4385132063

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Merge pull request #316 from jecisc/divers-cleanings

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/src/Math-Distributions/PMNormalDistribution.class.st

Class {
        #name : #PMNormalDistribution,
        #superclass : #PMProbabilityDensity,
        #instVars : [
                'mu',
                'sigma'
        ],
        #classVars : [
                'NextRandom'
        ],
        #category : #'Math-Distributions-Normal'
}

{ #category : #information }
PMNormalDistribution class >> boxMullerTransform [
        | random v1 v2 w y |
        random := Random new.

        [
                v1 :=random next * 2 - 1.
                v2 := random next * 2 - 1.
                w := v1 squared + v2 squared.
                w > 1
        ] whileTrue.

        y := (w ln * 2 negated / w) sqrt.
        v1 := y * v1.
        NextRandom := y * v2.
        ^v1
]

{ #category : #information }
PMNormalDistribution class >> distributionName [

        ^'Normal distribution'
]

{ #category : #creation }
PMNormalDistribution class >> fromHistogram: aHistogram [
        "Create an instance of the receiver with parameters estimated from the given histogram using best guesses. This method can be used to find the initial values for a fit."

        ^ self
                new: aHistogram average
                sigma: aHistogram standardDeviation
]

{ #category : #creation }
PMNormalDistribution class >> new [
        "Create a new instance of the receiver with mu=0 and sigma=1."
        ^self new: 0 sigma: 1
]

{ #category : #creation }
PMNormalDistribution class >> new: aNumber1 sigma: aNumber2 [
                "Create a new instance of the receiver with given mu and sigma."
        ^super new initialize: aNumber1 sigma: aNumber2
]

{ #category : #information }
PMNormalDistribution class >> random [
        "Answer a random number distributed according to a (0,1) normal distribution."

        | v1  |
        NextRandom
                ifNil: [

                        v1 := self boxMullerTransform ]
                ifNotNil: [ v1 := NextRandom.
                        NextRandom := nil ].
        ^ v1
]

{ #category : #information }
PMNormalDistribution >> average [
        "Answer the average of the receiver."
        ^mu
]

{ #category : #transformation }
PMNormalDistribution >> changeParametersBy: aVector [
        "Modify the parameters of the receiver by aVector."
        mu := mu + ( aVector at: 1).
        sigma := sigma + ( aVector at: 2)
]

{ #category : #information }
PMNormalDistribution >> distributionValue: aNumber [
        "Answers the probability of observing a random variable distributed according to the receiver with a value lower than or equal to aNumber."
        ^PMErfApproximation new value: ( ( aNumber - mu) / sigma)
]

{ #category : #initialization }
PMNormalDistribution >> initialize: aNumber1 sigma: aNumber2 [
        mu := aNumber1.
        sigma := aNumber2
]

{ #category : #information }
PMNormalDistribution >> kurtosis [
        "Answer the kurtosis of the receiver."
        ^0
]

{ #category : #information }
PMNormalDistribution >> parameters [

        ^Array with: mu with: sigma
]

{ #category : #information }
PMNormalDistribution >> random [
        "Answer a random number distributed according to the receiver."

        ^ self class random * sigma + mu
]

{ #category : #information }
PMNormalDistribution >> skewness [
        "Answer the skewness of the receiver."
        ^0
]

{ #category : #information }
PMNormalDistribution >> standardDeviation [
        "Answer the standard deviation of the receiver."
        ^sigma
]

{ #category : #information }
PMNormalDistribution >> value: aNumber [
        "Answers the probability that a random variable distributed according to the receiver gives a value between aNumber and aNumber + espilon (infinitesimal interval)."

        ^( PMErfApproximation new normal: (aNumber - mu) / sigma) / sigma
]

{ #category : #information }
PMNormalDistribution >> valueAndGradient: aNumber [
        "Answers an Array containing the value of the receiver at aNumber and the gradient of the receiver's respective to the receiver's parameters evaluated at aNumber."
        | dp y |
        y := ( aNumber - mu) / sigma.
        dp := ( PMErfApproximation new normal: y) / sigma.
        ^Array with: dp
                   with: ( PMVector with: dp * y / sigma
                                                         with: dp * ( y squared - 1) / sigma)
]

1	Class {
2	#name : #PMNormalDistribution,
3	#superclass : #PMProbabilityDensity,
4	#instVars : [
5	'mu',
6	'sigma'
7	],
8	#classVars : [
9	'NextRandom'
10	],
11	#category : #'Math-Distributions-Normal'
12	}
13
14	{ #category : #information }
15	PMNormalDistribution class >> boxMullerTransform [	3✔
16	\| random v1 v2 w y \|	3✔
17	random := Random new.	3✔
18		3✔
19	[	3✔
20	v1 :=random next * 2 - 1.	3✔
21	v2 := random next * 2 - 1.	3✔
22	w := v1 squared + v2 squared.	3✔
23	w > 1	3✔
24	] whileTrue.	3✔
25		3✔
26	y := (w ln * 2 negated / w) sqrt.	3✔
27	v1 := y * v1.	3✔
28	NextRandom := y * v2.	3✔
29	^v1	3✔
30	]	3✔
31
32	{ #category : #information }
33	PMNormalDistribution class >> distributionName [	×
34		×
35	^'Normal distribution'	×
36	]	×
37
38	{ #category : #creation }
39	PMNormalDistribution class >> fromHistogram: aHistogram [	×
40	"Create an instance of the receiver with parameters estimated from the given histogram using best guesses. This method can be used to find the initial values for a fit."	×
41		×
42	^ self	×
43	new: aHistogram average	×
44	sigma: aHistogram standardDeviation	×
45	]	×
46
47	{ #category : #creation }
48	PMNormalDistribution class >> new [	3✔
49	"Create a new instance of the receiver with mu=0 and sigma=1."	3✔
50	^self new: 0 sigma: 1	3✔
51	]	3✔
52
53	{ #category : #creation }
54	PMNormalDistribution class >> new: aNumber1 sigma: aNumber2 [	3✔
55	"Create a new instance of the receiver with given mu and sigma."	3✔
56	^super new initialize: aNumber1 sigma: aNumber2	3✔
57	]	3✔
58
59	{ #category : #information }
60	PMNormalDistribution class >> random [	3✔
61	"Answer a random number distributed according to a (0,1) normal distribution."	3✔
62		3✔
63	\| v1 \|	3✔
64	NextRandom	3✔
65	ifNil: [	3✔
66		3✔
67	v1 := self boxMullerTransform ]	3✔
68	ifNotNil: [ v1 := NextRandom.	3✔
69	NextRandom := nil ].	3✔
70	^ v1	3✔
71	]	3✔
72
73	{ #category : #information }
74	PMNormalDistribution >> average [	3✔
75	"Answer the average of the receiver."	3✔
76	^mu	3✔
77	]	3✔
78
79	{ #category : #transformation }
80	PMNormalDistribution >> changeParametersBy: aVector [	×
81	"Modify the parameters of the receiver by aVector."	×
82	mu := mu + ( aVector at: 1).	×
83	sigma := sigma + ( aVector at: 2)	×
84	]	×
85
86	{ #category : #information }
87	PMNormalDistribution >> distributionValue: aNumber [	3✔
88	"Answers the probability of observing a random variable distributed according to the receiver with a value lower than or equal to aNumber."	3✔
89	^PMErfApproximation new value: ( ( aNumber - mu) / sigma)	3✔
90	]	3✔
91
92	{ #category : #initialization }
93	PMNormalDistribution >> initialize: aNumber1 sigma: aNumber2 [	3✔
94	mu := aNumber1.	3✔
95	sigma := aNumber2	3✔
96	]	3✔
97
98	{ #category : #information }
99	PMNormalDistribution >> kurtosis [	×
100	"Answer the kurtosis of the receiver."	×
101	^0	×
102	]	×
103
104	{ #category : #information }
105	PMNormalDistribution >> parameters [	×
106		×
107	^Array with: mu with: sigma	×
108	]	×
109
110	{ #category : #information }
111	PMNormalDistribution >> random [	3✔
112	"Answer a random number distributed according to the receiver."	3✔
113		3✔
114	^ self class random * sigma + mu	3✔
115	]	3✔
116
117	{ #category : #information }
118	PMNormalDistribution >> skewness [	×
119	"Answer the skewness of the receiver."	×
120	^0	×
121	]	×
122
123	{ #category : #information }
124	PMNormalDistribution >> standardDeviation [	3✔
125	"Answer the standard deviation of the receiver."	3✔
126	^sigma	3✔
127	]	3✔
128
129	{ #category : #information }
130	PMNormalDistribution >> value: aNumber [	3✔
131	"Answers the probability that a random variable distributed according to the receiver gives a value between aNumber and aNumber + espilon (infinitesimal interval)."	3✔
132		3✔
133	^( PMErfApproximation new normal: (aNumber - mu) / sigma) / sigma	3✔
134	]	3✔
135
136	{ #category : #information }
137	PMNormalDistribution >> valueAndGradient: aNumber [	×
138	"Answers an Array containing the value of the receiver at aNumber and the gradient of the receiver's respective to the receiver's parameters evaluated at aNumber."	×
139	\| dp y \|	×
140	y := ( aNumber - mu) / sigma.	×
141	dp := ( PMErfApproximation new normal: y) / sigma.	×
142	^Array with: dp	×
143	with: ( PMVector with: dp * y / sigma	×
144	with: dp * ( y squared - 1) / sigma)	×
145	]	×

PolyMathOrg / PolyMath / 4385132063

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