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java.lang.Objectorg.apache.commons.math.distribution.AbstractDistribution
org.apache.commons.math.distribution.AbstractIntegerDistribution
org.apache.commons.math.distribution.HypergeometricDistributionImpl
public class HypergeometricDistributionImpl
The default implementation of HypergeometricDistribution
.
Constructor Summary | |
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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. |
Method Summary | |
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double |
cumulativeProbability(int x)
For this distribution, X, this method returns P(X ≤ x). |
int |
getNumberOfSuccesses()
Access the number of successes. |
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). |
Methods inherited from class org.apache.commons.math.distribution.AbstractIntegerDistribution |
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cumulativeProbability, cumulativeProbability, cumulativeProbability, inverseCumulativeProbability, isSupportLowerBoundInclusive, isSupportUpperBoundInclusive, probability, reseedRandomGenerator, sample, sample |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Methods inherited from interface org.apache.commons.math.distribution.IntegerDistribution |
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cumulativeProbability, inverseCumulativeProbability |
Methods inherited from interface org.apache.commons.math.distribution.DiscreteDistribution |
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probability |
Methods inherited from interface org.apache.commons.math.distribution.Distribution |
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cumulativeProbability, cumulativeProbability |
Constructor Detail |
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public HypergeometricDistributionImpl(int populationSize, int numberOfSuccesses, int sampleSize)
populationSize
- the population size.numberOfSuccesses
- number of successes in the population.sampleSize
- the sample size.Method Detail |
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public double cumulativeProbability(int x)
cumulativeProbability
in interface IntegerDistribution
cumulativeProbability
in class AbstractIntegerDistribution
x
- the value at which the PDF is evaluated.
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)
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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