migrate useful functions from previous PR

pymc3/distributions/dist_math.py

@@ -95,3 +95,60 @@ def i1(x):
                      x**9 / 1474560 + x**11 / 176947200 + x**13 / 29727129600,
                      np.e**x / (2 * np.pi * x)**0.5 * (1 - 3 / (8 * x) + 15 / (128 * x**2) + 315 / (3072 * x**3)
                                                        + 14175 / (98304 * x**4)))
+
+
+def sd2rho(sd):
+    """
+    `sd -> rho` theano converter
+    :math:`mu + sd*e = mu + log(1+exp(rho))*e`"""
+    return tt.log(tt.exp(sd) - 1)
+
+
+def rho2sd(rho):
+    """
+    `rho -> sd` theano converter
+    :math:`mu + sd*e = mu + log(1+exp(rho))*e`"""
+    return tt.log1p(tt.exp(rho))
+
+
+def log_normal(x, mean, **kwargs):
+    """
+    Calculate logarithm of normal distribution at point `x`
+    with given `mean` and `std`
+    Parameters
+    ----------
+    x : Tensor
+        point of evaluation
+    mean : Tensor
+        mean of normal distribution
+    kwargs : one of parameters `{sd, tau, w, rho}`
+    Notes
+    -----
+    There are four variants for density parametrization.
+    They are:
+        1) standard deviation - `std`
+        2) `w`, logarithm of `std` :math:`w = log(std)`
+        3) `rho` that follows this equation :math:`rho = log(exp(std) - 1)`
+        4) `tau` that follows this equation :math:`tau = std^{-1}`
+    ----
+    """
+    sd = kwargs.get('sd')
+    w = kwargs.get('w')
+    rho = kwargs.get('rho')
+    tau = kwargs.get('tau')
+    eps = kwargs.get('eps', 0.0)
+    check = sum(map(lambda a: a is not None, [sd, w, rho, tau]))
+    if check > 1:
+        raise ValueError('more than one required kwarg is passed')
+    if check == 0:
+        raise ValueError('none of required kwarg is passed')
+    if sd is not None:
+        std = sd
+    elif w is not None:
+        std = tt.exp(w)
+    elif rho is not None:
+        std = rho2sd(rho)
+    else:
+        std = tau**(-1)
+    std += eps
+    return c - tt.log(tt.abs_(std)) - (x - mean) ** 2 / (2 * std ** 2)

pymc3/math.py

@@ -22,3 +22,6 @@ def invlogit(x, eps=sys.float_info.epsilon):
 
 def logit(p):
     return tt.log(p / (1 - p))
+
+def flatten_list(tensors):
+    return tt.concatenate([var.ravel() for var in tensors])

pymc3/theanof.py

@@ -4,16 +4,24 @@
 from theano import theano, scalar, tensor as tt
 from theano.gof.graph import inputs
 from theano.tensor import TensorVariable
+from theano.sandbox.rng_mrg import MRG_RandomStreams
 from theano.gof import Op
 from theano.configparser import change_flags
 from .memoize import memoize
 from .blocking import ArrayOrdering
 from .data import DataGenerator
 
-__all__ = ['gradient', 'hessian', 'hessian_diag', 'inputvars',
-           'cont_inputs', 'floatX', 'jacobian',
-           'CallableTensor', 'join_nonshared_inputs',
-           'make_shared_replacements', 'generator']
+__all__ = ['gradient',
+           'hessian',
+           'hessian_diag',
+           'inputvars',
+           'cont_inputs',
+           'floatX',
+           'jacobian',
+           'CallableTensor',
+           'join_nonshared_inputs',
+           'make_shared_replacements',
+           'generator']
 
 
 def inputvars(a):
@@ -307,3 +315,41 @@ def set_gen(self, gen):
 def generator(gen):
     """shortcut for `GeneratorOp`"""
     return GeneratorOp(gen)()
+
+@change_flags(compute_test_value='off')
+def launch_rng(rng):
+    """Helper function for safe launch of rng.
+    If not launched, there will be problems with test_value
+    Parameters
+    ----------
+    rng : theano.sandbox.rng_mrg.MRG_RandomStreams` instance
+    """
+    state = rng.rstate
+    rng.inc_rstate()
+    rng.set_rstate(state)
+
+_tt_rng = MRG_RandomStreams()
+launch_rng(_tt_rng)
+
+
+def tt_rng():
+    """Get the package-level random number generator.
+    Returns
+    -------
+    `theano.sandbox.rng_mrg.MRG_RandomStreams` instance
+        `theano.sandbox.rng_mrg.MRG_RandomStreams`
+        instance passed to the most recent call of `set_tt_rng`
+    """
+    return _tt_rng
+
+
+def set_tt_rng(new_rng):
+    """Set the package-level random number generator.
+    Parameters
+    ----------
+    new_rng : `theano.sandbox.rng_mrg.MRG_RandomStreams` instance
+        The random number generator to use.
+    """
+    global _tt_rng
+    _tt_rng = new_rng
+    launch_rng(_tt_rng)