Improve documentation (#16)

This is a massive documentation update in order to make the ReadTheDocs website as appealing it should be. In summary, the following were undertaken: - Small patch fix for importing modules - Small modification in console_utils for console output - Modified ReadTheDocs' sidebar theme with an overriding css file. - Added a use-case example on Basic Optimization - Updated docstrings for most module implementations to make it look nice in the API documentation. Even though most of these updates occured at different files, they were all done for the sole purpose of improving documentation in ReadTheDocs and introducing an API documentation at the end. Author: ljvmiranda921
ljvmiranda921 · Aug 4, 2017 · 29bcb92 · 29bcb92
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diff --git a/HISTORY.rst b/HISTORY.rst
@@ -8,7 +8,7 @@ History
 * First release on PyPI.
 
 0.1.1 (2017-7-25)
-------------------
+~~~~~~~~~~~~~~~~~
 
 * Bug fixes
 * Implemented Local Best PSO
diff --git a/README.rst b/README.rst
@@ -60,7 +60,7 @@ built-in sphere function, :code:`pyswarms.utils.functions.sphere_func()`, and th
 .. code-block:: python
 
     import pyswarms as ps
-    from pyswarms.utils.functions import sphere_func
+    from pyswarms.utils.functions import single_obj as fx
 
     # Set-up hyperparameters
     options = {'c1': 0.5, 'c2': 0.3, 'm':0.9}
@@ -69,7 +69,7 @@ built-in sphere function, :code:`pyswarms.utils.functions.sphere_func()`, and th
     optimizer = ps.single.GBestPSO(n_particles=10, dims=2, **options)
 
     # Perform optimization
-    stats = optimizer.optimize(sphere_func, iters=100)
+    stats = optimizer.optimize(fx.sphere_func, iters=100)
 
 
 Credits

diff --git a/docs/_static/theme_overrides.css b/docs/_static/theme_overrides.css
@@ -0,0 +1,17 @@
+.wy-menu-vertical header, .wy-menu-vertical p.caption {
+	color: gold;
+}
+
+.wy-menu-vertical a {
+	color: white;
+}
+
+.wy-side-nav-search 
+{
+	color: #cacaca;
+	background: #074E68
+}
+
+.wy-side-nav-search input[type=text] {
+	border-color: rgba(7, 78, 104, 0.83);
+}
diff --git a/docs/conf.py b/docs/conf.py
@@ -152,6 +152,11 @@
 # "default.css".
 html_static_path = ['_static']
 
+def setup(app):
+    # overrides for wide tables in RTD theme
+    app.add_stylesheet('theme_overrides.css') # path relative to static
+
+
 # If not '', a 'Last updated on:' timestamp is inserted at every page
 # bottom, using the given strftime format.
 html_last_updated_fmt = '%b %d, %Y'

diff --git a/docs/examples/basic_optimization.rst b/docs/examples/basic_optimization.rst
@@ -0,0 +1,186 @@
+
+Basic Optimization
+==================
+
+In this example, we'll be performing a simple optimization of
+single-objective functions using the global-best optimizer in
+``pyswarms.single.GBestPSO`` and the local-best optimizer in
+``pyswarms.single.LBestPSO``. This aims to demonstrate the basic
+capabilities of the library when applied to benchmark problems.
+
+.. code:: ipython3
+
+    import sys
+    sys.path.append('../')
+
+.. code-block:: python
+
+    # Import modules
+    import numpy as np
+    
+    # Import PySwarms
+    import pyswarms as ps
+    from pyswarms.utils.functions import single_obj as fx
+    
+    # Some more magic so that the notebook will reload external python modules;
+    # see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython
+    %load_ext autoreload
+    %autoreload 2
+
+Optimizing a function
+---------------------
+
+First, let's start by optimizing the sphere function. Recall that the
+minima of this function can be located at ``f(0,0..,0)`` with a value of
+``0``. In case you don't remember the characteristics of a given
+function, simply call ``help(<function>)``.
+
+For now let's just set some arbitrary parameters in our optimizers.
+There are, at minimum, three steps to perform optimization:
+
+1. Set the hyperparameters to configure the swarm as a ``dict``.
+2. Create an instance of the optimizer by passing the dictionary along
+   with the necessary arguments.
+3. Call the ``optimize()`` method and have it store the optimal cost and
+   position in a variable.
+
+The ``optimize()`` method returns a ``tuple`` of values, one of which
+includes the optimal cost and position after optimization. You can store
+it in a single variable and just index the values, or unpack it using
+several variables at once.
+
+.. code-block:: python
+
+    # Set-up hyperparameters
+    options = {'c1': 0.5, 'c2': 0.3, 'm':0.9}
+    
+    # Call instance of PSO
+    gbest_pso = ps.single.GBestPSO(n_particles=10, dims=2, **options)
+    
+    # Perform optimization
+    cost, pos = gbest_pso.optimize(fx.sphere_func, print_step=100, iters=1000, verbose=3)
+
+
+.. parsed-literal::
+
+    Iteration 1/1000, cost: 0.0035824017918
+    Iteration 101/1000, cost: 1.02538653288e-08
+    Iteration 201/1000, cost: 9.95696087972e-13
+    Iteration 301/1000, cost: 8.22034343822e-16
+    Iteration 401/1000, cost: 3.7188438887e-19
+    Iteration 501/1000, cost: 1.23935292549e-25
+    Iteration 601/1000, cost: 6.03016193248e-28
+    Iteration 701/1000, cost: 3.70755768681e-34
+    Iteration 801/1000, cost: 2.64385328058e-37
+    Iteration 901/1000, cost: 1.76488833461e-40
+    ================================
+    Optimization finished!
+    Final cost: 0.000
+    Best value: [-6.5732265560180066e-24, -7.4004230063696789e-22]
+    
+    
+
+We can see that the optimizer was able to find a good minima as shown
+above. You can control the verbosity of the output using the ``verbose``
+argument, and the number of steps to be printed out using the
+``print_step`` argument.
+
+Now, let's try this one using local-best PSO:
+
+.. code-block:: python
+
+    # Set-up hyperparameters
+    options = {'c1': 0.5, 'c2': 0.3, 'm':0.9}
+    
+    # Call instance of PSO
+    lbest_pso = ps.single.LBestPSO(n_particles=10, dims=2, k=2,p=2, **options)
+    
+    # Perform optimization
+    cost, pos = lbest_pso.optimize(fx.sphere_func, print_step=100, iters=1000, verbose=3)
+
+
+.. parsed-literal::
+
+    Iteration 1/1000, cost: 0.190175474818
+    Iteration 101/1000, cost: 1.14470953523e-06
+    Iteration 201/1000, cost: 6.79485221069e-11
+    Iteration 301/1000, cost: 1.00691597113e-14
+    Iteration 401/1000, cost: 2.98301783945e-18
+    Iteration 501/1000, cost: 2.13856158282e-20
+    Iteration 601/1000, cost: 5.49351926815e-25
+    Iteration 701/1000, cost: 1.7673389214e-29
+    Iteration 801/1000, cost: 1.83082804473e-33
+    Iteration 901/1000, cost: 1.75920918448e-36
+    ================================
+    Optimization finished!
+    Final cost: 3.000
+    Best value: [-8.2344756213578705e-21, -2.6563827831876976e-20]
+    
+    
+
+Optimizing a function with bounds
+---------------------------------
+
+Another thing that we can do is to set some bounds into our solution, so
+as to contain our candidate solutions within a specific range. We can do
+this simply by passing a ``bounds`` parameter, of type ``tuple``, when
+creating an instance of our swarm. Let's try this using the global-best
+PSO with the Rastrigin function (``rastrigin_func`` in
+``pyswarms.utils.functions.single_obj``).
+
+Recall that the Rastrigin function is bounded within ``[-5.12, 5.12]``.
+If we pass an unbounded swarm into this function, then a ``ValueError``
+might be raised. So what we'll do is to create a bound within the
+specified range. There are some things to remember when specifying a
+bound:
+
+-  A bound should be of type tuple with length 2.
+-  It should contain two ``numpy.ndarrays`` so that we have a
+   ``(min_bound, max_bound)``
+-  Obviously, all values in the ``max_bound`` should always be greater
+   than the ``min_bound``. Their shapes should match the dimensions of
+   the swarm.
+
+What we'll do now is to create a 10-particle, 2-dimensional swarm. This
+means that we have to set our maximum and minimum boundaries with the
+shape of 2. In case we want to initialize an n-dimensional swarm, we
+then have to set our bounds with the same shape n. A fast workaround for
+this would be to use the ``numpy.ones`` function multiplied by a
+constant.
+
+.. code-block:: python
+
+    # Create bounds
+    max_bound = 5.12 * np.ones(2)
+    min_bound = - max_bound
+    bounds = (min_bound, max_bound)
+
+.. code-block:: python
+
+    # Initialize swarm
+    options = {'c1': 0.5, 'c2': 0.3, 'm':0.9}
+    
+    # Call instance of PSO with bounds argument
+    optimizer = ps.single.GBestPSO(n_particles=10, dims=2, bounds=bounds, **options)
+    
+    # Perform optimization
+    cost, pos = optimizer.optimize(fx.rastrigin_func, print_step=100, iters=1000, verbose=3)
+
+
+.. parsed-literal::
+
+    Iteration 1/1000, cost: 10.3592595923
+    Iteration 101/1000, cost: 0.00381030608321
+    Iteration 201/1000, cost: 1.31982446305e-07
+    Iteration 301/1000, cost: 1.16529008665e-11
+    Iteration 401/1000, cost: 0.0
+    Iteration 501/1000, cost: 0.0
+    Iteration 601/1000, cost: 0.0
+    Iteration 701/1000, cost: 0.0
+    Iteration 801/1000, cost: 0.0
+    Iteration 901/1000, cost: 0.0
+    ================================
+    Optimization finished!
+    Final cost: 0.000
+    Best value: [8.9869507154871327e-10, -2.7262405947023504e-09]
+
diff --git a/docs/examples/usecases.rst b/docs/examples/usecases.rst
@@ -0,0 +1,8 @@
+Use-case examples
+=================
+Below are some of the applications where PySwarms can be used.
+If you wish to check the actual Jupyter Notebooks, please go to this `link <https://github.com/ljvmiranda921/pyswarms/tree/master/examples>`_
+
+.. toctree::
+
+   basic_optimization
diff --git a/docs/features.rst b/docs/features.rst
@@ -0,0 +1,31 @@
+========
+Features
+========
+
+
+
+Single-Objective Optimizers
+---------------------------
+
+These are standard optimization techniques that aims to find the optima of a single objective function.
+
+Continuous 
+~~~~~~~~~~
+
+Single-objective optimization where the search space is continuous.
+
+* :mod:`pyswarms.single.gb` - global-best Particle Swarm Optimization algorithm with a star-topology. Every particle compares itself with the best-performing particle in the swarm.
+
+* :mod:`pyswarms.single.lb` - local-best Particle Swarm Optimization algorithm with a ring-topology. Every particle compares itself only with its nearest-neighbours as computed by a distance metric.
+
+
+Utilities
+---------
+
+Test Functions
+~~~~~~~~~~~~~~
+
+These functions can be used as benchmark tests for assessing the performance of the optimization
+algorithm.
+
+* :mod:`pyswarms.utils.functions.single_obj` - single-objective test functions
diff --git a/docs/index.rst b/docs/index.rst
@@ -1,13 +1,41 @@
 Welcome to PySwarms's documentation!
 ======================================
 
-Contents:
+.. image:: https://img.shields.io/pypi/v/pyswarms.svg
+        :target: https://pypi.python.org/pypi/pyswarms
+
+.. image:: https://img.shields.io/travis/ljvmiranda921/pyswarms.svg
+        :target: https://travis-ci.org/ljvmiranda921/pyswarms
+
+.. image:: https://readthedocs.org/projects/pyswarms/badge/?version=latest
+        :target: https://pyswarms.readthedocs.io/en/latest/?badge=latest
+        :alt: Documentation Status
+
+.. image:: https://pyup.io/repos/github/ljvmiranda921/pyswarms/shield.svg
+     :target: https://pyup.io/repos/github/ljvmiranda921/pyswarms/
+     :alt: Updates
+
+PySwarms is a simple, Python-based, Particle Swarm Optimization (PSO) library.
+
+* Free software: MIT license
+* Github repository: https://github.com/ljvmiranda921/pyswarms
+
+Launching pad
+-------------
+
+* If you don't know what Particle Swarm Optimization is, read up this short `Introduction <http://pyswarms.readthedocs.io/en/latest/intro.html>`_! Then, if you plan to use PySwarms in your project, check the `Installation guide <https://pyswarms.readthedocs.io/en/latest/installation.html>`_ and the `use-case examples <https://pyswarms.readthedocs.io/en/latest/examples/usecases.html>`_ in this documentation.
+
+* If you are a researcher in the field of swarm intelligence, and would like to include your technique in our list of optimizers, check our `contributing <https://pyswarms.readthedocs.io/en/latest/contributing.html>`_ page to see how to implement your optimizer using the current base classes in the library.
+
+* If you are an open-source contributor, and would like to help PySwarms grow, be sure to check our `Issues <https://github.com/ljvmiranda921/pyswarms/issues>`_ page in Github, and see the open issues with the tag `help-wanted <https://github.com/ljvmiranda921/pyswarms/labels/help%20wanted>`_. Moreover, we accommodate contributions from first-time contributors! Just check our `first-timers-only <https://github.com/ljvmiranda921/pyswarms/labels/first-timers-only>`_ tag for open issues (Don't worry! We're happy to help you make your first PR!).
+
 
 .. toctree::
    :maxdepth: 2
    :caption: General
 
-   readme
+   intro
+   features
    installation
    authors
    history