def test_profit_q():
E = ell(r, np.array([0., 0.]))
P = profit_q_oracle(params, a, v)
_, _, ell_info = cutting_plane_q(P, E, 0.)
assert ell_info.feasible
assert ell_info.num_iters == 28
return ell_info.num_iters
def run_firfilter(no_trick, duration=0.000001):
"""[summary]
Arguments:
no_trick ([type]): [description]
Keyword Arguments:
duration (float): [description] (default: {0.000001})
Raises:
Exception: [description]
"""
h0 = np.zeros(n) # initial x0
E = ell(10., h0)
E.no_defer_trick = no_trick
P = my_oracle()
prob1 = Problem(E, P)
prob1.solve(100.)
time.sleep(duration)
print('Problem status:', prob1.status)
if prob1.status != 'optimal':
raise Exception('ELL Error')
print("optimal value", prob1.optim_value)
assert prob1.status == 'optimal'
return prob1._solver_stats.num_iters
def test_case_infeasible2():
"""[summary]
"""
x0 = np.array([1., 1.]) # initial x0
E = ell(10., x0)
P = my_quasicvx_oracle
_, _, ell_info = cutting_plane_dc(P, E, -100) # wrong initial best-so-far
assert not ell_info.feasible
def test_case_feasible():
"""[summary]
"""
x0 = np.array([0., 0.]) # initial x0
E = ell(10., x0)
P = my_oracle
_, _, ell_info = cutting_plane_dc(P, E, float('-inf'))
assert ell_info.feasible
def lsq_corr_core(Y, n, Q):
x = np.zeros(n) # cannot all zeros
x[0] = 1.
E = ell(256., x)
P = bsearch_adaptor(Q, E)
normY = np.linalg.norm(Y, 'fro')
_, bs_info = bsearch(P, [0., normY * normY])
return P.x_best, bs_info.num_iters, bs_info.feasible
def test_profit_rb():
e1 = 0.003
e2 = 0.007
e3 = e4 = e5 = 1.
E = ell(r, np.array([0., 0.]))
P = profit_rb_oracle(params, a, v, (e1, e2, e3, e4, e5))
_, _, ell_info = cutting_plane_dc(P, E, 0.)
assert ell_info.feasible
assert ell_info.num_iters == 42
def test_case_infeasible1():
"""[summary]
"""
x0 = np.array([100., 100.]) # wrong initial guess,
E = ell(10., x0) # or ellipsoid is too small
P = my_quasicvx_oracle
_, _, ell_info = cutting_plane_dc(P, E, 0.)
assert not ell_info.feasible
assert ell_info.status == CUTStatus.nosoln # no sol'n
def test_case_infeasible():
"""[summary]
"""
x0 = np.array([100., 100.]) # wrong initial guess
E = ell(10., x0)
P = my_oracle2
ell_info = cutting_plane_feas(P, E)
assert ell_info.status == CUTStatus.nosoln # no sol'n
assert ell_info.num_iters == 1 # small
assert not ell_info.feasible
def test_case_feasible():
"""[summary]
"""
x0 = np.array([0., 0.]) # initial guess
E = ell(10., x0)
P = my_oracle2
ell_info = cutting_plane_feas(P, E)
assert ell_info.feasible
assert ell_info.status == CUTStatus.success
print(ell_info.num_iters, ell_info.status)
def test_case_feasible():
"""[summary]
"""
x0 = np.array([1., 1.]) # initial x0
E = ell(10., x0)
P = my_quasicvx_oracle
xb, fb, ell_info = cutting_plane_dc(P, E, 0.)
assert ell_info.feasible
assert fb == approx(-0.4288673396685956)
assert xb[0] == approx(0.501315956)
assert xb[1] == approx(1.650886769)
def test_optscaling():
"""[summary]
Keyword Arguments:
duration (float): [description] (default: {0.000001})
Returns:
[type]: [description]
"""
x0 = np.array([cmax, cmin])
t = cmax - cmin
E = ell(1.5 * t, x0)
dist = list(0 for _ in G)
P = optscaling_oracle(G, dist, get_cost)
_, _, ell_info = cutting_plane_dc(P, E, float('inf'))
# fmt = '{:f} {} {} {}'
# print(np.exp(xb))
# print(fmt.format(np.exp(fb), niter, feasible, status))
assert ell_info.feasible
def mle_corr_core(Y: Arr, n: int, P: Callable):
"""[summary]
Arguments:
Y (Arr): [description]
n (int): [description]
P (Callable): [description]
Returns:
[type]: [description]
"""
x = np.zeros(n)
x[0] = 1.
E = ell(50., x)
# E.use_parallel_cut = False
# options = Options()
# options.max_it = 2000
# options.tol = 1e-8
xb, _, ell_info = cutting_plane_dc(P, E, float('inf'))
# print(num_iters, feasible, status)
return xb, ell_info.num_iters, ell_info.feasible
def lsq_corr_core2(Y: Arr, n: int, P: Callable):
"""[summary]
Arguments:
Y (Arr): [description]
n (int): [description]
P (Callable): [description]
Returns:
[type]: [description]
"""
normY = np.linalg.norm(Y, 'fro')
normY2 = 32 * normY * normY
val = 256 * np.ones(n + 1)
val[-1] = normY2 * normY2
x = np.zeros(n + 1) # cannot all zeros
x[0] = 1.
x[-1] = normY2 / 2
E = ell(val, x)
xb, _, ell_info = cutting_plane_dc(P, E, float('inf'))
return xb[:-1], ell_info.num_iters, ell_info.feasible
def run_lmi(oracle, duration=0.000001):
"""[summary]
Arguments:
oracle ([type]): [description]
Keyword Arguments:
duration (float): [description] (default: {0.000001})
Returns:
[type]: [description]
"""
x0 = np.array([0., 0., 0.]) # initial x0
E = ell(10., x0)
P = my_oracle(oracle)
_, _, ell_info = cutting_plane_dc(P, E, float('inf'))
time.sleep(duration)
# fmt = '{:f} {} {} {}'
# print(fmt.format(fb, niter, feasible, status))
# print(xb)
assert ell_info.feasible
return ell_info.num_iters