Package org.ojalgo.random
Class LogNormal
java.lang.Object
org.ojalgo.random.RandomNumber
org.ojalgo.random.AbstractContinuous
org.ojalgo.random.LogNormal
- All Implemented Interfaces:
Comparable<RandomNumber>
,DoubleSupplier
,Supplier<Double>
,BasicFunction
,NullaryFunction<Double>
,PrimitiveFunction.Nullary
,ContinuousDistribution
,Distribution
,AccessScalar<Double>
,ComparableNumber<RandomNumber>
,NumberDefinition
A continuous distribution in which the logarithm of a variable has a normal distribution. A log normal
distribution results if the variable is the product of a large number of independent,
identically-distributed variables in the same way that a normal distribution results if the variable is the
sum of a large number of independent, identically-distributed variables.
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Nested Class Summary
Nested classes/interfaces inherited from interface org.ojalgo.function.BasicFunction
BasicFunction.Differentiable<N extends Comparable<N>,
F extends BasicFunction>, BasicFunction.Integratable<N extends Comparable<N>, F extends BasicFunction>, BasicFunction.PlainUnary<T, R> -
Field Summary
Fields -
Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionstatic LogNormal
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
double
The geometric mean is also the mediandouble
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
Subclasses must override either getStandardDeviation() or getVariance()!static LogNormal
make
(double mean, double variance) void
setSeed
(long seed) Methods inherited from class org.ojalgo.random.RandomNumber
checkProbabilty, compareTo, doubleValue, floatValue, getStandardDeviation, intValue, invoke, longValue, newSampleSet, random, setRandom, toString
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
Methods inherited from interface org.ojalgo.random.ContinuousDistribution
getLowerConfidenceQuantile, getUpperConfidenceQuantile
Methods inherited from interface org.ojalgo.random.Distribution
getStandardDeviation
Methods inherited from interface org.ojalgo.function.NullaryFunction
andThen, get, getAsDouble
Methods inherited from interface org.ojalgo.type.NumberDefinition
booleanValue, byteValue, shortValue
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Field Details
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myNormal
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Constructor Details
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LogNormal
public LogNormal() -
LogNormal
public LogNormal(double location, double scale) The location and scale parameters are the mean and standard deviation of the variable's logarithm (by definition, the variable's logarithm is normally distributed).
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Method Details
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estimate
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make
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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() -
getGeometricMean
public double getGeometricMean()The geometric mean is also the median -
getGeometricStandardDeviation
public double getGeometricStandardDeviation() -
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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getVariance
public double getVariance()Description copied from class:RandomNumber
Subclasses must override either getStandardDeviation() or getVariance()!- Specified by:
getVariance
in interfaceDistribution
- Overrides:
getVariance
in classRandomNumber
- See Also:
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setSeed
public void setSeed(long seed) - Overrides:
setSeed
in classRandomNumber
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generate
protected double generate()- Overrides:
generate
in classAbstractContinuous
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