Merge branch 'similar_sort' into 'master'

modifu

See merge request alpha/physical!158

linucb/views/linucb.py

@@ -45,7 +45,7 @@ class LinUCB:
             Aa_list = list()
             theta_list = list()
 
-            for tag_id in redis_linucb_tag_data_dict:
+            for tag_id in tag_list:
                 tag_dict = pickle.loads(redis_linucb_tag_data_dict[tag_id])
                 Aa_list.append(tag_dict["Aa"])
                 theta_list.append(tag_dict["theta"])
@@ -60,17 +60,38 @@ class LinUCB:
             theta_tmp = np.array(theta_list)
 
             np_array = np.dot(xaT, theta_tmp) + cls.alpha * np.sqrt(np.dot(np.dot(xaT, AaI_tmp), xa))
-            top_tag_list_len = int(np_array.size/2)
-            top_np_ind = np.argpartition(np_array, -top_tag_list_len)[-top_tag_list_len:]
-
-            top_tag_list = list()
-            top_np_list = top_np_ind.tolist()
-            for tag_id in top_np_list:
-                top_tag_list.append(tag_id)
+            # top_tag_list_len = int(np_array.size/2)
+            # top_np_ind = np.argpartition(np_array, -top_tag_list_len)[-top_tag_list_len:]
+            #
+            # top_tag_list = list()
+            # top_np_list = top_np_ind.tolist()
+            # for tag_id in top_np_list:
+            #     top_tag_list.append(tag_id)
 
             #art_max = tag_list[np.argmax(np.dot(xaT, theta_tmp) + cls.alpha * np.sqrt(np.dot(np.dot(xaT, AaI_tmp), xa)))]
 
-            return top_tag_list
+
+            top_tag_set = set()
+            np_score_list = list()
+            np_score_dict = dict()
+
+            for score_index in range(0,np_array.size):
+                score = np_array.take(score_index)
+                np_score_list.append(score)
+                if score not in np_score_dict:
+                    np_score_dict[score] = [score_index]
+                else:
+                    np_score_dict[score].append(score_index)
+
+            sorted_np_score_list = sorted(np_score_list,reverse=True)
+            for top_score in sorted_np_score_list:
+                for top_score_index in np_score_dict[top_score]:
+                    top_tag_set.add(tag_list[top_score_index])
+
+                if len(top_tag_set) >= 10:
+                    break
+
+            return list(top_tag_set)
         except:
             logging.error("catch exception,err_msg:%s" % traceback.format_exc())
             return []