UniformDiscrete (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.distributions.Distribution
- - jsat.distributions.discrete.DiscreteDistribution
  - - jsat.distributions.discrete.UniformDiscrete

All Implemented Interfaces:

Serializable, Cloneable
```
public class UniformDiscrete
extends DiscreteDistribution
```
The discrete uniform distribution.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description
`UniformDiscrete()` Creates a new Uniform distribution with a min of 0 and a max of 10
`UniformDiscrete(int min, int max)` Creates a new discrete uniform distribution

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`double`	`cdf(int x)` Computes the value of the Cumulative Density Function (CDF) at the given point.
`DiscreteDistribution`	`clone()`
`int`	`getMax()`
`int`	`getMin()`
`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()` Computes the mean value of the distribution
`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`	`pmf(int x)`
`void`	`setMax(int max)` Sets the maximum value to occur from the distribution, must be greater than `getMin()`.
`void`	`setMin(int min)` Sets the minimum value to occur from the distribution, must be less than `getMax()`.
`void`	`setMinMax(int min, int max)` Sets the minimum and maximum values at the same time, this is useful if setting them one at a time may have caused a conflict with the previous values
`double`	`skewness()` Computes the skewness of the distribution.
`double`	`variance()` Computes the variance of the distribution.

Methods inherited from class jsat.distributions.discrete.DiscreteDistribution
cdf, invCdf, logPmf

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

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

- Constructor Detail
  - UniformDiscrete
```
public UniformDiscrete()
```
    Creates a new Uniform distribution with a min of 0 and a max of 10
  - UniformDiscrete
```
public UniformDiscrete(int min,
                       int max)
```
    Creates a new discrete uniform distribution
    
    Parameters:
    
    min - the minimum value to occur
    
    max - the maximum value to occur
- Method Detail
  - setMinMax
```
public void setMinMax(int min,
                      int max)
```
    Sets the minimum and maximum values at the same time, this is useful if setting them one at a time may have caused a conflict with the previous values
    
    Parameters:
    
    min - the new minimum value to occur
    
    max - the new maximum value to occur
  - setMin
```
public void setMin(int min)
```
    Sets the minimum value to occur from the distribution, must be less than getMax().
    
    Parameters:
    
    min - the minimum value to occur
  - getMin
```
public int getMin()
```
  - setMax
```
public void setMax(int max)
```
    Sets the maximum value to occur from the distribution, must be greater than getMin().
    
    Parameters:
    
    max - the maximum value to occur
  - getMax
```
public int getMax()
```
  - pmf
```
public double pmf(int x)
```
    Specified by:
    
    pmf in class DiscreteDistribution
  - cdf
```
public double cdf(int x)
```
    Description copied from class: DiscreteDistribution
    
    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 DiscreteDistribution
    
    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 DiscreteDistribution
    
    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
  - 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.
    
    Specified by:
    
    mode in class Distribution
    
    Returns:
    
    the mode 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.
  - 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.
  - clone
```
public DiscreteDistribution clone()
```
    Specified by:
    
    clone in class DiscreteDistribution

Class UniformDiscrete

Constructor Summary

Method Summary

Methods inherited from class jsat.distributions.discrete.DiscreteDistribution

Methods inherited from class jsat.distributions.Distribution

Methods inherited from class java.lang.Object

Constructor Detail

UniformDiscrete

UniformDiscrete

Method Detail

setMinMax

setMin

getMin

setMax

getMax

pmf

cdf

invCdf

mean

median

mode

variance

skewness

min

max

clone