JezK
Edit File: rng.py
"""Re-create the RNG interface from Numeric. Replace import RNG with import numpy.oldnumeric.rng as RNG. It is for backwards compatibility only. """ from __future__ import division, absolute_import, print_function __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)