let MVNormal Proposal return 2d step matrix, SMC uses this, remove unused import

Author: hvasbath

pymc3/step_methods/metropolis.py

@@ -50,8 +50,12 @@ def __init__(self, s):
         self.chol = scipy.linalg.cholesky(s, lower=True)
 
     def __call__(self, num_draws=None):
-        b = np.random.randn(self.n)
-        return np.dot(self.chol, b)
+        if num_draws is not None:
+            b = np.random.randn(self.n, num_draws)
+            return np.dot(self.chol, b).T
+        else:
+            b = np.random.randn(self.n)
+            return np.dot(self.chol, b)
 
 
 class Metropolis(ArrayStepShared):

pymc3/step_methods/smc.py

@@ -25,6 +25,7 @@
 from ..theanof import inputvars, make_shared_replacements, join_nonshared_inputs
 import numpy.random as nr
 
+from .metropolis import MultivariateNormalProposal
 from .arraystep import metrop_select
 from ..backends import smc_text as atext
 
@@ -33,25 +34,6 @@
 EXPERIMENTAL_WARNING = "Warning: SMC is an experimental step method, and not yet"\
     " recommended for use in PyMC3!"
 
-
-class Proposal(object):
-    """Proposal distributions modified from pymc3 to initially create all the
-    Proposal steps without repeated execution of the RNG - significant speedup!
-
-    Parameters
-    ----------
-    s : :class:`numpy.ndarray`
-    """
-    def __init__(self, s):
-        self.s = np.atleast_1d(s)
-
-
-class MultivariateNormalProposal(Proposal):
-    def __call__(self, num_draws=None):
-        return np.random.multivariate_normal(
-                mean=np.zeros(self.s.shape[0]), cov=self.s, size=num_draws)
-
-
 proposal_dists = {
     'MultivariateNormal': MultivariateNormalProposal,
         }

pymc3/tests/test_step.py

@@ -5,7 +5,7 @@
 from .checks import close_to
 from .models import simple_categorical, mv_simple, mv_simple_discrete, simple_2model, mv_prior_simple
 from pymc3.sampling import assign_step_methods, sample
-from pymc3.model import Model, Deterministic
+from pymc3.model import Model
 from pymc3.step_methods import (NUTS, BinaryGibbsMetropolis, CategoricalGibbsMetropolis,
                                 Metropolis, Slice, CompoundStep, NormalProposal,
                                 MultivariateNormalProposal, HamiltonianMC,