4385132063

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Merge pull request #316 from jecisc/divers-cleanings

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57.14

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

Class {
        #name : #PMMultivariateNormalDistribution,
        #superclass : #PMProbabilityDensity,
        #instVars : [
                'meanVector',
                'covarianceMatrix'
        ],
        #category : #'Math-Distributions-Normal'
}

{ #category : #'instance creation' }
PMMultivariateNormalDistribution class >> meanVector: aMeanVector covarianceMatrix: aCovarianceMatrix [
        ^ self new
                initializeMeanVector: aMeanVector
                covarianceMatrix: aCovarianceMatrix;
                yourself
]

{ #category : #information }
PMMultivariateNormalDistribution >> average [
        ^ self meanVector
]

{ #category : #transformation }
PMMultivariateNormalDistribution >> changeParametersBy: aVector [
        "Modify the parameters of the receiver by aVector."
        meanVector := meanVector + aVector first.
        covarianceMatrix := covarianceMatrix + aVector second
]

{ #category : #information }
PMMultivariateNormalDistribution >> covarianceMatrix [
        ^ covarianceMatrix
]

{ #category : #information }
PMMultivariateNormalDistribution >> distributionValue: aNumber [
        "Answers the probability of observing a random variable distributed according to the receiver with a value lower than or equal to aNumber. Also known as the cumulative density function (CDF)."

        self shouldBeImplemented
]

{ #category : #initialization }
PMMultivariateNormalDistribution >> initializeMeanVector: aMeanVector covarianceMatrix: aCovarianceMatrix [
        meanVector := aMeanVector.
        covarianceMatrix := aCovarianceMatrix
]

{ #category : #information }
PMMultivariateNormalDistribution >> kurtosis [
        "Kurtosis is a measure of the 'tailedness' of the probability distribution of a real-valued random variable. Not defined for a multivariate normal distribution"
        self shouldNotImplement
]

{ #category : #information }
PMMultivariateNormalDistribution >> meanVector [
        ^ meanVector
]

{ #category : #information }
PMMultivariateNormalDistribution >> parameters [
        "Returns an Array containing the parameters of the distribution.
        It is used to print out the distribution and for fitting."

        ^ { meanVector . covarianceMatrix }
]

{ #category : #information }
PMMultivariateNormalDistribution >> power [
        "Number of dimensions of a multivariate normal distribution"
        ^ meanVector size
]

{ #category : #information }
PMMultivariateNormalDistribution >> random [
        "Answer a vector of random numbers distributed accroding to the receiver."
        | standardNormalRandom upperTriangular |

        standardNormalRandom := (1 to: self power) asPMVector
                collect: [ :each | PMNormalDistribution random ].

        upperTriangular := covarianceMatrix choleskyDecomposition.

        ^ upperTriangular transpose * standardNormalRandom + meanVector
]

{ #category : #information }
PMMultivariateNormalDistribution >> skewness [
        "Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. Not defined for a multivariate normal distribution"
        self shouldNotImplement
]

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

        ^ (-1/2 * (aVector - meanVector) * covarianceMatrix inverse * (aVector - meanVector)) exp / (((2 * Float pi) raisedTo: self power) * covarianceMatrix determinant) sqrt
]

1	Class {
2	#name : #PMMultivariateNormalDistribution,
3	#superclass : #PMProbabilityDensity,
4	#instVars : [
5	'meanVector',
6	'covarianceMatrix'
7	],
8	#category : #'Math-Distributions-Normal'
9	}
10
11	{ #category : #'instance creation' }
12	PMMultivariateNormalDistribution class >> meanVector: aMeanVector covarianceMatrix: aCovarianceMatrix [	3✔
13	^ self new	3✔
14	initializeMeanVector: aMeanVector	3✔
15	covarianceMatrix: aCovarianceMatrix;	3✔
16	yourself	3✔
17	]	3✔
18
19	{ #category : #information }
20	PMMultivariateNormalDistribution >> average [	3✔
21	^ self meanVector	3✔
22	]	3✔
23
24	{ #category : #transformation }
25	PMMultivariateNormalDistribution >> changeParametersBy: aVector [	×
26	"Modify the parameters of the receiver by aVector."	×
27	meanVector := meanVector + aVector first.	×
28	covarianceMatrix := covarianceMatrix + aVector second	×
29	]	×
30
31	{ #category : #information }
32	PMMultivariateNormalDistribution >> covarianceMatrix [	×
33	^ covarianceMatrix	×
34	]	×
35
36	{ #category : #information }
37	PMMultivariateNormalDistribution >> distributionValue: aNumber [	×
38	"Answers the probability of observing a random variable distributed according to the receiver with a value lower than or equal to aNumber. Also known as the cumulative density function (CDF)."	×
39		×
40	self shouldBeImplemented	×
41	]	×
42
43	{ #category : #initialization }
44	PMMultivariateNormalDistribution >> initializeMeanVector: aMeanVector covarianceMatrix: aCovarianceMatrix [	3✔
45	meanVector := aMeanVector.	3✔
46	covarianceMatrix := aCovarianceMatrix	3✔
47	]	3✔
48
49	{ #category : #information }
50	PMMultivariateNormalDistribution >> kurtosis [	×
51	"Kurtosis is a measure of the 'tailedness' of the probability distribution of a real-valued random variable. Not defined for a multivariate normal distribution"	×
52	self shouldNotImplement	×
53	]	×
54
55	{ #category : #information }
56	PMMultivariateNormalDistribution >> meanVector [	3✔
57	^ meanVector	3✔
58	]	3✔
59
60	{ #category : #information }
61	PMMultivariateNormalDistribution >> parameters [	×
62	"Returns an Array containing the parameters of the distribution.	×
63	It is used to print out the distribution and for fitting."	×
64		×
65	^ { meanVector . covarianceMatrix }	×
66	]	×
67
68	{ #category : #information }
69	PMMultivariateNormalDistribution >> power [	3✔
70	"Number of dimensions of a multivariate normal distribution"	3✔
71	^ meanVector size	3✔
72	]	3✔
73
74	{ #category : #information }
75	PMMultivariateNormalDistribution >> random [	3✔
76	"Answer a vector of random numbers distributed accroding to the receiver."	3✔
77	\| standardNormalRandom upperTriangular \|	3✔
78		3✔
79	standardNormalRandom := (1 to: self power) asPMVector	3✔
80	collect: [ :each \| PMNormalDistribution random ].	3✔
81		3✔
82	upperTriangular := covarianceMatrix choleskyDecomposition.	3✔
83		3✔
84	^ upperTriangular transpose * standardNormalRandom + meanVector	3✔
85	]	3✔
86
87	{ #category : #information }
88	PMMultivariateNormalDistribution >> skewness [	×
89	"Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. Not defined for a multivariate normal distribution"	×
90	self shouldNotImplement	×
91	]	×
92
93	{ #category : #information }
94	PMMultivariateNormalDistribution >> value: aVector [	3✔
95	"Answers the probability that a random variable distributed according to the receiver gives a value between aVector and aVector + espilon (infinitesimal interval)."	3✔
96		3✔
97	^ (-1/2 * (aVector - meanVector) * covarianceMatrix inverse * (aVector - meanVector)) exp / (((2 * Float pi) raisedTo: self power) * covarianceMatrix determinant) sqrt	3✔
98	]	3✔

PolyMathOrg / PolyMath / 4385132063

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