Change init_pos to center

Reference: #119

The name init_pos tends to be confusing. We're not really setting
the initial positions here. Instead, we're just defining where the
center is whenever the swarm is generated. Next, we'll set an
init_pos variable so that we can explicitly set the center

Signed-off-by: Lester James V. Miranda <[email protected]>

pyswarms/backend/generators.py

@@ -16,7 +16,7 @@
 from .swarms import Swarm
 
 
-def generate_swarm(n_particles, dimensions, bounds=None, init_pos=1.00):
+def generate_swarm(n_particles, dimensions, bounds=None, center=1.00):
     """Generates a swarm
 
     Parameters
@@ -29,8 +29,8 @@ def generate_swarm(n_particles, dimensions, bounds=None, init_pos=1.00):
         a tuple of size 2 where the first entry is the minimum bound while
         the second entry is the maximum bound. Each array must be of shape
         :code:`(dimensions,)`.
-    init_pos : :code:`numpy.ndarrray` (default is :code:`1`)
-        a list of initial positions for generating the swarm
+    center : :code:`numpy.ndarray` or :code:`float` (default is :code:`1`)
+        controls the mean or center whenever the swarm is generated randomly.
 
     Returns
     -------
@@ -43,7 +43,7 @@ def generate_swarm(n_particles, dimensions, bounds=None, init_pos=1.00):
             lb, ub = bounds
             min_bounds = np.repeat(np.array(lb)[np.newaxis, :], n_particles, axis=0)
             max_bounds = np.repeat(np.array(ub)[np.newaxis, :], n_particles, axis=0)
-        pos = init_pos * np.random.uniform(low=min_bounds, high=max_bounds,
+        pos = center * np.random.uniform(low=min_bounds, high=max_bounds,
                                            size=(n_particles, dimensions))
     except ValueError:
         raise
@@ -79,7 +79,7 @@ def generate_velocity(n_particles, dimensions, clamp=None):
     else:
         return velocity
 
-def create_swarm(n_particles, dimensions, behavior=None, bounds=None, init_pos=1.0, clamp=None):
+def create_swarm(n_particles, dimensions, behavior=None, bounds=None, center=1.0, clamp=None):
     """Abstracts the generate_swarm() and generate_velocity() methods
     
     Parameters
@@ -94,7 +94,7 @@ def create_swarm(n_particles, dimensions, behavior=None, bounds=None, init_pos=1
         a tuple of size 2 where the first entry is the minimum bound while
         the second entry is the maximum bound. Each array must be of shape
         :code:`(dimensions,)`.
-    init_pos : :code:`numpy.ndarrray` (default is :code:`1`)
+    center : :code:`numpy.ndarrray` (default is :code:`1`)
         a list of initial positions for generating the swarm
     clamp : tuple of floats (default is :code:`None`)
         a tuple of size 2 where the first entry is the minimum velocity
@@ -106,6 +106,6 @@ def create_swarm(n_particles, dimensions, behavior=None, bounds=None, init_pos=1
     pyswarms.backend.swarms.Swarm
         a Swarm class
     """
-    position = generate_swarm(n_particles, dimensions, bounds, init_pos)
+    position = generate_swarm(n_particles, dimensions, bounds, center)
     velocity = generate_velocity(n_particles, dimensions, clamp)
     return Swarm(position, velocity, behavior=behavior)

pyswarms/base/base_single.py

@@ -82,13 +82,13 @@ def assertions(self):
                 raise ValueError('Make sure that velocity_clamp is in the '
                                     'form (min, max)')
 
-        # Check setting of init_pos
-        if isinstance(self.init_pos, (list, np.ndarray)):
-            if not len(self.init_pos) == self.dimensions:
-                raise IndexError('Parameter `init_pos` must be the same shape '
+        # Check setting of center
+        if isinstance(self.center, (list, np.ndarray)):
+            if not len(self.center) == self.dimensions:
+                raise IndexError('Parameter `center` must be the same shape '
                                  'as dimensions.')
-            if isinstance(self.init_pos, np.ndarray) and self.init_pos.ndim != 1:
-                raise ValueError('Parameter `init_pos` must have a 1d array')
+            if isinstance(self.center, np.ndarray) and self.center.ndim != 1:
+                raise ValueError('Parameter `center` must have a 1d array')
 
 
         # Required keys in options argument
@@ -120,7 +120,7 @@ def setup_logging(self, default_path='./config/logging.yaml',
             logging.basicConfig(level=default_level)
 
     def __init__(self, n_particles, dimensions, options,
-                 bounds=None, velocity_clamp=None, init_pos=1.0, ftol=-np.inf):
+                 bounds=None, velocity_clamp=None, center=1.0, ftol=-np.inf):
         """Initializes the swarm.
 
         Creates a Swarm class depending on the values initialized
@@ -148,7 +148,7 @@ def __init__(self, n_particles, dimensions, options,
             a tuple of size 2 where the first entry is the minimum velocity
             and the second entry is the maximum velocity. It
             sets the limits for velocity clamping.
-        init_pos : list (default is :code:`None`)
+        center : list (default is :code:`None`)
             an array of size :code:`dimensions`
         ftol : float
             relative error in objective_func(best_pos) acceptable for convergence
@@ -161,7 +161,7 @@ def __init__(self, n_particles, dimensions, options,
         self.velocity_clamp = velocity_clamp
         self.swarm_size = (n_particles, dimensions)
         self.options = options
-        self.init_pos = init_pos
+        self.center = center
         self.ftol = ftol
         # Initialize named tuple for populating the history list
         self.ToHistory = namedtuple('ToHistory',
@@ -275,5 +275,5 @@ def reset(self):
         # Initialize the swarm
         self.swarm = create_swarm(n_particles=self.n_particles,
                                   dimensions=self.dimensions,
-                                  bounds=self.bounds, init_pos=self.init_pos, 
+                                  bounds=self.bounds, center=self.center, 
                                   clamp=self.velocity_clamp, behavior=self.options)

pyswarms/single/global_best.py

@@ -71,7 +71,7 @@
 class GlobalBestPSO(SwarmOptimizer):
 
     def __init__(self, n_particles, dimensions, options,
-                 bounds=None, velocity_clamp=None, init_pos=1.00, ftol=-np.inf):
+                 bounds=None, velocity_clamp=None, center=1.00, ftol=-np.inf):
         """Initializes the swarm.
 
         Attributes
@@ -97,15 +97,15 @@ def __init__(self, n_particles, dimensions, options,
             a tuple of size 2 where the first entry is the minimum velocity
             and the second entry is the maximum velocity. It
             sets the limits for velocity clamping.
-        init_pos : list (default is :code:`None`)
+        center : list (default is :code:`None`)
             an array of size :code:`dimensions`
         ftol : float
             relative error in objective_func(best_pos) acceptable for
             convergence
         """
         super(GlobalBestPSO, self).__init__(n_particles, dimensions, options,
                                             bounds, velocity_clamp,
-                                            init_pos, ftol)
+                                            center, ftol)
 
         # Initialize logger
         self.logger = logging.getLogger(__name__)

pyswarms/single/local_best.py

@@ -103,7 +103,7 @@ def assertions(self):
                              'or 2 (for L2/Euclidean).')
 
     def __init__(self, n_particles, dimensions, options, bounds=None,
-                 velocity_clamp=None, init_pos=1.00, ftol=-np.inf):
+                 velocity_clamp=None, center=1.00, ftol=-np.inf):
         """Initializes the swarm.
 
         Attributes
@@ -120,7 +120,7 @@ def __init__(self, n_particles, dimensions, options, bounds=None,
             a tuple of size 2 where the first entry is the minimum velocity
             and the second entry is the maximum velocity. It
             sets the limits for velocity clamping.
-        init_pos : list (default is :code:`None`)
+        center : list (default is :code:`None`)
             an array of size :code:`dimensions`
         ftol : float
             relative error in objective_func(best_pos) acceptable for
@@ -148,7 +148,7 @@ def __init__(self, n_particles, dimensions, options, bounds=None,
         self.k, self.p = options['k'], options['p']
         # Initialize parent class
         super(LocalBestPSO, self).__init__(n_particles, dimensions, options,
-                                           bounds, velocity_clamp, init_pos, ftol)
+                                           bounds, velocity_clamp, center, ftol)
         # Invoke assertions
         self.assertions()
         # Initialize the resettable attributes

tests/backend/test_generators.py

@@ -10,17 +10,17 @@
 
 
 @pytest.mark.parametrize('bounds', [None, ([2,2,2], [5,5,5]), ([-1,-1,0], [2,2,5])])
-@pytest.mark.parametrize('init_pos', [1, [3,3,3], [0.2,0.2,0.1]])
-def test_generate_swarm_return_values(bounds, init_pos):
+@pytest.mark.parametrize('center', [1, [3,3,3], [0.2,0.2,0.1]])
+def test_generate_swarm_return_values(bounds, center):
     """Tests if generate_swarm() returns expected values"""
     pos = P.generate_swarm(n_particles=2, dimensions=3, bounds=bounds,
-                           init_pos=init_pos)
+                           center=center)
     if bounds is None:
         min_bounds, max_bounds = (0.0, 1.00)
     else:
         min_bounds, max_bounds = bounds
-    lower_bound = init_pos * np.array(min_bounds)
-    upper_bound = init_pos * np.array(max_bounds)
+    lower_bound = center * np.array(min_bounds)
+    upper_bound = center * np.array(max_bounds)
     assert (pos <= upper_bound).all() and (pos >= lower_bound).all()
 
 @pytest.mark.parametrize('clamp', [None, (0,1), (2,5), (1,6)])

tests/optimizers/test_global_best.py

@@ -61,12 +61,12 @@ def test_vclamp_maxmin_exception(velocity_clamp, options):
     with pytest.raises(ValueError):
         GlobalBestPSO(5, 2, velocity_clamp=velocity_clamp, options=options)
 
-@pytest.mark.parametrize('err, init_pos',
+@pytest.mark.parametrize('err, center',
     [(IndexError, [1.5, 3.2, 2.5])])
-def test_init_pos_exception(err, init_pos, options):
-    """Tests if exception is thrown when init_pos is not a list or of different shape"""
+def test_center_exception(err, center, options):
+    """Tests if exception is thrown when center is not a list or of different shape"""
     with pytest.raises(err):
-        GlobalBestPSO(5, 2, init_pos=init_pos, options=options)
+        GlobalBestPSO(5, 2, center=center, options=options)
 
 def test_reset_default_values(gbest_reset):
     """Tests if best cost and best pos are set properly when the reset()

tests/optimizers/test_local_best.py

@@ -74,12 +74,12 @@ def test_vclamp_maxmin_exception(velocity_clamp, options):
     with pytest.raises(ValueError):
         LocalBestPSO(5, 2, velocity_clamp=velocity_clamp, options=options)
 
-@pytest.mark.parametrize('err, init_pos',
+@pytest.mark.parametrize('err, center',
     [(IndexError, [1.5, 3.2, 2.5])])
-def test_init_pos_exception(err, init_pos, options):
-    """Tests if exception is thrown when init_pos is not a list or of different shape"""
+def test_center_exception(err, center, options):
+    """Tests if exception is thrown when center is not a list or of different shape"""
     with pytest.raises(err):
-        LocalBestPSO(5, 2, init_pos=init_pos, options=options)
+        LocalBestPSO(5, 2, center=center, options=options)
 
 def test_reset_default_values(lbest_reset):
     """Tests if best cost and best pos are set properly when the reset()