Package org.ojalgo.random
Class Exponential
- java.lang.Object
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- org.ojalgo.random.RandomNumber
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- org.ojalgo.random.AbstractContinuous
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- org.ojalgo.random.Exponential
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- All Implemented Interfaces:
java.lang.Comparable<RandomNumber>
,java.util.function.DoubleSupplier
,java.util.function.Supplier<java.lang.Double>
,BasicFunction
,NullaryFunction<java.lang.Double>
,PrimitiveFunction.Nullary
,ContinuousDistribution
,Distribution
,AccessScalar<java.lang.Double>
,ComparableNumber<RandomNumber>
,NumberDefinition
public class Exponential extends AbstractContinuous
Distribution of length of life when no aging. Describes the time between events in a Poisson process, i.e. a process in which events occur continuously and independently at a constant average rate. It is the continuous analogue of the geometric distribution.
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Nested Class Summary
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Nested classes/interfaces inherited from interface org.ojalgo.function.BasicFunction
BasicFunction.Differentiable<N extends java.lang.Comparable<N>,F extends BasicFunction>, BasicFunction.Integratable<N extends java.lang.Comparable<N>,F extends BasicFunction>, BasicFunction.PlainUnary<T,R>
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Field Summary
Fields Modifier and Type Field Description private double
myRate
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Constructor Summary
Constructors Constructor Description Exponential()
Exponential(double rate)
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description protected double
generate()
double
getDensity(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
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
getQuantile(double probability)
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
getStandardDeviation()
Subclasses must override either getStandardDeviation() or getVariance()!static Exponential
of(double rate)
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Methods inherited from class org.ojalgo.random.RandomNumber
checkProbabilty, compareTo, doubleValue, floatValue, getVariance, intValue, invoke, longValue, newSampleSet, random, setRandom, setSeed, toString
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Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
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Methods inherited from interface org.ojalgo.random.ContinuousDistribution
getLowerConfidenceQuantile, getUpperConfidenceQuantile
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Methods inherited from interface org.ojalgo.random.Distribution
getVariance
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Methods inherited from interface org.ojalgo.function.NullaryFunction
andThen, get, getAsDouble
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Methods inherited from interface org.ojalgo.type.NumberDefinition
booleanValue, byteValue, shortValue
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Method Detail
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of
public static Exponential of(double rate)
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getDensity
public double getDensity(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)
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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)
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getExpected
public double getExpected()
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getQuantile
public double getQuantile(double probability)
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:
probability
- P(<=x)- Returns:
- x
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getStandardDeviation
public double getStandardDeviation()
Description copied from class:RandomNumber
Subclasses must override either getStandardDeviation() or getVariance()!- Specified by:
getStandardDeviation
in interfaceDistribution
- Overrides:
getStandardDeviation
in classRandomNumber
- See Also:
Distribution.getStandardDeviation()
,Distribution.getVariance()
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generate
protected double generate()
- Overrides:
generate
in classAbstractContinuous
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