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‎src/BaselineOfMathRandomNumbers/BaselineOfMathRandomNumbers.class.st‎

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"
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I am a baseline to define the structure and dependencies of the MathRandomNumbers project.
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"
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Class {
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#name : #BaselineOfMathRandomNumbers,
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#superclass : #BaselineOf,
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#category : #BaselineOfMathRandomNumbers
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}
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{ #category : #baseline }
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BaselineOfMathRandomNumbers >> baseline: spec [
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<baseline>
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spec
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for: #common
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do: [
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"Packages"
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spec
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package: 'Math-RandomNumbers';
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package: 'Math-Tests-RandomNumbers' with: [ spec requires: #('Math-RandomNumbers') ].
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"Groups"
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spec
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group: 'Core' with: #('Math-RandomNumbers');
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group: 'Tests' with: #('Math-Tests-RandomNumbers') ]
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]
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{ #category : #accessing }
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BaselineOfMathRandomNumbers >> projectClass [
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^ MetacelloCypressBaselineProject
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]

‎src/BaselineOfMathRandomNumbers/package.st‎

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Package { #name : #BaselineOfMathRandomNumbers }

‎src/Math-RandomNumbers/PMBernoulliGenerator.class.st‎

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"
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A PMBernoulliGenerator is simulates a Bernoulli Process. This is a discrete process, with probability of p for success, and 1-p for failure.
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next
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answer 1 if success event, 0 otherwise
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generator:
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provide a uniform [0,1] random number generator
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p:
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set the probability of success events
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class methods
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withProbability:
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create a generator with probability set to p
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defaultGeneratorClass
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class used for generator in new instances
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"
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Class {
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#name : #PMBernoulliGenerator,
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#superclass : #PMNumberGenerator,
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#instVars : [
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'probability'
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],
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#category : #'Math-RandomNumbers'
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}
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{ #category : #'instance creation' }
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PMBernoulliGenerator class >> fair [
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^ self withProbability: 0.5
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]
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{ #category : #private }
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PMBernoulliGenerator class >> validProbability: aProbability [
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"reject probabilities outside of [0,1]"
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self assert: [ (aProbability < 0) not and: (aProbability > 1) not ]
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]
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{ #category : #'instance creation' }
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PMBernoulliGenerator class >> withProbability: p [
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self validProbability: p.
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^ self new probability: p
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]
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{ #category : #initialization }
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PMBernoulliGenerator >> initialize [
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super initialize.
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self generator: self class defaultGeneratorClass new.
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self probability: 0.5
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]
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{ #category : #'stream accessing' }
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PMBernoulliGenerator >> next [
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^ randomNumberGenerator next < probability
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ifTrue: [ 1 ]
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ifFalse: [ 0 ]
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]
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{ #category : #accessing }
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PMBernoulliGenerator >> probability [
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^ probability
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]
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{ #category : #accessing }
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PMBernoulliGenerator >> probability: aProbability [
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self class validProbability: aProbability.
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probability := aProbability
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]

‎src/Math-RandomNumbers/PMBinomialGenerator.class.st‎

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"
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A PMBinomialGenerator yields results from a binomial distribution with probability p and n trials. The generator is the underlying random source.
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If each independent event has probability 0<p<1, and n trials are performed, next returns number of successes.
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"
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Class {
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#name : #PMBinomialGenerator,
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#superclass : #PMNumberGenerator,
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#instVars : [
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'numberOfTrials',
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'probability'
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],
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#category : #'Math-RandomNumbers'
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}
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{ #category : #'instance-creation' }
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PMBinomialGenerator class >> numberOfTrials: numberOfTrials probabilityOfSuccess: probabilityOfSuccess [
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^ self new
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numberOfTrials: numberOfTrials;
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probability: probabilityOfSuccess;
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yourself
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]
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{ #category : #accessing }
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PMBinomialGenerator >> expectedValue [
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^ numberOfTrials * probability
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]
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{ #category : #initialization }
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PMBinomialGenerator >> initialize [
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self generator: PMParkMillerMinimumRandomGenerator new.
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self numberOfTrials: 10; probability: 0.5
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]
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{ #category : #'stream access' }
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PMBinomialGenerator >> next [
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| x |
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x := 0.
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numberOfTrials timesRepeat: [ randomNumberGenerator next <= probability ifTrue: [ x := x + 1 ] ].
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^ x
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]
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{ #category : #accessing }
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PMBinomialGenerator >> numberOfTrials: anInteger [
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numberOfTrials := anInteger
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]
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{ #category : #accessing }
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PMBinomialGenerator >> probability: aProbability [
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probability := aProbability
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]

‎src/Math-RandomNumbers/PMCombinedRandomGenerator.class.st‎

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"
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This Combined Random Number Generator is based on the algorithm described by PIERRE L'ECUYER in ""Efficient and Portable Combined Random Number Generators"" [Communications of the ACM, June 19, Volume 31, Number 6, pp. 742-749, references p.774]. Taking into account its two-dimensional behaviour (from abovementioned article), this generator is suitable to produce the pairs of consecutive numbers.
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For the first linear congruential generator (generator A):
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m = 2147483563; a = 40014; q = 53668; r = 12211.
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For the second linear congruential generator (generator B):
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m = 2147483399; a = 40692; q = 52774; r = 3791.
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To produce initial seedA (for the first generator) the method #nextInt: 2147483562 of Random is used; to produce seedB (for the second) - the method #nextInt: 2147483398. Corresponding seeds are represented as Floats. The result of work of two generators (the next seedA and seedB) are combined.
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Developed by Konstantin Nizheradze <[email protected]>
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Instance Variables:
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random <Random>
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seedA <Number>
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seedB <Number>
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"
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Class {
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#name : #PMCombinedRandomGenerator,
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#superclass : #Random,
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#instVars : [
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'random',
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'seedA',
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'seedB'
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],
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#category : #'Math-RandomNumbers'
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}
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{ #category : #initialize }
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PMCombinedRandomGenerator >> initialize [
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super initialize.
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random := Random new.
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seedA := (random nextInt: 2147483562) asFloat.
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seedB := (random nextInt: 2147483398) asFloat
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]
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{ #category : #accessing }
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PMCombinedRandomGenerator >> next [
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"Combine seedA and seedB to produce new seed. Seed is in the interval [0, 1]. "
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seed := (self nextValueA) - (self nextValueB).
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seed < 1 ifTrue: [seed := seed + 2.147483562e9].
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seed := seed * 4.656613057391769e-10.
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^ seed
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]
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{ #category : #private }
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PMCombinedRandomGenerator >> nextValueA [
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"Evaluate next value of seedA using m and a of generatorA"
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| lo hi aLoRHi |
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hi := (seedA quo: 53668.0) asFloat.
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lo := seedA - (hi * 53668.0). " = seed rem: q"
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aLoRHi := ( 40014.0 * lo) - ( 12211.0 * hi).
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seedA := (aLoRHi > 0.0)
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ifTrue: [aLoRHi]
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ifFalse: [aLoRHi + 2.147483563e9].
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^ seedA
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]
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{ #category : #private }
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PMCombinedRandomGenerator >> nextValueB [
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"Evaluate next value of seedB using m, a, q and r of generatorB"
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| lo hi aLoRHi |
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hi := (seedB quo: 52774.0) asFloat.
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lo := seedB - (hi * 52774.0). " = seed rem: q"
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aLoRHi := (40692.0 * lo) - (3791.0 * hi).
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seedB := (aLoRHi > 0.0)
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ifTrue: [aLoRHi]
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ifFalse: [aLoRHi + 2.147483399e9].
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^ seedB
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]

‎src/Math-RandomNumbers/PMConstantGenerator.class.st‎

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"
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A PMConstantGenerator is still a number generator but a simple one :)
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"
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Class {
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#name : #PMConstantGenerator,
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#superclass : #PMNumberGenerator,
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#instVars : [
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'constant'
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],
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#category : #'Math-RandomNumbers'
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}
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{ #category : #'instance-creation' }
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PMConstantGenerator class >> constant: aNumber [
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^ self new constant: aNumber ; yourself
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]
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{ #category : #accessing }
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PMConstantGenerator >> constant: aConstant [
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constant := aConstant
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]
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{ #category : #accessing }
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PMConstantGenerator >> generator: aRandomGenerator [
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"Do nothing."
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^ self
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]
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{ #category : #'stream access' }
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PMConstantGenerator >> next [
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^ constant
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]

‎src/Math-RandomNumbers/PMExplicitInverseCongruentialRandomGenerator.class.st‎

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"
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A PMExplicitInverseCongruentialGenerator is an explicit inversive congruential generator, constructed according to ""Good random number generators are (not so) easy to find"" by P. Hellekalek (1998) and extended euclidean algorithm.
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Developed by Konstantin Nizheradze <[email protected]>
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Instance Variables
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nextN: <Object>
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nextValue: <Object>
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p: <Object>
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nextN
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- next number of the sequence of random numbers. It is also the parameter at extended euclidean algorythm
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nextValue
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- next modulo inverse value, calculated by extended euclidean algorithm
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p
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- parameter at extended euclidean algorithm
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"
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Class {
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#name : #PMExplicitInverseCongruentialRandomGenerator,
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#superclass : #Random,
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#instVars : [
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'p',
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'nextN',
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'nextValue',
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'nextModInv'
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],
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#category : #'Math-RandomNumbers'
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}
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{ #category : #initialization }
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PMExplicitInverseCongruentialRandomGenerator >> initialize [
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"Initialize using Random and define the modulo (p)"
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| random |
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super initialize.
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p := 16r7FFFFFFF asFloat.
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random := Random new.
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nextN := random nextValue.
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]
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{ #category : #accessing }
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PMExplicitInverseCongruentialRandomGenerator >> next [
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"This method is used by nextInt:, answer a random Float in the interval [0 to 1]"
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nextModInv := self nextRandomValue / p.
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^ nextModInv
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]
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{ #category : #private }
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PMExplicitInverseCongruentialRandomGenerator >> nextRandomValue [
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"Calculate the next modulo inverse value for n+1"
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| rem newrem inv newinv transrem transinv quotient |
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inv := 0.
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newinv := 1.
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rem := p.
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newrem := nextN.
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[newrem ~= 0] whileTrue: [quotient := rem // newrem.
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transrem := newrem.
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newrem := rem \\ newrem.
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rem := transrem.
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transinv := newinv.
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newinv := inv - (quotient * newinv).
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inv := transinv].
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(inv < 0) ifTrue: [inv := inv + p].
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nextValue := inv.
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nextN := nextN + 1.
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^nextValue
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]
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{ #category : #tests }
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PMExplicitInverseCongruentialRandomGenerator >> testNextValue: anInteger [
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"This is to test the principle of modulo inverse"
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| rem newrem inv newinv transrem transinv quotient |
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inv := 0.
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newinv := 1.
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rem := 240.
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newrem := anInteger.
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[newrem ~= 0] whileTrue: [quotient := rem // newrem.
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transrem := newrem.
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newrem := rem \\ newrem.
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rem := transrem.
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transinv := newinv.
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newinv := inv - (quotient * newinv).
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inv := transinv].
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^ inv
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]

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