def test_generic(args):
"""Test against the General function"""
(tol,cons,sol,test_func,low,high,shape) = args
#if shape == 0:
#x0 = np.random.uniform(0, 2, (1000, 5))
#print('here')
x0 = init_feasible(cons, low=low, high=high, shape=shape)
t0 = time.time()
res = minimize_qpso(test_func, x0, tol=tol)
t1= time.time()
converged = res.success
qpso_converged = 0
qpso_nit = res.nit
try:
np.testing.assert_array_almost_equal(sol, res.x, 3)
except:
qpso_converged = 1
# if high is None:
#x0 = np.random.uniform(0, 2, (1000, 5))
# else:
x0 = init_feasible(cons, low=low, high=high, shape=shape)
t2= time.time()
res = minimize(test_func,x0, tol=tol)
t3 = time.time()
converged = res.success
pso_converged = 0
pso_nit = res.nit
assert converged, res.message
try:
np.testing.assert_array_almost_equal(sol, res.x, 3)
except:
pso_converged = 1
return qpso_converged, qpso_nit ,t1-t0, pso_converged , pso_nit , t3-t2
def test_constrained(self):
"""Test against the following function::
y = (x0 - 1)^2 + (x1 - 2.5)^2
under the constraints::
x0 - 2.x1 + 2 >= 0
-x0 - 2.x1 + 6 >= 0
-x0 + 2.x1 + 2 >= 0
x0, x1 >= 0
"""
cons = ({'type': 'ineq', 'fun': lambda x: x[0] - 2 * x[1] + 2},
{'type': 'ineq', 'fun': lambda x: -x[0] - 2 * x[1] + 6},
{'type': 'ineq', 'fun': lambda x: -x[0] + 2 * x[1] + 2},
{'type': 'ineq', 'fun': lambda x: x[0]},
{'type': 'ineq', 'fun': lambda x: x[1]})
x0 = init_feasible(cons, low=0, high=2, shape=(1000, 2))
options = {'stable_iter': 50}
sol = np.array([1.4, 1.7])
res = minimize_qpso(lambda x: (x[0] - 1)**2 + (x[1] - 2.5)**2, x0,
constraints=cons, options=options)
converged = res.success
assert converged, res.message
np.testing.assert_array_almost_equal(sol, res.x, 3)
def test_unconstrained(self):
"""Test against the Rosenbrock function."""
x0 = np.random.uniform(0, 2, (1000, 5))
sol = np.array([1., 1., 1., 1., 1.])
res = minimize_qpso(rosen, x0)
converged = res.success
assert converged, res.message
np.testing.assert_array_almost_equal(sol, res.x, 3)
def test_rosendisc(self):
"""Test against the Rosenbrock function constrained to a disk."""
cons = ({'type': 'ineq', 'fun': lambda x: -x[0]**2 - x[1]**2 + 2}, )
x0 = init_feasible(cons, low=-1.5, high=1.5, shape=(1000, 2))
sol = np.array([1., 1.])
def rosen(x):
return (1 - x[0])**2 + 100 * (x[1] - x[0]**2)**2
res = minimize_qpso(rosen, x0)
converged = res.success
assert converged, res.message
np.testing.assert_array_almost_equal(sol, res.x, 3)
def test_ackley(self):
"""Test against the Ackley function."""
x0 = np.random.uniform(-5, 5, (1000, 2))
sol = np.array([0., 0.])
def ackley(x):
return -20 * np.exp(-.2 * np.sqrt(0.5 * (x[0]**2 + x[1]**2))) - \
np.exp(.5 * (np.cos(2*np.pi*x[0]) + np.cos(2*np.pi*x[1]))) + \
np.e + 20
res = minimize_qpso(ackley, x0)
converged = res.success
assert converged, res.message
np.testing.assert_array_almost_equal(sol, res.x, 3)
def test_levi(self):
"""Test against the Levi function."""
x0 = np.random.uniform(-10, 10, (1000, 2))
sol = np.array([1., 1.])
def levi(x):
sin3x = np.sin(3*np.pi*x[0]) ** 2
sin2y = np.sin(2*np.pi*x[1]) ** 2
return sin3x + (x[0] - 1)**2 * (1 + sin3x) + \
(x[1] - 1)**2 * (1 + sin2y)
res = minimize_qpso(levi, x0)
converged = res.success
assert converged, res.message
np.testing.assert_array_almost_equal(sol, res.x, 3)
fd = open('qpso_levy.csv','a')
low = -1.5
high=1.5
shape = (1000,2)
sol = np.array([1.,1.])
print("Testing qpso with levy")
print("rosen")
fd = open('qpso_levy_rosen.csv','a')
wr = csv.writer(fd, dialect='excel')
for i in range(30):
print(i)
cons = ({'type': 'ineq', 'fun': rosen},)
x0 = init_feasible(cons, low=low, high=high, shape=shape)
res = minimize_qpso(rosen, x0, options={'levy_rate':1})
res_p = [res.fun, res.nit, res.nsit, res.status, res.success, res.x[0], res.x[1]]
wr.writerow(res_p)
fd.close()
print("rosen_def")
fd = open('qpso_levy_rosen_def.csv','a')
wr = csv.writer(fd, dialect='excel')
for i in range(30):
print(i)
cons = ({'type': 'ineq', 'fun': rosen_def},)
x0 = init_feasible(cons, low=low, high=high, shape=shape)
res = minimize_qpso(rosen_def, x0, options={'levy_rate':1})
res_p = [res.fun, res.nit, res.nsit, res.status, res.success, res.x[0], res.x[1]]
wr.writerow(res_p)
fd.close()
print("mishra")