[pypy-svn] r3463 - pypy/trunk/src/pypy/appspace
sanxiyn at codespeak.net
sanxiyn at codespeak.net
Mon Mar 29 03:46:08 CEST 2004
Author: sanxiyn
Date: Mon Mar 29 03:46:07 2004
New Revision: 3463
Added:
pypy/trunk/src/pypy/appspace/random.py
Log:
Imports random.py version 1.60 from CPython CVS.
Added: pypy/trunk/src/pypy/appspace/random.py
==============================================================================
--- (empty file)
+++ pypy/trunk/src/pypy/appspace/random.py Mon Mar 29 03:46:07 2004
@@ -0,0 +1,805 @@
+"""Random variable generators.
+
+ integers
+ --------
+ uniform within range
+
+ sequences
+ ---------
+ pick random element
+ pick random sample
+ generate random permutation
+
+ distributions on the real line:
+ ------------------------------
+ uniform
+ normal (Gaussian)
+ lognormal
+ negative exponential
+ gamma
+ beta
+ pareto
+ Weibull
+
+ distributions on the circle (angles 0 to 2pi)
+ ---------------------------------------------
+ circular uniform
+ von Mises
+
+General notes on the underlying Mersenne Twister core generator:
+
+* The period is 2**19937-1.
+* It is one of the most extensively tested generators in existence
+* Without a direct way to compute N steps forward, the
+ semantics of jumpahead(n) are weakened to simply jump
+ to another distant state and rely on the large period
+ to avoid overlapping sequences.
+* The random() method is implemented in C, executes in
+ a single Python step, and is, therefore, threadsafe.
+
+"""
+
+from warnings import warn as _warn
+from types import MethodType as _MethodType, BuiltinMethodType as _BuiltinMethodType
+from math import log as _log, exp as _exp, pi as _pi, e as _e
+from math import sqrt as _sqrt, acos as _acos, cos as _cos, sin as _sin
+from math import floor as _floor
+
+__all__ = ["Random","seed","random","uniform","randint","choice","sample",
+ "randrange","shuffle","normalvariate","lognormvariate",
+ "expovariate","vonmisesvariate","gammavariate",
+ "gauss","betavariate","paretovariate","weibullvariate",
+ "getstate","setstate","jumpahead", "WichmannHill", "getrandbits"]
+
+NV_MAGICCONST = 4 * _exp(-0.5)/_sqrt(2.0)
+TWOPI = 2.0*_pi
+LOG4 = _log(4.0)
+SG_MAGICCONST = 1.0 + _log(4.5)
+BPF = 53 # Number of bits in a float
+
+# Translated by Guido van Rossum from C source provided by
+# Adrian Baddeley. Adapted by Raymond Hettinger for use with
+# the Mersenne Twister core generator.
+
+import _random
+
+class Random(_random.Random):
+ """Random number generator base class used by bound module functions.
+
+ Used to instantiate instances of Random to get generators that don't
+ share state. Especially useful for multi-threaded programs, creating
+ a different instance of Random for each thread, and using the jumpahead()
+ method to ensure that the generated sequences seen by each thread don't
+ overlap.
+
+ Class Random can also be subclassed if you want to use a different basic
+ generator of your own devising: in that case, override the following
+ methods: random(), seed(), getstate(), setstate() and jumpahead().
+ Optionally, implement a getrandombits() method so that randrange()
+ can cover arbitrarily large ranges.
+
+ """
+
+ VERSION = 2 # used by getstate/setstate
+
+ def __init__(self, x=None):
+ """Initialize an instance.
+
+ Optional argument x controls seeding, as for Random.seed().
+ """
+
+ self.seed(x)
+ self.gauss_next = None
+
+ def seed(self, a=None):
+ """Initialize internal state from hashable object.
+
+ None or no argument seeds from current time.
+
+ If a is not None or an int or long, hash(a) is used instead.
+ """
+
+ if a is None:
+ import time
+ a = long(time.time() * 256) # use fractional seconds
+ super(Random, self).seed(a)
+ self.gauss_next = None
+
+ def getstate(self):
+ """Return internal state; can be passed to setstate() later."""
+ return self.VERSION, super(Random, self).getstate(), self.gauss_next
+
+ def setstate(self, state):
+ """Restore internal state from object returned by getstate()."""
+ version = state[0]
+ if version == 2:
+ version, internalstate, self.gauss_next = state
+ super(Random, self).setstate(internalstate)
+ else:
+ raise ValueError("state with version %s passed to "
+ "Random.setstate() of version %s" %
+ (version, self.VERSION))
+
+## ---- Methods below this point do not need to be overridden when
+## ---- subclassing for the purpose of using a different core generator.
+
+## -------------------- pickle support -------------------
+
+ def __getstate__(self): # for pickle
+ return self.getstate()
+
+ def __setstate__(self, state): # for pickle
+ self.setstate(state)
+
+ def __reduce__(self):
+ return self.__class__, (), self.getstate()
+
+## -------------------- integer methods -------------------
+
+ def randrange(self, start, stop=None, step=1, int=int, default=None,
+ maxwidth=1L<<BPF):
+ """Choose a random item from range(start, stop[, step]).
+
+ This fixes the problem with randint() which includes the
+ endpoint; in Python this is usually not what you want.
+ Do not supply the 'int', 'default', and 'maxwidth' arguments.
+ """
+
+ # This code is a bit messy to make it fast for the
+ # common case while still doing adequate error checking.
+ istart = int(start)
+ if istart != start:
+ raise ValueError, "non-integer arg 1 for randrange()"
+ if stop is default:
+ if istart > 0:
+ if istart >= maxwidth:
+ return self._randbelow(istart)
+ return int(self.random() * istart)
+ raise ValueError, "empty range for randrange()"
+
+ # stop argument supplied.
+ istop = int(stop)
+ if istop != stop:
+ raise ValueError, "non-integer stop for randrange()"
+ width = istop - istart
+ if step == 1 and width > 0:
+ # Note that
+ # int(istart + self.random()*width)
+ # instead would be incorrect. For example, consider istart
+ # = -2 and istop = 0. Then the guts would be in
+ # -2.0 to 0.0 exclusive on both ends (ignoring that random()
+ # might return 0.0), and because int() truncates toward 0, the
+ # final result would be -1 or 0 (instead of -2 or -1).
+ # istart + int(self.random()*width)
+ # would also be incorrect, for a subtler reason: the RHS
+ # can return a long, and then randrange() would also return
+ # a long, but we're supposed to return an int (for backward
+ # compatibility).
+
+ if width >= maxwidth:
+ return int(istart + self._randbelow(width))
+ return int(istart + int(self.random()*width))
+ if step == 1:
+ raise ValueError, "empty range for randrange() (%d,%d, %d)" % (istart, istop, width)
+
+ # Non-unit step argument supplied.
+ istep = int(step)
+ if istep != step:
+ raise ValueError, "non-integer step for randrange()"
+ if istep > 0:
+ n = (width + istep - 1) / istep
+ elif istep < 0:
+ n = (width + istep + 1) / istep
+ else:
+ raise ValueError, "zero step for randrange()"
+
+ if n <= 0:
+ raise ValueError, "empty range for randrange()"
+
+ if n >= maxwidth:
+ return istart + self._randbelow(n)
+ return istart + istep*int(self.random() * n)
+
+ def randint(self, a, b):
+ """Return random integer in range [a, b], including both end points.
+ """
+
+ return self.randrange(a, b+1)
+
+ def _randbelow(self, n, _log=_log, int=int, _maxwidth=1L<<BPF,
+ _Method=_MethodType, _BuiltinMethod=_BuiltinMethodType):
+ """Return a random int in the range [0,n)
+
+ Handles the case where n has more bits than returned
+ by a single call to the underlying generator.
+ """
+
+ try:
+ getrandbits = self.getrandbits
+ except AttributeError:
+ pass
+ else:
+ # Only call self.getrandbits if the original random() builtin method
+ # has not been overridden or if a new getrandbits() was supplied.
+ # This assures that the two methods correspond.
+ if type(self.random) is _BuiltinMethod or type(getrandbits) is _Method:
+ k = int(1.00001 + _log(n-1, 2.0)) # 2**k > n-1 > 2**(k-2)
+ r = getrandbits(k)
+ while r >= n:
+ r = getrandbits(k)
+ return r
+ if n >= _maxwidth:
+ _warn("Underlying random() generator does not supply \n"
+ "enough bits to choose from a population range this large")
+ return int(self.random() * n)
+
+## -------------------- sequence methods -------------------
+
+ def choice(self, seq):
+ """Choose a random element from a non-empty sequence."""
+ return seq[int(self.random() * len(seq))]
+
+ def shuffle(self, x, random=None, int=int):
+ """x, random=random.random -> shuffle list x in place; return None.
+
+ Optional arg random is a 0-argument function returning a random
+ float in [0.0, 1.0); by default, the standard random.random.
+
+ Note that for even rather small len(x), the total number of
+ permutations of x is larger than the period of most random number
+ generators; this implies that "most" permutations of a long
+ sequence can never be generated.
+ """
+
+ if random is None:
+ random = self.random
+ for i in reversed(xrange(1, len(x))):
+ # pick an element in x[:i+1] with which to exchange x[i]
+ j = int(random() * (i+1))
+ x[i], x[j] = x[j], x[i]
+
+ def sample(self, population, k):
+ """Chooses k unique random elements from a population sequence.
+
+ Returns a new list containing elements from the population while
+ leaving the original population unchanged. The resulting list is
+ in selection order so that all sub-slices will also be valid random
+ samples. This allows raffle winners (the sample) to be partitioned
+ into grand prize and second place winners (the subslices).
+
+ Members of the population need not be hashable or unique. If the
+ population contains repeats, then each occurrence is a possible
+ selection in the sample.
+
+ To choose a sample in a range of integers, use xrange as an argument.
+ This is especially fast and space efficient for sampling from a
+ large population: sample(xrange(10000000), 60)
+ """
+
+ # Sampling without replacement entails tracking either potential
+ # selections (the pool) in a list or previous selections in a
+ # dictionary.
+
+ # When the number of selections is small compared to the
+ # population, then tracking selections is efficient, requiring
+ # only a small dictionary and an occasional reselection. For
+ # a larger number of selections, the pool tracking method is
+ # preferred since the list takes less space than the
+ # dictionary and it doesn't suffer from frequent reselections.
+
+ n = len(population)
+ if not 0 <= k <= n:
+ raise ValueError, "sample larger than population"
+ random = self.random
+ _int = int
+ result = [None] * k
+ if n < 6 * k: # if n len list takes less space than a k len dict
+ pool = list(population)
+ for i in xrange(k): # invariant: non-selected at [0,n-i)
+ j = _int(random() * (n-i))
+ result[i] = pool[j]
+ pool[j] = pool[n-i-1] # move non-selected item into vacancy
+ else:
+ try:
+ n > 0 and (population[0], population[n//2], population[n-1])
+ except (TypeError, KeyError): # handle sets and dictionaries
+ population = tuple(population)
+ selected = {}
+ for i in xrange(k):
+ j = _int(random() * n)
+ while j in selected:
+ j = _int(random() * n)
+ result[i] = selected[j] = population[j]
+ return result
+
+## -------------------- real-valued distributions -------------------
+
+## -------------------- uniform distribution -------------------
+
+ def uniform(self, a, b):
+ """Get a random number in the range [a, b)."""
+ return a + (b-a) * self.random()
+
+## -------------------- normal distribution --------------------
+
+ def normalvariate(self, mu, sigma):
+ """Normal distribution.
+
+ mu is the mean, and sigma is the standard deviation.
+
+ """
+ # mu = mean, sigma = standard deviation
+
+ # Uses Kinderman and Monahan method. Reference: Kinderman,
+ # A.J. and Monahan, J.F., "Computer generation of random
+ # variables using the ratio of uniform deviates", ACM Trans
+ # Math Software, 3, (1977), pp257-260.
+
+ random = self.random
+ while True:
+ u1 = random()
+ u2 = 1.0 - random()
+ z = NV_MAGICCONST*(u1-0.5)/u2
+ zz = z*z/4.0
+ if zz <= -_log(u2):
+ break
+ return mu + z*sigma
+
+## -------------------- lognormal distribution --------------------
+
+ def lognormvariate(self, mu, sigma):
+ """Log normal distribution.
+
+ If you take the natural logarithm of this distribution, you'll get a
+ normal distribution with mean mu and standard deviation sigma.
+ mu can have any value, and sigma must be greater than zero.
+
+ """
+ return _exp(self.normalvariate(mu, sigma))
+
+## -------------------- exponential distribution --------------------
+
+ def expovariate(self, lambd):
+ """Exponential distribution.
+
+ lambd is 1.0 divided by the desired mean. (The parameter would be
+ called "lambda", but that is a reserved word in Python.) Returned
+ values range from 0 to positive infinity.
+
+ """
+ # lambd: rate lambd = 1/mean
+ # ('lambda' is a Python reserved word)
+
+ random = self.random
+ u = random()
+ while u <= 1e-7:
+ u = random()
+ return -_log(u)/lambd
+
+## -------------------- von Mises distribution --------------------
+
+ def vonmisesvariate(self, mu, kappa):
+ """Circular data distribution.
+
+ mu is the mean angle, expressed in radians between 0 and 2*pi, and
+ kappa is the concentration parameter, which must be greater than or
+ equal to zero. If kappa is equal to zero, this distribution reduces
+ to a uniform random angle over the range 0 to 2*pi.
+
+ """
+ # mu: mean angle (in radians between 0 and 2*pi)
+ # kappa: concentration parameter kappa (>= 0)
+ # if kappa = 0 generate uniform random angle
+
+ # Based upon an algorithm published in: Fisher, N.I.,
+ # "Statistical Analysis of Circular Data", Cambridge
+ # University Press, 1993.
+
+ # Thanks to Magnus Kessler for a correction to the
+ # implementation of step 4.
+
+ random = self.random
+ if kappa <= 1e-6:
+ return TWOPI * random()
+
+ a = 1.0 + _sqrt(1.0 + 4.0 * kappa * kappa)
+ b = (a - _sqrt(2.0 * a))/(2.0 * kappa)
+ r = (1.0 + b * b)/(2.0 * b)
+
+ while True:
+ u1 = random()
+
+ z = _cos(_pi * u1)
+ f = (1.0 + r * z)/(r + z)
+ c = kappa * (r - f)
+
+ u2 = random()
+
+ if not (u2 >= c * (2.0 - c) and u2 > c * _exp(1.0 - c)):
+ break
+
+ u3 = random()
+ if u3 > 0.5:
+ theta = (mu % TWOPI) + _acos(f)
+ else:
+ theta = (mu % TWOPI) - _acos(f)
+
+ return theta
+
+## -------------------- gamma distribution --------------------
+
+ def gammavariate(self, alpha, beta):
+ """Gamma distribution. Not the gamma function!
+
+ Conditions on the parameters are alpha > 0 and beta > 0.
+
+ """
+
+ # alpha > 0, beta > 0, mean is alpha*beta, variance is alpha*beta**2
+
+ # Warning: a few older sources define the gamma distribution in terms
+ # of alpha > -1.0
+ if alpha <= 0.0 or beta <= 0.0:
+ raise ValueError, 'gammavariate: alpha and beta must be > 0.0'
+
+ random = self.random
+ if alpha > 1.0:
+
+ # Uses R.C.H. Cheng, "The generation of Gamma
+ # variables with non-integral shape parameters",
+ # Applied Statistics, (1977), 26, No. 1, p71-74
+
+ ainv = _sqrt(2.0 * alpha - 1.0)
+ bbb = alpha - LOG4
+ ccc = alpha + ainv
+
+ while True:
+ u1 = random()
+ if not 1e-7 < u1 < .9999999:
+ continue
+ u2 = 1.0 - random()
+ v = _log(u1/(1.0-u1))/ainv
+ x = alpha*_exp(v)
+ z = u1*u1*u2
+ r = bbb+ccc*v-x
+ if r + SG_MAGICCONST - 4.5*z >= 0.0 or r >= _log(z):
+ return x * beta
+
+ elif alpha == 1.0:
+ # expovariate(1)
+ u = random()
+ while u <= 1e-7:
+ u = random()
+ return -_log(u) * beta
+
+ else: # alpha is between 0 and 1 (exclusive)
+
+ # Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle
+
+ while True:
+ u = random()
+ b = (_e + alpha)/_e
+ p = b*u
+ if p <= 1.0:
+ x = pow(p, 1.0/alpha)
+ else:
+ # p > 1
+ x = -_log((b-p)/alpha)
+ u1 = random()
+ if not (((p <= 1.0) and (u1 > _exp(-x))) or
+ ((p > 1) and (u1 > pow(x, alpha - 1.0)))):
+ break
+ return x * beta
+
+## -------------------- Gauss (faster alternative) --------------------
+
+ def gauss(self, mu, sigma):
+ """Gaussian distribution.
+
+ mu is the mean, and sigma is the standard deviation. This is
+ slightly faster than the normalvariate() function.
+
+ Not thread-safe without a lock around calls.
+
+ """
+
+ # When x and y are two variables from [0, 1), uniformly
+ # distributed, then
+ #
+ # cos(2*pi*x)*sqrt(-2*log(1-y))
+ # sin(2*pi*x)*sqrt(-2*log(1-y))
+ #
+ # are two *independent* variables with normal distribution
+ # (mu = 0, sigma = 1).
+ # (Lambert Meertens)
+ # (corrected version; bug discovered by Mike Miller, fixed by LM)
+
+ # Multithreading note: When two threads call this function
+ # simultaneously, it is possible that they will receive the
+ # same return value. The window is very small though. To
+ # avoid this, you have to use a lock around all calls. (I
+ # didn't want to slow this down in the serial case by using a
+ # lock here.)
+
+ random = self.random
+ z = self.gauss_next
+ self.gauss_next = None
+ if z is None:
+ x2pi = random() * TWOPI
+ g2rad = _sqrt(-2.0 * _log(1.0 - random()))
+ z = _cos(x2pi) * g2rad
+ self.gauss_next = _sin(x2pi) * g2rad
+
+ return mu + z*sigma
+
+## -------------------- beta --------------------
+## See
+## http://sourceforge.net/bugs/?func=detailbug&bug_id=130030&group_id=5470
+## for Ivan Frohne's insightful analysis of why the original implementation:
+##
+## def betavariate(self, alpha, beta):
+## # Discrete Event Simulation in C, pp 87-88.
+##
+## y = self.expovariate(alpha)
+## z = self.expovariate(1.0/beta)
+## return z/(y+z)
+##
+## was dead wrong, and how it probably got that way.
+
+ def betavariate(self, alpha, beta):
+ """Beta distribution.
+
+ Conditions on the parameters are alpha > -1 and beta} > -1.
+ Returned values range between 0 and 1.
+
+ """
+
+ # This version due to Janne Sinkkonen, and matches all the std
+ # texts (e.g., Knuth Vol 2 Ed 3 pg 134 "the beta distribution").
+ y = self.gammavariate(alpha, 1.)
+ if y == 0:
+ return 0.0
+ else:
+ return y / (y + self.gammavariate(beta, 1.))
+
+## -------------------- Pareto --------------------
+
+ def paretovariate(self, alpha):
+ """Pareto distribution. alpha is the shape parameter."""
+ # Jain, pg. 495
+
+ u = 1.0 - self.random()
+ return 1.0 / pow(u, 1.0/alpha)
+
+## -------------------- Weibull --------------------
+
+ def weibullvariate(self, alpha, beta):
+ """Weibull distribution.
+
+ alpha is the scale parameter and beta is the shape parameter.
+
+ """
+ # Jain, pg. 499; bug fix courtesy Bill Arms
+
+ u = 1.0 - self.random()
+ return alpha * pow(-_log(u), 1.0/beta)
+
+## -------------------- Wichmann-Hill -------------------
+
+class WichmannHill(Random):
+
+ VERSION = 1 # used by getstate/setstate
+
+ def seed(self, a=None):
+ """Initialize internal state from hashable object.
+
+ None or no argument seeds from current time.
+
+ If a is not None or an int or long, hash(a) is used instead.
+
+ If a is an int or long, a is used directly. Distinct values between
+ 0 and 27814431486575L inclusive are guaranteed to yield distinct
+ internal states (this guarantee is specific to the default
+ Wichmann-Hill generator).
+ """
+
+ if a is None:
+ # Initialize from current time
+ import time
+ a = long(time.time() * 256)
+
+ if not isinstance(a, (int, long)):
+ a = hash(a)
+
+ a, x = divmod(a, 30268)
+ a, y = divmod(a, 30306)
+ a, z = divmod(a, 30322)
+ self._seed = int(x)+1, int(y)+1, int(z)+1
+
+ self.gauss_next = None
+
+ def random(self):
+ """Get the next random number in the range [0.0, 1.0)."""
+
+ # Wichman-Hill random number generator.
+ #
+ # Wichmann, B. A. & Hill, I. D. (1982)
+ # Algorithm AS 183:
+ # An efficient and portable pseudo-random number generator
+ # Applied Statistics 31 (1982) 188-190
+ #
+ # see also:
+ # Correction to Algorithm AS 183
+ # Applied Statistics 33 (1984) 123
+ #
+ # McLeod, A. I. (1985)
+ # A remark on Algorithm AS 183
+ # Applied Statistics 34 (1985),198-200
+
+ # This part is thread-unsafe:
+ # BEGIN CRITICAL SECTION
+ x, y, z = self._seed
+ x = (171 * x) % 30269
+ y = (172 * y) % 30307
+ z = (170 * z) % 30323
+ self._seed = x, y, z
+ # END CRITICAL SECTION
+
+ # Note: on a platform using IEEE-754 double arithmetic, this can
+ # never return 0.0 (asserted by Tim; proof too long for a comment).
+ return (x/30269.0 + y/30307.0 + z/30323.0) % 1.0
+
+ def getstate(self):
+ """Return internal state; can be passed to setstate() later."""
+ return self.VERSION, self._seed, self.gauss_next
+
+ def setstate(self, state):
+ """Restore internal state from object returned by getstate()."""
+ version = state[0]
+ if version == 1:
+ version, self._seed, self.gauss_next = state
+ else:
+ raise ValueError("state with version %s passed to "
+ "Random.setstate() of version %s" %
+ (version, self.VERSION))
+
+ def jumpahead(self, n):
+ """Act as if n calls to random() were made, but quickly.
+
+ n is an int, greater than or equal to 0.
+
+ Example use: If you have 2 threads and know that each will
+ consume no more than a million random numbers, create two Random
+ objects r1 and r2, then do
+ r2.setstate(r1.getstate())
+ r2.jumpahead(1000000)
+ Then r1 and r2 will use guaranteed-disjoint segments of the full
+ period.
+ """
+
+ if not n >= 0:
+ raise ValueError("n must be >= 0")
+ x, y, z = self._seed
+ x = int(x * pow(171, n, 30269)) % 30269
+ y = int(y * pow(172, n, 30307)) % 30307
+ z = int(z * pow(170, n, 30323)) % 30323
+ self._seed = x, y, z
+
+ def __whseed(self, x=0, y=0, z=0):
+ """Set the Wichmann-Hill seed from (x, y, z).
+
+ These must be integers in the range [0, 256).
+ """
+
+ if not type(x) == type(y) == type(z) == int:
+ raise TypeError('seeds must be integers')
+ if not (0 <= x < 256 and 0 <= y < 256 and 0 <= z < 256):
+ raise ValueError('seeds must be in range(0, 256)')
+ if 0 == x == y == z:
+ # Initialize from current time
+ import time
+ t = long(time.time() * 256)
+ t = int((t&0xffffff) ^ (t>>24))
+ t, x = divmod(t, 256)
+ t, y = divmod(t, 256)
+ t, z = divmod(t, 256)
+ # Zero is a poor seed, so substitute 1
+ self._seed = (x or 1, y or 1, z or 1)
+
+ self.gauss_next = None
+
+ def whseed(self, a=None):
+ """Seed from hashable object's hash code.
+
+ None or no argument seeds from current time. It is not guaranteed
+ that objects with distinct hash codes lead to distinct internal
+ states.
+
+ This is obsolete, provided for compatibility with the seed routine
+ used prior to Python 2.1. Use the .seed() method instead.
+ """
+
+ if a is None:
+ self.__whseed()
+ return
+ a = hash(a)
+ a, x = divmod(a, 256)
+ a, y = divmod(a, 256)
+ a, z = divmod(a, 256)
+ x = (x + a) % 256 or 1
+ y = (y + a) % 256 or 1
+ z = (z + a) % 256 or 1
+ self.__whseed(x, y, z)
+
+## -------------------- test program --------------------
+
+def _test_generator(n, func, args):
+ import time
+ print n, 'times', func.__name__
+ total = 0.0
+ sqsum = 0.0
+ smallest = 1e10
+ largest = -1e10
+ t0 = time.time()
+ for i in range(n):
+ x = func(*args)
+ total += x
+ sqsum = sqsum + x*x
+ smallest = min(x, smallest)
+ largest = max(x, largest)
+ t1 = time.time()
+ print round(t1-t0, 3), 'sec,',
+ avg = total/n
+ stddev = _sqrt(sqsum/n - avg*avg)
+ print 'avg %g, stddev %g, min %g, max %g' % \
+ (avg, stddev, smallest, largest)
+
+
+def _test(N=2000):
+ _test_generator(N, random, ())
+ _test_generator(N, normalvariate, (0.0, 1.0))
+ _test_generator(N, lognormvariate, (0.0, 1.0))
+ _test_generator(N, vonmisesvariate, (0.0, 1.0))
+ _test_generator(N, gammavariate, (0.01, 1.0))
+ _test_generator(N, gammavariate, (0.1, 1.0))
+ _test_generator(N, gammavariate, (0.1, 2.0))
+ _test_generator(N, gammavariate, (0.5, 1.0))
+ _test_generator(N, gammavariate, (0.9, 1.0))
+ _test_generator(N, gammavariate, (1.0, 1.0))
+ _test_generator(N, gammavariate, (2.0, 1.0))
+ _test_generator(N, gammavariate, (20.0, 1.0))
+ _test_generator(N, gammavariate, (200.0, 1.0))
+ _test_generator(N, gauss, (0.0, 1.0))
+ _test_generator(N, betavariate, (3.0, 3.0))
+
+# Create one instance, seeded from current time, and export its methods
+# as module-level functions. The functions share state across all uses
+#(both in the user's code and in the Python libraries), but that's fine
+# for most programs and is easier for the casual user than making them
+# instantiate their own Random() instance.
+
+_inst = Random()
+seed = _inst.seed
+random = _inst.random
+uniform = _inst.uniform
+randint = _inst.randint
+choice = _inst.choice
+randrange = _inst.randrange
+sample = _inst.sample
+shuffle = _inst.shuffle
+normalvariate = _inst.normalvariate
+lognormvariate = _inst.lognormvariate
+expovariate = _inst.expovariate
+vonmisesvariate = _inst.vonmisesvariate
+gammavariate = _inst.gammavariate
+gauss = _inst.gauss
+betavariate = _inst.betavariate
+paretovariate = _inst.paretovariate
+weibullvariate = _inst.weibullvariate
+getstate = _inst.getstate
+setstate = _inst.setstate
+jumpahead = _inst.jumpahead
+getrandbits = _inst.getrandbits
+
+if __name__ == '__main__':
+ _test()
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