'clean up' for Scott

ml-agents/mlagents/trainers/stats.py

             self._maybe_create_summary_writer(category)
             # adapted from https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514
             counts, bin_edges = np.histogram(value, bins=len(value))
-            
+
-            #print(float(np.min(value)))
-            #print(float(np.max(value)))
-            #print(int(np.prod(value.shape)))
-            #print(float(np.sum(value)))
-            #print(float(np.sum(value**2)))
-            #print(bin_edges[1:])
-            #hist.min = float(np.min(value))
-            #hist.max = float(np.max(value))
-            #hist.num = int(np.prod(value.shape))
-            #hist.sum = float(np.sum(value))
-            #hist.sum_squares = float(np.sum(value**2))
-            #value = np.log(value)
-            for obs, grad in sorted(enumerate(value), reverse=True, key=lambda x: x[1]):
-                print(f"Observation {obs} has relevance {grad}")
+            # for obs, grad in sorted(enumerate(value), reverse=True, key=lambda x: x[1]):
+            #    print(f"Observation {obs} has relevance {grad}")
-            hist.max = float(len(value))#float(np.max(value))
-            hist.num = len(value)#int(np.prod(value.shape))
+            hist.max = float(len(value))
+            hist.num = len(value)
-            hist.sum_squares = float(np.sum(value**2))
+            hist.sum_squares = float(np.sum(value ** 2))
-            for edge in range(len(value)):#counts:
-                #print(edge)
-                hist.bucket_limit.append(edge+.5)
+            for edge in range(len(value)):
+                hist.bucket_limit.append(edge + 0.5)
-                #print(c)
-            # Add bin edges and counts
-        #    for edge,i in zip(range(1,len(value)), bin_edges):
-        #        hist.bucket_limit.append(i)
-        #    for c,i in zip(value, counts):
-        #        hist.bucket.append(i)
-            #summary = tf.Summary()
-            #summary.value.add(tag="Saliency", histo=hist)
-            #self.summary_writers[category].add_summary(summary)#, self.trajectories)
-            #self.summary_writers[category].flush()
-
-            #with tf.Session(config=generate_session_config()) as sess:
-            #    hist_op = tf.summary.histogram(category, value)
-            #    hist = sess.run(hist_op)
     def _dict_to_tensorboard(
         self, name: str, input_dict: Dict[str, Any]
     ) -> Optional[bytes]: