Fix axis handling of randommethod in GRW

RELEASE-NOTES.md

@@ -9,6 +9,7 @@
 - Pass the `tune` argument from `sample` when using `advi+adapt_diag_grad` (see issue [#3965](https://github.com/pymc-devs/pymc3/issues/3965), fixed by [#3979](https://github.com/pymc-devs/pymc3/pull/3979)).
 - Add simple test case for new coords and dims feature in `pm.Model` (see [#3977](https://github.com/pymc-devs/pymc3/pull/3977)). 
 - Require ArviZ >= 0.9.0 (see [#3977](https://github.com/pymc-devs/pymc3/pull/3977)).
+- Temporarily fixed issue [#3962](https://github.com/pymc-devs/pymc3/issues/3962) by making change in the `_random()` method of `GaussianRandomWalk` class, refer to PR [#3985].Furthur testing revealed a new issue which is being tracked [#4010](https://github.com/pymc-devs/pymc3/issues/4010) 
 
 _NB: The `docs/*` folder is still removed from the tarball due to an upload size limit on PyPi._
 
@@ -54,6 +55,8 @@ Though we had to temporarily remove the `docs/*` folder from the tarball due to
 - Forced the `Beta` distribution's `random` method to generate samples that are in the open interval $(0, 1)$, i.e. no value can be equal to zero or equal to one (issue [#3898](https://github.com/pymc-devs/pymc3/issues/3898) fixed by [#3924](https://github.com/pymc-devs/pymc3/pull/3924)).
 - Fixed an issue that happened on Windows, that was introduced by the clipped beta distribution rvs function ([#3924](https://github.com/pymc-devs/pymc3/pull/3924)). Windows does not support the `float128` dtype, but we had assumed that it had to be available. The solution was to only support `float128` on Linux and Darwin systems (see issue [#3929](https://github.com/pymc-devs/pymc3/issues/3849) fixed by [#3930](https://github.com/pymc-devs/pymc3/pull/3930)).
 
+
+
 ### Deprecations
 - Remove `sample_ppc` and `sample_ppc_w` that were deprecated in 3.6.
 - Deprecated `sd` has been replaced by `sigma` (already in version 3.7) in continuous, mixed and timeseries distributions and now raises `DeprecationWarning` when `sd` is used. (see [#3837](https://github.com/pymc-devs/pymc3/pull/3837) and [#3688](https://github.com/pymc-devs/pymc3/issues/3688)).

docs/release-notes/pymc3-3.0.md

@@ -1 +0,0 @@
-

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):