/usr/lib64/python2.6/site-packages/numpy/oldnumeric
Edit: /usr/lib64/python2.6/site-packages/numpy/oldnumeric/rng.py (3754B)
# This module re-creates the RNG interface from Numeric
# Replace import RNG with import numpy.oldnumeric.rng as RNG
#
# It is for backwards compatibility only.
__all__ = ['CreateGenerator','ExponentialDistribution','LogNormalDistribution',
'NormalDistribution', 'UniformDistribution', 'error', 'ranf',
'default_distribution', 'random_sample', 'standard_generator']
import numpy.random.mtrand as mt
import math
class error(Exception):
pass
class Distribution(object):
def __init__(self, meth, *args):
self._meth = meth
self._args = args
def density(self,x):
raise NotImplementedError
def __call__(self, x):
return self.density(x)
def _onesample(self, rng):
return getattr(rng, self._meth)(*self._args)
def _sample(self, rng, n):
kwds = {'size' : n}
return getattr(rng, self._meth)(*self._args, **kwds)
class ExponentialDistribution(Distribution):
def __init__(self, lambda_):
if (lambda_ <= 0):
raise error, "parameter must be positive"
Distribution.__init__(self, 'exponential', lambda_)
def density(x):
if x < 0:
return 0.0
else:
lambda_ = self._args[0]
return lambda_*math.exp(-lambda_*x)
class LogNormalDistribution(Distribution):
def __init__(self, m, s):
m = float(m)
s = float(s)
if (s <= 0):
raise error, "standard deviation must be positive"
Distribution.__init__(self, 'lognormal', m, s)
sn = math.log(1.0+s*s/(m*m));
self._mn = math.log(m)-0.5*sn
self._sn = math.sqrt(sn)
self._fac = 1.0/math.sqrt(2*math.pi)/self._sn
def density(x):
m,s = self._args
y = (math.log(x)-self._mn)/self._sn
return self._fac*math.exp(-0.5*y*y)/x
class NormalDistribution(Distribution):
def __init__(self, m, s):
m = float(m)
s = float(s)
if (s <= 0):
raise error, "standard deviation must be positive"
Distribution.__init__(self, 'normal', m, s)
self._fac = 1.0/math.sqrt(2*math.pi)/s
def density(x):
m,s = self._args
y = (x-m)/s
return self._fac*math.exp(-0.5*y*y)
class UniformDistribution(Distribution):
def __init__(self, a, b):
a = float(a)
b = float(b)
width = b-a
if (width <=0):
raise error, "width of uniform distribution must be > 0"
Distribution.__init__(self, 'uniform', a, b)
self._fac = 1.0/width
def density(x):
a, b = self._args
if (x < a) or (x >= b):
return 0.0
else:
return self._fac
default_distribution = UniformDistribution(0.0,1.0)
class CreateGenerator(object):
def __init__(self, seed, dist=None):
if seed <= 0:
self._rng = mt.RandomState()
elif seed > 0:
self._rng = mt.RandomState(seed)
if dist is None:
dist = default_distribution
if not isinstance(dist, Distribution):
raise error, "Not a distribution object"
self._dist = dist
def ranf(self):
return self._dist._onesample(self._rng)
def sample(self, n):
return self._dist._sample(self._rng, n)
standard_generator = CreateGenerator(-1)
def ranf():
"ranf() = a random number from the standard generator."
return standard_generator.ranf()
def random_sample(*n):
"""random_sample(n) = array of n random numbers;
random_sample(n1, n2, ...)= random array of shape (n1, n2, ..)"""
if not n:
return standard_generator.ranf()
m = 1
for i in n:
m = m * i
return standard_generator.sample(m).reshape(*n)