Lib/random.py

"""Random variable generators.

    integers
    --------
           uniform within range

    sequences
    ---------
           pick random element
           generate random permutation

    distributions on the real line:
    ------------------------------
           uniform
           normal (Gaussian)
           lognormal
           negative exponential
           gamma
           beta

    distributions on the circle (angles 0 to 2pi)
    ---------------------------------------------
           circular uniform
           von Mises

Translated from anonymously contributed C/C++ source.

Multi-threading note: the random number generator used here is not
thread-safe; it is possible that two calls return the same random
value.
"""
# XXX The docstring sucks.

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

def _verify(name, expected):
    computed = eval(name)
    if abs(computed - expected) > 1e-7:
        raise ValueError(
            "computed value for %s deviates too much "
            "(computed %g, expected %g)" % (name, computed, expected))

NV_MAGICCONST = 4 * _exp(-0.5)/_sqrt(2.0)
_verify('NV_MAGICCONST', 1.71552776992141)

TWOPI = 2.0*_pi
_verify('TWOPI', 6.28318530718)

LOG4 = _log(4.0)
_verify('LOG4', 1.38629436111989)

SG_MAGICCONST = 1.0 + _log(4.5)
_verify('SG_MAGICCONST', 2.50407739677627)

del _verify

# Translated by Guido van Rossum from C source provided by
# Adrian Baddeley.

class Random:

    VERSION = 1     # 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

    # Specific to Wichmann-Hill generator.  Subclasses wishing to use a
    # different core generator should override seed(), random(),  getstate()
    # and setstate().

    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) == type(0):
            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)

    def seed(self, a=None):
        """Seed from hashable value

        None or no argument seeds from current time.
        """

        if a is None:
            self.__whseed()
        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)

    def getstate(self):
        """Return internal state; can be passed to setstate() later."""

        return self.VERSION, self._seed, self.gauss_next

    def __getstate__(self): # for pickle
        self.getstate()

    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 __setstate__(self, state):  # for pickle
        self.setstate(state)

    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 randrange(self, start, stop=None, step=1, int=int, default=None):
        """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' and 'default' 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:
                return int(self.random() * istart)
            raise ValueError, "empty range for randrange()"
        istop = int(stop)
        if istop != stop:
            raise ValueError, "non-integer stop for randrange()"
        if step == 1:
            if istart < istop:
                return istart + int(self.random() *
                                   (istop - istart))
            raise ValueError, "empty range for randrange()"
        istep = int(step)
        if istep != step:
            raise ValueError, "non-integer step for randrange()"
        if istep > 0:
            n = (istop - istart + istep - 1) / istep
        elif istep < 0:
            n = (istop - istart + istep + 1) / istep
        else:
            raise ValueError, "zero step for randrange()"

        if n <= 0:
            raise ValueError, "empty range for randrange()"
        return istart + istep*int(self.random() * n)

    def randint(self, a, b):
        """Get a random integer in the range [a, b] including
        both end points.

        (Deprecated; use randrange below.)
        """

        return self.randrange(a, b+1)

    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 xrange(len(x)-1, 0, -1):
        # 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]

# -------------------- 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):
        # 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 1:
            u1 = random()
            u2 = 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):
        return _exp(self.normalvariate(mu, sigma))

# -------------------- circular uniform --------------------

    def cunifvariate(self, mean, arc):
        # mean: mean angle (in radians between 0 and pi)
        # arc:  range of distribution (in radians between 0 and pi)

        return (mean + arc * (self.random() - 0.5)) % _pi

# -------------------- exponential distribution --------------------

    def expovariate(self, lambd):
        # 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):
        # 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 1:
            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):
        # beta times standard gamma
        ainv = _sqrt(2.0 * alpha - 1.0)
        return beta * self.stdgamma(alpha, ainv, alpha - LOG4, alpha + ainv)

    def stdgamma(self, alpha, ainv, bbb, ccc):
        # ainv = sqrt(2 * alpha - 1)
        # bbb = alpha - log(4)
        # ccc = alpha + ainv

        random = self.random
        if alpha <= 0.0:
            raise ValueError, 'stdgamma: alpha must be > 0.0'

        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

            while 1:
                u1 = random()
                u2 = 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

        elif alpha == 1.0:
            # expovariate(1)
            u = random()
            while u <= 1e-7:
                u = random()
            return -_log(u)

        else:   # alpha is between 0 and 1 (exclusive)

            # Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle

            while 1:
                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


# -------------------- Gauss (faster alternative) --------------------

    def gauss(self, mu, sigma):

        # 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 --------------------

    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)

# -------------------- Pareto --------------------

    def paretovariate(self, alpha):
        # Jain, pg. 495

        u = self.random()
        return 1.0 / pow(u, 1.0/alpha)

# -------------------- Weibull --------------------

    def weibullvariate(self, alpha, beta):
        # Jain, pg. 499; bug fix courtesy Bill Arms

        u = self.random()
        return alpha * pow(-_log(u), 1.0/beta)

# -------------------- test program --------------------

def _test_generator(n, funccall):
    import time
    print n, 'times', funccall
    code = compile(funccall, funccall, 'eval')
    sum = 0.0
    sqsum = 0.0
    smallest = 1e10
    largest = -1e10
    t0 = time.time()
    for i in range(n):
        x = eval(code)
        sum = sum + x
        sqsum = sqsum + x*x
        smallest = min(x, smallest)
        largest = max(x, largest)
    t1 = time.time()
    print round(t1-t0, 3), 'sec,',
    avg = sum/n
    stddev = _sqrt(sqsum/n - avg*avg)
    print 'avg %g, stddev %g, min %g, max %g' % \
              (avg, stddev, smallest, largest)

def _test(N=200):
    print 'TWOPI         =', TWOPI
    print 'LOG4          =', LOG4
    print 'NV_MAGICCONST =', NV_MAGICCONST
    print 'SG_MAGICCONST =', SG_MAGICCONST
    _test_generator(N, 'random()')
    _test_generator(N, 'normalvariate(0.0, 1.0)')
    _test_generator(N, 'lognormvariate(0.0, 1.0)')
    _test_generator(N, 'cunifvariate(0.0, 1.0)')
    _test_generator(N, 'expovariate(1.0)')
    _test_generator(N, 'vonmisesvariate(0.0, 1.0)')
    _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)')
    _test_generator(N, 'paretovariate(1.0)')
    _test_generator(N, 'weibullvariate(1.0, 1.0)')

# Initialize from current time.
_inst = Random()
seed = _inst.seed
random = _inst.random
uniform = _inst.uniform
randint = _inst.randint
choice = _inst.choice
randrange = _inst.randrange
shuffle = _inst.shuffle
normalvariate = _inst.normalvariate
lognormvariate = _inst.lognormvariate
cunifvariate = _inst.cunifvariate
expovariate = _inst.expovariate
vonmisesvariate = _inst.vonmisesvariate
gammavariate = _inst.gammavariate
stdgamma = _inst.stdgamma
gauss = _inst.gauss
betavariate = _inst.betavariate
paretovariate = _inst.paretovariate
weibullvariate = _inst.weibullvariate
getstate = _inst.getstate
setstate = _inst.setstate

if __name__ == '__main__':
    _test()