4385132063

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Merge pull request #316 from jecisc/divers-cleanings

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

Class {
        #name : #PMProbabilityDensity,
        #superclass : #Object,
        #instVars : [
                'flatGenerator'
        ],
        #category : #'Math-Distributions-Core'
}

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

        ^'Unknown distribution'
]

{ #category : #creation }
PMProbabilityDensity 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. Default returns nil (must be implemented by subclass)."
        self flag: #todo. "Why not subclassResponsibility?"
        ^ nil
]

{ #category : #information }
PMProbabilityDensity >> acceptanceBetween: aNumber1 and: aNumber2 [
        "Answers the probability of observing a random variable distributed according to the receiver with a value larger than aNumber 1 and lower than or equal to aNumber2. "

        ^ (self distributionValue: aNumber2) - (self distributionValue: aNumber1)
]

{ #category : #information }
PMProbabilityDensity >> approximatedValueAndGradient: aNumber [
        "Private - gradients 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.
         The gradient is computed by approximation."
        | delta parameters dp gradient n |
        parameters := self parameters.
        n := parameters size.
        dp := self value: aNumber.
        delta := Array new: n.
        delta atAllPut: 0.
        gradient := PMVector new: n.
        1 to: n do:
                [ :k |
                  delta at: k put: ( parameters at: k) * 0.0001.
                  self changeParametersBy: delta.
                  gradient at: k put: ( ( ( self value: aNumber) - dp) / ( delta at: k)).
                  delta at: k put: ( delta at: k ) negated.
                  k > 1
                        ifTrue: [ delta at: ( k - 1) put: 0].
                ].
        self changeParametersBy: delta.
        ^Array with: dp with: gradient
]

{ #category : #information }
PMProbabilityDensity >> average [
        "Answer the average of the receiver."
        self subclassResponsibility
]

{ #category : #transformation }
PMProbabilityDensity >> changeParametersBy: aVector [
        self subclassResponsibility
]

{ #category : #creation }
PMProbabilityDensity >> distributionFunction [

        ^PMProbabilityDistributionFunction density: self
]

{ #category : #information }
PMProbabilityDensity >> distributionValue: aNumber [
        "Answers the probability of observing a random variable distributed according to the receiver with a value lower than or equal to aNumber."
        ^self subclassResponsibility
]

{ #category : #accessing }
PMProbabilityDensity >> flatGenerator: aGenerator [
        "To change the default generator (which by default is flat between 0 and 1).
        aGenerator should know how to answer floatValue."

        flatGenerator := aGenerator
]

{ #category : #initialization }
PMProbabilityDensity >> initialize [

        super initialize.
        flatGenerator := PMMitchellMooreGenerator new
]

{ #category : #information }
PMProbabilityDensity >> inverseDistributionValue: aNumber [
        "Answer the number whose distribution value is aNumber.
         NOTE: Subclass MUST NOT overwrite this method."
        ^(aNumber between: 0 and: 1)
                        ifTrue: [ self privateInverseDistributionValue: aNumber]
                        ifFalse:[ self error: 'Illegal argument for inverse distribution value']
]

{ #category : #information }
PMProbabilityDensity >> kurtosis [
        "Answer the kurtosis of the receiver.
        Must be implemented by subclass."

        self subclassResponsibility
]

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

        "Returns an Array containing the parameters of the distribution.
        It is used to print out the distribution and for fitting."

        self subclassResponsibility
]

{ #category : #display }
PMProbabilityDensity >> printOn: aStream [

        | params |
        aStream nextPutAll: self class distributionName.
        (params := self parameters) ifNotNil: [
                | first |
                first := true.
                aStream nextPut: $(.
                params do: [ :each |
                        first
                                ifTrue: [ first := false ]
                                ifFalse: [ aStream nextPut: $, ].
                        aStream space.
                        each printOn: aStream ].
                aStream nextPut: $) ]
]

{ #category : #private }
PMProbabilityDensity >> privateInverseDistributionValue: aNumber [
        "Private - Answer the number whose distribution is aNumber.
        NOTE: Subclass may overwrite this method for faster computation."
        ^( PMCDFNewtonZeroFinder function: [ :x | ( self distributionValue: x) - aNumber] derivative: self)
                initialValue: self average ; evaluate
]

{ #category : #information }
PMProbabilityDensity >> random [
        "Answer a random number distributed according to the receiver."
        ^self privateInverseDistributionValue: flatGenerator floatValue
]

{ #category : #information }
PMProbabilityDensity >> skewness [
        "Answer the skewness of the receiver.
         Must be implemented by subclass."

        self subclassResponsibility
]

{ #category : #information }
PMProbabilityDensity >> standardDeviation [
        "Answer the standard deviation of the receiver.
        NOTE: At least one of the methods variance or standardDeviation must be implemented by the subclass."
        ^self variance sqrt
]

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

{ #category : #information }
PMProbabilityDensity >> 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."
        ^self approximatedValueAndGradient: aNumber
]

{ #category : #information }
PMProbabilityDensity >> variance [
        "Answer the variance of the receiver.
        NOTE: At least one of the methods variance or standardDeviation must be implemented by the subclass."
        ^self standardDeviation squared
]

1	Class {
2	#name : #PMProbabilityDensity,
3	#superclass : #Object,
4	#instVars : [
5	'flatGenerator'
6	],
7	#category : #'Math-Distributions-Core'
8	}
9
10	{ #category : #information }
11	PMProbabilityDensity class >> distributionName [	×
12		×
13	^'Unknown distribution'	×
14	]	×
15
16	{ #category : #creation }
17	PMProbabilityDensity class >> fromHistogram: aHistogram [	×
18	"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. Default returns nil (must be implemented by subclass)."	×
19	self flag: #todo. "Why not subclassResponsibility?"	×
20	^ nil	×
21	]	×
22
23	{ #category : #information }
24	PMProbabilityDensity >> acceptanceBetween: aNumber1 and: aNumber2 [	3✔
25	"Answers the probability of observing a random variable distributed according to the receiver with a value larger than aNumber 1 and lower than or equal to aNumber2. "	3✔
26		3✔
27	^ (self distributionValue: aNumber2) - (self distributionValue: aNumber1)	3✔
28	]	3✔
29
30	{ #category : #information }
31	PMProbabilityDensity >> approximatedValueAndGradient: aNumber [	3✔
32	"Private - gradients 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.	3✔
33	The gradient is computed by approximation."	3✔
34	\| delta parameters dp gradient n \|	3✔
35	parameters := self parameters.	3✔
36	n := parameters size.	3✔
37	dp := self value: aNumber.	3✔
38	delta := Array new: n.	3✔
39	delta atAllPut: 0.	3✔
40	gradient := PMVector new: n.	3✔
41	1 to: n do:	3✔
42	[ :k \|	3✔
43	delta at: k put: ( parameters at: k) * 0.0001.	3✔
44	self changeParametersBy: delta.	3✔
45	gradient at: k put: ( ( ( self value: aNumber) - dp) / ( delta at: k)).	3✔
46	delta at: k put: ( delta at: k ) negated.	3✔
47	k > 1	3✔
48	ifTrue: [ delta at: ( k - 1) put: 0].	3✔
49	].	3✔
50	self changeParametersBy: delta.	3✔
51	^Array with: dp with: gradient	3✔
52	]	3✔
53
54	{ #category : #information }
55	PMProbabilityDensity >> average [
56	"Answer the average of the receiver."
57	self subclassResponsibility
58	]
59
60	{ #category : #transformation }
61	PMProbabilityDensity >> changeParametersBy: aVector [
62	self subclassResponsibility
63	]
64
65	{ #category : #creation }
66	PMProbabilityDensity >> distributionFunction [	×
67		×
68	^PMProbabilityDistributionFunction density: self	×
69	]	×
70
71	{ #category : #information }
72	PMProbabilityDensity >> distributionValue: aNumber [
73	"Answers the probability of observing a random variable distributed according to the receiver with a value lower than or equal to aNumber."
74	^self subclassResponsibility
75	]
76
77	{ #category : #accessing }
78	PMProbabilityDensity >> flatGenerator: aGenerator [	×
79	"To change the default generator (which by default is flat between 0 and 1).	×
80	aGenerator should know how to answer floatValue."	×
81		×
82	flatGenerator := aGenerator	×
83	]	×
84
85	{ #category : #initialization }
86	PMProbabilityDensity >> initialize [	3✔
87		3✔
88	super initialize.	3✔
89	flatGenerator := PMMitchellMooreGenerator new	3✔
90	]	3✔
91
92	{ #category : #information }
93	PMProbabilityDensity >> inverseDistributionValue: aNumber [	3✔
94	"Answer the number whose distribution value is aNumber.	3✔
95	NOTE: Subclass MUST NOT overwrite this method."	3✔
96	^(aNumber between: 0 and: 1)	3✔
97	ifTrue: [ self privateInverseDistributionValue: aNumber]	3✔
98	ifFalse:[ self error: 'Illegal argument for inverse distribution value']	3✔
99	]	3✔
100
101	{ #category : #information }
102	PMProbabilityDensity >> kurtosis [
103	"Answer the kurtosis of the receiver.
104	Must be implemented by subclass."
105
106	self subclassResponsibility
107	]
108
109	{ #category : #information }
110	PMProbabilityDensity >> parameters [
111
112	"Returns an Array containing the parameters of the distribution.
113	It is used to print out the distribution and for fitting."
114
115	self subclassResponsibility
116	]
117
118	{ #category : #display }
119	PMProbabilityDensity >> printOn: aStream [	3✔
120		3✔
121	\| params \|	3✔
122	aStream nextPutAll: self class distributionName.	3✔
123	(params := self parameters) ifNotNil: [	3✔
124	\| first \|	3✔
125	first := true.	3✔
126	aStream nextPut: $(.	3✔
127	params do: [ :each \|	3✔
128	first	3✔
129	ifTrue: [ first := false ]	3✔
130	ifFalse: [ aStream nextPut: $, ].	3✔
131	aStream space.	3✔
132	each printOn: aStream ].	3✔
133	aStream nextPut: $) ]	3✔
134	]	3✔
135
136	{ #category : #private }
137	PMProbabilityDensity >> privateInverseDistributionValue: aNumber [	3✔
138	"Private - Answer the number whose distribution is aNumber.	3✔
139	NOTE: Subclass may overwrite this method for faster computation."	3✔
140	^( PMCDFNewtonZeroFinder function: [ :x \| ( self distributionValue: x) - aNumber] derivative: self)	3✔
141	initialValue: self average ; evaluate	3✔
142	]	3✔
143
144	{ #category : #information }
145	PMProbabilityDensity >> random [	×
146	"Answer a random number distributed according to the receiver."	×
147	^self privateInverseDistributionValue: flatGenerator floatValue	×
148	]	×
149
150	{ #category : #information }
151	PMProbabilityDensity >> skewness [
152	"Answer the skewness of the receiver.
153	Must be implemented by subclass."
154
155	self subclassResponsibility
156	]
157
158	{ #category : #information }
159	PMProbabilityDensity >> standardDeviation [	3✔
160	"Answer the standard deviation of the receiver.	3✔
161	NOTE: At least one of the methods variance or standardDeviation must be implemented by the subclass."	3✔
162	^self variance sqrt	3✔
163	]	3✔
164
165	{ #category : #information }
166	PMProbabilityDensity >> value: aNumber [
167	"Answers the probability that a random variable distributed according to the receiver
168	gives a value between aNumber and aNumber + espilon (infinitesimal interval)."
169	self subclassResponsibility
170	]
171
172	{ #category : #information }
173	PMProbabilityDensity >> valueAndGradient: aNumber [	3✔
174	"Answers an Array containing the value of the receiver at aNumber	3✔
175	and the gradient of the receiver's respective to the receiver's	3✔
176	parameters evaluated at aNumber."	3✔
177	^self approximatedValueAndGradient: aNumber	3✔
178	]	3✔
179
180	{ #category : #information }
181	PMProbabilityDensity >> variance [	3✔
182	"Answer the variance of the receiver.	3✔
183	NOTE: At least one of the methods variance or standardDeviation must be implemented by the subclass."	3✔
184	^self standardDeviation squared	3✔
185	]	3✔

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