Branch numpy.distutils to distutils-revamp

1 files changed, 0 insertions, 136 deletions

diff --git a/numpy/random/oldrng.py b/numpy/random/oldrng.py
deleted file mode 100644
index 1e6b2fbd6..000000000
--- a/numpy/random/oldrng.py
+++ /dev/null
@@ -1,136 +0,0 @@
-# This module re-creates the RNG interface from Numeric
-# Replace import RNG with import numpy.random.oldrng as RNG
-#
-# It is for backwards compatibility only. 
-
-
-__all__ = ['CreateGenerator','ExponentialDistribution','LogNormalDistribution','NormalDistribution',
-           'UniformDistribution', 'error', 'default_distribution', 'random_sample', 'ranf',
-           '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_*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*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*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)
-