From b347ccd076e27034cde97f736cf301ce7f892340 Mon Sep 17 00:00:00 2001
From: Jake <souter_downhill@hotmail.com>
Date: Sat, 23 Mar 2019 17:48:42 +1100
Subject: [PATCH 1/7] Fix bug where optimise methods return incorrect pos. This
 resolves #321

---
 pyswarms/backend/handlers.py               | 12 +--
 pyswarms/backend/topology/ring.py          |  4 +-
 pyswarms/discrete/binary.py                |  4 +-
 pyswarms/single/general_optimizer.py       |  4 +-
 pyswarms/single/local_best.py              |  4 +-
 tests.py                                   | 93 ++++++++++++++++++++++
 tests/backend/conftest.py                  | 74 ++++++++++-------
 tests/backend/test_handlers.py             | 44 ++++++----
 tests/backend/test_operators.py            | 16 ++--
 tests/backend/topology/test_pyramid.py     |  4 +-
 tests/backend/topology/test_random.py      | 13 ++-
 tests/backend/topology/test_ring.py        | 13 ++-
 tests/backend/topology/test_von_neumann.py | 13 ++-
 tests/optimizers/test_binary.py            | 22 +++++
 tests/optimizers/test_general_optimizer.py | 12 +++
 tests/optimizers/test_global_best.py       |  9 +++
 tests/optimizers/test_local_best.py        |  9 +++
 17 files changed, 275 insertions(+), 75 deletions(-)
 create mode 100644 tests.py

diff --git a/pyswarms/backend/handlers.py b/pyswarms/backend/handlers.py
index ab755950..d0d2ff26 100644
--- a/pyswarms/backend/handlers.py
+++ b/pyswarms/backend/handlers.py
@@ -196,13 +196,15 @@ def reflective(self, position, bounds, **kwargs):
         new_pos = position
         while lower_than_bound[0].size != 0 or greater_than_bound[0].size != 0:
             if lower_than_bound[0].size > 0:
-                new_pos[lower_than_bound] = 2 * lb[lower_than_bound[1]] - \
-                new_pos[lower_than_bound]
+                new_pos[lower_than_bound] = (
+                    2 * lb[lower_than_bound[1]] - new_pos[lower_than_bound]
+                )
             if greater_than_bound[0].size > 0:
-                new_pos[greater_than_bound] = 2 * ub[greater_than_bound[1]] - \
-                new_pos[greater_than_bound]
+                new_pos[greater_than_bound] = (
+                    2 * ub[greater_than_bound[1]] - new_pos[greater_than_bound]
+                )
             lower_than_bound, greater_than_bound = self._out_of_bounds(
-                    new_pos, bounds
+                new_pos, bounds
             )
 
         return new_pos
diff --git a/pyswarms/backend/topology/ring.py b/pyswarms/backend/topology/ring.py
index 573e0fae..0f1ca901 100644
--- a/pyswarms/backend/topology/ring.py
+++ b/pyswarms/backend/topology/ring.py
@@ -65,9 +65,7 @@ def compute_gbest(self, swarm, p, k, **kwargs):
             if (self.static and self.neighbor_idx is None) or not self.static:
                 # Obtain the nearest-neighbors for each particle
                 tree = cKDTree(swarm.position)
-                _, self.neighbor_idx = tree.query(
-                    swarm.position, p=p, k=k
-                )
+                _, self.neighbor_idx = tree.query(swarm.position, p=p, k=k)
 
             # Map the computed costs to the neighbour indices and take the
             # argmin. If k-neighbors is equal to 1, then the swarm acts
diff --git a/pyswarms/discrete/binary.py b/pyswarms/discrete/binary.py
index 49804452..caaff700 100644
--- a/pyswarms/discrete/binary.py
+++ b/pyswarms/discrete/binary.py
@@ -202,7 +202,9 @@ def optimize(self, objective_func, iters, **kwargs):
             self.swarm.position = self._compute_position(self.swarm)
         # Obtain the final best_cost and the final best_position
         final_best_cost = self.swarm.best_cost.copy()
-        final_best_pos = self.swarm.position[self.swarm.pbest_cost.argmin(axis=0)].copy()
+        final_best_pos = self.swarm.best_pos[
+            self.swarm.pbest_cost.argmin(axis=0)
+        ].copy()
         self.rep.log(
             "Optimization finished | best cost: {}, best pos: {}".format(
                 final_best_cost, final_best_pos
diff --git a/pyswarms/single/general_optimizer.py b/pyswarms/single/general_optimizer.py
index 35f24ac0..d473334c 100644
--- a/pyswarms/single/general_optimizer.py
+++ b/pyswarms/single/general_optimizer.py
@@ -248,7 +248,9 @@ def optimize(self, objective_func, iters, **kwargs):
             )
         # Obtain the final best_cost and the final best_position
         final_best_cost = self.swarm.best_cost.copy()
-        final_best_pos = self.swarm.position[self.swarm.pbest_cost.argmin()].copy()
+        final_best_pos = self.swarm.best_pos[
+            self.swarm.best_cost.argmin()
+        ].copy()
         # Write report in log and return final cost and position
         self.rep.log(
             "Optimization finished | best cost: {}, best pos: {}".format(
diff --git a/pyswarms/single/local_best.py b/pyswarms/single/local_best.py
index 446eba44..eec2bbbd 100644
--- a/pyswarms/single/local_best.py
+++ b/pyswarms/single/local_best.py
@@ -235,7 +235,9 @@ def optimize(self, objective_func, iters, **kwargs):
             )
         # Obtain the final best_cost and the final best_position
         final_best_cost = self.swarm.best_cost.copy()
-        final_best_pos = self.swarm.position[self.swarm.pbest_cost.argmin(axis=0)].copy()
+        final_best_pos = self.swarm.best_pos[
+            self.swarm.pbest_cost.argmin(axis=0)
+        ].copy()
         # Write report in log and return final cost and position
         self.rep.log(
             "Optimization finished | best cost: {}, best pos: {}".format(
diff --git a/tests.py b/tests.py
new file mode 100644
index 00000000..199bfb8a
--- /dev/null
+++ b/tests.py
@@ -0,0 +1,93 @@
+
+
+#######################################
+## TEST: BinaryPSO
+"""
+print("Testing BinaryPSO...\n")
+
+def f_per_particle(m):
+    return sum(m)
+
+def f(x):
+    n_particles = x.shape[0]
+    j = [f_per_particle(x[i]) for i in range(n_particles)]
+    return np.array(j)
+
+options = {'c1': 0.5, 'c2': 0.5, 'w':0.9, 'k': 30, 'p':2}
+optimizer = ps.discrete.BinaryPSO(n_particles=30, dimensions=15, options=options)
+cost, pos = optimizer.optimize(f, iters=5)
+
+assert f_per_particle(pos) == cost
+
+######################
+## TEST: GlobalBestPOS
+print("\nTesting GlobalBestPSO...\n")
+options = {'c1': 0.5, 'c2': 0.3, 'w':0.9}
+optimizer = ps.single.GlobalBestPSO(n_particles=10, dimensions=2, options=options)
+cost, pos = optimizer.optimize(fx.sphere, iters=5)
+
+assert sum(pos ** 2) == cost
+
+#####################
+## TEST: LocalBestPOS
+print("\nTesting LocalBestPSO...\n")
+options = {'c1': 0.5, 'c2': 0.3, 'w':0.9, 'k': 2, 'p': 2}
+optimizer = ps.single.LocalBestPSO(n_particles=10, dimensions=2, options=options)
+cost, pos = optimizer.optimize(fx.sphere, iters=5)
+
+assert sum(pos ** 2) == cost
+
+
+#######################################
+## TEST: GeneralBestPOS
+# No test implemented
+"""
+
+import numpy as np
+import pyswarms as ps
+from pyswarms.utils.functions import single_obj as fx
+from pyswarms.backend.topology import Pyramid
+import unittest
+
+class TestOptimizer(unittest.TestCase):
+    def test_binary(self):
+        dim = 10
+        x = np.random.rand(dim,dim)
+        def f_per_particle(m):
+            # Get the subset of the features from the binary mask
+            if np.count_nonzero(m) == 0:
+                return sum(x)
+            return sum(x[:, m==1]).mean()
+        def f(x):
+            n_particles = x.shape[0]
+            j = [f_per_particle(x[i]) for i in range(n_particles)]
+            return np.array(j)
+        options = {'c1': 0.5, 'c2': 0.5, 'w':0.9, 'k': 2, 'p':2}
+        optimizer = ps.discrete.BinaryPSO(n_particles=5, dimensions=dim, options=options)
+        cost, pos = optimizer.optimize(f, iters=5)
+        self.assertTrue(f_per_particle(pos) == cost)
+
+    def test_global(self):
+        options = {'c1': 0.5, 'c2': 0.3, 'w':0.9}
+        optimizer = ps.single.GlobalBestPSO(n_particles=10, dimensions=2, options=options)
+        cost, pos = optimizer.optimize(fx.sphere, iters=5)
+        self.assertTrue(sum(pos ** 2) == cost)
+
+    def test_local(self):
+        options = {'c1': 0.5, 'c2': 0.3, 'w':0.9, 'k': 2, 'p': 2}
+        optimizer = ps.single.LocalBestPSO(n_particles=10, dimensions=2, options=options)
+        cost, pos = optimizer.optimize(fx.sphere, iters=5)
+        self.assertTrue(sum(pos ** 2) == cost)
+
+    def test_general(self):
+        options = {'c1': 0.5, 'c2': 0.3, 'w':0.9}
+        my_topology = Pyramid(static=False)
+        optimizer = ps.single.GeneralOptimizerPSO(n_particles=10, dimensions=2,
+                                            options=options, topology=my_topology)
+        cost, pos = optimizer.optimize(fx.sphere, iters=5)
+        print("\n\n", pos)
+        print(sum(pos ** 2), '\n\n')
+        self.assertTrue(sum(pos ** 2) == cost)
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/tests/backend/conftest.py b/tests/backend/conftest.py
index 2d4baf56..0a3581aa 100644
--- a/tests/backend/conftest.py
+++ b/tests/backend/conftest.py
@@ -27,56 +27,74 @@ def swarm():
     }
     return Swarm(**attrs_at_t)
 
+
 @pytest.fixture
 def bounds():
     bounds_ = (np.array([2, 3, 1]), np.array([4, 7, 8]))
     return bounds_
 
+
 @pytest.fixture
 def clamp():
     clamp_ = (np.array([2, 3, 1]), np.array([4, 7, 8]))
     return clamp_
 
+
 @pytest.fixture
 def positions_inbound():
-    pos_ = np.array([[3.3, 4.4, 2.3],
-                     [3.7, 5.2, 7.0],
-                     [2.5, 6.8, 2.3],
-                     [2.1, 6.9, 4.7],
-                     [2.7, 3.2, 3.5],
-                     [2.5, 5.1, 1.2]
-                    ])
+    pos_ = np.array(
+        [
+            [3.3, 4.4, 2.3],
+            [3.7, 5.2, 7.0],
+            [2.5, 6.8, 2.3],
+            [2.1, 6.9, 4.7],
+            [2.7, 3.2, 3.5],
+            [2.5, 5.1, 1.2],
+        ]
+    )
     return pos_
 
+
 @pytest.fixture
 def positions_out_of_bound():
-    pos_ = np.array([[5.3, 4.4, 2.3],
-                     [3.7, 9.2, 7.0],
-                     [8.5, 0.8, 2.3],
-                     [2.1, 6.9, 0.7],
-                     [2.7, 9.2, 3.5],
-                     [1.5, 5.1, 9.2]
-                    ])
+    pos_ = np.array(
+        [
+            [5.3, 4.4, 2.3],
+            [3.7, 9.2, 7.0],
+            [8.5, 0.8, 2.3],
+            [2.1, 6.9, 0.7],
+            [2.7, 9.2, 3.5],
+            [1.5, 5.1, 9.2],
+        ]
+    )
     return pos_
 
+
 @pytest.fixture
 def velocities_inbound():
-    pos_ = np.array([[3.3, 4.4, 2.3],
-                     [3.7, 5.2, 7.0],
-                     [2.5, 6.8, 2.3],
-                     [2.1, 6.9, 4.7],
-                     [2.7, 3.2, 3.5],
-                     [2.5, 5.1, 1.2]
-                    ])
+    pos_ = np.array(
+        [
+            [3.3, 4.4, 2.3],
+            [3.7, 5.2, 7.0],
+            [2.5, 6.8, 2.3],
+            [2.1, 6.9, 4.7],
+            [2.7, 3.2, 3.5],
+            [2.5, 5.1, 1.2],
+        ]
+    )
     return pos_
 
+
 @pytest.fixture
 def velocities_out_of_bound():
-    pos_ = np.array([[5.3, 4.4, 2.3],
-                     [3.7, 9.2, 7.0],
-                     [8.5, 0.8, 2.3],
-                     [2.1, 6.9, 0.7],
-                     [2.7, 9.2, 3.5],
-                     [1.5, 5.1, 9.2]
-                    ])
+    pos_ = np.array(
+        [
+            [5.3, 4.4, 2.3],
+            [3.7, 9.2, 7.0],
+            [8.5, 0.8, 2.3],
+            [2.1, 6.9, 0.7],
+            [2.7, 9.2, 3.5],
+            [1.5, 5.1, 9.2],
+        ]
+    )
     return pos_
diff --git a/tests/backend/test_handlers.py b/tests/backend/test_handlers.py
index 33852efa..6b887102 100644
--- a/tests/backend/test_handlers.py
+++ b/tests/backend/test_handlers.py
@@ -9,10 +9,21 @@
 )
 import pyswarms.backend.handlers as h
 
-bh_strategies = [name for name, _ in inspect.getmembers(h.BoundaryHandler(""),
-    predicate=inspect.ismethod) if not name.startswith(("__", "_"))]
-vh_strategies = [name for name, _ in inspect.getmembers(h.VelocityHandler(""),
-    predicate=inspect.ismethod) if not name.startswith(("__", "_"))]
+bh_strategies = [
+    name
+    for name, _ in inspect.getmembers(
+        h.BoundaryHandler(""), predicate=inspect.ismethod
+    )
+    if not name.startswith(("__", "_"))
+]
+vh_strategies = [
+    name
+    for name, _ in inspect.getmembers(
+        h.VelocityHandler(""), predicate=inspect.ismethod
+    )
+    if not name.startswith(("__", "_"))
+]
+
 
 def test_out_of_bounds(bounds, positions_inbound, positions_out_of_bound):
     hm = HandlerMixin()
@@ -34,9 +45,8 @@ def test_out_of_bounds(bounds, positions_inbound, positions_out_of_bound):
         np.ravel(idx_out_of_bounds[1]).all() == np.ravel(expected_idx[1]).all()
     )
 
-@pytest.mark.parametrize(
-    "strategy", bh_strategies
-)
+
+@pytest.mark.parametrize("strategy", bh_strategies)
 def test_bound_handling(
     bounds, positions_inbound, positions_out_of_bound, strategy
 ):
@@ -52,6 +62,7 @@ def test_bound_handling(
     assert lower_than_bound.all()
     assert greater_than_bound.all()
 
+
 def test_nearest_strategy(bounds, positions_inbound, positions_out_of_bound):
     bh = BoundaryHandler(strategy="nearest")
     # TODO Add strategy specific tests
@@ -86,16 +97,17 @@ def test_periodic_strategy(bounds, positions_inbound, positions_out_of_bound):
     bh = BoundaryHandler(strategy="periodic")
     # TODO Add strategy specific tests
 
+
 def assert_clamp(
-clamp,
-velocities_inbound,
-velocities_out_of_bound,
-positions_inbound,
-positions_out_of_bound,
-vh,
-bounds=None,
+    clamp,
+    velocities_inbound,
+    velocities_out_of_bound,
+    positions_inbound,
+    positions_out_of_bound,
+    vh,
+    bounds=None,
 ):
-# Test if it doesn't handle inclamp velocities
+    # Test if it doesn't handle inclamp velocities
     inbound_handled = vh(
         velocities_inbound, clamp, position=positions_inbound, bounds=bounds
     )
@@ -114,8 +126,6 @@ def assert_clamp(
     assert not greater_than_clamp.all()
 
 
-
-
 def test_unmodified_strategy(
     clamp, velocities_inbound, velocities_out_of_bound
 ):
diff --git a/tests/backend/test_operators.py b/tests/backend/test_operators.py
index 94e6dd6b..716a3cfa 100644
--- a/tests/backend/test_operators.py
+++ b/tests/backend/test_operators.py
@@ -41,7 +41,9 @@ def test_return_values(self, swarm, clamp):
             assert (clamp[0] <= v).all() and (clamp[1] >= v).all()
 
     @pytest.mark.parametrize("swarm", [0, (1, 2, 3)])
-    @pytest.mark.parametrize("vh_strat", ["unmodified", "zero", "invert", "adjust"])
+    @pytest.mark.parametrize(
+        "vh_strat", ["unmodified", "zero", "invert", "adjust"]
+    )
     def test_input_swarm(self, swarm, vh_strat):
         """Test if method raises AttributeError with wrong swarm"""
         vh = VelocityHandler(strategy=vh_strat)
@@ -49,7 +51,9 @@ def test_input_swarm(self, swarm, vh_strat):
             P.compute_velocity(swarm, clamp=(0, 1), vh=vh)
 
     @pytest.mark.parametrize("options", [{"c1": 0.5, "c2": 0.3}])
-    @pytest.mark.parametrize("vh_strat", ["unmodified", "zero", "invert", "adjust"])
+    @pytest.mark.parametrize(
+        "vh_strat", ["unmodified", "zero", "invert", "adjust"]
+    )
     def test_missing_kwargs(self, swarm, options, vh_strat):
         """Test if method raises KeyError with missing kwargs"""
         vh = VelocityHandler(strategy=vh_strat)
@@ -74,13 +78,13 @@ def test_return_values(self, swarm, bounds, bh_strat):
         assert p.shape == swarm.velocity.shape
         if bounds is not None:
             assert (bounds[0] <= p).all() and (bounds[1] >= p).all()
-            
+
     @pytest.mark.parametrize("swarm", [0, (1, 2, 3)])
-    @pytest.mark.parametrize("bh_strat", ["nearest", "random", "shrink",
-    "intermediate"])
+    @pytest.mark.parametrize(
+        "bh_strat", ["nearest", "random", "shrink", "intermediate"]
+    )
     def test_input_swarm(self, swarm, bh_strat):
         """Test if method raises AttributeError with wrong swarm"""
         bh = BoundaryHandler(strategy=bh_strat)
         with pytest.raises(AttributeError):
             P.compute_position(swarm, bounds=([-5, -5], [5, 5]), bh=bh)
-
diff --git a/tests/backend/topology/test_pyramid.py b/tests/backend/topology/test_pyramid.py
index eb3e0b16..02cd131e 100644
--- a/tests/backend/topology/test_pyramid.py
+++ b/tests/backend/topology/test_pyramid.py
@@ -28,7 +28,9 @@ def test_compute_gbest_return_values(
         """Test if compute_gbest() gives the expected return values"""
         topo = topology(static=static)
         expected_cost = 1.0002528364353296
-        expected_pos = np.array([9.90438476e-01, 2.50379538e-03, 1.87405987e-05])
+        expected_pos = np.array(
+            [9.90438476e-01, 2.50379538e-03, 1.87405987e-05]
+        )
         pos, cost = topo.compute_gbest(swarm, **options)
         assert cost == pytest.approx(expected_cost)
         assert pos[np.argmin(cost)] == pytest.approx(expected_pos)
diff --git a/tests/backend/topology/test_random.py b/tests/backend/topology/test_random.py
index 50ef3235..8c675036 100644
--- a/tests/backend/topology/test_random.py
+++ b/tests/backend/topology/test_random.py
@@ -29,12 +29,17 @@ def test_compute_gbest_return_values(
         """Test if update_gbest_neighborhood gives the expected return values"""
         topo = topology(static=static)
         expected_cost = 1.0002528364353296
-        expected_pos = np.array([9.90438476e-01, 2.50379538e-03, 1.87405987e-05])
-        expected_pos_2 = np.array([9.98033031e-01, 4.97392619e-03, 3.07726256e-03])
+        expected_pos = np.array(
+            [9.90438476e-01, 2.50379538e-03, 1.87405987e-05]
+        )
+        expected_pos_2 = np.array(
+            [9.98033031e-01, 4.97392619e-03, 3.07726256e-03]
+        )
         pos, cost = topo.compute_gbest(swarm, **options)
         assert cost == pytest.approx(expected_cost)
-        assert ((pos[np.argmin(cost)] == pytest.approx(expected_pos)) or
-        (pos[np.argmin(cost)] == pytest.approx(expected_pos_2)))
+        assert (pos[np.argmin(cost)] == pytest.approx(expected_pos)) or (
+            pos[np.argmin(cost)] == pytest.approx(expected_pos_2)
+        )
 
     @pytest.mark.parametrize("static", [True, False])
     @pytest.mark.parametrize("k", [1, 2])
diff --git a/tests/backend/topology/test_ring.py b/tests/backend/topology/test_ring.py
index 03fa8948..8d55c81c 100644
--- a/tests/backend/topology/test_ring.py
+++ b/tests/backend/topology/test_ring.py
@@ -30,8 +30,13 @@ def test_compute_gbest_return_values(self, swarm, topology, p, k, static):
         topo = topology(static=static)
         pos, cost = topo.compute_gbest(swarm, p=p, k=k)
         expected_cost = 1.0002528364353296
-        expected_pos = np.array([9.90438476e-01, 2.50379538e-03, 1.87405987e-05])
-        expected_pos_2 = np.array([9.98033031e-01, 4.97392619e-03, 3.07726256e-03])
+        expected_pos = np.array(
+            [9.90438476e-01, 2.50379538e-03, 1.87405987e-05]
+        )
+        expected_pos_2 = np.array(
+            [9.98033031e-01, 4.97392619e-03, 3.07726256e-03]
+        )
         assert cost == pytest.approx(expected_cost)
-        assert ((pos[np.argmin(cost)] == pytest.approx(expected_pos)) or
-                (pos[np.argmin(cost)] == pytest.approx(expected_pos_2)))
+        assert (pos[np.argmin(cost)] == pytest.approx(expected_pos)) or (
+            pos[np.argmin(cost)] == pytest.approx(expected_pos_2)
+        )
diff --git a/tests/backend/topology/test_von_neumann.py b/tests/backend/topology/test_von_neumann.py
index 7e3eed53..7331189b 100644
--- a/tests/backend/topology/test_von_neumann.py
+++ b/tests/backend/topology/test_von_neumann.py
@@ -27,12 +27,17 @@ def test_update_gbest_neighborhood(self, swarm, topology, p, r):
         """Test if update_gbest_neighborhood gives the expected return values"""
         topo = topology()
         pos, cost = topo.compute_gbest(swarm, p=p, r=r)
-        expected_pos = np.array([9.90438476e-01, 2.50379538e-03, 1.87405987e-05])
-        expected_pos_2 = np.array([9.98033031e-01, 4.97392619e-03, 3.07726256e-03])
+        expected_pos = np.array(
+            [9.90438476e-01, 2.50379538e-03, 1.87405987e-05]
+        )
+        expected_pos_2 = np.array(
+            [9.98033031e-01, 4.97392619e-03, 3.07726256e-03]
+        )
         expected_cost = 1.0002528364353296
         assert cost == pytest.approx(expected_cost)
-        assert ((pos[np.argmin(cost)] == pytest.approx(expected_pos)) or
-                (pos[np.argmin(cost)] == pytest.approx(expected_pos_2)))
+        assert (pos[np.argmin(cost)] == pytest.approx(expected_pos)) or (
+            pos[np.argmin(cost)] == pytest.approx(expected_pos_2)
+        )
 
     @pytest.mark.parametrize("m", [i for i in range(3)])
     @pytest.mark.parametrize("n", [i for i in range(3)])
diff --git a/tests/optimizers/test_binary.py b/tests/optimizers/test_binary.py
index 0fca5fc4..87080893 100644
--- a/tests/optimizers/test_binary.py
+++ b/tests/optimizers/test_binary.py
@@ -3,6 +3,7 @@
 
 # Import modules
 import pytest
+import numpy as np
 
 # Import from pyswarms
 from pyswarms.discrete import BinaryPSO
@@ -28,3 +29,24 @@ def optimizer_reset(self, options):
         opt.optimize(sphere, 10)
         opt.reset()
         return opt
+
+    def test_binary_correct_pos(self, options):
+        dim = 10
+        x = np.random.rand(dim, dim)
+
+        def f_per_particle(m):
+            if np.count_nonzero(m) == 0:
+                return sum(x)
+            return sum(x[:, m == 1]).mean()
+
+        def binary_eg(options):
+            def f(x):
+                n_particles = x.shape[0]
+                j = [f_per_particle(x[i]) for i in range(n_particles)]
+                return np.array(j)
+
+            opt = BinaryPSO(n_particles=5, dimensions=dim, options=options)
+            return opt.optimize(f, iters=5)
+
+        cost, pos = binary_eg(options)
+        assert f_per_particle(pos) == cost
diff --git a/tests/optimizers/test_general_optimizer.py b/tests/optimizers/test_general_optimizer.py
index 96cee252..93249b24 100644
--- a/tests/optimizers/test_general_optimizer.py
+++ b/tests/optimizers/test_general_optimizer.py
@@ -12,6 +12,7 @@
 import pyswarms.backend.topology as t
 from pyswarms.single import GeneralOptimizerPSO
 from pyswarms.utils.functions.single_obj import sphere
+from pyswarms.backend.topology import Pyramid
 
 from .abc_test_optimizer import ABCTestOptimizer
 
@@ -95,3 +96,14 @@ def test_obj_incorrect_kwargs(self, obj_with_args, optimizer):
         with pytest.raises(TypeError):
             # Wrong kwargs
             cost, pos = optimizer.optimize(obj_with_args, 1000, c=1, d=100)
+
+    def general_eg(self, options):
+        my_topology = Pyramid(static=False)
+        opt = GeneralOptimizerPSO(
+            n_particles=10, dimensions=2, options=options, topology=my_topology
+        )
+        return opt.optimize(sphere, iters=5)
+
+    def test_general_correct_pos(self, options):
+        cost, pos = self.general_eg(options)
+        assert sum(pos ** 2) == cost
diff --git a/tests/optimizers/test_global_best.py b/tests/optimizers/test_global_best.py
index 58187288..59b2e486 100644
--- a/tests/optimizers/test_global_best.py
+++ b/tests/optimizers/test_global_best.py
@@ -28,3 +28,12 @@ def optimizer_reset(self, options):
         opt.optimize(sphere, 10)
         opt.reset()
         return opt
+
+    def global_eg(self, options):
+        opt = GlobalBestPSO(n_particles=10, dimensions=2, options=options)
+        return opt.optimize(sphere, iters=5)
+
+    def test_global_correct_pos(self, options):
+        print("Running local test")
+        cost, pos = self.global_eg(options)
+        assert sum(pos ** 2) == cost
diff --git a/tests/optimizers/test_local_best.py b/tests/optimizers/test_local_best.py
index 40df0e47..a1f6a859 100644
--- a/tests/optimizers/test_local_best.py
+++ b/tests/optimizers/test_local_best.py
@@ -28,3 +28,12 @@ def optimizer_reset(self, options):
         opt.optimize(sphere, 10)
         opt.reset()
         return opt
+
+    def local_eg(self, options):
+        opt = LocalBestPSO(n_particles=10, dimensions=2, options=options)
+        return opt.optimize(sphere, iters=5)
+
+    def test_local_correct_pos(self, options):
+        print("Running local test")
+        cost, pos = self.local_eg(options)
+        assert sum(pos ** 2) == cost

From 226a56ec1fce1d2826b24ced0ba0338bfeb01f6d Mon Sep 17 00:00:00 2001
From: ichbinjakes <souter_downhill@hotmail.com>
Date: Sat, 23 Mar 2019 17:54:51 +1100
Subject: [PATCH 2/7] Delete tests.py

---
 tests.py | 93 --------------------------------------------------------
 1 file changed, 93 deletions(-)
 delete mode 100644 tests.py

diff --git a/tests.py b/tests.py
deleted file mode 100644
index 199bfb8a..00000000
--- a/tests.py
+++ /dev/null
@@ -1,93 +0,0 @@
-
-
-#######################################
-## TEST: BinaryPSO
-"""
-print("Testing BinaryPSO...\n")
-
-def f_per_particle(m):
-    return sum(m)
-
-def f(x):
-    n_particles = x.shape[0]
-    j = [f_per_particle(x[i]) for i in range(n_particles)]
-    return np.array(j)
-
-options = {'c1': 0.5, 'c2': 0.5, 'w':0.9, 'k': 30, 'p':2}
-optimizer = ps.discrete.BinaryPSO(n_particles=30, dimensions=15, options=options)
-cost, pos = optimizer.optimize(f, iters=5)
-
-assert f_per_particle(pos) == cost
-
-######################
-## TEST: GlobalBestPOS
-print("\nTesting GlobalBestPSO...\n")
-options = {'c1': 0.5, 'c2': 0.3, 'w':0.9}
-optimizer = ps.single.GlobalBestPSO(n_particles=10, dimensions=2, options=options)
-cost, pos = optimizer.optimize(fx.sphere, iters=5)
-
-assert sum(pos ** 2) == cost
-
-#####################
-## TEST: LocalBestPOS
-print("\nTesting LocalBestPSO...\n")
-options = {'c1': 0.5, 'c2': 0.3, 'w':0.9, 'k': 2, 'p': 2}
-optimizer = ps.single.LocalBestPSO(n_particles=10, dimensions=2, options=options)
-cost, pos = optimizer.optimize(fx.sphere, iters=5)
-
-assert sum(pos ** 2) == cost
-
-
-#######################################
-## TEST: GeneralBestPOS
-# No test implemented
-"""
-
-import numpy as np
-import pyswarms as ps
-from pyswarms.utils.functions import single_obj as fx
-from pyswarms.backend.topology import Pyramid
-import unittest
-
-class TestOptimizer(unittest.TestCase):
-    def test_binary(self):
-        dim = 10
-        x = np.random.rand(dim,dim)
-        def f_per_particle(m):
-            # Get the subset of the features from the binary mask
-            if np.count_nonzero(m) == 0:
-                return sum(x)
-            return sum(x[:, m==1]).mean()
-        def f(x):
-            n_particles = x.shape[0]
-            j = [f_per_particle(x[i]) for i in range(n_particles)]
-            return np.array(j)
-        options = {'c1': 0.5, 'c2': 0.5, 'w':0.9, 'k': 2, 'p':2}
-        optimizer = ps.discrete.BinaryPSO(n_particles=5, dimensions=dim, options=options)
-        cost, pos = optimizer.optimize(f, iters=5)
-        self.assertTrue(f_per_particle(pos) == cost)
-
-    def test_global(self):
-        options = {'c1': 0.5, 'c2': 0.3, 'w':0.9}
-        optimizer = ps.single.GlobalBestPSO(n_particles=10, dimensions=2, options=options)
-        cost, pos = optimizer.optimize(fx.sphere, iters=5)
-        self.assertTrue(sum(pos ** 2) == cost)
-
-    def test_local(self):
-        options = {'c1': 0.5, 'c2': 0.3, 'w':0.9, 'k': 2, 'p': 2}
-        optimizer = ps.single.LocalBestPSO(n_particles=10, dimensions=2, options=options)
-        cost, pos = optimizer.optimize(fx.sphere, iters=5)
-        self.assertTrue(sum(pos ** 2) == cost)
-
-    def test_general(self):
-        options = {'c1': 0.5, 'c2': 0.3, 'w':0.9}
-        my_topology = Pyramid(static=False)
-        optimizer = ps.single.GeneralOptimizerPSO(n_particles=10, dimensions=2,
-                                            options=options, topology=my_topology)
-        cost, pos = optimizer.optimize(fx.sphere, iters=5)
-        print("\n\n", pos)
-        print(sum(pos ** 2), '\n\n')
-        self.assertTrue(sum(pos ** 2) == cost)
-
-if __name__ == '__main__':
-    unittest.main()

From 1192964954608f0ed36871f5cb674a04fd6fc204 Mon Sep 17 00:00:00 2001
From: Jake <souter_downhill@hotmail.com>
Date: Sat, 30 Mar 2019 14:12:49 +1100
Subject: [PATCH 3/7] Revert flake formatting

---
 pyswarms/backend/handlers.py               | 12 ++--
 pyswarms/backend/topology/ring.py          |  4 +-
 tests/backend/conftest.py                  | 74 ++++++++--------------
 tests/backend/test_handlers.py             | 44 +++++--------
 tests/backend/test_operators.py            | 16 ++---
 tests/backend/topology/test_pyramid.py     |  4 +-
 tests/backend/topology/test_random.py      | 13 ++--
 tests/backend/topology/test_ring.py        | 13 ++--
 tests/backend/topology/test_von_neumann.py | 13 ++--
 9 files changed, 72 insertions(+), 121 deletions(-)

diff --git a/pyswarms/backend/handlers.py b/pyswarms/backend/handlers.py
index d0d2ff26..ab755950 100644
--- a/pyswarms/backend/handlers.py
+++ b/pyswarms/backend/handlers.py
@@ -196,15 +196,13 @@ def reflective(self, position, bounds, **kwargs):
         new_pos = position
         while lower_than_bound[0].size != 0 or greater_than_bound[0].size != 0:
             if lower_than_bound[0].size > 0:
-                new_pos[lower_than_bound] = (
-                    2 * lb[lower_than_bound[1]] - new_pos[lower_than_bound]
-                )
+                new_pos[lower_than_bound] = 2 * lb[lower_than_bound[1]] - \
+                new_pos[lower_than_bound]
             if greater_than_bound[0].size > 0:
-                new_pos[greater_than_bound] = (
-                    2 * ub[greater_than_bound[1]] - new_pos[greater_than_bound]
-                )
+                new_pos[greater_than_bound] = 2 * ub[greater_than_bound[1]] - \
+                new_pos[greater_than_bound]
             lower_than_bound, greater_than_bound = self._out_of_bounds(
-                new_pos, bounds
+                    new_pos, bounds
             )
 
         return new_pos
diff --git a/pyswarms/backend/topology/ring.py b/pyswarms/backend/topology/ring.py
index 0f1ca901..573e0fae 100644
--- a/pyswarms/backend/topology/ring.py
+++ b/pyswarms/backend/topology/ring.py
@@ -65,7 +65,9 @@ def compute_gbest(self, swarm, p, k, **kwargs):
             if (self.static and self.neighbor_idx is None) or not self.static:
                 # Obtain the nearest-neighbors for each particle
                 tree = cKDTree(swarm.position)
-                _, self.neighbor_idx = tree.query(swarm.position, p=p, k=k)
+                _, self.neighbor_idx = tree.query(
+                    swarm.position, p=p, k=k
+                )
 
             # Map the computed costs to the neighbour indices and take the
             # argmin. If k-neighbors is equal to 1, then the swarm acts
diff --git a/tests/backend/conftest.py b/tests/backend/conftest.py
index 0a3581aa..2d4baf56 100644
--- a/tests/backend/conftest.py
+++ b/tests/backend/conftest.py
@@ -27,74 +27,56 @@ def swarm():
     }
     return Swarm(**attrs_at_t)
 
-
 @pytest.fixture
 def bounds():
     bounds_ = (np.array([2, 3, 1]), np.array([4, 7, 8]))
     return bounds_
 
-
 @pytest.fixture
 def clamp():
     clamp_ = (np.array([2, 3, 1]), np.array([4, 7, 8]))
     return clamp_
 
-
 @pytest.fixture
 def positions_inbound():
-    pos_ = np.array(
-        [
-            [3.3, 4.4, 2.3],
-            [3.7, 5.2, 7.0],
-            [2.5, 6.8, 2.3],
-            [2.1, 6.9, 4.7],
-            [2.7, 3.2, 3.5],
-            [2.5, 5.1, 1.2],
-        ]
-    )
+    pos_ = np.array([[3.3, 4.4, 2.3],
+                     [3.7, 5.2, 7.0],
+                     [2.5, 6.8, 2.3],
+                     [2.1, 6.9, 4.7],
+                     [2.7, 3.2, 3.5],
+                     [2.5, 5.1, 1.2]
+                    ])
     return pos_
 
-
 @pytest.fixture
 def positions_out_of_bound():
-    pos_ = np.array(
-        [
-            [5.3, 4.4, 2.3],
-            [3.7, 9.2, 7.0],
-            [8.5, 0.8, 2.3],
-            [2.1, 6.9, 0.7],
-            [2.7, 9.2, 3.5],
-            [1.5, 5.1, 9.2],
-        ]
-    )
+    pos_ = np.array([[5.3, 4.4, 2.3],
+                     [3.7, 9.2, 7.0],
+                     [8.5, 0.8, 2.3],
+                     [2.1, 6.9, 0.7],
+                     [2.7, 9.2, 3.5],
+                     [1.5, 5.1, 9.2]
+                    ])
     return pos_
 
-
 @pytest.fixture
 def velocities_inbound():
-    pos_ = np.array(
-        [
-            [3.3, 4.4, 2.3],
-            [3.7, 5.2, 7.0],
-            [2.5, 6.8, 2.3],
-            [2.1, 6.9, 4.7],
-            [2.7, 3.2, 3.5],
-            [2.5, 5.1, 1.2],
-        ]
-    )
+    pos_ = np.array([[3.3, 4.4, 2.3],
+                     [3.7, 5.2, 7.0],
+                     [2.5, 6.8, 2.3],
+                     [2.1, 6.9, 4.7],
+                     [2.7, 3.2, 3.5],
+                     [2.5, 5.1, 1.2]
+                    ])
     return pos_
 
-
 @pytest.fixture
 def velocities_out_of_bound():
-    pos_ = np.array(
-        [
-            [5.3, 4.4, 2.3],
-            [3.7, 9.2, 7.0],
-            [8.5, 0.8, 2.3],
-            [2.1, 6.9, 0.7],
-            [2.7, 9.2, 3.5],
-            [1.5, 5.1, 9.2],
-        ]
-    )
+    pos_ = np.array([[5.3, 4.4, 2.3],
+                     [3.7, 9.2, 7.0],
+                     [8.5, 0.8, 2.3],
+                     [2.1, 6.9, 0.7],
+                     [2.7, 9.2, 3.5],
+                     [1.5, 5.1, 9.2]
+                    ])
     return pos_
diff --git a/tests/backend/test_handlers.py b/tests/backend/test_handlers.py
index 6b887102..33852efa 100644
--- a/tests/backend/test_handlers.py
+++ b/tests/backend/test_handlers.py
@@ -9,21 +9,10 @@
 )
 import pyswarms.backend.handlers as h
 
-bh_strategies = [
-    name
-    for name, _ in inspect.getmembers(
-        h.BoundaryHandler(""), predicate=inspect.ismethod
-    )
-    if not name.startswith(("__", "_"))
-]
-vh_strategies = [
-    name
-    for name, _ in inspect.getmembers(
-        h.VelocityHandler(""), predicate=inspect.ismethod
-    )
-    if not name.startswith(("__", "_"))
-]
-
+bh_strategies = [name for name, _ in inspect.getmembers(h.BoundaryHandler(""),
+    predicate=inspect.ismethod) if not name.startswith(("__", "_"))]
+vh_strategies = [name for name, _ in inspect.getmembers(h.VelocityHandler(""),
+    predicate=inspect.ismethod) if not name.startswith(("__", "_"))]
 
 def test_out_of_bounds(bounds, positions_inbound, positions_out_of_bound):
     hm = HandlerMixin()
@@ -45,8 +34,9 @@ def test_out_of_bounds(bounds, positions_inbound, positions_out_of_bound):
         np.ravel(idx_out_of_bounds[1]).all() == np.ravel(expected_idx[1]).all()
     )
 
-
-@pytest.mark.parametrize("strategy", bh_strategies)
+@pytest.mark.parametrize(
+    "strategy", bh_strategies
+)
 def test_bound_handling(
     bounds, positions_inbound, positions_out_of_bound, strategy
 ):
@@ -62,7 +52,6 @@ def test_bound_handling(
     assert lower_than_bound.all()
     assert greater_than_bound.all()
 
-
 def test_nearest_strategy(bounds, positions_inbound, positions_out_of_bound):
     bh = BoundaryHandler(strategy="nearest")
     # TODO Add strategy specific tests
@@ -97,17 +86,16 @@ def test_periodic_strategy(bounds, positions_inbound, positions_out_of_bound):
     bh = BoundaryHandler(strategy="periodic")
     # TODO Add strategy specific tests
 
-
 def assert_clamp(
-    clamp,
-    velocities_inbound,
-    velocities_out_of_bound,
-    positions_inbound,
-    positions_out_of_bound,
-    vh,
-    bounds=None,
+clamp,
+velocities_inbound,
+velocities_out_of_bound,
+positions_inbound,
+positions_out_of_bound,
+vh,
+bounds=None,
 ):
-    # Test if it doesn't handle inclamp velocities
+# Test if it doesn't handle inclamp velocities
     inbound_handled = vh(
         velocities_inbound, clamp, position=positions_inbound, bounds=bounds
     )
@@ -126,6 +114,8 @@ def assert_clamp(
     assert not greater_than_clamp.all()
 
 
+
+
 def test_unmodified_strategy(
     clamp, velocities_inbound, velocities_out_of_bound
 ):
diff --git a/tests/backend/test_operators.py b/tests/backend/test_operators.py
index 716a3cfa..94e6dd6b 100644
--- a/tests/backend/test_operators.py
+++ b/tests/backend/test_operators.py
@@ -41,9 +41,7 @@ def test_return_values(self, swarm, clamp):
             assert (clamp[0] <= v).all() and (clamp[1] >= v).all()
 
     @pytest.mark.parametrize("swarm", [0, (1, 2, 3)])
-    @pytest.mark.parametrize(
-        "vh_strat", ["unmodified", "zero", "invert", "adjust"]
-    )
+    @pytest.mark.parametrize("vh_strat", ["unmodified", "zero", "invert", "adjust"])
     def test_input_swarm(self, swarm, vh_strat):
         """Test if method raises AttributeError with wrong swarm"""
         vh = VelocityHandler(strategy=vh_strat)
@@ -51,9 +49,7 @@ def test_input_swarm(self, swarm, vh_strat):
             P.compute_velocity(swarm, clamp=(0, 1), vh=vh)
 
     @pytest.mark.parametrize("options", [{"c1": 0.5, "c2": 0.3}])
-    @pytest.mark.parametrize(
-        "vh_strat", ["unmodified", "zero", "invert", "adjust"]
-    )
+    @pytest.mark.parametrize("vh_strat", ["unmodified", "zero", "invert", "adjust"])
     def test_missing_kwargs(self, swarm, options, vh_strat):
         """Test if method raises KeyError with missing kwargs"""
         vh = VelocityHandler(strategy=vh_strat)
@@ -78,13 +74,13 @@ def test_return_values(self, swarm, bounds, bh_strat):
         assert p.shape == swarm.velocity.shape
         if bounds is not None:
             assert (bounds[0] <= p).all() and (bounds[1] >= p).all()
-
+            
     @pytest.mark.parametrize("swarm", [0, (1, 2, 3)])
-    @pytest.mark.parametrize(
-        "bh_strat", ["nearest", "random", "shrink", "intermediate"]
-    )
+    @pytest.mark.parametrize("bh_strat", ["nearest", "random", "shrink",
+    "intermediate"])
     def test_input_swarm(self, swarm, bh_strat):
         """Test if method raises AttributeError with wrong swarm"""
         bh = BoundaryHandler(strategy=bh_strat)
         with pytest.raises(AttributeError):
             P.compute_position(swarm, bounds=([-5, -5], [5, 5]), bh=bh)
+
diff --git a/tests/backend/topology/test_pyramid.py b/tests/backend/topology/test_pyramid.py
index 02cd131e..eb3e0b16 100644
--- a/tests/backend/topology/test_pyramid.py
+++ b/tests/backend/topology/test_pyramid.py
@@ -28,9 +28,7 @@ def test_compute_gbest_return_values(
         """Test if compute_gbest() gives the expected return values"""
         topo = topology(static=static)
         expected_cost = 1.0002528364353296
-        expected_pos = np.array(
-            [9.90438476e-01, 2.50379538e-03, 1.87405987e-05]
-        )
+        expected_pos = np.array([9.90438476e-01, 2.50379538e-03, 1.87405987e-05])
         pos, cost = topo.compute_gbest(swarm, **options)
         assert cost == pytest.approx(expected_cost)
         assert pos[np.argmin(cost)] == pytest.approx(expected_pos)
diff --git a/tests/backend/topology/test_random.py b/tests/backend/topology/test_random.py
index 8c675036..50ef3235 100644
--- a/tests/backend/topology/test_random.py
+++ b/tests/backend/topology/test_random.py
@@ -29,17 +29,12 @@ def test_compute_gbest_return_values(
         """Test if update_gbest_neighborhood gives the expected return values"""
         topo = topology(static=static)
         expected_cost = 1.0002528364353296
-        expected_pos = np.array(
-            [9.90438476e-01, 2.50379538e-03, 1.87405987e-05]
-        )
-        expected_pos_2 = np.array(
-            [9.98033031e-01, 4.97392619e-03, 3.07726256e-03]
-        )
+        expected_pos = np.array([9.90438476e-01, 2.50379538e-03, 1.87405987e-05])
+        expected_pos_2 = np.array([9.98033031e-01, 4.97392619e-03, 3.07726256e-03])
         pos, cost = topo.compute_gbest(swarm, **options)
         assert cost == pytest.approx(expected_cost)
-        assert (pos[np.argmin(cost)] == pytest.approx(expected_pos)) or (
-            pos[np.argmin(cost)] == pytest.approx(expected_pos_2)
-        )
+        assert ((pos[np.argmin(cost)] == pytest.approx(expected_pos)) or
+        (pos[np.argmin(cost)] == pytest.approx(expected_pos_2)))
 
     @pytest.mark.parametrize("static", [True, False])
     @pytest.mark.parametrize("k", [1, 2])
diff --git a/tests/backend/topology/test_ring.py b/tests/backend/topology/test_ring.py
index 8d55c81c..03fa8948 100644
--- a/tests/backend/topology/test_ring.py
+++ b/tests/backend/topology/test_ring.py
@@ -30,13 +30,8 @@ def test_compute_gbest_return_values(self, swarm, topology, p, k, static):
         topo = topology(static=static)
         pos, cost = topo.compute_gbest(swarm, p=p, k=k)
         expected_cost = 1.0002528364353296
-        expected_pos = np.array(
-            [9.90438476e-01, 2.50379538e-03, 1.87405987e-05]
-        )
-        expected_pos_2 = np.array(
-            [9.98033031e-01, 4.97392619e-03, 3.07726256e-03]
-        )
+        expected_pos = np.array([9.90438476e-01, 2.50379538e-03, 1.87405987e-05])
+        expected_pos_2 = np.array([9.98033031e-01, 4.97392619e-03, 3.07726256e-03])
         assert cost == pytest.approx(expected_cost)
-        assert (pos[np.argmin(cost)] == pytest.approx(expected_pos)) or (
-            pos[np.argmin(cost)] == pytest.approx(expected_pos_2)
-        )
+        assert ((pos[np.argmin(cost)] == pytest.approx(expected_pos)) or
+                (pos[np.argmin(cost)] == pytest.approx(expected_pos_2)))
diff --git a/tests/backend/topology/test_von_neumann.py b/tests/backend/topology/test_von_neumann.py
index 7331189b..7e3eed53 100644
--- a/tests/backend/topology/test_von_neumann.py
+++ b/tests/backend/topology/test_von_neumann.py
@@ -27,17 +27,12 @@ def test_update_gbest_neighborhood(self, swarm, topology, p, r):
         """Test if update_gbest_neighborhood gives the expected return values"""
         topo = topology()
         pos, cost = topo.compute_gbest(swarm, p=p, r=r)
-        expected_pos = np.array(
-            [9.90438476e-01, 2.50379538e-03, 1.87405987e-05]
-        )
-        expected_pos_2 = np.array(
-            [9.98033031e-01, 4.97392619e-03, 3.07726256e-03]
-        )
+        expected_pos = np.array([9.90438476e-01, 2.50379538e-03, 1.87405987e-05])
+        expected_pos_2 = np.array([9.98033031e-01, 4.97392619e-03, 3.07726256e-03])
         expected_cost = 1.0002528364353296
         assert cost == pytest.approx(expected_cost)
-        assert (pos[np.argmin(cost)] == pytest.approx(expected_pos)) or (
-            pos[np.argmin(cost)] == pytest.approx(expected_pos_2)
-        )
+        assert ((pos[np.argmin(cost)] == pytest.approx(expected_pos)) or
+                (pos[np.argmin(cost)] == pytest.approx(expected_pos_2)))
 
     @pytest.mark.parametrize("m", [i for i in range(3)])
     @pytest.mark.parametrize("n", [i for i in range(3)])

From 8c64a4de3106c9f643f9d24926d324380b2933c3 Mon Sep 17 00:00:00 2001
From: Jake <souter_downhill@hotmail.com>
Date: Sun, 31 Mar 2019 13:58:55 +1100
Subject: [PATCH 4/7] Correct return best pos code

---
 pyswarms/discrete/binary.py                |  4 +---
 pyswarms/single/general_optimizer.py       |  4 +---
 pyswarms/single/global_best.py             |  2 +-
 pyswarms/single/local_best.py              |  4 +---
 tests/optimizers/test_binary.py            | 23 ++++------------------
 tests/optimizers/test_general_optimizer.py | 14 ++++---------
 tests/optimizers/test_global_best.py       |  9 +++------
 tests/optimizers/test_local_best.py        |  9 +++------
 8 files changed, 18 insertions(+), 51 deletions(-)

diff --git a/pyswarms/discrete/binary.py b/pyswarms/discrete/binary.py
index caaff700..01ca32ca 100644
--- a/pyswarms/discrete/binary.py
+++ b/pyswarms/discrete/binary.py
@@ -202,9 +202,7 @@ def optimize(self, objective_func, iters, **kwargs):
             self.swarm.position = self._compute_position(self.swarm)
         # Obtain the final best_cost and the final best_position
         final_best_cost = self.swarm.best_cost.copy()
-        final_best_pos = self.swarm.best_pos[
-            self.swarm.pbest_cost.argmin(axis=0)
-        ].copy()
+        final_best_pos = self.swarm.pbest_pos[self.swarm.pbest_cost.argmin()].copy()
         self.rep.log(
             "Optimization finished | best cost: {}, best pos: {}".format(
                 final_best_cost, final_best_pos
diff --git a/pyswarms/single/general_optimizer.py b/pyswarms/single/general_optimizer.py
index d473334c..07004f6a 100644
--- a/pyswarms/single/general_optimizer.py
+++ b/pyswarms/single/general_optimizer.py
@@ -248,9 +248,7 @@ def optimize(self, objective_func, iters, **kwargs):
             )
         # Obtain the final best_cost and the final best_position
         final_best_cost = self.swarm.best_cost.copy()
-        final_best_pos = self.swarm.best_pos[
-            self.swarm.best_cost.argmin()
-        ].copy()
+        final_best_pos = self.swarm.pbest_pos[self.swarm.pbest_cost.argmin()].copy()
         # Write report in log and return final cost and position
         self.rep.log(
             "Optimization finished | best cost: {}, best pos: {}".format(
diff --git a/pyswarms/single/global_best.py b/pyswarms/single/global_best.py
index 3f9e9724..b815d6d4 100644
--- a/pyswarms/single/global_best.py
+++ b/pyswarms/single/global_best.py
@@ -207,7 +207,7 @@ def optimize(self, objective_func, iters, **kwargs):
             )
         # Obtain the final best_cost and the final best_position
         final_best_cost = self.swarm.best_cost.copy()
-        final_best_pos = self.swarm.best_pos.copy()
+        final_best_pos = self.swarm.pbest_pos[self.swarm.pbest_cost.argmin()].copy()
         # Write report in log and return final cost and position
         self.rep.log(
             "Optimization finished | best cost: {}, best pos: {}".format(
diff --git a/pyswarms/single/local_best.py b/pyswarms/single/local_best.py
index eec2bbbd..a89a2692 100644
--- a/pyswarms/single/local_best.py
+++ b/pyswarms/single/local_best.py
@@ -235,9 +235,7 @@ def optimize(self, objective_func, iters, **kwargs):
             )
         # Obtain the final best_cost and the final best_position
         final_best_cost = self.swarm.best_cost.copy()
-        final_best_pos = self.swarm.best_pos[
-            self.swarm.pbest_cost.argmin(axis=0)
-        ].copy()
+        final_best_pos = self.swarm.pbest_pos[self.swarm.pbest_cost.argmin()].copy()
         # Write report in log and return final cost and position
         self.rep.log(
             "Optimization finished | best cost: {}, best pos: {}".format(
diff --git a/tests/optimizers/test_binary.py b/tests/optimizers/test_binary.py
index 87080893..bcb59b40 100644
--- a/tests/optimizers/test_binary.py
+++ b/tests/optimizers/test_binary.py
@@ -31,22 +31,7 @@ def optimizer_reset(self, options):
         return opt
 
     def test_binary_correct_pos(self, options):
-        dim = 10
-        x = np.random.rand(dim, dim)
-
-        def f_per_particle(m):
-            if np.count_nonzero(m) == 0:
-                return sum(x)
-            return sum(x[:, m == 1]).mean()
-
-        def binary_eg(options):
-            def f(x):
-                n_particles = x.shape[0]
-                j = [f_per_particle(x[i]) for i in range(n_particles)]
-                return np.array(j)
-
-            opt = BinaryPSO(n_particles=5, dimensions=dim, options=options)
-            return opt.optimize(f, iters=5)
-
-        cost, pos = binary_eg(options)
-        assert f_per_particle(pos) == cost
+        """ Test to check binary optimiser returns the correct position corresponding to the best cost """
+        opt = BinaryPSO(10, 2, options=options)
+        cost, pos = opt.optimize(sphere, 10)
+        assert sum(pos**2) == cost
diff --git a/tests/optimizers/test_general_optimizer.py b/tests/optimizers/test_general_optimizer.py
index 93249b24..1b4c1568 100644
--- a/tests/optimizers/test_general_optimizer.py
+++ b/tests/optimizers/test_general_optimizer.py
@@ -12,7 +12,6 @@
 import pyswarms.backend.topology as t
 from pyswarms.single import GeneralOptimizerPSO
 from pyswarms.utils.functions.single_obj import sphere
-from pyswarms.backend.topology import Pyramid
 
 from .abc_test_optimizer import ABCTestOptimizer
 
@@ -97,13 +96,8 @@ def test_obj_incorrect_kwargs(self, obj_with_args, optimizer):
             # Wrong kwargs
             cost, pos = optimizer.optimize(obj_with_args, 1000, c=1, d=100)
 
-    def general_eg(self, options):
-        my_topology = Pyramid(static=False)
-        opt = GeneralOptimizerPSO(
-            n_particles=10, dimensions=2, options=options, topology=my_topology
-        )
-        return opt.optimize(sphere, iters=5)
-
-    def test_general_correct_pos(self, options):
-        cost, pos = self.general_eg(options)
+    def test_general_correct_pos(self, options, optimizer):
+        """ Test to check general optimiser returns the correct position corresponding to the best cost """
+        cost, pos = optimizer.optimize(sphere, iters=5)
+        print(cost, '---', pos)
         assert sum(pos ** 2) == cost
diff --git a/tests/optimizers/test_global_best.py b/tests/optimizers/test_global_best.py
index 59b2e486..401398b9 100644
--- a/tests/optimizers/test_global_best.py
+++ b/tests/optimizers/test_global_best.py
@@ -29,11 +29,8 @@ def optimizer_reset(self, options):
         opt.reset()
         return opt
 
-    def global_eg(self, options):
-        opt = GlobalBestPSO(n_particles=10, dimensions=2, options=options)
-        return opt.optimize(sphere, iters=5)
-
     def test_global_correct_pos(self, options):
-        print("Running local test")
-        cost, pos = self.global_eg(options)
+        """ Test to check global optimiser returns the correct position corresponding to the best cost """
+        opt = GlobalBestPSO(n_particles=10, dimensions=2, options=options)
+        cost, pos = opt.optimize(sphere, iters=5)
         assert sum(pos ** 2) == cost
diff --git a/tests/optimizers/test_local_best.py b/tests/optimizers/test_local_best.py
index a1f6a859..945ce3e6 100644
--- a/tests/optimizers/test_local_best.py
+++ b/tests/optimizers/test_local_best.py
@@ -29,11 +29,8 @@ def optimizer_reset(self, options):
         opt.reset()
         return opt
 
-    def local_eg(self, options):
-        opt = LocalBestPSO(n_particles=10, dimensions=2, options=options)
-        return opt.optimize(sphere, iters=5)
-
     def test_local_correct_pos(self, options):
-        print("Running local test")
-        cost, pos = self.local_eg(options)
+        """ Test to check local optimiser returns the correct position corresponding to the best cost """
+        opt = LocalBestPSO(n_particles=10, dimensions=2, options=options)
+        cost, pos = opt.optimize(sphere, iters=5)
         assert sum(pos ** 2) == cost

From ebf564d7272be22ea59689ccedd34236494b8cb6 Mon Sep 17 00:00:00 2001
From: Jake <souter_downhill@hotmail.com>
Date: Sun, 31 Mar 2019 14:00:22 +1100
Subject: [PATCH 5/7] Remove numpy import

---
 tests/optimizers/test_binary.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tests/optimizers/test_binary.py b/tests/optimizers/test_binary.py
index bcb59b40..937f71a6 100644
--- a/tests/optimizers/test_binary.py
+++ b/tests/optimizers/test_binary.py
@@ -3,7 +3,6 @@
 
 # Import modules
 import pytest
-import numpy as np
 
 # Import from pyswarms
 from pyswarms.discrete import BinaryPSO

From 487ec7d4312f9801917126d74f4aaad7012bc0e5 Mon Sep 17 00:00:00 2001
From: Jake <souter_downhill@hotmail.com>
Date: Thu, 25 Apr 2019 09:43:07 +1000
Subject: [PATCH 6/7] Change test to use optimizer history

---
 examples/basic_optimization.ipynb          | 667 ++++++++++++++++++++-
 tests/optimizers/test_binary.py            |   9 +-
 tests/optimizers/test_general_optimizer.py |   6 +-
 tests/optimizers/test_global_best.py       |   6 +-
 tests/optimizers/test_local_best.py        |   6 +-
 5 files changed, 666 insertions(+), 28 deletions(-)

diff --git a/examples/basic_optimization.ipynb b/examples/basic_optimization.ipynb
index 1e395c21..46698191 100644
--- a/examples/basic_optimization.ipynb
+++ b/examples/basic_optimization.ipynb
@@ -28,8 +28,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on Python version: 3.6.3 |Anaconda custom (64-bit)| (default, Oct 13 2017, 12:02:49) \n",
-      "[GCC 7.2.0]\n"
+      "Running on Python version: 3.7.2 (default, Dec 29 2018, 06:19:36) \n",
+      "[GCC 7.3.0]\n"
      ]
     }
    ],
@@ -79,36 +79,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:pyswarms.single.global_best:Iteration 1/1000, cost: 0.11075768527574707\n",
-      "INFO:pyswarms.single.global_best:Iteration 101/1000, cost: 7.521863508083004e-08\n",
-      "INFO:pyswarms.single.global_best:Iteration 201/1000, cost: 2.8159915186067273e-11\n",
-      "INFO:pyswarms.single.global_best:Iteration 301/1000, cost: 8.794923638889175e-17\n",
-      "INFO:pyswarms.single.global_best:Iteration 401/1000, cost: 1.4699516547190895e-21\n",
-      "INFO:pyswarms.single.global_best:Iteration 501/1000, cost: 5.111264897313781e-23\n",
-      "INFO:pyswarms.single.global_best:Iteration 601/1000, cost: 8.329697430155943e-27\n",
-      "INFO:pyswarms.single.global_best:Iteration 701/1000, cost: 1.662161785541961e-30\n",
-      "INFO:pyswarms.single.global_best:Iteration 801/1000, cost: 6.140424420222279e-34\n",
-      "INFO:pyswarms.single.global_best:Iteration 901/1000, cost: 2.0523902169204634e-39\n",
-      "INFO:pyswarms.single.global_best:================================\n",
-      "Optimization finished!\n",
-      "Final cost: 0.0000\n",
-      "Best value: [-2.431421462417008e-22, -9.502018378214418e-23]\n",
-      "\n"
+      "2019-04-24 18:23:08,556 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}\n",
+      "\n",
+      "pyswarms.single.global_best:   0%|          |0/10\u001b[A\n",
+      "pyswarms.single.global_best:   0%|          |0/10, best_cost=0.0605\u001b[A\n",
+      "pyswarms.single.global_best:   0%|          |0/10, best_cost=0.0605\u001b[A\n",
+      "pyswarms.single.global_best:   0%|          |0/10, best_cost=0.0605\u001b[A\n",
+      "pyswarms.single.global_best:   0%|          |0/10, best_cost=0.0605\u001b[A\n",
+      "pyswarms.single.global_best:   0%|          |0/10, best_cost=0.0605\u001b[A\n",
+      "pyswarms.single.global_best:   0%|          |0/10, best_cost=0.0424\u001b[A\n",
+      "pyswarms.single.global_best:   0%|          |0/10, best_cost=8.36e-06\u001b[A\n",
+      "pyswarms.single.global_best:   0%|          |0/10, best_cost=8.36e-06\u001b[A\n",
+      "pyswarms.single.global_best:  80%|████████  |8/10, best_cost=8.36e-06\u001b[A\n",
+      "pyswarms.single.global_best:  80%|████████  |8/10, best_cost=8.36e-06\u001b[A\n",
+      "pyswarms.single.global_best:  80%|████████  |8/10, best_cost=8.36e-06\u001b[A\n",
+      "pyswarms.single.global_best: 100%|██████████|10/10, best_cost=8.36e-06\u001b[A2019-04-24 18:23:08,708 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 8.356164158276717e-06, best pos: [ 0.00249681 -0.00145674]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 144 ms, sys: 14.8 ms, total: 159 ms\n",
-      "Wall time: 151 ms\n"
+      "CPU times: user 52 ms, sys: 0 ns, total: 52 ms\n",
+      "Wall time: 157 ms\n"
      ]
     }
    ],
@@ -121,9 +121,632 @@
     "optimizer = ps.single.GlobalBestPSO(n_particles=10, dimensions=2, options=options)\n",
     "\n",
     "# Perform optimization\n",
-    "cost, pos = optimizer.optimize(fx.sphere, print_step=100, iters=1000, verbose=3)"
+    "cost, pos = optimizer.optimize(fx.sphere, iters=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z=np.array(optimizer.cost_history)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8.356164158276717e-06"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z[z.argmin()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AxisError",
+     "evalue": "axis 1 is out of bounds for array of dimension 1",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAxisError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-18-581cfa7918b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msphere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pyswarms/utils/functions/single_obj.py\u001b[0m in \u001b[0;36msphere\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    715\u001b[0m         \u001b[0mcomputed\u001b[0m \u001b[0mcost\u001b[0m \u001b[0mof\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_particles\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    716\u001b[0m     \"\"\"\n\u001b[0;32m--> 717\u001b[0;31m     \u001b[0mj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m2.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    718\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    719\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_sum\u001b[0;34m(a, axis, dtype, out, keepdims, initial)\u001b[0m\n\u001b[1;32m     34\u001b[0m def _sum(a, axis=None, dtype=None, out=None, keepdims=False,\n\u001b[1;32m     35\u001b[0m          initial=_NoValue):\n\u001b[0;32m---> 36\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mumr_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m def _prod(a, axis=None, dtype=None, out=None, keepdims=False,\n",
+      "\u001b[0;31mAxisError\u001b[0m: axis 1 is out of bounds for array of dimension 1"
+     ]
+    }
+   ],
+   "source": [
+    "fx.sphere(pos.T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.00249681, -0.00145674])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pos.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([6.23406772e-06, 2.12209644e-06])"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(pos.reshape(-1, 1) ** 2).sum(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([6.23406772e-06, 2.12209644e-06])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fx.sphere(pos.reshape(-1, 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.00249681, -0.00145674])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8.356164158276717e-06"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ToHistory',\n",
+       " '__abstractmethods__',\n",
+       " '__class__',\n",
+       " '__delattr__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__module__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__slots__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " '__weakref__',\n",
+       " '_abc_impl',\n",
+       " '_populate_history',\n",
+       " 'bh',\n",
+       " 'bounds',\n",
+       " 'center',\n",
+       " 'cost_history',\n",
+       " 'dimensions',\n",
+       " 'ftol',\n",
+       " 'init_pos',\n",
+       " 'mean_neighbor_history',\n",
+       " 'mean_pbest_history',\n",
+       " 'n_particles',\n",
+       " 'name',\n",
+       " 'optimize',\n",
+       " 'options',\n",
+       " 'pos_history',\n",
+       " 'rep',\n",
+       " 'reset',\n",
+       " 'swarm',\n",
+       " 'swarm_size',\n",
+       " 'top',\n",
+       " 'velocity_clamp',\n",
+       " 'velocity_history',\n",
+       " 'vh']"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(optimizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array([[0.90124935, 0.17491093],\n",
+       "        [0.80474654, 0.92141545],\n",
+       "        [0.82737053, 0.08275101],\n",
+       "        [0.43055521, 0.16029267],\n",
+       "        [0.7748099 , 0.83678948],\n",
+       "        [0.21088674, 0.49822131],\n",
+       "        [0.35918509, 0.7553067 ],\n",
+       "        [0.10156838, 0.22410303],\n",
+       "        [0.58793823, 0.30151857],\n",
+       "        [0.17084816, 0.75461516]]), array([[1.43727288, 0.58528291],\n",
+       "        [0.93435275, 1.42905162],\n",
+       "        [1.13856742, 0.33461069],\n",
+       "        [0.72586063, 0.32709616],\n",
+       "        [1.5094666 , 1.18965563],\n",
+       "        [0.90403286, 0.49314525],\n",
+       "        [0.67649282, 1.00266235],\n",
+       "        [0.5043016 , 0.67703065],\n",
+       "        [0.75097075, 0.41294503],\n",
+       "        [0.4881155 , 0.80689386]]), array([[1.65419275, 0.77490286],\n",
+       "        [0.77212441, 1.42704143],\n",
+       "        [0.98198008, 0.50241268],\n",
+       "        [0.79360331, 0.46185756],\n",
+       "        [1.7394455 , 1.39718321],\n",
+       "        [1.34641821, 0.40948887],\n",
+       "        [0.9158877 , 1.14369282],\n",
+       "        [0.67208624, 0.96319035],\n",
+       "        [0.70009302, 0.44887179],\n",
+       "        [0.57512907, 0.69416884]]), array([[1.21344488, 0.61992065],\n",
+       "        [0.45232105, 1.17197039],\n",
+       "        [0.66917004, 0.41051598],\n",
+       "        [0.68143011, 0.47378703],\n",
+       "        [1.65180215, 1.39952274],\n",
+       "        [1.41919708, 0.35495412],\n",
+       "        [0.82031158, 1.13631049],\n",
+       "        [0.63125209, 0.98728533],\n",
+       "        [0.55975409, 0.38715874],\n",
+       "        [0.53502928, 0.52391748]]), array([[0.5447976 , 0.46467561],\n",
+       "        [0.33144311, 0.71443207],\n",
+       "        [0.30345822, 0.31535249],\n",
+       "        [0.49027688, 0.35467089],\n",
+       "        [1.22966712, 0.99730451],\n",
+       "        [0.7226531 , 0.29773023],\n",
+       "        [0.39199028, 0.80877984],\n",
+       "        [0.54763719, 0.59748084],\n",
+       "        [0.3568341 , 0.24559238],\n",
+       "        [0.43745753, 0.33405719]]), array([[-0.18582241,  0.30254416],\n",
+       "        [ 0.18521129,  0.19873369],\n",
+       "        [-0.02654187,  0.2203464 ],\n",
+       "        [ 0.25351304,  0.17935622],\n",
+       "        [ 0.47076599,  0.47001543],\n",
+       "        [-0.18585261,  0.29629828],\n",
+       "        [-0.05865941,  0.42130528],\n",
+       "        [ 0.18595814,  0.13376584],\n",
+       "        [ 0.17034079,  0.11556653],\n",
+       "        [ 0.332588  ,  0.15416082]]), array([[-0.75208666,  0.13358625],\n",
+       "        [ 0.05030533, -0.27457037],\n",
+       "        [-0.30552283,  0.12318309],\n",
+       "        [ 0.03198256,  0.00709269],\n",
+       "        [-0.23476459, -0.06529374],\n",
+       "        [-0.9488298 ,  0.25177129],\n",
+       "        [-0.40068151,  0.07099387],\n",
+       "        [-0.14094749, -0.28401897],\n",
+       "        [ 0.00249681, -0.00145674],\n",
+       "        [ 0.19438228, -0.01001684]]), array([[-0.94229611,  0.02982517],\n",
+       "        [-0.06892539, -0.58493354],\n",
+       "        [-0.38847327,  0.0277909 ],\n",
+       "        [-0.17233487, -0.14907231],\n",
+       "        [-0.81222803, -0.53376366],\n",
+       "        [-1.26384299,  0.16073674],\n",
+       "        [-0.69344037, -0.2511363 ],\n",
+       "        [-0.34909747, -0.43797897],\n",
+       "        [-0.14856277, -0.10677768],\n",
+       "        [ 0.03806729, -0.15522386]]), array([[-0.72402378,  0.00299605],\n",
+       "        [-0.10746193, -0.5584984 ],\n",
+       "        [-0.2264908 , -0.0401891 ],\n",
+       "        [-0.3201045 , -0.23810478],\n",
+       "        [-1.25419726, -0.72882366],\n",
+       "        [-0.9590918 ,  0.09803825],\n",
+       "        [-0.89113748, -0.47244531],\n",
+       "        [-0.32266201, -0.55880999],\n",
+       "        [-0.23650561, -0.14437043],\n",
+       "        [-0.10854115, -0.25264529]]), array([[-0.34292605,  0.05533529],\n",
+       "        [ 0.00479474, -0.22896048],\n",
+       "        [ 0.0409274 , -0.00397033],\n",
+       "        [-0.33712626, -0.20313865],\n",
+       "        [-1.44693963, -0.52632436],\n",
+       "        [-0.62908024,  0.05772904],\n",
+       "        [-0.86707901, -0.30730053],\n",
+       "        [-0.08983331, -0.5830162 ],\n",
+       "        [-0.22397181, -0.12428935],\n",
+       "        [-0.21618396, -0.31741636]])]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "optimizer.pos_history"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.06053830594849705,\n",
+       " 0.06053830594849705,\n",
+       " 0.06053830594849705,\n",
+       " 0.06053830594849705,\n",
+       " 0.06053830594849705,\n",
+       " 0.042371607406169756,\n",
+       " 8.356164158276717e-06,\n",
+       " 8.356164158276717e-06,\n",
+       " 8.356164158276717e-06,\n",
+       " 8.356164158276717e-06]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "optimizer.cost_history"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.argmin(fx.sphere(optimizer.pos_history[np.argmin(optimizer.cost_history)]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.argmin(optimizer.cost_history)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.75208666,  0.13358625],\n",
+       "       [ 0.05030533, -0.27457037],\n",
+       "       [-0.30552283,  0.12318309],\n",
+       "       [ 0.03198256,  0.00709269],\n",
+       "       [-0.23476459, -0.06529374],\n",
+       "       [-0.9488298 ,  0.25177129],\n",
+       "       [-0.40068151,  0.07099387],\n",
+       "       [-0.14094749, -0.28401897],\n",
+       "       [ 0.00249681, -0.00145674],\n",
+       "       [ 0.19438228, -0.01001684]])"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "optimizer.pos_history[6]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.00249681, -0.00145674])"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "optimizer.pos_history[np.argmin(optimizer.cost_history)][np.argmin(fx.sphere(optimizer.pos_history[np.argmin(optimizer.cost_history)]))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.00249681, -0.00145674])"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min_cost_idx = np.argmin(optimizer.cost_history)\n",
+    "min_cost_idx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min_pos_idx = np.argmin(fx.sphere(optimizer.pos_history[min_cost_idx]))\n",
+    "min_pos_idx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.00249681, -0.00145674])"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "optimizer.pos_history[min_cost_idx][min_pos_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "optimizer.cost_history[min_cost_idx] == cost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.array_equal(optimizer.pos_history[min_cost_idx][min_pos_idx], pos)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -498,7 +1121,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.0"
+   "version": "3.7.2"
   }
  },
  "nbformat": 4,
diff --git a/tests/optimizers/test_binary.py b/tests/optimizers/test_binary.py
index 937f71a6..8a2f5a2f 100644
--- a/tests/optimizers/test_binary.py
+++ b/tests/optimizers/test_binary.py
@@ -3,6 +3,7 @@
 
 # Import modules
 import pytest
+import numpy as np
 
 # Import from pyswarms
 from pyswarms.discrete import BinaryPSO
@@ -30,7 +31,11 @@ def optimizer_reset(self, options):
         return opt
 
     def test_binary_correct_pos(self, options):
-        """ Test to check binary optimiser returns the correct position corresponding to the best cost """
+        """ Test to check binary optimiser returns the correct position
+        corresponding to the best cost """
         opt = BinaryPSO(10, 2, options=options)
         cost, pos = opt.optimize(sphere, 10)
-        assert sum(pos**2) == cost
+        # find best pos from history
+        min_cost_idx = np.argmin(opt.cost_history)
+        min_pos_idx = np.argmin(sphere(opt.pos_history[min_cost_idx]))
+        assert np.array_equal(opt.pos_history[min_cost_idx][min_pos_idx], pos)
diff --git a/tests/optimizers/test_general_optimizer.py b/tests/optimizers/test_general_optimizer.py
index 1b4c1568..20a4bef9 100644
--- a/tests/optimizers/test_general_optimizer.py
+++ b/tests/optimizers/test_general_optimizer.py
@@ -99,5 +99,7 @@ def test_obj_incorrect_kwargs(self, obj_with_args, optimizer):
     def test_general_correct_pos(self, options, optimizer):
         """ Test to check general optimiser returns the correct position corresponding to the best cost """
         cost, pos = optimizer.optimize(sphere, iters=5)
-        print(cost, '---', pos)
-        assert sum(pos ** 2) == cost
+        # find best pos from history
+        min_cost_idx = np.argmin(optimizer.cost_history)
+        min_pos_idx = np.argmin(sphere(optimizer.pos_history[min_cost_idx]))
+        assert np.array_equal(optimizer.pos_history[min_cost_idx][min_pos_idx], pos)
diff --git a/tests/optimizers/test_global_best.py b/tests/optimizers/test_global_best.py
index 401398b9..1f32fc67 100644
--- a/tests/optimizers/test_global_best.py
+++ b/tests/optimizers/test_global_best.py
@@ -3,6 +3,7 @@
 
 # Import modules
 import pytest
+import numpy as np
 
 # Import from pyswarms
 from pyswarms.single import GlobalBestPSO
@@ -33,4 +34,7 @@ def test_global_correct_pos(self, options):
         """ Test to check global optimiser returns the correct position corresponding to the best cost """
         opt = GlobalBestPSO(n_particles=10, dimensions=2, options=options)
         cost, pos = opt.optimize(sphere, iters=5)
-        assert sum(pos ** 2) == cost
+        # find best pos from history
+        min_cost_idx = np.argmin(opt.cost_history)
+        min_pos_idx = np.argmin(sphere(opt.pos_history[min_cost_idx]))
+        assert np.array_equal(opt.pos_history[min_cost_idx][min_pos_idx], pos)
diff --git a/tests/optimizers/test_local_best.py b/tests/optimizers/test_local_best.py
index 945ce3e6..3650e697 100644
--- a/tests/optimizers/test_local_best.py
+++ b/tests/optimizers/test_local_best.py
@@ -3,6 +3,7 @@
 
 # Import modules
 import pytest
+import numpy as np
 
 # Import from pyswarms
 from pyswarms.single import LocalBestPSO
@@ -33,4 +34,7 @@ def test_local_correct_pos(self, options):
         """ Test to check local optimiser returns the correct position corresponding to the best cost """
         opt = LocalBestPSO(n_particles=10, dimensions=2, options=options)
         cost, pos = opt.optimize(sphere, iters=5)
-        assert sum(pos ** 2) == cost
+        # find best pos from history
+        min_cost_idx = np.argmin(opt.cost_history)
+        min_pos_idx = np.argmin(sphere(opt.pos_history[min_cost_idx]))
+        assert np.array_equal(opt.pos_history[min_cost_idx][min_pos_idx], pos)

From 8aedc979787863e0b134c093b0d91f61f2ca868c Mon Sep 17 00:00:00 2001
From: Jake <souter_downhill@hotmail.com>
Date: Thu, 25 Apr 2019 09:47:51 +1000
Subject: [PATCH 7/7] Revert notebook changes

---
 examples/basic_optimization.ipynb | 667 +-----------------------------
 1 file changed, 22 insertions(+), 645 deletions(-)

diff --git a/examples/basic_optimization.ipynb b/examples/basic_optimization.ipynb
index 46698191..1e395c21 100644
--- a/examples/basic_optimization.ipynb
+++ b/examples/basic_optimization.ipynb
@@ -28,8 +28,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on Python version: 3.7.2 (default, Dec 29 2018, 06:19:36) \n",
-      "[GCC 7.3.0]\n"
+      "Running on Python version: 3.6.3 |Anaconda custom (64-bit)| (default, Oct 13 2017, 12:02:49) \n",
+      "[GCC 7.2.0]\n"
      ]
     }
    ],
@@ -79,36 +79,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2019-04-24 18:23:08,556 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}\n",
-      "\n",
-      "pyswarms.single.global_best:   0%|          |0/10\u001b[A\n",
-      "pyswarms.single.global_best:   0%|          |0/10, best_cost=0.0605\u001b[A\n",
-      "pyswarms.single.global_best:   0%|          |0/10, best_cost=0.0605\u001b[A\n",
-      "pyswarms.single.global_best:   0%|          |0/10, best_cost=0.0605\u001b[A\n",
-      "pyswarms.single.global_best:   0%|          |0/10, best_cost=0.0605\u001b[A\n",
-      "pyswarms.single.global_best:   0%|          |0/10, best_cost=0.0605\u001b[A\n",
-      "pyswarms.single.global_best:   0%|          |0/10, best_cost=0.0424\u001b[A\n",
-      "pyswarms.single.global_best:   0%|          |0/10, best_cost=8.36e-06\u001b[A\n",
-      "pyswarms.single.global_best:   0%|          |0/10, best_cost=8.36e-06\u001b[A\n",
-      "pyswarms.single.global_best:  80%|████████  |8/10, best_cost=8.36e-06\u001b[A\n",
-      "pyswarms.single.global_best:  80%|████████  |8/10, best_cost=8.36e-06\u001b[A\n",
-      "pyswarms.single.global_best:  80%|████████  |8/10, best_cost=8.36e-06\u001b[A\n",
-      "pyswarms.single.global_best: 100%|██████████|10/10, best_cost=8.36e-06\u001b[A2019-04-24 18:23:08,708 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 8.356164158276717e-06, best pos: [ 0.00249681 -0.00145674]\n"
+      "INFO:pyswarms.single.global_best:Iteration 1/1000, cost: 0.11075768527574707\n",
+      "INFO:pyswarms.single.global_best:Iteration 101/1000, cost: 7.521863508083004e-08\n",
+      "INFO:pyswarms.single.global_best:Iteration 201/1000, cost: 2.8159915186067273e-11\n",
+      "INFO:pyswarms.single.global_best:Iteration 301/1000, cost: 8.794923638889175e-17\n",
+      "INFO:pyswarms.single.global_best:Iteration 401/1000, cost: 1.4699516547190895e-21\n",
+      "INFO:pyswarms.single.global_best:Iteration 501/1000, cost: 5.111264897313781e-23\n",
+      "INFO:pyswarms.single.global_best:Iteration 601/1000, cost: 8.329697430155943e-27\n",
+      "INFO:pyswarms.single.global_best:Iteration 701/1000, cost: 1.662161785541961e-30\n",
+      "INFO:pyswarms.single.global_best:Iteration 801/1000, cost: 6.140424420222279e-34\n",
+      "INFO:pyswarms.single.global_best:Iteration 901/1000, cost: 2.0523902169204634e-39\n",
+      "INFO:pyswarms.single.global_best:================================\n",
+      "Optimization finished!\n",
+      "Final cost: 0.0000\n",
+      "Best value: [-2.431421462417008e-22, -9.502018378214418e-23]\n",
+      "\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 52 ms, sys: 0 ns, total: 52 ms\n",
-      "Wall time: 157 ms\n"
+      "CPU times: user 144 ms, sys: 14.8 ms, total: 159 ms\n",
+      "Wall time: 151 ms\n"
      ]
     }
    ],
@@ -121,632 +121,9 @@
     "optimizer = ps.single.GlobalBestPSO(n_particles=10, dimensions=2, options=options)\n",
     "\n",
     "# Perform optimization\n",
-    "cost, pos = optimizer.optimize(fx.sphere, iters=10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "z=np.array(optimizer.cost_history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8.356164158276717e-06"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "z[z.argmin()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AxisError",
-     "evalue": "axis 1 is out of bounds for array of dimension 1",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAxisError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-18-581cfa7918b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msphere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pyswarms/utils/functions/single_obj.py\u001b[0m in \u001b[0;36msphere\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    715\u001b[0m         \u001b[0mcomputed\u001b[0m \u001b[0mcost\u001b[0m \u001b[0mof\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_particles\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    716\u001b[0m     \"\"\"\n\u001b[0;32m--> 717\u001b[0;31m     \u001b[0mj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m2.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    718\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    719\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_sum\u001b[0;34m(a, axis, dtype, out, keepdims, initial)\u001b[0m\n\u001b[1;32m     34\u001b[0m def _sum(a, axis=None, dtype=None, out=None, keepdims=False,\n\u001b[1;32m     35\u001b[0m          initial=_NoValue):\n\u001b[0;32m---> 36\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mumr_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m def _prod(a, axis=None, dtype=None, out=None, keepdims=False,\n",
-      "\u001b[0;31mAxisError\u001b[0m: axis 1 is out of bounds for array of dimension 1"
-     ]
-    }
-   ],
-   "source": [
-    "fx.sphere(pos.T)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.00249681, -0.00145674])"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pos.T"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([6.23406772e-06, 2.12209644e-06])"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(pos.reshape(-1, 1) ** 2).sum(axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([6.23406772e-06, 2.12209644e-06])"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fx.sphere(pos.reshape(-1, 1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.00249681, -0.00145674])"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pos"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8.356164158276717e-06"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cost"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['ToHistory',\n",
-       " '__abstractmethods__',\n",
-       " '__class__',\n",
-       " '__delattr__',\n",
-       " '__dict__',\n",
-       " '__dir__',\n",
-       " '__doc__',\n",
-       " '__eq__',\n",
-       " '__format__',\n",
-       " '__ge__',\n",
-       " '__getattribute__',\n",
-       " '__gt__',\n",
-       " '__hash__',\n",
-       " '__init__',\n",
-       " '__init_subclass__',\n",
-       " '__le__',\n",
-       " '__lt__',\n",
-       " '__module__',\n",
-       " '__ne__',\n",
-       " '__new__',\n",
-       " '__reduce__',\n",
-       " '__reduce_ex__',\n",
-       " '__repr__',\n",
-       " '__setattr__',\n",
-       " '__sizeof__',\n",
-       " '__slots__',\n",
-       " '__str__',\n",
-       " '__subclasshook__',\n",
-       " '__weakref__',\n",
-       " '_abc_impl',\n",
-       " '_populate_history',\n",
-       " 'bh',\n",
-       " 'bounds',\n",
-       " 'center',\n",
-       " 'cost_history',\n",
-       " 'dimensions',\n",
-       " 'ftol',\n",
-       " 'init_pos',\n",
-       " 'mean_neighbor_history',\n",
-       " 'mean_pbest_history',\n",
-       " 'n_particles',\n",
-       " 'name',\n",
-       " 'optimize',\n",
-       " 'options',\n",
-       " 'pos_history',\n",
-       " 'rep',\n",
-       " 'reset',\n",
-       " 'swarm',\n",
-       " 'swarm_size',\n",
-       " 'top',\n",
-       " 'velocity_clamp',\n",
-       " 'velocity_history',\n",
-       " 'vh']"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(optimizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[array([[0.90124935, 0.17491093],\n",
-       "        [0.80474654, 0.92141545],\n",
-       "        [0.82737053, 0.08275101],\n",
-       "        [0.43055521, 0.16029267],\n",
-       "        [0.7748099 , 0.83678948],\n",
-       "        [0.21088674, 0.49822131],\n",
-       "        [0.35918509, 0.7553067 ],\n",
-       "        [0.10156838, 0.22410303],\n",
-       "        [0.58793823, 0.30151857],\n",
-       "        [0.17084816, 0.75461516]]), array([[1.43727288, 0.58528291],\n",
-       "        [0.93435275, 1.42905162],\n",
-       "        [1.13856742, 0.33461069],\n",
-       "        [0.72586063, 0.32709616],\n",
-       "        [1.5094666 , 1.18965563],\n",
-       "        [0.90403286, 0.49314525],\n",
-       "        [0.67649282, 1.00266235],\n",
-       "        [0.5043016 , 0.67703065],\n",
-       "        [0.75097075, 0.41294503],\n",
-       "        [0.4881155 , 0.80689386]]), array([[1.65419275, 0.77490286],\n",
-       "        [0.77212441, 1.42704143],\n",
-       "        [0.98198008, 0.50241268],\n",
-       "        [0.79360331, 0.46185756],\n",
-       "        [1.7394455 , 1.39718321],\n",
-       "        [1.34641821, 0.40948887],\n",
-       "        [0.9158877 , 1.14369282],\n",
-       "        [0.67208624, 0.96319035],\n",
-       "        [0.70009302, 0.44887179],\n",
-       "        [0.57512907, 0.69416884]]), array([[1.21344488, 0.61992065],\n",
-       "        [0.45232105, 1.17197039],\n",
-       "        [0.66917004, 0.41051598],\n",
-       "        [0.68143011, 0.47378703],\n",
-       "        [1.65180215, 1.39952274],\n",
-       "        [1.41919708, 0.35495412],\n",
-       "        [0.82031158, 1.13631049],\n",
-       "        [0.63125209, 0.98728533],\n",
-       "        [0.55975409, 0.38715874],\n",
-       "        [0.53502928, 0.52391748]]), array([[0.5447976 , 0.46467561],\n",
-       "        [0.33144311, 0.71443207],\n",
-       "        [0.30345822, 0.31535249],\n",
-       "        [0.49027688, 0.35467089],\n",
-       "        [1.22966712, 0.99730451],\n",
-       "        [0.7226531 , 0.29773023],\n",
-       "        [0.39199028, 0.80877984],\n",
-       "        [0.54763719, 0.59748084],\n",
-       "        [0.3568341 , 0.24559238],\n",
-       "        [0.43745753, 0.33405719]]), array([[-0.18582241,  0.30254416],\n",
-       "        [ 0.18521129,  0.19873369],\n",
-       "        [-0.02654187,  0.2203464 ],\n",
-       "        [ 0.25351304,  0.17935622],\n",
-       "        [ 0.47076599,  0.47001543],\n",
-       "        [-0.18585261,  0.29629828],\n",
-       "        [-0.05865941,  0.42130528],\n",
-       "        [ 0.18595814,  0.13376584],\n",
-       "        [ 0.17034079,  0.11556653],\n",
-       "        [ 0.332588  ,  0.15416082]]), array([[-0.75208666,  0.13358625],\n",
-       "        [ 0.05030533, -0.27457037],\n",
-       "        [-0.30552283,  0.12318309],\n",
-       "        [ 0.03198256,  0.00709269],\n",
-       "        [-0.23476459, -0.06529374],\n",
-       "        [-0.9488298 ,  0.25177129],\n",
-       "        [-0.40068151,  0.07099387],\n",
-       "        [-0.14094749, -0.28401897],\n",
-       "        [ 0.00249681, -0.00145674],\n",
-       "        [ 0.19438228, -0.01001684]]), array([[-0.94229611,  0.02982517],\n",
-       "        [-0.06892539, -0.58493354],\n",
-       "        [-0.38847327,  0.0277909 ],\n",
-       "        [-0.17233487, -0.14907231],\n",
-       "        [-0.81222803, -0.53376366],\n",
-       "        [-1.26384299,  0.16073674],\n",
-       "        [-0.69344037, -0.2511363 ],\n",
-       "        [-0.34909747, -0.43797897],\n",
-       "        [-0.14856277, -0.10677768],\n",
-       "        [ 0.03806729, -0.15522386]]), array([[-0.72402378,  0.00299605],\n",
-       "        [-0.10746193, -0.5584984 ],\n",
-       "        [-0.2264908 , -0.0401891 ],\n",
-       "        [-0.3201045 , -0.23810478],\n",
-       "        [-1.25419726, -0.72882366],\n",
-       "        [-0.9590918 ,  0.09803825],\n",
-       "        [-0.89113748, -0.47244531],\n",
-       "        [-0.32266201, -0.55880999],\n",
-       "        [-0.23650561, -0.14437043],\n",
-       "        [-0.10854115, -0.25264529]]), array([[-0.34292605,  0.05533529],\n",
-       "        [ 0.00479474, -0.22896048],\n",
-       "        [ 0.0409274 , -0.00397033],\n",
-       "        [-0.33712626, -0.20313865],\n",
-       "        [-1.44693963, -0.52632436],\n",
-       "        [-0.62908024,  0.05772904],\n",
-       "        [-0.86707901, -0.30730053],\n",
-       "        [-0.08983331, -0.5830162 ],\n",
-       "        [-0.22397181, -0.12428935],\n",
-       "        [-0.21618396, -0.31741636]])]"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "optimizer.pos_history"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0.06053830594849705,\n",
-       " 0.06053830594849705,\n",
-       " 0.06053830594849705,\n",
-       " 0.06053830594849705,\n",
-       " 0.06053830594849705,\n",
-       " 0.042371607406169756,\n",
-       " 8.356164158276717e-06,\n",
-       " 8.356164158276717e-06,\n",
-       " 8.356164158276717e-06,\n",
-       " 8.356164158276717e-06]"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "optimizer.cost_history"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.argmin(fx.sphere(optimizer.pos_history[np.argmin(optimizer.cost_history)]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.argmin(optimizer.cost_history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[-0.75208666,  0.13358625],\n",
-       "       [ 0.05030533, -0.27457037],\n",
-       "       [-0.30552283,  0.12318309],\n",
-       "       [ 0.03198256,  0.00709269],\n",
-       "       [-0.23476459, -0.06529374],\n",
-       "       [-0.9488298 ,  0.25177129],\n",
-       "       [-0.40068151,  0.07099387],\n",
-       "       [-0.14094749, -0.28401897],\n",
-       "       [ 0.00249681, -0.00145674],\n",
-       "       [ 0.19438228, -0.01001684]])"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "optimizer.pos_history[6]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.00249681, -0.00145674])"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "optimizer.pos_history[np.argmin(optimizer.cost_history)][np.argmin(fx.sphere(optimizer.pos_history[np.argmin(optimizer.cost_history)]))]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.00249681, -0.00145674])"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pos"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "min_cost_idx = np.argmin(optimizer.cost_history)\n",
-    "min_cost_idx"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "min_pos_idx = np.argmin(fx.sphere(optimizer.pos_history[min_cost_idx]))\n",
-    "min_pos_idx"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.00249681, -0.00145674])"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "optimizer.pos_history[min_cost_idx][min_pos_idx]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "optimizer.cost_history[min_cost_idx] == cost"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.array_equal(optimizer.pos_history[min_cost_idx][min_pos_idx], pos)"
+    "cost, pos = optimizer.optimize(fx.sphere, print_step=100, iters=1000, verbose=3)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1121,7 +498,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.6.0"
   }
  },
  "nbformat": 4,