Release v.0.1.4 development version

The update to v.0.1.4. neccesitated the following changes: - Modified setup.py to the current version - Fixed broken examples from documentation tutorials - Updated Features in README.rst Moreso, some changes were created to prepare the package before v.0.1.5dev: - Excluded '/docs' and '/tests' directory when uploading to PyPI
ljvmiranda921 · Aug 4, 2017 · 8be5e6b · 8be5e6b
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diff --git a/HISTORY.rst b/HISTORY.rst
@@ -20,4 +20,10 @@ History
 * Pre-release
 * Implemented Binary PSO
 * More efficient API for gbest and lbest
-* Documentation and tests
+* Documentation and tests
+
+0.1.4 (2017-08-03)
+~~~~~~~~~~~~~~~~~
+
+* Well, this is embarassing...
+* Added a patch to fix :code:`pip` installation
diff --git a/README.rst b/README.rst
@@ -13,15 +13,15 @@ PySwarms
         :alt: Documentation Status
 
 .. image:: https://landscape.io/github/ljvmiranda921/pyswarms/master/landscape.svg?style=flat
-   :target: https://landscape.io/github/ljvmiranda921/pyswarms/master
-   :alt: Code Health
+        :target: https://landscape.io/github/ljvmiranda921/pyswarms/master
+        :alt: Code Health
 
 .. image:: https://pyup.io/repos/github/ljvmiranda921/pyswarms/shield.svg
-     :target: https://pyup.io/repos/github/ljvmiranda921/pyswarms/
-     :alt: Updates
+        :target: https://pyup.io/repos/github/ljvmiranda921/pyswarms/
+        :alt: Updates
 
 .. image:: https://img.shields.io/badge/license-MIT-blue.svg   
-     :target: https://raw.githubusercontent.com/ljvmiranda921/pyswarms/master/LICENSE
+        :target: https://raw.githubusercontent.com/ljvmiranda921/pyswarms/master/LICENSE
 
 
 PySwarms is a simple, Python-based, Particle Swarm Optimization (PSO) library.

diff --git a/docs/api/pyswarms.rst b/docs/api/pyswarms.rst
@@ -18,13 +18,11 @@ It supports a simple skeleton to construct a customized PSO algorithm.
 Optimizers
 -----------
 
-The optimizers include the actual PSO implementations for various tasks. Generally,
-there are two ways to implement an optimizer from this library: (1) as an easy
-off-the-shelf algorithm, and (2) as an experimental custom-made algorithm.
-
-1. Easy off-the-shelf implementations include those that are already considered as standard in literature. This may include the classics such as global-best and local-best. Their topologies are hardcoded already, and there is no need for prior set-up in order to use. This is useful for quick-and-easy optimization problems.
-
-2. Experimental PSO algorithms are like standard PSO algorithms but without a defined topology. Instead, an object that inherits from a :code:`Topology` class is passed to an optimizer to define swarm behavior. Although the standard PSO implementations can be done through this, this is more experimental.
+The optimizers include the actual PSO implementations for various tasks.
+These include easy, off-the-shelf implementations include those that are
+already considered as standard in literature. This may include the
+classics such as global-best and local-best. Useful for quick-and-easy
+optimization problems.
 
 .. toctree::
 

diff --git a/docs/examples/train_neural_network.rst b/docs/examples/train_neural_network.rst
@@ -180,7 +180,7 @@ parameters arbitrarily.
     # Initialize swarm
     options = {'c1': 0.5, 'c2': 0.3, 'w':0.9}
     
-    # Call instance of PSO with bounds argument
+    # Call instance of PSO
     dims = (4 * 20) + (20 * 3) + 20 + 3 
     optimizer = ps.single.GBestPSO(n_particles=100, dims=dims, **options)
     

diff --git a/docs/features.rst b/docs/features.rst
@@ -2,16 +2,6 @@
 Features
 ========
 
-There are two ways in which optimizers are implemented in PySwarms. The first involves
-quick-and-easy implementations of classic PSO algorithms. Here, the topologies (or the way
-a swarm behaves) is hardcoded in the source code. This is useful for fast implementations that
-doesn't need prior set-up.
-
-The second involves a set of experimental classes where topology is not defined. Instead, one
-should create an object that inherits from a :code:`Topology` class, and pass it as a parameter
-in the experimental PSO classes. There are some topologies that are already implemented, but it's also possible
-to define a custom-made one. This is perfect for researchers who wanted to try out various swarm
-behaviours and movements.
 
 Single-Objective Optimizers
 ---------------------------
@@ -28,7 +18,6 @@ functions.
 
 * :mod:`pyswarms.single.lb` - classic 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.
 
-* :mod:`pyswarms.single.exp` - experimental Particle Swarm Optimization algorithm. 
 
 Discrete 
 ~~~~~~~~

diff --git a/docs/index.rst b/docs/index.rst
@@ -12,15 +12,15 @@ Welcome to PySwarms's documentation!
         :alt: Documentation Status
 
 .. image:: https://landscape.io/github/ljvmiranda921/pyswarms/master/landscape.svg?style=flat
-   :target: https://landscape.io/github/ljvmiranda921/pyswarms/master
-   :alt: Code Health
+        :target: https://landscape.io/github/ljvmiranda921/pyswarms/master
+        :alt: Code Health
 
 .. image:: https://pyup.io/repos/github/ljvmiranda921/pyswarms/shield.svg
-     :target: https://pyup.io/repos/github/ljvmiranda921/pyswarms/
-     :alt: Updates
+        :target: https://pyup.io/repos/github/ljvmiranda921/pyswarms/
+        :alt: Updates
 
 .. image:: https://img.shields.io/badge/license-MIT-blue.svg   
-     :target: https://raw.githubusercontent.com/ljvmiranda921/pyswarms/master/LICENSE
+        :target: https://raw.githubusercontent.com/ljvmiranda921/pyswarms/master/LICENSE
 
 PySwarms is a simple, Python-based, Particle Swarm Optimization (PSO) library.
 

diff --git a/examples/.ipynb_checkpoints/basic_optimization-checkpoint.ipynb b/examples/.ipynb_checkpoints/basic_optimization-checkpoint.ipynb
@@ -23,7 +23,9 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Import modules\n",
@@ -69,27 +71,27 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration 1/1000, cost: 0.241810034912\n",
-      "Iteration 101/1000, cost: 1.24792343462e-07\n",
-      "Iteration 201/1000, cost: 3.37290413126e-11\n",
-      "Iteration 301/1000, cost: 1.69154015e-16\n",
-      "Iteration 401/1000, cost: 7.45197149386e-20\n",
-      "Iteration 501/1000, cost: 3.10721498746e-24\n",
-      "Iteration 601/1000, cost: 1.88640993989e-30\n",
-      "Iteration 701/1000, cost: 8.54586648864e-34\n",
-      "Iteration 801/1000, cost: 5.35080705362e-35\n",
-      "Iteration 901/1000, cost: 2.8556550659e-37\n",
+      "Iteration 1/1000, cost: 0.172625433638\n",
+      "Iteration 101/1000, cost: 9.78790435841e-08\n",
+      "Iteration 201/1000, cost: 1.06971376846e-10\n",
+      "Iteration 301/1000, cost: 8.14953145258e-16\n",
+      "Iteration 401/1000, cost: 3.12401159696e-20\n",
+      "Iteration 501/1000, cost: 1.48621699689e-22\n",
+      "Iteration 601/1000, cost: 3.66990623598e-25\n",
+      "Iteration 701/1000, cost: 9.59668571497e-31\n",
+      "Iteration 801/1000, cost: 1.4444452957e-34\n",
+      "Iteration 901/1000, cost: 6.65383019372e-38\n",
       "================================\n",
       "Optimization finished!\n",
-      "Final cost: 0.000\n",
-      "Best value: [1.1619287820351099e-23, -9.6008484982928834e-23]\n",
+      "Final cost: 0.0000\n",
+      "Best value: [-5.4976282085417155e-21, 5.2520478160189662e-22]\n",
       "\n"
      ]
     }
    ],
    "source": [
     "# Set-up hyperparameters\n",
-    "options = {'c1': 0.5, 'c2': 0.3, 'm':0.9}\n",
+    "options = {'c1': 0.5, 'c2': 0.3, 'w':0.9}\n",
     "\n",
     "# Call instance of PSO\n",
     "gbest_pso = ps.single.GBestPSO(n_particles=10, dims=2, **options)\n",
@@ -123,30 +125,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration 1/1000, cost: 0.04003185568\n",
-      "Iteration 101/1000, cost: 6.3794607757e-06\n",
-      "Iteration 201/1000, cost: 7.09436163329e-12\n",
-      "Iteration 301/1000, cost: 1.5461139723e-15\n",
-      "Iteration 401/1000, cost: 1.19646365726e-16\n",
-      "Iteration 501/1000, cost: 1.69249151459e-19\n",
-      "Iteration 601/1000, cost: 4.50338461028e-25\n",
-      "Iteration 701/1000, cost: 5.19268925253e-28\n",
-      "Iteration 801/1000, cost: 1.56302357735e-30\n",
-      "Iteration 901/1000, cost: 9.65270491034e-36\n",
+      "Iteration 1/1000, cost: 0.0242054338711\n",
+      "Iteration 101/1000, cost: 2.97581624567e-08\n",
+      "Iteration 201/1000, cost: 7.11602482139e-10\n",
+      "Iteration 301/1000, cost: 2.03603327891e-13\n",
+      "Iteration 401/1000, cost: 2.06532397175e-17\n",
+      "Iteration 501/1000, cost: 1.91037623424e-22\n",
+      "Iteration 601/1000, cost: 1.6553317776e-27\n",
+      "Iteration 701/1000, cost: 2.01430762053e-33\n",
+      "Iteration 801/1000, cost: 9.56525068774e-37\n",
+      "Iteration 901/1000, cost: 4.88187595682e-37\n",
       "================================\n",
       "Optimization finished!\n",
-      "Final cost: 0.000\n",
-      "Best value: [1.083763152840743e-19, 1.1881341950447718e-19]\n",
+      "Final cost: 5.0000\n",
+      "Best value: [3.2487681005019184e-19, -5.7752296538288309e-20]\n",
       "\n"
      ]
     }
    ],
    "source": [
     "# Set-up hyperparameters\n",
-    "options = {'c1': 0.5, 'c2': 0.3, 'm':0.9}\n",
+    "options = {'c1': 0.5, 'c2': 0.3, 'w':0.9, 'k': 2, 'p': 2}\n",
     "\n",
     "# Call instance of PSO\n",
-    "lbest_pso = ps.single.LBestPSO(n_particles=10, dims=2, k=2,p=2, **options)\n",
+    "lbest_pso = ps.single.LBestPSO(n_particles=10, dims=2, **options)\n",
     "\n",
     "# Perform optimization\n",
     "cost, pos = lbest_pso.optimize(fx.sphere_func, print_step=100, iters=1000, verbose=3)"
@@ -180,55 +182,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Create bounds\n",
+    "max_bound = 5.12 * np.ones(2)\n",
+    "min_bound = - max_bound\n",
+    "bounds = (min_bound, max_bound)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Help on function rastrigin_func in module pyswarms.utils.functions.single_obj:\n",
-      "\n",
-      "rastrigin_func(x)\n",
-      "    Rastrigin objective function.\n",
-      "    \n",
-      "    Has a global minimum at :code:`f(0,0,...,0)` with a search\n",
-      "    domain of :code:`[-5.12, 5.12]`\n",
-      "    \n",
-      "    Parameters\n",
-      "    ----------\n",
-      "    x : numpy.ndarray \n",
-      "        set of inputs of shape :code:`(n_particles, dims)`\n",
-      "    \n",
-      "    Returns\n",
-      "    -------\n",
-      "    numpy.ndarray \n",
-      "        computed cost of size :code:`(n_particles, )`\n",
-      "    \n",
-      "    Raises\n",
-      "    ------\n",
-      "    ValueError\n",
-      "        When the input is out of bounds with respect to the function\n",
-      "        domain\n",
+      "Iteration 1/1000, cost: 11.3842045778\n",
+      "Iteration 101/1000, cost: 0.00601961330037\n",
+      "Iteration 201/1000, cost: 1.06143502876e-08\n",
+      "Iteration 301/1000, cost: 1.12052589429e-11\n",
+      "Iteration 401/1000, cost: 7.1054273576e-15\n",
+      "Iteration 501/1000, cost: 0.0\n",
+      "Iteration 601/1000, cost: 0.0\n",
+      "Iteration 701/1000, cost: 0.0\n",
+      "Iteration 801/1000, cost: 0.0\n",
+      "Iteration 901/1000, cost: 0.0\n",
+      "================================\n",
+      "Optimization finished!\n",
+      "Final cost: 0.0000\n",
+      "Best value: [-3.5535227100370778e-09, -8.3294662576708084e-10]\n",
       "\n"
      ]
     }
    ],
    "source": [
-    "# Create bounds\n",
-    "max_bound = 5.12 * np.ones(2)\n",
-    "min_bound = - max_bound\n",
-    "bounds = tuple(max_bound, min_bound)"
+    "# Initialize swarm\n",
+    "options = {'c1': 0.5, 'c2': 0.3, 'w':0.9}\n",
+    "\n",
+    "# Call instance of PSO with bounds argument\n",
+    "optimizer = ps.single.GBestPSO(n_particles=10, dims=2, bounds=bounds, **options)\n",
+    "\n",
+    "# Perform optimization\n",
+    "cost, pos = optimizer.optimize(fx.rastrigin_func, print_step=100, iters=1000, verbose=3)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

diff --git a/examples/.ipynb_checkpoints/train_neural_network-checkpoint.ipynb b/examples/.ipynb_checkpoints/train_neural_network-checkpoint.ipynb
@@ -215,29 +215,29 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration 1/1000, cost: 1.11338932053\n",
-      "Iteration 101/1000, cost: 0.0541135752532\n",
-      "Iteration 201/1000, cost: 0.0468046270747\n",
-      "Iteration 301/1000, cost: 0.0434828849533\n",
-      "Iteration 401/1000, cost: 0.0358833340106\n",
-      "Iteration 501/1000, cost: 0.0312474981647\n",
-      "Iteration 601/1000, cost: 0.0150869267541\n",
-      "Iteration 701/1000, cost: 0.01267166403\n",
-      "Iteration 801/1000, cost: 0.00632312205821\n",
-      "Iteration 901/1000, cost: 0.00194080306565\n",
+      "Iteration 1/1000, cost: 1.10288192551\n",
+      "Iteration 101/1000, cost: 0.0720619629541\n",
+      "Iteration 201/1000, cost: 0.0428161767369\n",
+      "Iteration 301/1000, cost: 0.0341626636208\n",
+      "Iteration 401/1000, cost: 0.0283418687066\n",
+      "Iteration 501/1000, cost: 0.023736130164\n",
+      "Iteration 601/1000, cost: 0.0187020687472\n",
+      "Iteration 701/1000, cost: 0.0175513666795\n",
+      "Iteration 801/1000, cost: 0.0155400739943\n",
+      "Iteration 901/1000, cost: 0.0142253459508\n",
       "================================\n",
       "Optimization finished!\n",
-      "Final cost: 0.0015\n",
-      "Best value: -0.356506 0.441392 -0.605476 0.620517 -0.156904 0.206396 ...\n",
+      "Final cost: 0.0139\n",
+      "Best value: 0.931199 -2.853534 0.348705 -4.812333 -0.258052 -0.490002 ...\n",
       "\n"
      ]
     }
    ],
    "source": [
     "# Initialize swarm\n",
-    "options = {'c1': 0.5, 'c2': 0.3, 'm':0.9}\n",
+    "options = {'c1': 0.5, 'c2': 0.3, 'w':0.9}\n",
     "\n",
-    "# Call instance of PSO with bounds argument\n",
+    "# Call instance of PSO\n",
     "dims = (4 * 20) + (20 * 3) + 20 + 3 \n",
     "optimizer = ps.single.GBestPSO(n_particles=100, dims=dims, **options)\n",
     "\n",
@@ -257,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {
     "collapsed": true
    },
@@ -305,16 +305,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1.0"
+       "0.99333333333333329"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }