Fix multiple random failing tests by using a random generator with a fixed seed

src/Math-Core-Process/PMIterativeProcess.class.st

@@ -82,8 +82,7 @@ PMIterativeProcess >> initialize [
 	super initialize.
 
 	desiredPrecision := self class defaultPrecision.
-	maximumIterations := self class defaultMaximumIterations.
-	^ self
+	maximumIterations := self class defaultMaximumIterations
 ]
 
 { #category : #operation }

src/Math-FunctionFit/PMAnotherChromosomeManager.class.st

@@ -8,7 +8,8 @@ Class {
 	#instVars : [
 		'hammersley',
 		'rateOfLC',
-		'rateOfEir'
+		'rateOfEir',
+		'randomGenerator'
 	],
 	#classVars : [
 		'Primes'
@@ -33,28 +34,29 @@ PMAnotherChromosomeManager class >> integerDigitsFor: anInteger base: aBase [
 ]
 
 { #category : #utilities }
-PMAnotherChromosomeManager class >> numberOfHamersleyPoints: n dimension: d randomized: aBoolean [
+PMAnotherChromosomeManager class >> numberOfHamersleyPoints: n dimension: d randomGenerator: randomGenerator [
 	"a bit randomized "
-	| dist randomNumberGenerator |
+
+	| dist |
 	dist := 1.0 / n.
-	randomNumberGenerator := Random new.
-
-	^(1 to: n) collect: [ :number |
-		(1 to: d) collect: [ :dim |
-			(dim=1)
-				ifTrue: [ aBoolean
-					ifTrue: [ (number/n) - (randomNumberGenerator nextBetween: 0 and: dist) ]
-					ifFalse: [ number/n ] ]
-				ifFalse: [ |sum prime|
-					sum := 0.
-					prime := Primes at: dim - 1.
-
-					(self integerDigitsFor: number base: prime) reverse withIndexDo: [ :i :index |
-						sum := i / (prime raisedToInteger: index) + sum ].
-
-					aBoolean
-						ifTrue: [ sum + (randomNumberGenerator nextBetween: 0 and: dist) ]
-						ifFalse: [ sum ] ] ]].
+
+	^ (1 to: n) collect: [ :number |
+		  (1 to: d) collect: [ :dim |
+			  dim = 1
+				  ifTrue: [
+					  randomGenerator
+						  ifNotNil: [ number / n - (randomGenerator nextBetween: 0 and: dist) ]
+						  ifNil: [ number / n ] ]
+				  ifFalse: [
+					  | sum prime |
+					  sum := 0.
+					  prime := Primes at: dim - 1.
+
+					  (self integerDigitsFor: number base: prime) reverse withIndexDo: [ :i :index | sum := i / (prime raisedToInteger: index) + sum ].
+
+					  randomGenerator
+						  ifNotNil: [ sum + (randomGenerator nextBetween: 0 and: dist) ]
+						  ifNil: [ sum ] ] ] ]
 ]
 
 { #category : #'instance creation' }
@@ -108,14 +110,15 @@ PMAnotherChromosomeManager >> eirCrossover: aChromosome1 and: aChromosome2 [
 
 { #category : #initialization }
 PMAnotherChromosomeManager >> initialize [
-super initialize .
-populationSize :=100.
-hammersley :=true.
-rateOfEir :=0.16.
-rateOfLC:=0.29.
-rateOfMutation :=0.4.
-rateOfCrossover :=0.15.
-Primes  ifNil:[Primes :=Integer primesUpTo: 500].	"sufficient for up to 95 dimensions (parameters)"
+
+	super initialize.
+	populationSize := 100.
+	hammersley := true.
+	rateOfEir := 0.16.
+	rateOfLC := 0.29.
+	rateOfMutation := 0.4.
+	rateOfCrossover := 0.15.
+	Primes ifNil: [ Primes := Integer primesUpTo: 500 ] "sufficient for up to 95 dimensions (parameters)"
 ]
 
 { #category : #information }
@@ -205,16 +208,29 @@ PMAnotherChromosomeManager >> process: aChromosome1 and: aChromosome2 [
 								add: (self clone: aChromosome2)]]]]
 ]
 
+{ #category : #accessing }
+PMAnotherChromosomeManager >> randomGenerator [
+
+	^ randomGenerator
+]
+
+{ #category : #accessing }
+PMAnotherChromosomeManager >> randomGenerator: anObject [
+
+	randomGenerator := anObject
+]
+
 { #category : #private }
 PMAnotherChromosomeManager >> randomRangeAt: aPosition [
 ^(range at: aPosition )*(self smallDistribution )
 ]
 
 { #category : #operation }
 PMAnotherChromosomeManager >> randomizePopulation [
-hammersley ifFalse: [^super randomizePopulation ].
-population :=self class numberOfHamersleyPoints: populationSize  dimension: origin size randomized: true.
-population := population collect: [:aChr| aChr *range +origin ]
+
+	hammersley ifFalse: [ ^ super randomizePopulation ].
+	population := self class numberOfHamersleyPoints: populationSize dimension: origin size randomGenerator: (self randomGenerator ifNil: [ Random new ]).
+	population := population collect: [ :aChr | aChr * range + origin ]
 ]
 
 { #category : #accessing }

src/Math-FunctionFit/PMAnotherGeneticOptimizer.class.st

@@ -95,7 +95,8 @@ PMAnotherGeneticOptimizer >> computePrecision: whateverData [  "can be changed t
 
 { #category : #operation }
 PMAnotherGeneticOptimizer >> evaluate [
-^Cursor wait showWhile: [super evaluate]
+
+	^ Cursor wait showWhile: [ super evaluate ]
 ]
 
 { #category : #operation }
@@ -129,12 +130,12 @@ statistics :=false
 
 { #category : #operation }
 PMAnotherGeneticOptimizer >> initializeIterations [
-	bestPoints size>=chromosomeManager populationSize
-		ifTrue: [bestPoints := bestPoints copyFrom: 1 to: (bestPoints size // 2 max: 1)].
+
+	bestPoints size >= chromosomeManager populationSize ifTrue: [ bestPoints := bestPoints copyFrom: 1 to: (bestPoints size // 2 max: 1) ].
 	super initializeIterations.
-	bestValueHistory:=OrderedCollection new.
-	worstValueHistory:=OrderedCollection new.
-	whateverHistory:=OrderedCollection new
+	bestValueHistory := OrderedCollection new.
+	worstValueHistory := OrderedCollection new.
+	whateverHistory := OrderedCollection new
 ]
 
 { #category : #operation }

src/Math-FunctionFit/PMErrorMinimizer.class.st

@@ -29,6 +29,7 @@ PMErrorMinimizer class >> function: aBlock data: aCollection [
 
 { #category : #accessing }
 PMErrorMinimizer >> maxFunction [
-"The number of data partitions used."
-^dataHolder size
+	"The number of data partitions used."
+
+	^ dataHolder size
 ]

src/Math-FunctionFit/PMFunctionFit.class.st

@@ -43,7 +43,12 @@ PMFunctionFit >> accumulate: aWeightedPoint [
 
 { #category : #operation }
 PMFunctionFit >> computeChanges [
-	^[super computeChanges]on: PMSingularMatrixError  do:[:signal|signal messageText: 'singular error matrix, set better parameters'.signal pass]
+
+	^ [ super computeChanges ]
+		  on: PMSingularMatrixError
+		  do: [ :signal |
+			  signal messageText: 'singular error matrix, set better parameters'.
+			  signal pass ]
 ]
 
 { #category : #information }

src/Math-FunctionFit/PMGeneralFunctionFit.class.st

@@ -10,14 +10,14 @@ Class {
 	#name : #PMGeneralFunctionFit,
 	#superclass : #Object,
 	#instVars : [
-		'go',
 		'manager',
 		'errorFunction',
 		'result',
 		'firstResult',
 		'verbose',
 		'data',
-		'dataTruncated'
+		'dataTruncated',
+		'geneticOptimizer'
 	],
 	#category : #'Math-FunctionFit'
 }
@@ -92,27 +92,31 @@ self resetResult.
 
 { #category : #operation }
 PMGeneralFunctionFit >> evaluate [
-|ff|
-ff :=PMErrorMinimizer function: errorFunction.
-firstResult :=[ff evaluate .ff parameters]
-	onErrorDo: [ verbose ifTrue: [self inform: 'ErrorMinimizer was not successful']. nil].
-firstResult ifNotNil: [go addPointAt: firstResult] .
-firstResult := go evaluate .
-self errorType =#squared
-	ifTrue: [ff :=PMFunctionFit function: errorFunction function data: errorFunction data ].
-ff parameters: firstResult.
-ff desiredPrecision: PMFloatingPointMachine new machinePrecision.
-ff maximumIterations: 1000 .
-result:=[ff evaluate .ff parameters]
-			onErrorDo: [
-				verbose ifTrue: [self inform: 'last FunctionFit was not successful'].
-				ff result parameters].
-((errorFunction value: result) > (errorFunction value: firstResult)) ifTrue:[
-	ff:=result.
-	result :=firstResult.
-	firstResult :=ff.
-	verbose ifTrue: [self inform: 'first result was better than final result'  ] ].
-^result
+
+	| ff |
+	ff := PMErrorMinimizer function: errorFunction.
+	firstResult := [
+	               ff evaluate.
+	               ff parameters ] onErrorDo: [
+		               verbose ifTrue: [ self inform: 'ErrorMinimizer was not successful' ].
+		               nil ].
+	firstResult ifNotNil: [ geneticOptimizer addPointAt: firstResult ].
+	firstResult := geneticOptimizer evaluate.
+	self errorType = #squared ifTrue: [ ff := PMFunctionFit function: errorFunction function data: errorFunction data ].
+	ff parameters: firstResult.
+	ff desiredPrecision: PMFloatingPointMachine new machinePrecision.
+	ff maximumIterations: 1000.
+	result := [
+	          ff evaluate.
+	          ff parameters ] onErrorDo: [
+		          verbose ifTrue: [ self inform: 'last FunctionFit was not successful' ].
+		          ff result parameters ].
+	(errorFunction value: result) > (errorFunction value: firstResult) ifTrue: [
+		ff := result.
+		result := firstResult.
+		firstResult := ff.
+		verbose ifTrue: [ self inform: 'first result was better than final result' ] ].
+	^ result
 ]
 
 { #category : #operation }
@@ -165,16 +169,19 @@ errorFunction function: aBlock
 
 { #category : #initialization }
 PMGeneralFunctionFit >> initialize [
-super initialize .
-errorFunction :=PMErrorOfParameterFunction new.
-manager :=PMAnotherChromosomeManager new.
-manager populationSize: 50.
-go :=PMAnotherGeneticOptimizer minimizingFunction: errorFunction .
-go chromosomeManager: manager.
-go maximumIterations: 170.
-self errorType: #squared.
-verbose:=false.
-dataTruncated :=false
+
+	super initialize.
+	errorFunction := PMErrorOfParameterFunction new.
+	manager := PMAnotherChromosomeManager new
+		           populationSize: 50;
+		           yourself.
+	geneticOptimizer := (PMAnotherGeneticOptimizer minimizingFunction: errorFunction)
+		                    chromosomeManager: manager;
+		                    maximumIterations: 170;
+		                    yourself.
+	self errorType: #squared.
+	verbose := false.
+	dataTruncated := false
 ]
 
 { #category : #accessing }
@@ -186,7 +193,7 @@ PMGeneralFunctionFit >> manager [
 { #category : #accessing }
 PMGeneralFunctionFit >> maximumIterations: anInteger [
 "default is 170."
-go maximumIterations: anInteger.
+geneticOptimizer maximumIterations: anInteger.
 ^anInteger
 ]
 
@@ -207,7 +214,7 @@ manager range: (array2 - array1)
 { #category : #accessing }
 PMGeneralFunctionFit >> optimizer [
 "if you want to change parameters of AnotherGeneticOptimizer not directly available in GeneralFunctionFit"
-^go
+^geneticOptimizer
 ]
 
 { #category : #accessing }
@@ -221,7 +228,7 @@ manager populationSize: anInteger .
 { #category : #accessing }
 PMGeneralFunctionFit >> precision [
 "not really useful to look at, you might better look at #error"
-^go precision
+^geneticOptimizer precision
 ]
 
 { #category : #printing }
@@ -230,7 +237,7 @@ PMGeneralFunctionFit >> printOn: aStream [
 		nextPutAll: 'a ';
 		nextPutAll: self class name;
 		nextPut: $(;
-		print: go;
+		print: geneticOptimizer;
 		nextPutAll: ' with data of size: ';
 	  	print: data size .
 	 dataTruncated ifTrue:
@@ -272,7 +279,7 @@ self data: data
 PMGeneralFunctionFit >> resetResult [
 result :=nil.
 firstResult :=nil.
-go resetBestPoints
+geneticOptimizer resetBestPoints
 ]
 
 { #category : #accessing }

src/Math-Numerical/PMFunctionOptimizer.class.st

@@ -60,9 +60,10 @@ PMFunctionOptimizer >> initialValue: aVector [
 
 { #category : #initialization }
 PMFunctionOptimizer >> initialize [
-		"Private"
-	bestPoints := SortedCollection sortBlock: [ :a :b | a betterThan: b].
-	^super initialize
+	"Private"
+
+	super initialize.
+	bestPoints := SortedCollection sortBlock: [ :a :b | a betterThan: b ]
 ]
 
 { #category : #initialization }