Rebase again

pymc3/step_methods/hmc/base_hmc.py

@@ -9,7 +9,8 @@
 
 
 class BaseHMC(arraystep.GradientSharedStep):
-    """Superclass to implement Hamiltonian/hybrid monte carlo"""
+    """Superclass to implement Hamiltonian/hybrid monte carlo."""
+
     default_blocked = True
 
     def __init__(self, vars=None, scaling=None, step_scale=0.25, is_cov=False,

pymc3/step_methods/hmc/integration.py

@@ -8,7 +8,10 @@
 
 
 class CpuLeapfrogIntegrator(object):
+    """Optimized leapfrog integration using numpy."""
+
     def __init__(self, ndim, potential, logp_dlogp_func):
+        """Leapfrog integrator using CPU."""
         self._ndim = ndim
         self._potential = potential
         self._logp_dlogp_func = logp_dlogp_func
@@ -19,6 +22,7 @@ def __init__(self, ndim, potential, logp_dlogp_func):
                              % (self._potential.dtype, self._dtype))
 
     def compute_state(self, q, p):
+        """Compute Hamiltonian functions using a position and momentum."""
         if q.dtype != self._dtype or p.dtype != self._dtype:
             raise ValueError('Invalid dtype. Must be %s' % self._dtype)
         logp, dlogp = self._logp_dlogp_func(q)
@@ -28,6 +32,23 @@ def compute_state(self, q, p):
         return State(q, p, v, dlogp, energy)
 
     def step(self, epsilon, state, out=None):
+        """Leapfrog integrator step.
+
+        Half a momentum update, full position update, half momentum update.
+
+        Parameters
+        ----------
+        epsilon: float, > 0
+            step scale
+        state: State namedtuple,
+            current position data
+        out: (optional) State namedtuple,
+            preallocated arrays to write to in place
+
+        Returns
+        -------
+        None if `out` is provided, else a State namedtuple
+        """
         pot = self._potential
         axpy = linalg.blas.get_blas_funcs('axpy', dtype=self._dtype)
 

pymc3/step_methods/hmc/quadpotential.py

@@ -262,6 +262,7 @@ def current_mean(self):
 
 class QuadPotentialDiag(QuadPotential):
     """Quad potential using a diagonal covariance matrix."""
+
     def __init__(self, v, dtype=None):
         """Use a vector to represent a diagonal matrix for a covariance matrix.