples - the number of random samples to take
    
    rand - the source of randomness
    
    Returns:
    
    a vector of the random sample values
  - clone
```
public abstract Distribution clone()
```
    Overrides:
    
    clone in class Object
  - getFunctionCDF
```
public static Function getFunctionCDF(Distribution dist)
```
    Wraps the cdf(double) function of the given distribution in a function object for use.
    
    Parameters:
    
    dist - the distribution to wrap the cdf of
    
    Returns:
    
    a function for evaluating the cdf of the given distribution

Class Distribution

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

Distribution

Method Detail

cdf

invCdf

invCdf

mean

median

mode

variance

skewness

standardDeviation

min

max

sample

sampleVec

clone

getFunctionCDF