Use machine precision of Pharo

Author: jecisc

src/Math-FunctionFit/PMAnotherGeneticOptimizer.class.st

@@ -19,7 +19,8 @@ Class {
 
 { #category : #information }
 PMAnotherGeneticOptimizer class >> defaultPrecision [
-	^PMFloatingPointMachine new machinePrecision
+
+	^ Float machineEpsilon
 ]
 
 { #category : #'instance creation' }

src/Math-FunctionFit/PMGeneralFunctionFit.class.st

@@ -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: [ go addPointAt: firstResult ].
+	firstResult := go evaluate.
+	self errorType = #squared ifTrue: [ ff := PMFunctionFit function: errorFunction function data: errorFunction data ].
+	ff parameters: firstResult.
+	ff desiredPrecision: Float machineEpsilon.
+	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 }

src/Math-Helpers/PMFloatingPointMachine.class.st

@@ -15,7 +15,6 @@ Class {
 	#superclass : #Object,
 	#instVars : [
 		'defaultNumericalPrecision',
-		'machinePrecision',
 		'negativeMachinePrecision'
 	],
 	#classVars : [
@@ -37,19 +36,6 @@ PMFloatingPointMachine class >> reset [
 	UniqueInstance := nil
 ]
 
-{ #category : #information }
-PMFloatingPointMachine >> computeMachinePrecision [
-
-	| one zero inverseRadix tmp |
-	one := 1.0.
-	zero := 0.0.
-	inverseRadix := one / Float radix asFloat.
-	machinePrecision := one.
-	[
-	tmp := one + machinePrecision.
-	tmp - one = zero ] whileFalse: [ machinePrecision := machinePrecision * inverseRadix ]
-]
-
 { #category : #information }
 PMFloatingPointMachine >> computeNegativeMachinePrecision [
 
@@ -67,17 +53,10 @@ PMFloatingPointMachine >> computeNegativeMachinePrecision [
 { #category : #information }
 PMFloatingPointMachine >> defaultNumericalPrecision [
 
-	defaultNumericalPrecision ifNil: [ defaultNumericalPrecision := self machinePrecision sqrt ].
+	defaultNumericalPrecision ifNil: [ defaultNumericalPrecision := Float machineEpsilon sqrt ].
 	^ defaultNumericalPrecision
 ]
 
-{ #category : #information }
-PMFloatingPointMachine >> machinePrecision [
-
-	machinePrecision ifNil: [ self computeMachinePrecision ].
-	^ machinePrecision
-]
-
 { #category : #information }
 PMFloatingPointMachine >> negativeMachinePrecision [
 

src/Math-Tests-Numerical/PMFloatingPointMachineTestCase.class.st

@@ -32,14 +32,13 @@ PMNumericalMethodsTestCase >> testBissection [
 	"Code Example 5.1"
 
 	| zeroFinder result |
-	zeroFinder := PMBisectionZeroFinder
-		function: [ :x | x errorFunction - 0.9 ].
+	zeroFinder := PMBisectionZeroFinder function: [ :x | x errorFunction - 0.9 ].
 	zeroFinder
 		setPositiveX: 10.0;
 		setNegativeX: 0.0.
 	result := zeroFinder evaluate.
 	self assert: zeroFinder hasConverged.
-	self assert: (result - 1.28155193291605) abs < 1.0e-14
+	self assert: result closeTo: 1.28155193291605
 ]
 
 { #category : #'linear algebra' }
@@ -95,7 +94,7 @@ PMNumericalMethodsTestCase >> testErrorFunction [
 	"simple cases to expect"
 
 	self assert: 0 errorFunction equals: 1 / 2.
-	self assert: Float fmax errorFunction > (1 - PMFloatingPointMachine new machinePrecision).
+	self assert: Float fmax errorFunction > (1 - Float machineEpsilon).
 	"add some code to require initialize to run"
 	PMErfApproximation reset.
 	self assert: Float fmax negated errorFunction < Float fmin