Class EmpiricalWalker
- java.lang.Object
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- cern.colt.PersistentObject
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- cern.jet.random.AbstractDistribution
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- cern.jet.random.AbstractDiscreteDistribution
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- cern.jet.random.EmpiricalWalker
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- All Implemented Interfaces:
DoubleFunction
,IntFunction
,java.io.Serializable
,java.lang.Cloneable
public class EmpiricalWalker extends AbstractDiscreteDistribution
Discrete Empirical distribution (pdf's can be specified).The probability distribution function (pdf) must be provided by the user as an array of positive real numbers. The pdf does not need to be provided in the form of relative probabilities, absolute probabilities are also accepted.
Instance methods operate on a user supplied uniform random number generator; they are unsynchronized.
- Static methods operate on a default uniform random number generator; they are synchronized.
Implementation: Walker's algorithm. Generating a random number takes O(1), i.e. constant time, as opposed to commonly used algorithms with logarithmic time complexity. Preprocessing time (on object construction) is O(k) where k is the number of elements of the provided empirical pdf. Space complexity is O(k).
This is a port of discrete.c which was written by James Theiler and is distributed with GSL 0.4.1. Theiler's implementation in turn is based upon
Alastair J. Walker, An efficient method for generating discrete random variables with general distributions, ACM Trans Math Soft 3, 253-256 (1977).
See also: D. E. Knuth, The Art of Computer Programming, Volume 2 (Seminumerical algorithms), 3rd edition, Addison-Wesley (1997), p120.
- Version:
- 1.0, 09/24/99
- See Also:
- Serialized Form
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Field Summary
Fields Modifier and Type Field Description protected int[]
A
protected double[]
cdf
protected double[]
F
protected int
K
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Fields inherited from class cern.jet.random.AbstractDistribution
randomGenerator
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Fields inherited from class cern.colt.PersistentObject
serialVersionUID
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Constructor Summary
Constructors Constructor Description EmpiricalWalker(double[] pdf, int interpolationType, RandomEngine randomGenerator)
Constructs an Empirical distribution.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description double
cdf(int k)
Returns the cumulative distribution function.java.lang.Object
clone()
Returns a deep copy of the receiver; the copy will produce identical sequences.int
nextInt()
Returns a random integer k with probability pdf(k).double
pdf(int k)
Returns the probability distribution function.void
setState(double[] pdf, int interpolationType)
Sets the distribution parameters.void
setState2(double[] pdf)
Sets the distribution parameters.java.lang.String
toString()
Returns a String representation of the receiver.-
Methods inherited from class cern.jet.random.AbstractDiscreteDistribution
nextDouble
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Methods inherited from class cern.jet.random.AbstractDistribution
apply, apply, getRandomGenerator, makeDefaultGenerator, setRandomGenerator
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Constructor Detail
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EmpiricalWalker
public EmpiricalWalker(double[] pdf, int interpolationType, RandomEngine randomGenerator)
Constructs an Empirical distribution. The probability distribution function (pdf) is an array of positive real numbers. It need not be provided in the form of relative probabilities, absolute probabilities are also accepted. The pdf must satisfy both of the following conditions- 0.0 <= pdf[i] : 0<=i<=pdf.length-1
- 0.0 < Sum(pdf[i]) : 0<=i<=pdf.length-1
- Parameters:
pdf
- the probability distribution function.interpolationType
- can be either Empirical.NO_INTERPOLATION or Empirical.LINEAR_INTERPOLATION.randomGenerator
- a uniform random number generator.- Throws:
java.lang.IllegalArgumentException
- if at least one of the three conditions above is violated.
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Method Detail
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cdf
public double cdf(int k)
Returns the cumulative distribution function.
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clone
public java.lang.Object clone()
Returns a deep copy of the receiver; the copy will produce identical sequences. After this call has returned, the copy and the receiver have equal but separate state.- Overrides:
clone
in classAbstractDistribution
- Returns:
- a copy of the receiver.
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nextInt
public int nextInt()
Returns a random integer k with probability pdf(k).- Specified by:
nextInt
in classAbstractDiscreteDistribution
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pdf
public double pdf(int k)
Returns the probability distribution function.
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setState
public void setState(double[] pdf, int interpolationType)
Sets the distribution parameters. The pdf must satisfy all of the following conditions- pdf != null && pdf.length > 0
- 0.0 <= pdf[i] : 0 < =i <= pdf.length-1
- 0.0 < Sum(pdf[i]) : 0 <=i <= pdf.length-1
- Parameters:
pdf
- probability distribution function.- Throws:
java.lang.IllegalArgumentException
- if at least one of the three conditions above is violated.
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setState2
public void setState2(double[] pdf)
Sets the distribution parameters. The pdf must satisfy both of the following conditions- 0.0 <= pdf[i] : 0 < =i <= pdf.length-1
- 0.0 < Sum(pdf[i]) : 0 <=i <= pdf.length-1
- Parameters:
pdf
- probability distribution function.- Throws:
java.lang.IllegalArgumentException
- if at least one of the three conditions above is violated.
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toString
public java.lang.String toString()
Returns a String representation of the receiver.- Overrides:
toString
in classjava.lang.Object
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