[MNT] Add reuse workflow

docs/workflow/reuse.rst

@@ -2,3 +2,39 @@
 Getting Started-Workflow-Reuse learnware
 ==========================================
 
+This part introduces two baseline methods for reusing a given list of learnwares, namely ``JobSelectorReuser`` and ``AveragingReuser``.
+Instead of training a model from scratch, you can easily reuse a list of learnwares ``learnware_list (List[Learnware])`` to make predictions on your own data ``test_data (numpy.ndarray or torch.Tensor)`` in the following way:
+
+.. code-block:: python
+
+    from learnware.learnware import JobSelectorReuser, AveragingReuser
+
+    # using jobselector reuser to reuse the searched learnwares to make prediction
+    reuse_job_selector = JobSelectorReuser(learnware_list=learnware_list)
+    job_selector_predict_y = reuse_job_selector.predict(user_data=test_data)
+
+    # using averaging ensemble reuser to reuse the searched learnwares to make prediction
+    reuse_ensemble = AveragingReuser(learnware_list=learnware_list)
+    ensemble_predict_y = reuse_ensemble.predict(user_data=test_data)
+
+
+JobSelectorReuser
+====================
+
+The ``JobSelectorReuser`` is a class that inherits from the base reuse class ``BaseReuser``.
+Its purpose is to create a job selector that identifies the optimal learnware for each data point in user data.
+There are three parameters required to initialize the class:
+
+- ``learnware_list``: A list of objects of type ``Learnware``. Each ``Learnware`` object should have an RKME specification.
+- ``herding_num``: An optional integer that specifies the number of items to herd, which defaults to 1000 if not provided.
+- ``use_herding``: A boolean flag indicating whether to use kernel herding.
+
+The job selector is essentially a multi-class classifier :math:`g(\boldsymbol{x}\rightarrow \mathcal{I})` with :math:`\mathcal{I}=\{1,\ldots, C\}`.
+Given a testing sample :math:`\boldsymbol{x}`, the ``JobSelectorReuser`` predicts it by using the :math:`g(\boldsymbol{x})`-th learnware in ``learnware_list``.
+If ``use_herding`` is set to false, the ``JobSelectorReuser`` uses data points in each learware's RKME spefication with the corresponding learnware index to train a job selector.
+If ``use_herding`` is true, the algorithm estimates the mixture weight based on RKME specifications and raw user data, uses the weight to generate ``herding_num`` auxiliary data points mimicking the user distribution through the kernel herding method, and learns a job selector on these data.
+
+
+AveragingReuser
+====================
+