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src/p/y/pymc-2.3.2/pymc/gp/gp_submodel.py   pymc(Download)
class GaussianProcess(pm.Stochastic):
 
    """
    G=GaussianProcess(name, submodel, **kwds)
 

src/k/a/kabuki-0.5.4/kabuki/distributions.py   kabuki(Download)
import warnings
import numpy as np
import pymc as pm
 
from pymc import Stochastic, utils
    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

src/k/a/kabuki-HEAD/kabuki/distributions.py   kabuki(Download)
import warnings
import numpy as np
import pymc as pm
 
from pymc import Stochastic, utils
    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

src/p/y/pymc-2.3.2/pymc/tests/test_instantiation.py   pymc(Download)
from numpy.testing import *
import pymc
from pymc import Sampler, observed, stochastic, deterministic, \
    Stochastic, Deterministic
from numpy import array, log, sum, ones, concatenate, inf