Class UniformDistribution
- java.lang.Object
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- org.nd4j.linalg.api.rng.distribution.BaseDistribution
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- org.nd4j.linalg.api.rng.distribution.impl.UniformDistribution
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- All Implemented Interfaces:
Distribution
public class UniformDistribution extends BaseDistribution
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Field Summary
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Fields inherited from class org.nd4j.linalg.api.rng.distribution.BaseDistribution
random, solverAbsoluteAccuracy
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Constructor Summary
Constructors Constructor Description UniformDistribution(double lower, double upper)
Create a uniform real distribution using the given lower and upper bounds.UniformDistribution(Random rng, double lower, double upper)
Creates a uniform distribution.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description double
cumulativeProbability(double x)
For a random variableX
whose values are distributed according to this distribution, this method returnsP(X <= x)
.double
cumulativeProbability(double x0, double x1)
For a random variableX
whose values are distributed according to this distribution, this method returnsP(x0 < X <= x1)
.double
density(double x)
Returns the probability density function (PDF) of this distribution evaluated at the specified pointx
.double
getNumericalMean()
Use this method to get the numerical value of the mean of this distribution.double
getNumericalVariance()
Use this method to get the numerical value of the variance of this distribution.double
getSupportLowerBound()
Access the lower bound of the support.double
getSupportUpperBound()
Access the upper bound of the support.double
inverseCumulativeProbability(double p)
Computes the quantile function of this distribution.boolean
isSupportConnected()
Use this method to get information about whether the support is connected, i.e.boolean
isSupportLowerBoundInclusive()
Whether or not the lower bound of support is in the domain of the density function.boolean
isSupportUpperBoundInclusive()
Whether or not the upper bound of support is in the domain of the density function.double
sample()
Generate a random value sampled from this distribution.INDArray
sample(int[] shape)
Sample the given shapeINDArray
sample(INDArray ret)
Fill the target array by sampling from the distribution-
Methods inherited from class org.nd4j.linalg.api.rng.distribution.BaseDistribution
getSolverAbsoluteAccuracy, probability, probability, reseedRandomGenerator, sample, sample
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Constructor Detail
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UniformDistribution
public UniformDistribution(double lower, double upper) throws org.apache.commons.math3.exception.NumberIsTooLargeException
Create a uniform real distribution using the given lower and upper bounds.- Parameters:
lower
- Lower bound of this distribution (inclusive).upper
- Upper bound of this distribution (exclusive).- Throws:
org.apache.commons.math3.exception.NumberIsTooLargeException
- iflower >= upper
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UniformDistribution
public UniformDistribution(Random rng, double lower, double upper) throws org.apache.commons.math3.exception.NumberIsTooLargeException
Creates a uniform distribution.- Parameters:
rng
- Random number generator.lower
- Lower bound of this distribution (inclusive).upper
- Upper bound of this distribution (exclusive).- Throws:
org.apache.commons.math3.exception.NumberIsTooLargeException
- iflower >= upper
.- Since:
- 3.1
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Method Detail
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density
public double density(double x)
Returns the probability density function (PDF) of this distribution evaluated at the specified pointx
. In general, the PDF is the derivative of theCDF
. If the derivative does not exist atx
, then an appropriate replacement should be returned, e.g.Double.POSITIVE_INFINITY
,Double.NaN
, or the limit inferior or limit superior of the difference quotient.- Parameters:
x
- the point at which the PDF is evaluated- Returns:
- the value of the probability density function at point
x
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cumulativeProbability
public double cumulativeProbability(double x)
For a random variableX
whose values are distributed according to this distribution, this method returnsP(X <= x)
. In other words, this method represents the (cumulative) distribution function (CDF) for this distribution.- Parameters:
x
- the point at which the CDF is evaluated- Returns:
- the probability that a random variable with this
distribution takes a value less than or equal to
x
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cumulativeProbability
public double cumulativeProbability(double x0, double x1) throws org.apache.commons.math3.exception.NumberIsTooLargeException
Description copied from interface:Distribution
For a random variableX
whose values are distributed according to this distribution, this method returnsP(x0 < X <= x1)
.- Parameters:
x0
- the exclusive lower boundx1
- the inclusive upper bound- Returns:
- the probability that a random variable with this distribution
takes a value between
x0
andx1
, excluding the lower and including the upper endpoint - Throws:
org.apache.commons.math3.exception.NumberIsTooLargeException
- ifx0 > x1
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inverseCumulativeProbability
public double inverseCumulativeProbability(double p) throws org.apache.commons.math3.exception.OutOfRangeException
Description copied from class:BaseDistribution
Computes the quantile function of this distribution. For a random variableX
distributed according to this distribution, the returned value isinf{x in R | P(X<=x) >= p}
for0 < p <= 1
,inf{x in R | P(X<=x) > 0}
forp = 0
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Distribution.getSupportLowerBound()
forp = 0
,Distribution.getSupportUpperBound()
forp = 1
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- Specified by:
inverseCumulativeProbability
in interfaceDistribution
- Overrides:
inverseCumulativeProbability
in classBaseDistribution
- Parameters:
p
- the cumulative probability- Returns:
- the smallest
p
-quantile of this distribution (largest 0-quantile forp = 0
) - Throws:
org.apache.commons.math3.exception.OutOfRangeException
- ifp < 0
orp > 1
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getNumericalMean
public double getNumericalMean()
Use this method to get the numerical value of the mean of this distribution. For lower boundlower
and upper boundupper
, the mean is0.5 * (lower + upper)
.- Returns:
- the mean or
Double.NaN
if it is not defined
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getNumericalVariance
public double getNumericalVariance()
Use this method to get the numerical value of the variance of this distribution. For lower boundlower
and upper boundupper
, the variance is(upper - lower)^2 / 12
.- Returns:
- the variance (possibly
Double.POSITIVE_INFINITY
as for certain cases inTDistribution
) orDouble.NaN
if it is not defined
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getSupportLowerBound
public double getSupportLowerBound()
Access the lower bound of the support. This method must return the same value asinverseCumulativeProbability(0)
. In other words, this method must return
The lower bound of the support is equal to the lower bound parameter of the distribution.inf {x in R | P(X <= x) > 0}
.- Returns:
- lower bound of the support
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getSupportUpperBound
public double getSupportUpperBound()
Access the upper bound of the support. This method must return the same value asinverseCumulativeProbability(1)
. In other words, this method must return
The upper bound of the support is equal to the upper bound parameter of the distribution.inf {x in R | P(X <= x) = 1}
.- Returns:
- upper bound of the support
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isSupportLowerBoundInclusive
public boolean isSupportLowerBoundInclusive()
Whether or not the lower bound of support is in the domain of the density function. Returns true iffgetSupporLowerBound()
is finite anddensity(getSupportLowerBound())
returns a non-NaN, non-infinite value.- Returns:
- true if the lower bound of support is finite and the density function returns a non-NaN, non-infinite value there
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isSupportUpperBoundInclusive
public boolean isSupportUpperBoundInclusive()
Whether or not the upper bound of support is in the domain of the density function. Returns true iffgetSupportUpperBound()
is finite anddensity(getSupportUpperBound())
returns a non-NaN, non-infinite value.- Returns:
- true if the upper bound of support is finite and the density function returns a non-NaN, non-infinite value there
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isSupportConnected
public boolean isSupportConnected()
Use this method to get information about whether the support is connected, i.e. whether all values between the lower and upper bound of the support are included in the support. The support of this distribution is connected.- Returns:
true
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sample
public double sample()
Generate a random value sampled from this distribution. The default implementation uses the inversion method.- Specified by:
sample
in interfaceDistribution
- Overrides:
sample
in classBaseDistribution
- Returns:
- a random value.
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sample
public INDArray sample(int[] shape)
Description copied from interface:Distribution
Sample the given shape- Specified by:
sample
in interfaceDistribution
- Overrides:
sample
in classBaseDistribution
- Parameters:
shape
- the given shape- Returns:
- an ndarray with random samples from this distribution
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sample
public INDArray sample(INDArray ret)
Description copied from interface:Distribution
Fill the target array by sampling from the distribution- Specified by:
sample
in interfaceDistribution
- Overrides:
sample
in classBaseDistribution
- Parameters:
ret
- target array- Returns:
- an ndarray with random samples from this distribution
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