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__module__( ( ( t9 /usr/lib64/python2.4/site-packages/Numeric/RandomArray.pyR
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| | ƒ d S( sb seed(x, y), set the seed using the integers x, y;
Set a random one from clock if y == 0
s seed requires integer arguments.i Ni
i i ( t typet xt IntTypet yR t timet tt intt matht log10t ndigitst baset ranlibt set_seeds( R R R R R
R ( ( R t seed s &
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t i ƒ S( s Return the current seed pairN( R t get_seeds( ( ( R t get_seed! s c C s‘ t | t ƒ o
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isinstancet shapeR t lent Numerict multiplyt reducet nt applyt funt argst s( R R R R R ( ( R t _build_random_array% s
c C s t t i f | ƒ S( s@ random(n) or random([n, m, ...]) returns array of random numbersN( R R t sampleR ( R ( ( R t random6 s c C s | | | t | ƒ S( sd uniform(minimum, maximum, shape=[]) returns array of given shape of random reals
in given rangeN( t minimumt maximumR" R ( R# R$ R ( ( R t uniform: s c C s¨ t | t ƒ p
t d ‚ n | d j o | } d } n t | t ƒ p
t d ‚ n | | t | ƒ } t | t i
ƒ o | | i t i ƒ Sn | t
| ƒ Sd S( sk randint(min, max, shape=[]) = random integers >=min, < max
If max not given, random integers >= 0, <mins' randint requires first argument integeri s( randint requires second argument integerN( R R# R R R$ t NoneR" R t aR t ArrayTypet astypet IntR ( R# R$ R R' ( ( R t randint? s
i c C s t | | d | ƒ S( sR random_integers(max, min=1, shape=[]) = random integers in range min-max inclusivei N( R+ R# R$ R ( R$ R# R ( ( R t random_integersO s c C s t i t | ƒ ƒ S( s2 permutation(n) = a permutation of indices range(n)N( R t argsortR" R ( R ( ( R t permutationS s c C s t t i f | ƒ S( s¢ standard_normal(n) or standard_normal([n, m, ...]) returns array of
random numbers normally distributed with mean 0 and standard
deviation 1N( R R t standard_normalR ( R ( ( R R/ W s c C s t | ƒ } | | | S( s¬ normal(mean, std, n) or normal(mean, std, [n, m, ...]) returns
array of random numbers randomly distributed with specified mean and
standard deviationN( R/ R R t stdt mean( R1 R0 R R ( ( R t normal] s c C sŠ t i | ƒ } t i | ƒ } t | i ƒ d j o
t d ‚ n t | i ƒ d j p | i d | i d j o
t d ‚ n | i d | i d j o
t d ‚ n t | t ƒ o
| g } n t | ƒ } | i | i d ƒ t i
t i i | ƒ ƒ } t i i | d t | ƒ d !ƒ | i d f | _ t i | ƒ \ } } } t i | t i | ƒ | ƒ } t i | | | ƒ | | _ | S( s‹ multivariate_normal(mean, cov) or multivariate_normal(mean, cov, [m, n, ...])
returns an array containing multivariate normally distributed random numbers
with specified mean and covariance.
mean must be a 1 dimensional array. cov must be a square two dimensional
array with the same number of rows and columns as mean has elements.
The first form returns a single 1-D array containing a multivariate
normal.
The second form returns an array of shape (m, n, ..., cov.shape[0]).
In this case, output[i,j,...,:] is a 1-D array containing a multivariate
normal.i s mean must be 1 dimensional.i i s% cov must be 2 dimensional and square.s# mean and cov must have same length.N( R t arrayR1 t covR R R R R t listt final_shapet appendR R/ R R R t
LinearAlgebrat singular_value_decompositiont uR t vt matrixmultiplyt sqrtt add( R1 R4 R R6 R R: R; R ( ( R t multivariate_normald s(
1
3 c C sF t | ƒ } t i | | ƒ t i d | | ƒ t i | | | ƒ | S( sŠ exponential(mean, n) or exponential(mean, [n, m, ...]) returns array
of random numbers exponentially distributed with specified meanf0.0N( R" R R R t logt subtractR R1 ( R1 R R ( ( R t exponential“ s c C s t t i | | f | ƒ S( sW beta(a, b) or beta(a, b, [n, m, ...]) returns array of beta distributed random numbers.N( R R t betaR' t bR ( R' RD R ( ( R RC Ÿ s c C s t t i | | f | ƒ S( sZ gamma(a, r) or gamma(a, r, [n, m, ...]) returns array of gamma distributed random numbers.N( R R t gammaR' t rR ( R' RF R ( ( R RE £ s c C s" t | | ƒ | t | | ƒ | S( s± F(dfn, dfd) or F(dfn, dfd, [n, m, ...]) returns array of F distributed random numbers with dfn degrees of freedom in the numerator and dfd degrees of freedom in the denominator.N( t
chi_squaret dfnR t dfd( RH RI R ( ( R t F§ s c C s% t | | | ƒ | t | | ƒ | S( s noncentral_F(dfn, dfd, nonc) or noncentral_F(dfn, dfd, nonc, [n, m, ...]) returns array of noncentral F distributed random numbers with dfn degrees of freedom in the numerator and dfd degrees of freedom in the denominator, and noncentrality parameter nconc.N( t noncentral_chi_squareRH t nconcR RG RI ( RH RI RL R ( ( R t noncentral_F« s c C s t t i | f | ƒ S( s chi_square(df) or chi_square(df, [n, m, ...]) returns array of chi squared distributed random numbers with df degrees of freedom.N( R R t chisquaret dfR ( RO R ( ( R RG ¯ s c C s t t i | | f | ƒ S( sÁ noncentral_chi_square(df, nconc) or chi_square(df, nconc, [n, m, ...]) returns array of noncentral chi squared distributed random numbers with df degrees of freedom and noncentrality parameter.N( R R t noncentral_chisquareRO RL R ( RO RL R ( ( R RK ³ s c C s t t i | | f | ƒ S( s binomial(trials, p) or binomial(trials, p, [n, m, ...]) returns array of binomially distributed random integers.
trials is the number of trials in the binomial distribution.
p is the probability of an event in each trial of the binomial distribution.N( R R t binomialt trialst pR ( RR RS R ( ( R RQ · s c C s t t i | | f | ƒ S( sI negative_binomial(trials, p) or negative_binomial(trials, p, [n, m, ...]) returns
array of negative binomially distributed random integers.
trials is the number of trials in the negative binomial distribution.
p is the probability of an event in each trial of the negative binomial distribution.N( R R t negative_binomialRR RS R ( RR RS R ( ( R RT ¾ s c C s± t i | ƒ } t | i ƒ d j o
t d ‚ n t | ƒ t d ƒ j o
| g } n | } | i | i d d ƒ t i
| | i t i
ƒ t i i | ƒ ƒ } | | _ | S( sï multinomial(trials, probs) or multinomial(trials, probs, [n, m, ...]) returns
array of multinomial distributed integer vectors.
trials is the number of trials in each multinomial distribution.
probs is a one dimensional array. There are len(prob)+1 events.
prob[i] is the probability of the i-th event, 0<=i<len(prob).
The probability of event len(prob) is 1.-Numeric.sum(prob).
The first form returns a single 1-D array containing one multinomially
distributed vector.
The second form returns an array of shape (m, n, ..., len(probs)).
In this case, output[i,j,...,:] is a 1-D array containing a multinomially
distributed integer 1-D array.i s probs must be 1 dimensional.i N( R R3 t probsR R R R R6 R7 R t multinomialRR R) t Float32R R R ( RR RU R R6 R ( ( R RV Æ s
- c C s t t i | f | ƒ S( s poisson(mean) or poisson(mean, [n, m, ...]) returns array of poisson
distributed random integers with specifed mean.N( R R t poissonR1 R ( R1 R ( ( R RX â s c
C sÊ t | ƒ d } t i | ƒ | } | | } t i | | ƒ | d } d Gt | ƒ G| GHd G| Gd G| GHd G| Gd G| GH| g j o< t i | | | ƒ d | d d } d G| Gd G| GHn d S(
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Average of s (should be about s ):s2 Variance of those random numbers (should be about f9998.0f3.0f2.0s2 Skewness of those random numbers (should be about (
R R R R t sumt x_meant x_minus_meant x_varR R1 t vart skewt x_skew(
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ƒ t d d ƒ } t | d | f | | d d ƒ t t i d d g ƒ t i d d g d d g f ƒ ƒ } d! G| GH| i dF j o
t d" ‚ n t t i d d g ƒ t i d d g d d g g ƒ d d g ƒ } d# GH| GH| i dG j o
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d% g ƒ t i d d d g d d d g d d d g g ƒ d ƒ } t i | ƒ d } d& GH| GH| | } d' GHt i t i | ƒ | ƒ d( GHt d) d* d ƒ } t | d+ d, d- ƒ t d. d d ƒ } t | d/ d d% d d% d% ƒ t d0 d ƒ } t | d1 d2 d3 d t i d d0 ƒ ƒ t! d) d* d ƒ } t | d4 d5 d6 ƒ t" d7 d ƒ } t | d8 d d d9 ƒ d: GHt# d; d d; ƒ GHd< GHt$ d; d d; g ƒ GHd= GHt% d; d> d d> g d? ƒ } | GHd@ Gt i | ƒ dA GHd S(H Ns Initial seeds Failed seed test.s First random number iss" Average of 10000 random numbers isi' f10000.0i
iè i i i s random returned wrong shapes' Average of 100 by 100 random numbers isf0.5f0.59999999999999998s uniform returned wrong shapes% uniform returned out of desired ranges randint(1, 10, shape=[50])R i2 s permutation(10)s randint(3,9)i i s random_integers(10, shape=[20])i f3.0f2.0s$ standard_normal returned wrong shapes8 normally distributed numbers with mean 2 and variance %fs5 random numbers exponentially distributed with mean %fi s
A multivariate normals( multivariate_normal returned wrong shapes- A 4x3x2 array containing multivariate normalsiœÿÿÿid s< Average of 10000 multivariate normals with mean [-100,0,100]s\ Estimated covariance of 10000 multivariate normals with covariance [[3,2,1],[2,2,1],[1,1,1]]f9999.0f5.0f10.0s beta(5.,10.) random numbersf0.33300000000000002f0.014f0.01s gamma(.01,2.) random numbersf11.0s5 chi squared random numbers with 11 degrees of freedomi i s1 F random numbers with 5 and 10 degrees of freedomf1.25f1.3500000000000001f50.0s# poisson random numbers with mean 50f0.14000000000000001sG
Each element is the result of 16 binomial trials with probability 0.5:i sP
Each element is the result of 16 negative binomial trials with probability 0.5:sX
Each row is the result of 16 multinomial trials with probabilities [0.1, 0.5, 0.1 0.3]:f0.10000000000000001i s Mean = f8.0( i' ( iè i
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