Class ConstantDistribution
- 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.ConstantDistribution
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- All Implemented Interfaces:
Distribution
public class ConstantDistribution extends BaseDistribution
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Field Summary
Fields Modifier and Type Field Description static double
DEFAULT_INVERSE_ABSOLUTE_ACCURACY
Default inverse cumulative probability accuracy.-
Fields inherited from class org.nd4j.linalg.api.rng.distribution.BaseDistribution
random, solverAbsoluteAccuracy
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Constructor Summary
Constructors Constructor Description ConstantDistribution(double value)
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Method Summary
All Methods Instance Methods Concrete Methods Deprecated 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)
Deprecated.SeeRealDistribution.cumulativeProbability(double, double)
double
density(double x)
Returns the probability density function (PDF) of this distribution evaluated at the specified pointx
.double
getMean()
Access the mean.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.protected double
getSolverAbsoluteAccuracy()
Returns the solver absolute accuracy for inverse cumulative computation.double
getStandardDeviation()
Access the standard deviation.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
probability(double x0, double x1)
For a random variableX
whose values are distributed according to this distribution, this method returnsP(x0 < X <= x1)
.double
sample()
Generate a random value sampled from this distribution.INDArray
sample(int[] shape)
Sample the given shapeINDArray
sample(INDArray target)
Fill the target array by sampling from the distribution-
Methods inherited from class org.nd4j.linalg.api.rng.distribution.BaseDistribution
probability, reseedRandomGenerator, sample, sample
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Field Detail
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DEFAULT_INVERSE_ABSOLUTE_ACCURACY
public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY
Default inverse cumulative probability accuracy.- Since:
- 2.1
- See Also:
- Constant Field Values
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Method Detail
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getMean
public double getMean()
Access the mean.- Returns:
- the mean for this distribution.
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getStandardDeviation
public double getStandardDeviation()
Access the standard deviation.- Returns:
- the standard deviation for this distribution.
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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. Ifx
is more than 40 standard deviations from the mean, 0 or 1 is returned, as in these cases the actual value is withinDouble.MIN_VALUE
of 0 or 1.- 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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inverseCumulativeProbability
public double inverseCumulativeProbability(double p) throws org.apache.commons.math3.exception.OutOfRangeException
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
.
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
- Since:
- 3.2
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cumulativeProbability
@Deprecated public double cumulativeProbability(double x0, double x1) throws org.apache.commons.math3.exception.NumberIsTooLargeException
Deprecated.SeeRealDistribution.cumulativeProbability(double, double)
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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probability
public double probability(double x0, double x1) throws org.apache.commons.math3.exception.NumberIsTooLargeException
For a random variableX
whose values are distributed according to this distribution, this method returnsP(x0 < X <= x1)
.- Overrides:
probability
in classBaseDistribution
- Parameters:
x0
- Lower bound (excluded).x1
- Upper bound (included).- 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
. The default implementation uses the identityP(x0 < X <= x1) = P(X <= x1) - P(X <= x0)
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getSolverAbsoluteAccuracy
protected double getSolverAbsoluteAccuracy()
Returns the solver absolute accuracy for inverse cumulative computation. You can override this method in order to use a Brent solver with an absolute accuracy different from the default.- Overrides:
getSolverAbsoluteAccuracy
in classBaseDistribution
- Returns:
- the maximum absolute error in inverse cumulative probability estimates
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getNumericalMean
public double getNumericalMean()
Use this method to get the numerical value of the mean of this distribution. For mean parametermu
, the mean ismu
.- 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 standard deviation parameters
, the variance iss^2
.- 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 always negative infinity no matter the parameters.inf {x in R | P(X <= x) > 0}
.- Returns:
- lower bound of the support (always
Double.NEGATIVE_INFINITY
)
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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 always positive infinity no matter the parameters.inf {x in R | P(X <= x) = 1}
.- Returns:
- upper bound of the support (always
Double.POSITIVE_INFINITY
)
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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 target)
Description copied from interface:Distribution
Fill the target array by sampling from the distribution- Specified by:
sample
in interfaceDistribution
- Overrides:
sample
in classBaseDistribution
- Parameters:
target
- target array- Returns:
- an ndarray with random samples from this distribution
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