[DOC] update advanced topics

docs/advanced/anchor.rst

@@ -4,15 +4,15 @@ Anchor learnware
 
 Anchor learnwares are a small fraction of representative learnwares that helps locate user's requirements through user feedback. The learnware market can choose or generate several learnwares as anchor learnwares corresponding to the specification island. If the user does not have sufficient training data for constructing an RKME requirement, the learnware market can send several anchor learnwares to the user. By feeding her own data to these anchor learnwares, some information such as (precision, recall) or other performance indicators, can be generated and returned to the market. These information could help the market identify potentially helpful models, e.g., by identifying models that are far from anchors exhibiting poor performance whereas close to anchors exhibiting relatively better performance in the specification island.
 
-To fulfill the anchor learnware method, you need to implement the following functions in ``anchor.py``: 
+To fulfill the anchor learnware method, you need to implement the following functions in ``learnware/market/anchor/``: 
 
-- First, you should design how the market chooses or generates anchor learnwares. This can be realized by selecting prototype models through functional space clustering, and more interesting designs can be explored. The function ``AnchoredMarket.update_anchor_learnware_list`` is reserved for it. The functions ``AnchoredMarket._update_anchor_learnware`` and ``AnchoredMarket._delete_anchor_learnware`` have been completed as auxiliary.
+- First, you should design how the market chooses or generates anchor learnwares. This can be realized by selecting prototype models through functional space clustering, and more interesting designs can be explored. The function ``AnchoredOrganizer.update_anchor_learnware_list`` is reserved for it. The functions ``AnchoredOrganizer._update_anchor_learnware`` and ``AnchoredOrganizer._delete_anchor_learnware`` have been completed as auxiliary.
 
-- Second, when a user comes with no RKME(or other statistical) specifications, the market should choose several anchor learnwares  and send them to the user. This process is done by ``AnchoredMarket.search_anchor_learnware``, and the chosen anchors are stored in ``AnchoredUserInfo`` by ``AnchoredUserInfo.add_anchor_learnware``.
+- Second, when a user comes with no RKME(or other statistical) specifications, the market should choose several anchor learnwares and send them to the user. This process is done by ``AnchoredSearcher.search_anchor_learnware``, and the chosen anchors are stored in ``AnchoredUserInfo`` by ``AnchoredUserInfo.add_anchor_learnware_ids``.
   
 - Third, the market should specify which performance indicator should the user return. By feeding the user's data to these anchor learnwares, the returned information is calculated and stored in ``AnchoredUserInfo`` by ``AnchoredUserInfo.update_stat_info``.
   
-- Fourth, according to the returned information from the user, the market should identify the helpful learnwares for the user. This process is done in ``AnchoredMarket.search_learnware``.
+- Fourth, according to the returned information from the user, the market should identify the helpful learnwares for the user. This process is done in ``AnchoredSearcher.search_learnware``.
 
 
 

docs/advanced/evolve.rst

@@ -1,5 +1,5 @@
 ==============================
-Specification Evolvement
+Evolvable Specification
 ==============================
 
 The specification is the core of the learnware paradigm.
@@ -8,16 +8,16 @@ As the number of learnwares in the market increases, the knowledge held in the l
 This growth makes it possible for specification evolvement, enabling the market to generate new specifications for each learnware that more accurately characterize the properties of each model and its relationships with others.
 As a result, the learnware market can more effectively identify learnwares beneficial for user tasks.
 
-To achieve the evolvement of specifications, you need to implement the class ``EvolvedMarket`` in the following way:
+To achieve evolvable specifications, you need to implement the class ``EvolvedOrganizer`` in ``learnware/market/evolve/``:
 
-- First, design a method for the learnware market to generate new statistical specifications for learnwares and implement the function ``EvolvedMarket.generate_new_stat_specification``.
-- Second, use the function ``EvolvedMarket.generate_new_stat_specification`` to implement the function ``EvolvedMarket.evolve_learnware_list``, which enables learnwares to evolve by assigning new statistical specifications.
+- First, design a method for the learnware market to generate new statistical specifications for learnwares and implement the function ``EvolvedOrganizer.generate_new_stat_specification``.
+- Second, use the function ``EvolvedOrganizer.generate_new_stat_specification`` to implement the function ``EvolvedOrganizer.evolve_learnware_list``, which enables learnwares to evolve by assigning new statistical specifications.
 
 When implementing the anchor design, it is essential to develop an appropriate evolvement method for anchor learnwares based on the specific anchor selection method.
 In the anchor design, the learnware market sends anchor learnware to users, who then provide statistical information about the anchor learnwares on their tasks to the market.
 Based on this statistical feedback from users, the market can more accurately characterize anchor learnwares and continuously evolve them.
 
-To realize specification evolvement, including anchor learnwares, you need to additionally implement the class ``EvolvedAnchoredMarket`` in the following way:
+To realize evolvable specifications, including anchor learnwares, you need to additionally implement the class ``EvolvedAnchoredOrganizer`` in ``learnware/market/evolve_anchor/``:
 
-- First, based on the specific anchor selection method, design an appropriate evolvement method for anchor learnwares and implement the function ``EvolvedAnchoredMarket.evolve_anchor_learnware_list``.
-- Second, utilize the statistical feedback from users to implement the function ``EvolvedAnchoredMarket.evolve_anchor_learnware_by_user``, which enables anchor learnwares to evolve continually as users interact with the learnware market.
+- First, based on the specific anchor selection method, design an appropriate evolvement method for anchor learnwares and implement the function ``EvolvedAnchoredOrganizer.evolve_anchor_learnware_list``.
+- Second, utilize the statistical feedback from users to implement the function ``EvolvedAnchoredOrganizer.evolve_anchor_learnware_by_user``, which enables anchor learnwares to evolve continually as users interact with the learnware market.