Comments

pymc3/step_methods/hmc/base_hmc.py

@@ -1,11 +1,11 @@
+import numpy as np
+
 from ..arraystep import ArrayStepShared
 from .trajectory import get_theano_hamiltonian_functions
-
 from pymc3.tuning import guess_scaling
 from pymc3.model import modelcontext, Point
 from .quadpotential import quad_potential
 from pymc3.theanof import inputvars, make_shared_replacements, floatX
-import numpy as np
 
 
 class BaseHMC(ArrayStepShared):

pymc3/step_methods/hmc/hmc.py

@@ -1,10 +1,10 @@
+import numpy as np
+
 from ..arraystep import metrop_select, Competence
 from .base_hmc import BaseHMC
 from pymc3.vartypes import discrete_types
 from pymc3.theanof import floatX
 
-import numpy as np
-
 
 __all__ = ['HamiltonianMC']
 
@@ -20,7 +20,7 @@ def unif(step_size, elow=.85, ehigh=1.15):
 class HamiltonianMC(BaseHMC):
     R"""A sampler for continuous variables based on Hamiltonian mechanics.
 
-    See NUTS sampler for automatically tuned stopping time.
+    See NUTS sampler for automatically tuned stopping time and step size scaling.
     """
 
     name = 'hmc'

pymc3/step_methods/hmc/nuts.py

@@ -1,17 +1,17 @@
 from collections import namedtuple
 import warnings
 
+import numpy as np
+import numpy.random as nr
+from scipy import stats, linalg
+import six
+
 from ..arraystep import Competence
 from pymc3.exceptions import SamplingError
 from .base_hmc import BaseHMC
 from pymc3.theanof import floatX
 from pymc3.vartypes import continuous_types
 
-import numpy as np
-import numpy.random as nr
-from scipy import stats, linalg
-import six
-
 __all__ = ['NUTS']
 
 
@@ -28,7 +28,7 @@ class NUTS(BaseHMC):
     sample. A detailed description can be found at [1], "Algorithm 6:
     Efficient No-U-Turn Sampler with Dual Averaging".
 
-    Nuts provides a number of statistics that can be accessed with
+    NUTS provides a number of statistics that can be accessed with
     `trace.get_sampler_stats`:
 
     - `mean_tree_accept`: The mean acceptance probability for the tree

pymc3/step_methods/hmc/quadpotential.py

@@ -1,4 +1,4 @@
-from numpy import dot
+import numpy as np
 from numpy.random import normal
 import scipy.linalg
 import theano.tensor as tt
@@ -7,7 +7,6 @@
 
 from pymc3.theanof import floatX
 
-import numpy as np
 
 __all__ = ['quad_potential', 'ElemWiseQuadPotential', 'QuadPotential',
            'QuadPotential_Inv', 'isquadpotential']
@@ -137,7 +136,7 @@ def velocity(self, x):
     def random(self):
         """Draw random value from QuadPotential."""
         n = floatX(normal(size=self.L.shape[0]))
-        return dot(self.L, n)
+        return np.dot(self.L, n)
 
     def energy(self, x):
         """Compute kinetic energy at a position in parameter space."""

pymc3/step_methods/hmc/trajectory.py

@@ -1,11 +1,10 @@
 from collections import namedtuple
 
-from pymc3.theanof import join_nonshared_inputs, gradient, CallableTensor, floatX
-
 import theano
 import theano.tensor as tt
 import numpy as np
 
+from pymc3.theanof import join_nonshared_inputs, gradient, CallableTensor, floatX
 
 Hamiltonian = namedtuple("Hamiltonian", "logp, dlogp, pot")