def binary_reset():
"""Returns a BinaryPSO instance that has been run and reset to check
default value"""
pso = BinaryPSO(10, 2, {"c1": 0.5, "c2": 0.7, "w": 0.5, "k": 2, "p": 2})
pso.optimize(sphere_func, 10, verbose=0)
pso.reset()
return pso
def binary_reset():
"""Returns a BinaryPSO instance that has been run and reset to check
default value"""
pso = BinaryPSO(10, 2, {'c1': 0.5, 'c2': 0.7, 'w': 0.5, 'k': 2, 'p': 2})
pso.optimize(sphere_func, 10, verbose=0)
pso.reset()
return pso
def test_reset_best_pos_none(self):
"""Tests if best pos is set to NoneType when reset() is called"""
# Perform a simple optimization
optimizer = BinaryPSO(5,2, options=self.options)
optimizer.optimize(sphere_func, 100, verbose=0)
optimizer.reset()
self.assertIsNone(optimizer.best_pos)
def test_reset_best_cost_inf(self):
"""Tests if best cost is set to infinity when reset() is called"""
# Perform a simple optimization
optimizer = BinaryPSO(5,2, options=self.options)
optimizer.optimize(sphere_func, 100, verbose=0)
optimizer.reset()
self.assertEqual(optimizer.best_cost, np.inf)
def test_reset_best_pos_none(self):
"""Tests if best pos is set to NoneType when reset() is called"""
# Perform a simple optimization
optimizer = BinaryPSO(5, 2, options=self.options)
optimizer.optimize(sphere_func, 100, verbose=0)
optimizer.reset()
self.assertIsNone(optimizer.best_pos)
def test_reset_best_cost_inf(self):
"""Tests if best cost is set to infinity when reset() is called"""
# Perform a simple optimization
optimizer = BinaryPSO(5, 2, options=self.options)
optimizer.optimize(sphere_func, 100, verbose=0)
optimizer.reset()
self.assertEqual(optimizer.best_cost, np.inf)
def test_reset(self):
"""Tests if the reset method resets the attributes required"""
# Perform a simple optimization
optimizer = BinaryPSO(5, 2, options=self.options)
optimizer.optimize(sphere_func, 100, verbose=0)
# Reset the attributes
optimizer.reset()
# Perform testing
self.assertEqual(optimizer.best_cost, np.inf)
self.assertIsNone(optimizer.best_pos)
def test_binary_correct_pos(self, options):
"""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)
# 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)
def optimizer_reset(self, options):
opt = BinaryPSO(10, 2, options=options)
opt.optimize(sphere, 10)
opt.reset()
return opt
def optimizer_history(self, options):
opt = BinaryPSO(10, 2, options=options)
opt.optimize(sphere, 1000)
return opt
def binary_history():
"""Returns a BinaryPSO instance run for 1000 iterations for checking
history"""
pso = BinaryPSO(10, 2, {'c1': 0.5, 'c2': 0.7, 'w': 0.5, 'k': 2, 'p': 2})
pso.optimize(sphere_func, 1000, verbose=0)
return pso
def binary_history():
"""Returns a BinaryPSO instance run for 1000 iterations for checking
history"""
pso = BinaryPSO(10, 2, {"c1": 0.5, "c2": 0.7, "w": 0.5, "k": 2, "p": 2})
pso.optimize(sphere_func, 1000, verbose=0)
return pso