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mean_var_testt multinomialt multivariate_normalt negative_binomialt noncentral_Ft noncentral_chi_squaret normalt permutationt poissont randintt randomt random_integerst seedt standard_normalt uniformi����Ni c C s< | d k s | d k r% t j � n t j | | f � d S( Ni ( t mtR ( t xt y( ( sC /usr/lib64/python2.7/site-packages/numpy/oldnumeric/random_array.pyR s
c C s t d � � d S( Ns� If you want to save the state of the random number generator.
Then you should use obj = numpy.random.get_state() followed by.
numpy.random.set_state(obj).( t NotImplementedError( ( ( sC /usr/lib64/python2.7/site-packages/numpy/oldnumeric/random_array.pyR s c C s" | g k r d } n t j | � S( s@ random(n) or random([n, m, ...]) returns array of random numbersN( t NoneR t
random_sample( t shape( ( sC /usr/lib64/python2.7/site-packages/numpy/oldnumeric/random_array.pyR s c C s( | g k r d } n t j | | | � S( sc uniform(minimum, maximum, shape=[]) returns array of given shape of random reals
in given rangeN( R R R ( t minimumt maximumR ( ( sC /usr/lib64/python2.7/site-packages/numpy/oldnumeric/random_array.pyR ! s c C s� t | t � s t d � � n | d k r9 | } d } n t | t � sW t d � � n | | t | � } t | t j � r� | | j t j � S| t | � 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( t
isinstancet intR R R t npt ndarrayt astype( R R R t a( ( sC /usr/lib64/python2.7/site-packages/numpy/oldnumeric/random_array.pyR ( s i c C s t | | d | � S( sR random_integers(max, min=1, shape=[]) = random integers in range min-max inclusivei ( R ( R R R ( ( sC /usr/lib64/python2.7/site-packages/numpy/oldnumeric/random_array.pyR 8 s c C s
t j | � S( s2 permutation(n) = a permutation of indices range(n)( R R ( t n( ( sC /usr/lib64/python2.7/site-packages/numpy/oldnumeric/random_array.pyR < s c C s" | g k r d } n t j | � 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 R ( R ( ( sC /usr/lib64/python2.7/site-packages/numpy/oldnumeric/random_array.pyR @ s c C s( | g k r d } n t j | | | � 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 meant stdR ( ( sC /usr/lib64/python2.7/site-packages/numpy/oldnumeric/random_array.pyR H s c C s( | g k r d } n t j | | | � S( sU 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.N( R R R
( R' t covR ( ( sC /usr/lib64/python2.7/site-packages/numpy/oldnumeric/random_array.pyR
P s c C s% | g k r d } n t j | | � S( s� exponential(mean, n) or exponential(mean, [n, m, ...]) returns array
of random numbers exponentially distributed with specified meanN( R R R ( R' R ( ( sC /usr/lib64/python2.7/site-packages/numpy/oldnumeric/random_array.pyR b s c C s( | g k r d } n t j | | | � S( sW beta(a, b) or beta(a, b, [n, m, ...]) returns array of beta distributed random numbers.N( R R R ( R% t bR ( ( sC /usr/lib64/python2.7/site-packages/numpy/oldnumeric/random_array.pyR i s c C s( | g k r d } n t j | | | � S( sZ gamma(a, r) or gamma(a, r, [n, m, ...]) returns array of gamma distributed random numbers.N( R R R ( R% t rR ( ( sC /usr/lib64/python2.7/site-packages/numpy/oldnumeric/random_array.pyR o s c C s( | g k r d } n t j | | | � 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( R R t f( t dfnt dfdR ( ( sC /usr/lib64/python2.7/site-packages/numpy/oldnumeric/random_array.pyR u s c C s+ | g k r d } n t j | | | | � 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( R R t noncentral_f( R- R. t nconcR ( ( sC /usr/lib64/python2.7/site-packages/numpy/oldnumeric/random_array.pyR { s c C s% | g k r d } n t j | | � 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 chisquare( t dfR ( ( sC /usr/lib64/python2.7/site-packages/numpy/oldnumeric/random_array.pyR � s c C s( | g k r d } n t j | | | � S( s� 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_chisquare( R2 R0 R ( ( sC /usr/lib64/python2.7/site-packages/numpy/oldnumeric/random_array.pyR
� s c C s( | g k r d } n t j | | | � 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 R ( t trialst pR ( ( sC /usr/lib64/python2.7/site-packages/numpy/oldnumeric/random_array.pyR � s c C s( | g k r d } n t j | | | � 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 R ( R4 R5 R ( ( sC /usr/lib64/python2.7/site-packages/numpy/oldnumeric/random_array.pyR � s c C s( | g k r d } n t j | | | � S( s� 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.-np.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.N( R R R ( R4 t probsR ( ( sC /usr/lib64/python2.7/site-packages/numpy/oldnumeric/random_array.pyR � s c C s% | g k r d } n t j | | � S( s� poisson(mean) or poisson(mean, [n, m, ...]) returns array of poisson
distributed random integers with specified mean.N( R R R ( R' R ( ( sC /usr/lib64/python2.7/site-packages/numpy/oldnumeric/random_array.pyR � s c
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k rNt d � n dE | _ t | d | d f d | d d � t d d � } t | d | f | | d d � t t j d d g � t j d d g d d g f � � } d! G| GH| j dF k r t d" � n t t j d d g � t j d d g d d g g � d d g � } d# GH| GH| j dG k rpt d" � n t t j d$ d d% g � t j d d d g d d d g d d d g g � d � } t j | d d �d } d& GH| GH| | } d' GHt j t j | � | � d( GHt d) d* d � } t | d+ d, d- � t d. d d � } t | d/ dH dJ � t d0 d � } t | d1 d2 d3 d t j d d0 � � t d) d* d � } t | d4 d5 d6 � t d7 d � } t | d8 d d d9 � d: GHt d; d d; � GHd< GHt d; d d; g � GHd= GHt d; d> d d> g d? � } | GHd@ Gt j | d d �dA GHd S(K Ni s Failed seed test.s First random number iss" Average of 10000 random numbers isi' R7 i g ��@i
i� i s random returned wrong shapes' Average of 100 by 100 random numbers isg �?g333333�?s 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 g @g @s$ 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 normalsi����id 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]]g ���@g @g $@s beta(5.,10.) random numbersg Zd;�O�?gy�&1��?g{�G�z�?s gamma(.01,2.) random numbersg &@s5 chi squared random numbers with 11 degrees of freedomi i s1 F random numbers with 5 and 10 degrees of freedomg �?g�������?g I@s# poisson random numbers with mean 50g�Q� ��?sG
Each element is the result of 16 binomial trials with probability 0.5:i sP
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