public class ConstantDistribution extends BaseDistribution
Modifier and Type | Field and Description |
---|---|
static double |
DEFAULT_INVERSE_ABSOLUTE_ACCURACY
Default inverse cumulative probability accuracy.
|
random, solverAbsoluteAccuracy
Constructor and Description |
---|
ConstantDistribution(double value) |
Modifier and Type | Method and Description |
---|---|
double |
cumulativeProbability(double x)
For a random variable
X whose values are distributed according
to this distribution, this method returns P(X <= x) . |
double |
cumulativeProbability(double x0,
double x1)
Deprecated.
See
org.apache.commons.math3.distribution.RealDistribution#cumulativeProbability(double, double) |
double |
density(double x)
Returns the probability density function (PDF) of this distribution
evaluated at the specified point
x . |
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 variable
X whose values are distributed according
to this distribution, this method returns P(x0 < X <= x1) . |
double |
sample()
Generate a random value sampled from this distribution.
|
INDArray |
sample(INDArray target)
Fill the target array by sampling from the distribution
|
INDArray |
sample(int[] shape)
Sample the given shape
|
probability, reseedRandomGenerator, sample, sample
public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY
public double getMean()
public double getStandardDeviation()
public double density(double x)
x
. In general, the PDF is
the derivative of the CDF
.
If the derivative does not exist at x
, 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.x
- the point at which the PDF is evaluatedx
public double cumulativeProbability(double x)
X
whose values are distributed according
to this distribution, this method returns P(X <= x)
. In other
words, this method represents the (cumulative) distribution function
(CDF) for this distribution.
If x
is more than 40 standard deviations from the mean, 0 or 1
is returned, as in these cases the actual value is within
Double.MIN_VALUE
of 0 or 1.x
- the point at which the CDF is evaluatedx
public double inverseCumulativeProbability(double p) throws org.apache.commons.math3.exception.OutOfRangeException
X
distributed according to this distribution, the
returned value is
inf{x in R | P(X<=x) >= p}
for 0 < p <= 1
,inf{x in R | P(X<=x) > 0}
for p = 0
.Distribution.getSupportLowerBound()
for p = 0
,Distribution.getSupportUpperBound()
for p = 1
.inverseCumulativeProbability
in interface Distribution
inverseCumulativeProbability
in class BaseDistribution
p
- the cumulative probabilityp
-quantile of this distribution
(largest 0-quantile for p = 0
)org.apache.commons.math3.exception.OutOfRangeException
- if p < 0
or p > 1
@Deprecated public double cumulativeProbability(double x0, double x1) throws org.apache.commons.math3.exception.NumberIsTooLargeException
org.apache.commons.math3.distribution.RealDistribution#cumulativeProbability(double, double)
X
whose values are distributed according
to this distribution, this method returns P(x0 < X <= x1)
.x0
- the exclusive lower boundx1
- the inclusive upper boundx0
and x1
,
excluding the lower and including the upper endpointorg.apache.commons.math3.exception.NumberIsTooLargeException
- if x0 > x1
public double probability(double x0, double x1) throws org.apache.commons.math3.exception.NumberIsTooLargeException
X
whose values are distributed according
to this distribution, this method returns P(x0 < X <= x1)
.probability
in class BaseDistribution
x0
- Lower bound (excluded).x1
- Upper bound (included).x0
and x1
, excluding the lower
and including the upper endpoint.org.apache.commons.math3.exception.NumberIsTooLargeException
- if x0 > x1
.
The default implementation uses the identity
P(x0 < X <= x1) = P(X <= x1) - P(X <= x0)
protected double getSolverAbsoluteAccuracy()
getSolverAbsoluteAccuracy
in class BaseDistribution
public double getNumericalMean()
mu
, the mean is mu
.Double.NaN
if it is not definedpublic double getNumericalVariance()
s
, the variance is s^2
.Double.POSITIVE_INFINITY
as
for certain cases in org.apache.commons.math3.distribution.TDistribution
) or Double.NaN
if it
is not definedpublic double getSupportLowerBound()
inverseCumulativeProbability(0)
. In other words, this
method must return
inf {x in R | P(X <= x) > 0}
.
Double.NEGATIVE_INFINITY
)public double getSupportUpperBound()
inverseCumulativeProbability(1)
. In other words, this
method must return
inf {x in R | P(X <= x) = 1}
.
Double.POSITIVE_INFINITY
)public boolean isSupportLowerBoundInclusive()
getSupporLowerBound()
is finite and
density(getSupportLowerBound())
returns a non-NaN, non-infinite
value.public boolean isSupportUpperBoundInclusive()
getSupportUpperBound()
is finite and
density(getSupportUpperBound())
returns a non-NaN, non-infinite
value.public boolean isSupportConnected()
true
public double sample()
sample
in interface Distribution
sample
in class BaseDistribution
public INDArray sample(int[] shape)
Distribution
sample
in interface Distribution
sample
in class BaseDistribution
shape
- the given shapepublic INDArray sample(INDArray target)
Distribution
sample
in interface Distribution
sample
in class BaseDistribution
target
- target arrayCopyright © 2020. All rights reserved.