IronPython“随机”模块错误

时间:2013-05-25 18:30:23

标签: c# python windows-phone-8 windows-phone ironpython

我正在忙着将IronPython移植到Windows Phone 8,我可以运行Skeinforge而且我差不多完成了。我已经可以运行脚本并导入大多数模块。我的问题是我现在正试图实现“随机”模块。我最大的问题是库使用'SHA512'来计算随机数。这是一个问题,因为微软没有实现这个哈希以及IronPython在移动.Net框架中使用的其他哈希,因为它们是“不安全的”。我通过从hashlib.py中删除不受支持的哈希来解决这个问题(这似乎有效)。然后我尝试将random.py中对'SHA512'的引用更改为'SHA256'。我的问题是我现在得到这个非常随机的错误:

expected Random, got Random

如果有人知道怎么做,请帮助我。我会在完成之后记录这一点,因为每个人都可以在WP8上享受IPY。

这是random.py模块:

from __future__ import division
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, ceil as _ceil
from math import sqrt as _sqrt, acos as _acos, cos as _cos, sin as _sin
from os import urandom as _urandom
from binascii import hexlify as _hexlify
import hashlib as _hashlib

__all__ = ["Random","seed","random","uniform","randint","choice","sample",
           "randrange","shuffle","normalvariate","lognormvariate",
           "expovariate","vonmisesvariate","gammavariate","triangular",
           "gauss","betavariate","paretovariate","weibullvariate",
           "getstate","setstate","jumpahead", "WichmannHill", "getrandbits",
           "SystemRandom"]

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
RECIP_BPF = 2**-BPF

import _random

class Random(_random.Random):
    VERSION = 3     # used by getstate/setstate

    def __init__(self, x=None):
        self.seed(x)
        self.gauss_next = None

    def seed(self, a=None):
        if a is None:
            try:
                a = long(_hexlify(_urandom(16)), 16)
            except NotImplementedError:
                import time
                a = long(time.time() * 256) # use fractional seconds

        super(Random, self).seed(a)
        self.gauss_next = None

    def getstate(self):
        return self.VERSION, super(Random, self).getstate(), self.gauss_next

    def setstate(self, state):
        version = state[0]
        if version == 3:
            version, internalstate, self.gauss_next = state
            super(Random, self).setstate(internalstate)
        elif version == 2:
            version, internalstate, self.gauss_next = state
            try:
                internalstate = tuple( long(x) % (2**32) for x in internalstate )
            except ValueError, e:
                raise TypeError, e
            super(Random, self).setstate(internalstate)
        else:
            raise ValueError("state with version %s passed to "
                             "Random.setstate() of version %s" %
                             (version, self.VERSION))

    def jumpahead(self, n):
        s = repr(n) + repr(self.getstate())
        n = int(_hashlib.new('sha256', s).hexdigest(), 16)
        super(Random, self).jumpahead(n)

    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()

    def randrange(self, start, stop=None, step=1, int=int, default=None,
                  maxwidth=1L<<BPF):

        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()"

        istop = int(stop)
        if istop != stop:
            raise ValueError, "non-integer stop for randrange()"
        width = istop - istart
        if step == 1 and width > 0:

            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)

        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 + istep*self._randbelow(n)
        return istart + istep*int(self.random() * n)

    def randint(self, a, b):

        return self.randrange(a, b+1)

    def _randbelow(self, n, _log=_log, int=int, _maxwidth=1L<<BPF,
                   _Method=_MethodType, _BuiltinMethod=_BuiltinMethodType):

        try:
            getrandbits = self.getrandbits
        except AttributeError:
            pass
        else:
            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)

    def choice(self, seq):
        return seq[int(self.random() * len(seq))]  # raises IndexError if seq is empty

    def shuffle(self, x, random=None, int=int):

        if random is None:
            random = self.random
        for i in reversed(xrange(1, len(x))):
            j = int(random() * (i+1))
            x[i], x[j] = x[j], x[i]

    def sample(self, population, k):

        n = len(population)
        if not 0 <= k <= n:
            raise ValueError("sample larger than population")
        random = self.random
        _int = int
        result = [None] * k
        setsize = 21        # size of a small set minus size of an empty list
        if k > 5:
            setsize += 4 ** _ceil(_log(k * 3, 4)) # table size for big sets
        if n <= setsize or hasattr(population, "keys"):
            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:
                selected = set()
                selected_add = selected.add
                for i in xrange(k):
                    j = _int(random() * n)
                    while j in selected:
                        j = _int(random() * n)
                    selected_add(j)
                    result[i] = population[j]
            except (TypeError, KeyError):   # handle (at least) sets
                if isinstance(population, list):
                    raise
                return self.sample(tuple(population), k)
        return result

    def uniform(self, a, b):
        "Get a random number in the range [a, b) or [a, b] depending on rounding."
        return a + (b-a) * self.random()

    def triangular(self, low=0.0, high=1.0, mode=None):
        u = self.random()
        c = 0.5 if mode is None else (mode - low) / (high - low)
        if u > c:
            u = 1.0 - u
            c = 1.0 - c
            low, high = high, low
        return low + (high - low) * (u * c) ** 0.5

    def normalvariate(self, mu, sigma):

        random = self.random
        while 1:
            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

    def lognormvariate(self, mu, sigma):
        return _exp(self.normalvariate(mu, sigma))

    def expovariate(self, lambd):

        random = self.random
        u = random()
        while u <= 1e-7:
            u = random()
        return -_log(u)/lambd

    def vonmisesvariate(self, mu, kappa):

        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 u2 < c * (2.0 - c) or 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

    def gammavariate(self, alpha, beta):

        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:

            ainv = _sqrt(2.0 * alpha - 1.0)
            bbb = alpha - LOG4
            ccc = alpha + ainv

            while 1:
                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:
            u = random()
            while u <= 1e-7:
                u = random()
            return -_log(u) * beta

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

            while 1:
                u = random()
                b = (_e + alpha)/_e
                p = b*u
                if p <= 1.0:
                    x = p ** (1.0/alpha)
                else:
                    x = -_log((b-p)/alpha)
                u1 = random()
                if p > 1.0:
                    if u1 <= x ** (alpha - 1.0):
                        break
                elif u1 <= _exp(-x):
                    break
            return x * beta

    def gauss(self, mu, sigma):

        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

    def betavariate(self, alpha, beta):

        y = self.gammavariate(alpha, 1.)
        if y == 0:
            return 0.0
        else:
            return y / (y + self.gammavariate(beta, 1.))

    def paretovariate(self, alpha):

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

    def weibullvariate(self, alpha, beta):

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


class WichmannHill(Random):

    VERSION = 1     # used by getstate/setstate

    def seed(self, a=None):

        if a is None:
            try:
                a = long(_hexlify(_urandom(16)), 16)
            except NotImplementedError:
                import time
                a = long(time.time() * 256) # use fractional seconds

        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):

        x, y, z = self._seed
        x = (171 * x) % 30269
        y = (172 * y) % 30307
        z = (170 * z) % 30323
        self._seed = x, y, z
        return (x/30269.0 + y/30307.0 + z/30323.0) % 1.0

    def getstate(self):
        return self.VERSION, self._seed, self.gauss_next

    def setstate(self, state):
        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):

        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):

        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:
            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)
        self._seed = (x or 1, y or 1, z or 1)

        self.gauss_next = None

    def whseed(self, a=None):

        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)

class SystemRandom(Random):

    def random(self):
        return (long(_hexlify(_urandom(7)), 16) >> 3) * RECIP_BPF

    def getrandbits(self, k):
        if k <= 0:
            raise ValueError('number of bits must be greater than zero')
        if k != int(k):
            raise TypeError('number of bits should be an integer')
        bytes = (k + 7) // 8                    # bits / 8 and rounded up
        x = long(_hexlify(_urandom(bytes)), 16)
        return x >> (bytes * 8 - k)             # trim excess bits

    def _stub(self, *args, **kwds):
        "Stub method.  Not used for a system random number generator."
        return None
    seed = jumpahead = _stub

    def _notimplemented(self, *args, **kwds):
        "Method should not be called for a system random number generator."
        raise NotImplementedError('System entropy source does not have state.')
    getstate = setstate = _notimplemented

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))
    _test_generator(N, triangular, (0.0, 1.0, 1.0/3.0))


_inst = Random()
seed = _inst.seed
random = _inst.random
uniform = _inst.uniform
triangular = _inst.triangular
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()

经过一些劳动密集型调试后,我确定以下行最有可能导致错误:

_inst = new Random()

0 个答案:

没有答案