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src/p/y/pymc-2.3.2/pymc/distributions.py   pymc(Download)
from scipy.stats.kde import gaussian_kde
from .Node import ZeroProbability
from .PyMCObjects import Stochastic, Deterministic
from .CommonDeterministics import Lambda
from numpy import pi, inf
    class new_class(Stochastic):
        __doc__ = docstr
        def __init__(self, *args, **kwds):
            (dtype, name, parent_names, parents_default, docstr, logp, random, mv, logp_partial_gradients) = new_class_args
        value = random()
    return Stochastic(logp     = logp,
                      doc      = 'Non-parametric density with Gaussian Kernels.',
                      name     = name,
class Categorical(Stochastic):
    __doc__ = """
C = Categorical(name, p, value=None, dtype=np.int, observed=False,
size=1, trace=True, rseed=False, cache_depth=2, plot=None)
class Multinomial(Stochastic):
M = Multinomial(name, n, p, trace=True, value=None,
   rseed=False, observed=False, cache_depth=2, plot=None])

src/p/y/pymc-2.3.2/pymc/Model.py   pymc(Download)
from numpy.random import randint
from . import database
from .PyMCObjects import Stochastic, Deterministic, Node, Variable, Potential
from .Container import Container, ObjectContainer
import sys

src/p/y/pymc-2.3.2/pymc/InstantiationDecorators.py   pymc(Download)
import pdb
from imp import load_dynamic
from .PyMCObjects import Stochastic, Deterministic, Potential
from .Node import ZeroProbability, ContainerBase, Node, StochasticMeta
from .Container import Container

src/p/y/pymc-2.3.2/pymc/StepMethods.py   pymc(Download)
from numpy.random import normal as rnormal
from numpy.random import poisson as rpoisson
from .PyMCObjects import Stochastic, Potential, Deterministic
from .Container import Container
from .Node import ZeroProbability, Node, Variable, StochasticBase

src/p/y/pymc-2.3.2/pymc/CircularStochastic.py   pymc(Download)
from .PyMCObjects import Stochastic
from .Container import Container
from .InstantiationDecorators import stochastic
from .flib import mod_to_circle
from .distributions import rvon_mises, von_mises_like
class CircularStochastic(Stochastic):
    C = CircularStochastic(lo, hi, *args, **kwargs)