Remove _func in single_obj function names (#222)

README.md

@@ -101,7 +101,7 @@ import pyswarms as ps
 
 Suppose we want to find the minima of `f(x) = x^2` using global best
 PSO, simply import the built-in sphere function,
-`pyswarms.utils.functions.sphere_func()`, and the necessary optimizer:
+`pyswarms.utils.functions.sphere()`, and the necessary optimizer:
 
 ```python
 import pyswarms as ps
@@ -111,7 +111,7 @@ options = {'c1': 0.5, 'c2': 0.3, 'w':0.9}
 # Call instance of PSO
 optimizer = ps.single.GlobalBestPSO(n_particles=10, dimensions=2, options=options)
 # Perform optimization
-best_cost, best_pos = optimizer.optimize(fx.sphere_func, iters=100, verbose=3, print_step=25)
+best_cost, best_pos = optimizer.optimize(fx.sphere, iters=100, verbose=3, print_step=25)
 ```
 ```s
 >>> 2017-10-03 10:12:33,859 - pyswarms.single.global_best - INFO - Iteration 1/100, cost: 0.131244226714
@@ -170,7 +170,7 @@ options = {
 # n_selection_iters is the number of iterations to run the searcher
 # iters is the number of iterations to run the optimizer
 g = RandomSearch(ps.single.LocalBestPSO, n_particles=40,
-            dimensions=20, options=options, objective_func=fx.sphere_func,
+            dimensions=20, options=options, objective_func=fx.sphere,
             iters=10, n_selection_iters=100)
 
 best_score, best_options = g.search()
@@ -202,7 +202,7 @@ from pyswarms.utils.plotters import plot_cost_history
 # Set-up optimizer
 options = {'c1':0.5, 'c2':0.3, 'w':0.9}
 optimizer = ps.single.GlobalBestPSO(n_particles=50, dimensions=2, options=options)
-optimizer.optimize(fx.sphere_func, iters=100)
+optimizer.optimize(fx.sphere, iters=100)
 # Plot the cost
 plot_cost_history(optimizer.cost_history)
 plt.show()
@@ -216,7 +216,7 @@ We can also plot the animation...
 from pyswarms.utils.plotters.formatters import Mesher
 from pyswarms.utils.plotters.formatters import Designer
 # Plot the sphere function's mesh for better plots
-m = Mesher(func=fx.sphere_func)
+m = Mesher(func=fx.sphere)
 # Adjust figure limits
 d = Designer(limits=[(-1,1), (-1,1), (-0.1,1)],
              label=['x-axis', 'y-axis', 'z-axis'])

docs/examples/basic_optimization.rst

@@ -71,7 +71,7 @@ several variables at once.
     optimizer = ps.single.GlobalBestPSO(n_particles=10, dimensions=2, options=options)
     
     # Perform optimization
-    cost, pos = optimizer.optimize(fx.sphere_func, print_step=100, iters=1000, verbose=3)
+    cost, pos = optimizer.optimize(fx.sphere, print_step=100, iters=1000, verbose=3)
 
 
 .. parsed-literal::
@@ -116,7 +116,7 @@ Now, let’s try this one using local-best PSO:
     optimizer = ps.single.LocalBestPSO(n_particles=10, dimensions=2, options=options)
     
     # Perform optimization
-    cost, pos = optimizer.optimize(fx.sphere_func, print_step=100, iters=1000, verbose=3)
+    cost, pos = optimizer.optimize(fx.sphere, print_step=100, iters=1000, verbose=3)
 
 
 .. parsed-literal::
@@ -151,7 +151,7 @@ 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
+PSO with the Rastrigin function (``rastrigin`` in
 ``pyswarms.utils.functions.single_obj``).
 
 Recall that the Rastrigin function is bounded within ``[-5.12, 5.12]``.
@@ -191,7 +191,7 @@ constant.
     optimizer = ps.single.GlobalBestPSO(n_particles=10, dimensions=2, options=options, bounds=bounds)
     
     # Perform optimization
-    cost, pos = optimizer.optimize(fx.rastrigin_func, print_step=100, iters=1000, verbose=3)
+    cost, pos = optimizer.optimize(fx.rastrigin, print_step=100, iters=1000, verbose=3)
 
 
 .. parsed-literal::

docs/examples/custom_optimization_loop.rst

@@ -29,7 +29,7 @@ new attributes.
     import numpy as np
     
     # Import sphere function as objective function
-    from pyswarms.utils.functions.single_obj import sphere_func as f
+    from pyswarms.utils.functions.single_obj import sphere as f
     
     # Import backend modules
     import pyswarms.backend as P

docs/examples/visualization.rst

@@ -50,7 +50,7 @@ the ``optimize()`` method for 100 iterations.
 
     options = {'c1':0.5, 'c2':0.3, 'w':0.9}
     optimizer = ps.single.GlobalBestPSO(n_particles=50, dimensions=2, options=options)
-    cost, pos = optimizer.optimize(fx.sphere_func, iters=100)
+    cost, pos = optimizer.optimize(fx.sphere, iters=100)
 
 
 .. code-block::
@@ -112,7 +112,7 @@ with respect to our objective function. We can accomplish that using the
     from pyswarms.utils.plotters.formatters import Mesher
 
     # Initialize mesher with sphere function
-    m = Mesher(func=fx.sphere_func)
+    m = Mesher(func=fx.sphere)
 
 There are different formatters available in the
 ``pyswarms.utils.plotters.formatters`` module to customize your plots

examples/basic_optimization.ipynb

@@ -121,7 +121,7 @@
     "optimizer = ps.single.GlobalBestPSO(n_particles=10, dimensions=2, options=options)\n",
     "\n",
     "# Perform optimization\n",
-    "cost, pos = optimizer.optimize(fx.sphere_func, print_step=100, iters=1000, verbose=3)"
+    "cost, pos = optimizer.optimize(fx.sphere, print_step=100, iters=1000, verbose=3)"
    ]
   },
   {
@@ -184,15 +184,15 @@
     "optimizer = ps.single.LocalBestPSO(n_particles=10, dimensions=2, options=options)\n",
     "\n",
     "# Perform optimization\n",
-    "cost, pos = optimizer.optimize(fx.sphere_func, print_step=100, iters=1000, verbose=3)"
+    "cost, pos = optimizer.optimize(fx.sphere, print_step=100, iters=1000, verbose=3)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Optimizing a function with bounds\n",
-    "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`)."
+    "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` in `pyswarms.utils.functions.single_obj`)."
    ]
   },
   {
@@ -269,7 +269,7 @@
     "optimizer = ps.single.GlobalBestPSO(n_particles=10, dimensions=2, options=options, bounds=bounds)\n",
     "\n",
     "# Perform optimization\n",
-    "cost, pos = optimizer.optimize(fx.rastrigin_func, print_step=100, iters=1000, verbose=3)"
+    "cost, pos = optimizer.optimize(fx.rastrigin, print_step=100, iters=1000, verbose=3)"
    ]
   },
   {

examples/custom_optimization_loop.ipynb

@@ -60,7 +60,7 @@
     "import numpy as np\n",
     "\n",
     "# Import sphere function as objective function\n",
-    "from pyswarms.utils.functions.single_obj import sphere_func as f\n",
+    "from pyswarms.utils.functions.single_obj import sphere as f\n",
     "\n",
     "# Import backend modules\n",
     "import pyswarms.backend as P\n",

examples/visualization.ipynb

@@ -105,7 +105,7 @@
    "source": [
     "options = {'c1':0.5, 'c2':0.3, 'w':0.9}\n",
     "optimizer = ps.single.GlobalBestPSO(n_particles=50, dimensions=2, options=options)\n",
-    "cost, pos = optimizer.optimize(fx.sphere_func, iters=100)"
+    "cost, pos = optimizer.optimize(fx.sphere, iters=100)"
    ]
   },
   {
@@ -184,7 +184,7 @@
    "outputs": [],
    "source": [
     "# Initialize mesher with sphere function\n",
-    "m = Mesher(func=fx.sphere_func)"
+    "m = Mesher(func=fx.sphere)"
    ]
   },
   {

pyswarms/single/general_optimizer.py

@@ -46,7 +46,7 @@
                                         options=options, topology=my_topology)
 
     # Perform optimization
-    stats = optimizer.optimize(fx.sphere_func, iters=100)
+    stats = optimizer.optimize(fx.sphere, iters=100)
 
 This algorithm was adapted from the earlier works of J. Kennedy and
 R.C. Eberhart in Particle Swarm Optimization [IJCNN1995]_.

pyswarms/single/global_best.py

@@ -45,7 +45,7 @@
                                         options=options)
 
     # Perform optimization
-    stats = optimizer.optimize(fx.sphere_func, iters=100)
+    stats = optimizer.optimize(fx.sphere, iters=100)
 
 This algorithm was adapted from the earlier works of J. Kennedy and
 R.C. Eberhart in Particle Swarm Optimization [IJCNN1995]_.

pyswarms/single/local_best.py

@@ -49,7 +49,7 @@
                                        options=options)
 
     # Perform optimization
-    stats = optimizer.optimize(fx.sphere_func, iters=100)
+    stats = optimizer.optimize(fx.sphere, iters=100)
 
 This algorithm was adapted from one of the earlier works of
 J. Kennedy and R.C. Eberhart in Particle Swarm Optimization

pyswarms/utils/functions/single_obj.py

@@ -17,23 +17,23 @@
 the design pattern stated above.
 
 Function list:
-- Ackley's, ackley_func
-- Beale, beale_func
-- Booth, booth_func
-- Bukin's No 6, bukin6_func
-- Cross-in-Tray, crossintray_func
-- Easom, easom_func
-- Eggholder, eggholder_func
-- Goldstein, goldstein_func
-- Himmelblau's, himmelblau_func
-- Holder Table, holdertable_func
-- Levi, levi_func
-- Matyas, matyas_func
-- Rastrigin, rastrigin_func
-- Rosenbrock, rosenbrock_func
-- Schaffer No 2, schaffer2_func
-- Sphere, sphere_func
-- Three Hump Camel, threehump_func
+- Ackley's, ackley
+- Beale, beale
+- Booth, booth
+- Bukin's No 6, bukin6
+- Cross-in-Tray, crossintray
+- Easom, easom
+- Eggholder, eggholder
+- Goldstein, goldstein
+- Himmelblau's, himmelblau
+- Holder Table, holdertable
+- Levi, levi
+- Matyas, matyas
+- Rastrigin, rastrigin
+- Rosenbrock, rosenbrock
+- Schaffer No 2, schaffer2
+- Sphere, sphere
+- Three Hump Camel, threehump
 """
 
 # Import from __future__
@@ -45,7 +45,7 @@
 import numpy as np
 
 
-def ackley_func(x):
+def ackley(x):
     """Ackley's objective function.
 
     Has a global minimum of `0` at :code:`f(0,0,...,0)` with a search
@@ -81,7 +81,7 @@ def ackley_func(x):
     return j
 
 
-def beale_func(x):
+def beale(x):
     """Beale objective function.
 
     Only takes two dimensions and has a global minimum of `0` at
@@ -124,7 +124,7 @@ def beale_func(x):
     return j
 
 
-def booth_func(x):
+def booth(x):
     """Booth's objective function.
 
     Only takes two dimensions and has a global minimum of `0` at
@@ -161,7 +161,7 @@ def booth_func(x):
     return j
 
 
-def bukin6_func(x):
+def bukin6(x):
     """Bukin N. 6 Objective Function
 
     Only takes two dimensions and has a global minimum  of `0` at
@@ -210,7 +210,7 @@ def bukin6_func(x):
     return j
 
 
-def crossintray_func(x):
+def crossintray(x):
     """Cross-in-tray objective function.
 
     Only takes two dimensions and has a four equal global minimums
@@ -264,7 +264,7 @@ def crossintray_func(x):
     return j
 
 
-def easom_func(x):
+def easom(x):
     """Easom objective function.
 
     Only takes two dimensions and has a global minimum of
@@ -312,7 +312,7 @@ def easom_func(x):
     return j
 
 
-def eggholder_func(x):
+def eggholder(x):
     """Eggholder objective function.
 
     Only takes two dimensions and has a global minimum of
@@ -359,7 +359,7 @@ def eggholder_func(x):
     return j
 
 
-def goldstein_func(x):
+def goldstein(x):
     """Goldstein-Price's objective function.
 
     Only takes two dimensions and has a global minimum at
@@ -424,7 +424,7 @@ def goldstein_func(x):
     return j
 
 
-def himmelblau_func(x):
+def himmelblau(x):
     """Himmelblau's  objective function
 
     Only takes two dimensions and has a four equal global minimums
@@ -470,7 +470,7 @@ def himmelblau_func(x):
     return j
 
 
-def holdertable_func(x):
+def holdertable(x):
     """Holder Table objective function
 
     Only takes two dimensions and has a four equal global minimums
@@ -520,7 +520,7 @@ def holdertable_func(x):
     return j
 
 
-def levi_func(x):
+def levi(x):
     """Levi objective function
 
     Only takes two dimensions and has a global minimum at
@@ -569,7 +569,7 @@ def levi_func(x):
     return j
 
 
-def matyas_func(x):
+def matyas(x):
     """Matyas objective function
 
     Only takes two dimensions and has a global minimum at
@@ -599,7 +599,7 @@ def matyas_func(x):
     return j
 
 
-def rastrigin_func(x):
+def rastrigin(x):
     """Rastrigin objective function.
 
     Has a global minimum at :code:`f(0,0,...,0)` with a search
@@ -632,7 +632,7 @@ def rastrigin_func(x):
     return j
 
 
-def rosenbrock_func(x):
+def rosenbrock(x):
     """Rosenbrock objective function.
 
     Also known as the Rosenbrock's valley or Rosenbrock's banana
@@ -658,7 +658,7 @@ def rosenbrock_func(x):
     return r
 
 
-def schaffer2_func(x):
+def schaffer2(x):
     """Schaffer N.2 objective function
 
     Only takes two dimensions and has a global minimum at
@@ -703,7 +703,7 @@ def schaffer2_func(x):
     return j
 
 
-def sphere_func(x):
+def sphere(x):
     """Sphere objective function.
 
     Has a global minimum at :code:`0` and with a search domain of
@@ -724,7 +724,7 @@ def sphere_func(x):
     return j
 
 
-def threehump_func(x):
+def threehump(x):
     """Three-hump camel objective function
 
     Only takes two dimensions and has a global minimum of `0` at