def test_min_ntr_noble_atoms_lsr1():
for irep in xrange(10):
with numpy_random_seed(irep):
fn = Atoms(1.0, 1.0, 1, 10, np.random.normal(0, 2, 30))
hm = LSR1HessianModel(7)
wr = HessianModelWrapper(fn, hm)
minimize_objective_ntr(wr, maxiter=5125)
assert rms(fn.gradient()) < 1e-7
def test_min_ntr_noble_atoms_lsr1():
for irep in xrange(10):
with numpy_random_seed(irep):
fn = Atoms(1.0, 1.0, 1, 10, np.random.normal(0, 2, 30))
hm = LSR1HessianModel(7)
wr = HessianModelWrapper(fn, hm)
minimize_objective_ntr(wr, maxiter=5125)
assert rms(fn.gradient()) < 1e-7
def test_multi_gauss_diis():
completed = 0
for irep in xrange(100):
with numpy_random_seed(irep):
reference, xs = get_problem(4)
converged_a, counter_a, amps_a = run_mult_gauss_diis(reference, xs, 500, False)
converged_b, counter_b, amps_b = run_mult_gauss_diis(reference, xs, 200, True)
assert counter_a >= counter_b
assert converged_b
if converged_a and converged_b:
completed += 1
assert abs(amps_a - amps_b).max() < 1e-10
assert completed > 50
def test_multi_gauss_diis():
completed = 0
for irep in xrange(100):
with numpy_random_seed(irep):
reference, xs = get_problem(4)
converged_a, counter_a, amps_a = run_mult_gauss_diis(
reference, xs, 500, False)
converged_b, counter_b, amps_b = run_mult_gauss_diis(
reference, xs, 200, True)
assert counter_a >= counter_b
assert converged_b
if converged_a and converged_b:
completed += 1
assert abs(amps_a - amps_b).max() < 1e-10
assert completed > 50
def test_compare_sr1_lsr1_constant():
n = 5
for irep1 in xrange(100):
with numpy_random_seed(irep1):
A = np.random.normal(0, 1, (n, n))
A = (A + A.T)
hm = SR1HessianModel(eps_skip=0.0)
lhm = LSR1HessianModel(n, eps_skip=0.0)
for i in xrange(3):
delta_x = np.zeros(n)
delta_x[i] = 1.0
delta_g = np.dot(A, delta_x)
hm.feed(delta_x, delta_g)
lhm.feed(delta_x, delta_g)
for irep2 in xrange(100):
v = np.random.normal(0, 1, n)
o1 = hm.dot_hessian(v)
o2 = lhm.dot_hessian(v)
assert abs(o1 - o2).max() < 1e-8
def test_compare_sr1_lsr1_constant():
n = 5
for irep1 in xrange(100):
with numpy_random_seed(irep1):
A = np.random.normal(0, 1, (n, n))
A = (A + A.T)
hm = SR1HessianModel(eps_skip=0.0)
lhm = LSR1HessianModel(n, eps_skip=0.0)
for i in xrange(3):
delta_x = np.zeros(n)
delta_x[i] = 1.0
delta_g = np.dot(A, delta_x)
hm.feed(delta_x, delta_g)
lhm.feed(delta_x, delta_g)
for irep2 in xrange(100):
v = np.random.normal(0, 1, n)
o1 = hm.dot_hessian(v)
o2 = lhm.dot_hessian(v)
assert abs(o1 - o2).max() < 1e-8
def test_gradient():
fn = Atoms(1.0, 1.0, 6, 10, np.zeros(30, float))
with numpy_random_seed(2):
xs = [np.random.normal(0, 7, 30) for ix in xrange(100)]
fn.test_gradient(xs, nrep=4)
def test_gradient():
fn = Atoms(1.0, 1.0, 6, 10, np.zeros(30, float))
with numpy_random_seed(2):
xs = [np.random.normal(0, 7, 30) for ix in xrange(100)]
fn.test_gradient(xs, nrep=4)
