org.ojalgo.random

## Class Uniform

• ### Nested classes/interfaces inherited from interface org.ojalgo.function.BasicFunction

`BasicFunction.Differentiable<N extends Number,F extends BasicFunction<N>>, BasicFunction.Integratable<N extends Number,F extends BasicFunction<N>>`
• ### Constructor Summary

Constructors
Constructor and Description
`Uniform()`
```Uniform(double lower, double range)```
• ### Method Summary

All Methods
Modifier and Type Method and Description
`protected double` `generate()`
`double` `getDistribution(double value)`
In probability theory and statistics, the cumulative distribution function (CDF), or just distribution function, describes the probability that a real-valued random variable X with a given probability distribution will be found at a value less than or equal to x.
`double` `getExpected()`
`double` `getLowerConfidenceQuantile(double confidence)`
`double` `getProbability(double value)`
In probability theory, a probability density function (pdf), or density of a continuous random variable is a function that describes the relative likelihood for this random variable to occur at a given point.
`double` `getQuantile(double probality)`
The quantile function, for any distribution, is defined for real variables between zero and one and is mathematically the inverse of the cumulative distribution function.
`double` `getUpperConfidenceQuantile(double confidence)`
`double` `getVariance()`
Subclasses must override either getStandardDeviation() or getVariance()!
`static int` `randomInteger(int limit)`
`static int` ```randomInteger(int lower, int higher)```
`static long` `randomInteger(long limit)`
• ### Methods inherited from class org.ojalgo.random.RandomNumber

`checkProbabilty, doubleValue, floatValue, getStandardDeviation, intValue, invoke, longValue, random, toString`
• ### Methods inherited from class java.lang.Number

`byteValue, shortValue`
• ### Methods inherited from class java.lang.Object

`clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait`
• ### Methods inherited from interface org.ojalgo.random.Distribution

`getStandardDeviation`
• ### Methods inherited from interface org.ojalgo.function.NullaryFunction

`andThen, get, getAsDouble`
• ### Constructor Detail

• #### Uniform

`public Uniform()`
• #### Uniform

```public Uniform(double lower,
double range)```
• ### Method Detail

• #### randomInteger

`public static int randomInteger(int limit)`
Returns:
An integer: 0 <= ? < limit
• #### randomInteger

```public static int randomInteger(int lower,
int higher)```
Returns:
An integer: lower <= ? < higher
• #### randomInteger

`public static long randomInteger(long limit)`
Returns:
An integer: 0 <= ? < limit
• #### getDistribution

`public double getDistribution(double value)`
Description copied from interface: `ContinuousDistribution`
In probability theory and statistics, the cumulative distribution function (CDF), or just distribution function, describes the probability that a real-valued random variable X with a given probability distribution will be found at a value less than or equal to x. Intuitively, it is the "area so far" function of the probability distribution. Cumulative distribution functions are also used to specify the distribution of multivariate random variables. WikipediA
Parameters:
`value` - x
Returns:
P(<=x)
• #### getExpected

`public double getExpected()`
• #### getProbability

`public double getProbability(double value)`
Description copied from interface: `ContinuousDistribution`
In probability theory, a probability density function (pdf), or density of a continuous random variable is a function that describes the relative likelihood for this random variable to occur at a given point. The probability for the random variable to fall within a particular region is given by the integral of this variable's density over the region. The probability density function is nonnegative everywhere, and its integral over the entire space is equal to one. WikipediA
Parameters:
`value` - x
Returns:
P(x)
• #### getQuantile

`public double getQuantile(double probality)`
Description copied from interface: `ContinuousDistribution`
The quantile function, for any distribution, is defined for real variables between zero and one and is mathematically the inverse of the cumulative distribution function. WikipediA The input probability absolutely has to be [0.0, 1.0], but values close to 0.0 and 1.0 may be problematic
Parameters:
`probality` - P(<=x)
Returns:
x
• #### getVariance

`public double getVariance()`
Description copied from class: `RandomNumber`
Subclasses must override either getStandardDeviation() or getVariance()!
Specified by:
`getVariance` in interface `Distribution`
Overrides:
`getVariance` in class `RandomNumber`
`Distribution.getStandardDeviation()`, `Distribution.getVariance()`
• #### generate

`protected double generate()`
Specified by:
`generate` in class `RandomNumber`
• #### getLowerConfidenceQuantile

`public final double getLowerConfidenceQuantile(double confidence)`
• #### getUpperConfidenceQuantile

`public final double getUpperConfidenceQuantile(double confidence)`