public class HypergeometricDistributionImpl extends AbstractIntegerDistribution implements HypergeometricDistribution, Serializable
HypergeometricDistribution
.randomData
Constructor and Description |
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HypergeometricDistributionImpl(int populationSize,
int numberOfSuccesses,
int sampleSize)
Construct a new hypergeometric distribution with the given the population
size, the number of successes in the population, and the sample size.
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Modifier and Type | Method and Description |
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double |
cumulativeProbability(int x)
For this distribution, X, this method returns P(X ≤ x).
|
protected int |
getDomainLowerBound(double p)
Access the domain value lower bound, based on
p , used to
bracket a PDF root. |
protected int |
getDomainUpperBound(double p)
Access the domain value upper bound, based on
p , used to
bracket a PDF root. |
int |
getNumberOfSuccesses()
Access the number of successes.
|
protected double |
getNumericalMean()
Returns the mean.
|
double |
getNumericalVariance()
Returns the variance.
|
int |
getPopulationSize()
Access the population size.
|
int |
getSampleSize()
Access the sample size.
|
int |
getSupportLowerBound()
Returns the lower bound for the support for the distribution.
|
int |
getSupportUpperBound()
Returns the upper bound for the support of the distribution.
|
double |
probability(int x)
For this distribution, X, this method returns P(X = x).
|
void |
setNumberOfSuccesses(int num)
Deprecated.
as of 2.1 (class will become immutable in 3.0)
|
void |
setPopulationSize(int size)
Deprecated.
as of 2.1 (class will become immutable in 3.0)
|
void |
setSampleSize(int size)
Deprecated.
as of 2.1 (class will become immutable in 3.0)
|
double |
upperCumulativeProbability(int x)
For this distribution, X, this method returns P(X ≥ x).
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cumulativeProbability, cumulativeProbability, cumulativeProbability, inverseCumulativeProbability, isSupportLowerBoundInclusive, isSupportUpperBoundInclusive, probability, reseedRandomGenerator, sample, sample
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
cumulativeProbability, inverseCumulativeProbability
probability
cumulativeProbability, cumulativeProbability
public HypergeometricDistributionImpl(int populationSize, int numberOfSuccesses, int sampleSize)
populationSize
- the population size.numberOfSuccesses
- number of successes in the population.sampleSize
- the sample size.public double cumulativeProbability(int x)
cumulativeProbability
in interface IntegerDistribution
cumulativeProbability
in class AbstractIntegerDistribution
x
- the value at which the PDF is evaluated.protected int getDomainLowerBound(double p)
p
, used to
bracket a PDF root.getDomainLowerBound
in class AbstractIntegerDistribution
p
- the desired probability for the critical valuep
protected int getDomainUpperBound(double p)
p
, used to
bracket a PDF root.getDomainUpperBound
in class AbstractIntegerDistribution
p
- the desired probability for the critical valuep
public int getNumberOfSuccesses()
getNumberOfSuccesses
in interface HypergeometricDistribution
public int getPopulationSize()
getPopulationSize
in interface HypergeometricDistribution
public int getSampleSize()
getSampleSize
in interface HypergeometricDistribution
public double probability(int x)
probability
in interface IntegerDistribution
x
- the value at which the PMF is evaluated.@Deprecated public void setNumberOfSuccesses(int num)
setNumberOfSuccesses
in interface HypergeometricDistribution
num
- the new number of successes.IllegalArgumentException
- if num
is negative.@Deprecated public void setPopulationSize(int size)
setPopulationSize
in interface HypergeometricDistribution
size
- the new population size.IllegalArgumentException
- if size
is not positive.@Deprecated public void setSampleSize(int size)
setSampleSize
in interface HypergeometricDistribution
size
- the new sample size.IllegalArgumentException
- if size
is negative.public double upperCumulativeProbability(int x)
x
- the value at which the CDF is evaluated.public int getSupportLowerBound()
N
,
number of successes m
, and
sample size n
,
the lower bound of the support is
max(0, n + m - N)
public int getSupportUpperBound()
m
and
sample size n
,
the upper bound of the support is
min(m, n)
protected double getNumericalMean()
N
,
number of successes m
, and
sample size n
, the mean is
n * m / N
public double getNumericalVariance()
N
,
number of successes m
, and
sample size n
, the variance is
[ n * m * (N - n) * (N - m) ] / [ N^2 * (N - 1) ]
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