Fix axis handling of randommethod in GRW

pymc3/distributions/distribution.py

@@ -559,6 +559,7 @@ def draw_values(params, point=None, size=None):
     # draw_values in the context of sample_posterior_predictive
     ppc_sampler = vectorized_ppc.get(None)
     if ppc_sampler is not None:
+
         # this is being done inside new, vectorized sample_posterior_predictive
         return ppc_sampler(params, trace=point, samples=size)
 
@@ -592,7 +593,6 @@ def draw_values(params, point=None, size=None):
             else:
                 # param still needs to be drawn
                 symbolic_params.append((i, p))
-
         if not symbolic_params:
             # We only need to enforce the correct order if there are symbolic
             # params that could be drawn in variable order
@@ -995,7 +995,7 @@ def generate_samples(generator, *args, **kwargs):
     else:
         samples = generator(size=size_tup + dist_bcast_shape, *args, **kwargs)
     samples = np.asarray(samples)
-
+    
     # reshape samples here
     if samples.ndim > 0 and samples.shape[0] == 1 and size_tup == (1,):
         if (len(samples.shape) > len(dist_shape) and

pymc3/distributions/timeseries.py

@@ -17,6 +17,7 @@
 from scipy import stats
 import theano.tensor as tt
 from theano import scan
+import numpy as np
 
 from pymc3.util import get_variable_name
 from .continuous import get_tau_sigma, Normal, Flat
@@ -293,6 +294,7 @@ def random(self, point=None, size=None):
         sigma, mu = distribution.draw_values(
             [self.sigma, self.mu], point=point, size=size
         )
+
         return distribution.generate_samples(
             self._random,
             sigma=sigma,
@@ -307,10 +309,15 @@ def _random(self, sigma, mu, size, sample_shape):
         if size[len(sample_shape)] == sample_shape:
             axis = len(sample_shape)
         else:
-            axis = 0
+            axis = len(size) - 1
         rv = stats.norm(mu, sigma)
         data = rv.rvs(size).cumsum(axis=axis)
-        data = data - data[0]  # TODO: this should be a draw from `init`, if available
+        data = np.array(data)
+        if len(data.shape)>1:
+            for i in range(data.shape[0]):
+                data[i] = data[i] - data[i][0]
+        else:
+            data = data - data[0]
         return data
 
     def _repr_latex_(self, name=None, dist=None):