Update documentation for search

The docstrings for the search module are now
added into the API documentation.

Author: ljvmiranda921
Email: ljvmiranda921@gmail.com

docs/api/pyswarms.rst

@@ -39,4 +39,5 @@ module.
 
 .. toctree::
 
-   pyswarms.utils.functions
+   pyswarms.utils.functions
+   pyswarms.utils.search

docs/api/pyswarms.utils.search.rst

@@ -0,0 +1,37 @@
+pyswarms.utils.search package
+================================
+
+.. automodule:: pyswarms.utils.search
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Submodules
+----------
+
+pyswarms.utils.search.base_search module
+-----------------------------------------
+
+.. automodule:: pyswarms.utils.search.base_search
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :special-members:
+
+pyswarms.utils.search.grid_search module
+-----------------------------------------
+
+.. automodule:: pyswarms.utils.search.grid_search
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :special-members:
+
+pyswarms.utils.search.random_search module
+-------------------------------------------
+
+.. automodule:: pyswarms.utils.search.random_search
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :special-members:

pyswarms/utils/search/base_search.py

@@ -1,7 +1,5 @@
 # -*- coding: utf-8 -*-
-"""
-Base class for hyperparameter optimization search functions. 
-"""
+"""Base class for hyperparameter optimization search functions"""
 
 # Import from __future__
 from __future__ import with_statement

pyswarms/utils/search/grid_search.py

@@ -1,5 +1,4 @@
 # -*- coding: utf-8 -*-
-
 """
 Hyperparameter grid search.
 
@@ -13,22 +12,6 @@
 default returns the minimum objective function score and hyperparameters that
 yield the minimum score, yet maximum score can also be evaluated.
 
-Parameters
-----------
-* c1 : float
-    cognitive parameter
-* c2 : float
-    social parameter
-* w : float
-    inertia parameter
-* k : int
-    number of neighbors to be considered. Must be a
-    positive integer less than `n_particles`
-* p: int {1,2}
-    the Minkowski p-norm to use. 1 is the
-    sum-of-absolute values (or L1 distance) while 2 is
-    the Euclidean (or L2) distance.
-
 >>> options = {'c1': [1, 2, 3],
                'c2': [1, 2, 3],
                'w' : [2, 3, 5],

pyswarms/utils/search/random_search.py

@@ -13,21 +13,6 @@
 returns the minimum objective function score and hyperparameters that yield
 the minimum score, yet maximum score can also be evaluated.
 
-Parameters
-----------
-* c1 : float
-    cognitive parameter
-* c2 : float
-    social parameter
-* w : float
-    inertia parameter
-* k : int
-    number of neighbors to be considered. Must be a
-    positive integer less than `n_particles`
-* p: int {1,2}
-    the Minkowski p-norm to use. 1 is the
-    sum-of-absolute values (or L1 distance) while 2 is
-    the Euclidean (or L2) distance.
 
 >>> options = {'c1': [1, 5],
                'c2': [6, 10],
@@ -60,8 +45,6 @@
 # Import from package
 from pyswarms.utils.search.base_search import SearchBase
 
-
-
 class RandomSearch(SearchBase):
     """Search of optimal performance on selected objective function
     over combinations of randomly selected hyperparameter values