def lbest_reset():
"""Returns a LocalBestPSO instance that has been run and reset to check
default value"""
pso = LocalBestPSO(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 lbest_reset():
"""Returns a LocalBestPSO instance that has been run and reset to check
default value"""
pso = LocalBestPSO(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 = LocalBestPSO(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 = LocalBestPSO(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 = LocalBestPSO(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 = LocalBestPSO(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 = LocalBestPSO(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 optimizer_reset(self, options):
opt = LocalBestPSO(10, 2, options)
opt.optimize(sphere, 10)
opt.reset()
return opt