Package org.ojalgo.random
Class LogNormal
- java.lang.Object
-
- org.ojalgo.random.RandomNumber
-
- org.ojalgo.random.AbstractContinuous
-
- org.ojalgo.random.LogNormal
-
- 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 LogNormal extends AbstractContinuous
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.
-
-
Nested Class Summary
-
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>
-
-
Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description static LogNormal
estimate(Access1D<?> rawSamples)
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
getGeometricMean()
The geometric mean is also the mediandouble
getGeometricStandardDeviation()
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
getVariance()
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
-
-
-
-
Field Detail
-
myNormal
private final Normal myNormal
-
-
Method Detail
-
make
public static LogNormal make(double mean, double variance)
-
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)
-
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()
-
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
-
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:
Distribution.getStandardDeviation()
,Distribution.getVariance()
-
setSeed
public void setSeed(long seed)
- Overrides:
setSeed
in classRandomNumber
-
generate
protected double generate()
- Overrides:
generate
in classAbstractContinuous
-
-