Proposed rwalk change

• Don't add the initial point to the chain (prevents bad estimated of the act)
• Break strictly after maxmcmc and return a random draw from the prior

Closes #477 (closed)

bilby/core/sampler/dynesty.py

@@ -685,9 +685,9 @@ def sample_rwalk_bilby(args):
     reject = 0
     nfail = 0
     act = np.inf
-    u_list = [u]
-    v_list = [prior_transform(u)]
-    logl_list = [loglikelihood(v_list[-1])]
+    u_list = []
+    v_list = []
+    logl_list = []
     max_walk_warning = True
 
     while len(u_list) < nact * act:
@@ -748,7 +748,7 @@ def sample_rwalk_bilby(args):
                 old_act=old_act, maxmcmc=maxmcmc)
 
         # If we've taken too many likelihood evaluations then break
-        if accept + reject > maxmcmc and accept > 0:
+        if accept + reject > maxmcmc:
             if max_walk_warning:
                 warnings.warn(
                     "Hit maximum number of walks {} with accept={}, reject={}, "
@@ -765,17 +765,11 @@ def sample_rwalk_bilby(args):
         u = u_list[idx]
         v = v_list[idx]
         logl = logl_list[idx]
-    elif len(u_list) == 1:
-        logger.warning("Returning the only point in the chain")
-        u = u_list[-1]
-        v = v_list[-1]
-        logl = logl_list[-1]
     else:
-        idx = np.random.randint(int(len(u_list) / 2), len(u_list))
-        logger.warning("Returning random point in second half of the chain")
-        u = u_list[idx]
-        v = v_list[idx]
-        logl = logl_list[idx]
+        logger.warning("Unable to find a new point using walk: returning a random point")
+        u = np.random.uniform(size=n)
+        v = prior_transform(u)
+        logl = loglikelihood(v)
 
     blob = {'accept': accept, 'reject': reject, 'fail': nfail, 'scale': scale}
     kwargs["old_act"] = act