Merge pull request #192 from Learnware-LAMDA/check_docs

[DOC] check several pages in docs

CHANGES.rst

@@ -1,7 +1,7 @@
 Changelog
 =========
-Here you can see the full list of changes between each Learnware Market release.
+Here you can see the full list of changes between ``learnware`` release.
 
 Version 0.1.0
 -------------
-This is the initial release of Learnware Market.
+This is the initial release of ``learnware``.

docs/about/about.rst

@@ -2,10 +2,17 @@
 About Us
 ================
 
-We thank all the contributors for the development of learnware package:
+The R&D Team of ``learnware`` is from `Nanjing University's LAMDA Research Institute <https://www.lamda.nju.edu.cn/MainPage.ashx>`_, with Prof. `Zhi-Hua Zhou <http:https://cs.nju.edu.cn/zhouzh>`_ serving as the Founding Director.
 
-.. image:: https://github.com/Learnware-LAMDA/Learnware/graphs/contributors
- :align: center
+LAMDA is affiliated with the National Key Laboratory for Novel Software Technology, the Department of Computer Science & Technology and the School of Artificial Intelligence, Nanjing University, China. It locates at Computer Science and Technology Building in the Xianlin campus of Nanjing University, mainly in Rm910.
 
-In LAMDA Group, also many people participate the discussions, learnware package design and development and so on. 
-For more details about us, please refer to `LAMDA Group <https://www.lamda.nju.edu.cn/>`_.
+"LAMDA" means "Learning And Mining from DatA". The main research interests of LAMDA include machine learning, data mining, pattern recognition, information retrieval, evolutionary computation, neural computation, and some other related areas. Currently our research mainly involves: ensemble learning, semi-supervised and active learning, multi-instance and multi-label learning, cost-sensitive and class-imbalance learning, metric learning, dimensionality reduction and feature selection, structure learning and clustering, theoretical foundations of evolutionary computation, improving comprehensibility, content-based image retrieval, web search and mining, face recognition, computer-aided medical diagnosis, bioinformatics, etc.
+
+The ``learnware`` package is currently maintained by the LAMDA Beimingwu R&D Team, consisting of: Zhi-Hua Zhou, Yang Yu, Zhi-Hao Tan, Jian-Dong Liu, Peng Tan, Xiao-Dong Bi, Qin-Cheng Zheng, Xiao-Chuan Zou, Yi Xie, Hai-Tian Liu, Hao-Yu Shi, Xin-Yu Zhang and others.
+
+Contact
+==========
+
+``learnware`` is still young. It may contain bugs and issues. Contributions are welcome. If you encounter any problems or have any suggestions while using the ``learnware`` package, please contact us:
+
+- Email: [email protected]

docs/about/dev.rst

@@ -6,7 +6,7 @@ For Developer
 Install with Dev Mode
 =======================
 
-As a developer, you often want make changes to ``Learnware Market`` and hope it would reflect directly in your environment without reinstalling it. You can install ``Learnware Market`` in editable mode with following command.
+As a developer, you often want make changes to ``learnware`` and hope it would reflect directly in your environment without reinstalling it. You can install ``learnware`` in editable mode with following command.
 
 .. code-block:: bash
  
@@ -33,7 +33,7 @@ The suffix specifies the specific nature of the modification, with the initial l
 Examples: The following are all valid:
 
 - [DOC] Fix the document
-- [FIX, ENH] Fix the bug and add some feature"
+- [FIX, ENH] Fix the bug and add some features
 
 
 Docstring
@@ -47,7 +47,7 @@ Continuous Integration
 ======================
 Continuous Integration (CI) tools help you stick to the quality standards by running tests every time you push a new commit and reporting the results to a pull request.
 
-``Learnware Market`` will check the following tests when you pull a request:
+This project will check the following tests when you pull a request:
 1. We will check your code length, you can fix your code style by the following commands:
 
 .. code-block:: bash

docs/references/api.rst

@@ -3,7 +3,7 @@
 API Reference
 ================================
 
-Here you can find high-level ``Learnware`` interfaces.
+Here you can find high-level interfaces in the ``learnware`` package.
 
 Market
 ====================

docs/references/beimingwu.rst

@@ -24,7 +24,7 @@ In addition, the Beimingwu system also has the following features:
 - ``Diverse Learnware Search``: The Beimingwu system supports both semantic specifications and statistical specifications searches, covering data types such as tables, images, and text. In addition, for table-based tasks, the system also supports the search for heterogeneous table learnwares.
 - ``Local Learnware Deployment``: The Beimingwu system provides interfaces for learnware deployment and learnware reuse in the learnware package, facilitating users' convenient and secure learnware deployment.
 - ``Data Privacy Protection``: The Beimingwu system operations, including learnware upload, search, and deployment, do not require users to upload local data. All relevant statistical specifications are generated locally by users, ensuring data privacy.
-- ``Fully Open Source``: The Beimingwu system's source code is completely open-source, including the learnware package and frontend/backend code. The learnware package is highly extensible, making it easy to integrate new specification designs, learnware system designs, and learnware reuse methods in the future.
+- ``Open Source System``: The Beimingwu system's source code is open-source, including the learnware package and frontend/backend code. The learnware package is highly extensible, making it easy to integrate new specification designs, learnware system designs, and learnware reuse methods in the future.
 
 Beimingwu is the first system-level implementation of the learnware paradigm.
 This pioneering venture is just the beginning, with vast opportunities for enhancement and growth in the related technological fields still ahead.

docs/start/install.rst

@@ -8,9 +8,9 @@ Installation Guide
 ===================================
 .. note::
 
- The ``learnware`` package supports `Windows`, `Linux`. It's recommended to use ``Learnware`` in `Linux`. ``Learnware`` supports Python3, which is up to Python3.11.
+ The ``learnware`` package supports `Windows`, `Linux`. It's recommended to use ``learnware`` in `Linux`. This package supports Python3, which is up to Python3.11.
 
-Users can easily install ``Learnware`` by pip according to the following command:
+Users can easily install ``learnware`` by pip according to the following command:
 
 .. code-block:: bash
 
@@ -29,10 +29,10 @@ In the ``learnware`` package, besides the base classes, many core functionalitie
 Install ``learnware`` Package From Source
 ==========================================
 
-Also, Users can install ``Learnware`` by the source code according to the following steps:
+Also, Users can install ``learnware`` by the source code according to the following steps:
 
-- Enter the root directory of ``Learnware``, in which the file ``setup.py`` exists.
-- Then, please execute the following command to install the environment dependencies and install ``Learnware``:
+- Enter the root directory of this project, in which the file ``setup.py`` exists.
+- Then, please execute the following command to install the environment dependencies and install ``learnware``:
 
  .. code-block:: bash
  

docs/start/quick.rst

@@ -14,7 +14,7 @@ and utilizing ``Learnware`` to handle user tasks.
 Installation
 ====================
 
-Learnware is currently hosted on `PyPI <https://pypi.org/>`_. You can easily intsall ``Learnware`` by following these steps:
+Learnware is currently hosted on `PyPI <https://pypi.org/project/learnware/>`_. You can easily intsall the ``learnware`` package by following these steps:
 
 .. code-block:: bash
 
@@ -32,14 +32,14 @@ In the ``learnware`` package, besides the base classes, many core functionalitie
 Prepare Learnware
 ====================
 
-In learnware ``learnware`` package, each learnware is encapsulated in a ``zip`` package, which should contain at least the following four files:
+In the ``learnware`` package, each learnware is encapsulated in a ``zip`` package, which should contain at least the following four files:
 
 - ``learnware.yaml``: learnware configuration file.
 - ``__init__.py``: methods for using the model.
 - ``stat.json``: the statistical specification of the learnware. Its filename can be customized and recorded in learnware.yaml.
 - ``environment.yaml`` or ``requirements.txt``: specifies the environment for the model.
 
-To facilitate the construction of a learnware, we provide a `Learnware Template <https://www.bmwu.cloud/static/learnware-template.zip>`_ that the users can use as a basis for building your own learnware. We've also detailed the format of the learnware ``zip`` package in `Learnware Preparation<../workflows/upload:prepare-learnware>`.
+To facilitate the construction of a learnware, we provide a `Learnware Template <https://www.bmwu.cloud/static/learnware-template.zip>`_ that the users can use as a basis for building your own learnware. We've also detailed the format of the learnware ``zip`` package in `Learnware Preparation <../workflows/upload:prepare-learnware>`_.
 
 Learnware Package Workflow
 ============================
@@ -119,7 +119,7 @@ If you decide in favor of porviding your own statistical specification file, ``s
 the ``Learnware Market`` can further refine the selection of learnwares from the previous step. 
 This second-stage search leverages statistical information to identify one or more learnwares that are most likely to be beneficial for your task. 
 
-For example, the code below executes learnware search when using Reduced Set Kernel Embedding as the statistical specification:
+For example, the code below executes learnware search when using Reduced Kernel Mean Embedding (RKME) as the statistical specification:
 
 .. code-block:: python