我正忙着将 IronPython 移植到 Windows Phone 8，这样我就可以运行 Skeinforge 并且我快完成了。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。我最大的问题是该库使用“SHA512”来计算随机数。这是一个问题，因为微软没有实现这个散列以及 IronPython 在移动 .Net 框架中使用的其他一些散列，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
如果你们中的任何人知道如何，请帮助我。完成后我会记录下来，以便每个人都可以在 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()