def test_setup_number_of_weights(self):
"Test setup weight with number of weights"
for n in range(1, 20):
p = IMP.Particle(self.m)
w = Weight.setup_particle(p, n)
self.assertTrue(Weight.get_is_setup(p))
self.assertEqual(w.get_number_of_weights(), n)
for k in range(n):
self.assertAlmostEqual(w.get_weight(k), 1.0 / n, delta=1e-6)
def test_setup_weights(self):
"Test setup weight with initial values"
for n in range(1, 20):
p = IMP.Particle(self.m)
ws = np.random.uniform(size=n)
w = Weight.setup_particle(p, ws)
self.assertTrue(Weight.get_is_setup(p))
self.assertEqual(w.get_number_of_weights(), n)
self.assertSequenceAlmostEqual(
w.get_weights(),
IMP.algebra.get_projected(
w.get_unit_simplex(), IMP.algebra.VectorKD(ws)
),
)
def test_set_weights_zero(self):
p = IMP.Particle(self.m)
n = 5
w = Weight.setup_particle(p, n)
ws = np.zeros(n)
w.set_weights(ws)
for k in range(n):
self.assertAlmostEqual(w.get_weight(k), 1.0 / n, delta=1e-6)
def test_get_unit_simplex(self):
for n in range(1, 20):
p = IMP.Particle(self.m)
ws = np.random.uniform(size=n)
w = Weight.setup_particle(p, ws)
s = w.get_unit_simplex()
self.assertIsInstance(s, IMP.algebra.UnitSimplexKD)
self.assertEqual(s.get_dimension(), n)
def test_change_nweights_for_optimized_raises_error(self):
for n in range(1, 20):
w = Weight.setup_particle(IMP.Particle(self.m), n)
self.assertFalse(w.get_weights_are_optimized())
w.set_number_of_weights(n + 1)
w.set_number_of_weights(n)
w.set_weights_are_optimized(True)
self.assertRaisesUsageException(w.set_number_of_weights, n + 1)
def test_Setup_iw(self):
"Test weight_initial_values"
w = Weight.setup_particle(IMP.Particle(self.m))
w.add_weight()
for n in range(19):
for k in range(n + 1):
self.assertEqual(w.get_weight(k), 1.0 / (n + 1))
w.add_weight()
def test_setup_empty_add_weight(self):
"Test setup weight as empty and add weights"
w = Weight.setup_particle(IMP.Particle(self.m))
for n in range(1, 20):
w.add_weight()
self.assertEqual(w.get_number_of_weights(), n)
self.assertSequenceAlmostEqual(
list(w.get_weights()), [1] + [0] * (n - 1)
)
def test_Setup_number(self):
"Test weights_initial_values"
w = Weight.setup_particle(IMP.Particle(self.m))
w.add_weight()
for n in range(19):
ws = w.get_weights()
self.assertEqual(len(ws), n + 1)
for k in range(n + 1):
self.assertEqual(ws[k], 1.0 / (n + 1))
w.add_weight()
def test_Setup_size(self):
"Test weights_size"
w = Weight.setup_particle(IMP.Particle(self.m))
w.add_weight()
for n in range(19):
ws = w.get_weights()
ns = w.get_number_of_states()
self.assertEqual(n + 1, ns)
self.assertEqual(len(ws), ns)
w.add_weight()
def test_add_weight(self):
"Test add_weight"
w = Weight.setup_particle(IMP.Particle(self.m), 1)
for n in range(2, 20):
ws = list(w.get_weights())
wi = np.random.uniform()
w.add_weight(wi)
ws.append(wi)
self.assertEqual(w.get_number_of_weights(), n)
ws = list(IMP.algebra.get_projected(w.get_unit_simplex(), ws))
self.assertSequenceAlmostEqual(list(w.get_weights()), ws)
def test_add_to_weights_derivatives(self):
for n in range(1, 20):
w = Weight.setup_particle(IMP.Particle(self.m), n)
ws = np.random.uniform(size=n)
ws /= np.sum(ws)
w.set_weights(ws)
dw = np.random.normal(size=n)
w.add_to_weights_derivatives(dw, IMP.DerivativeAccumulator())
dw2 = w.get_weights_derivatives()
dw2 = [dw2[i] for i in range(n)]
self.assertSequenceAlmostEqual(list(dw), dw2, delta=1e-6)
def test_set_weights(self):
for n in range(1, 20):
p = IMP.Particle(self.m)
w = Weight.setup_particle(p, n)
ws = np.random.uniform(size=n)
w.set_weights(ws)
self.assertSequenceAlmostEqual(
w.get_weights(),
IMP.algebra.get_projected(
w.get_unit_simplex(), IMP.algebra.VectorKD(ws)
),
)
def test_assign_values(self):
"Test weights_set"
w = Weight.setup_particle(IMP.Particle(self.m))
w.add_weight()
ws = [1.0]
for n in range(19):
w.set_weights(ws)
ws2 = w.get_weights()
for k in range(n + 1):
self.assertEqual(ws[k], ws2[k])
ws.append(1.0)
w.add_weight()
def test_add_to_weight_derivative(self):
for n in range(1, 20):
w = Weight.setup_particle(IMP.Particle(self.m), n)
ws = np.random.uniform(size=n)
ws /= np.sum(ws)
w.set_weights(ws)
for k in range(0, n):
dwk = np.random.normal()
w.add_to_weight_derivative(k, dwk, IMP.DerivativeAccumulator())
self.assertAlmostEqual(
w.get_weight_derivative(k), dwk, delta=1e-6
)
def test_set_optimized(self):
"Test weights_optimized"
w = Weight.setup_particle(IMP.Particle(self.m))
w.add_weight()
for n in range(19):
w.set_weights_are_optimized(True)
for k in range(n + 1):
b = w.get_is_optimized(w.get_weight_key(k))
self.assertEqual(b, True)
w.set_weights_are_optimized(False)
for k in range(n + 1):
b = w.get_is_optimized(w.get_weight_key(k))
self.assertEqual(b, False)
w.add_weight()
def setUp(self):
IMP.test.TestCase.setUp(self)
# IMP.set_log_level(IMP.MEMORY)
IMP.set_log_level(0)
self.m = IMP.kernel.Model()
self.w = Weight.setup_particle(IMP.kernel.Particle(self.m))
self.w.set_weights_are_optimized(True)
self.w.add_weight()
self.w.add_weight()
self.wm = WeightMover(self.w, 0.1)
self.mc = IMP.core.MonteCarlo(self.m)
self.mc.set_return_best(False)
self.mc.set_kt(1.0)
self.mc.add_mover(self.wm)
def setUp(self):
IMP.test.TestCase.setUp(self)
# IMP.set_log_level(IMP.MEMORY)
IMP.set_log_level(0)
self.m = IMP.kernel.Model()
self.w = Weight.setup_particle(IMP.kernel.Particle(self.m))
self.w.set_weights_are_optimized(True)
self.w.add_weight()
self.w.add_weight()
self.wm = WeightMover(self.w, 0.1)
self.mc = IMP.core.MonteCarlo(self.m)
self.mc.set_return_best(False)
self.mc.set_kt(1.0)
self.mc.add_mover(self.wm)
def test_set_nweights_lazy(self):
for n in range(1, 20):
w = Weight.setup_particle(IMP.Particle(self.m), n)
ws = IMP.algebra.get_random_vector_on(w.get_unit_simplex())
w.set_weights(ws)
w.set_number_of_weights_lazy(n + 1)
self.assertEqual(w.get_number_of_weights(), n + 1)
self.assertSequenceAlmostEqual(
list(w.get_weights()), list(ws) + [0]
)
w.set_number_of_weights_lazy(n)
self.assertEqual(w.get_number_of_weights(), n)
self.assertSequenceAlmostEqual(list(w.get_weights()), list(ws))
def test_set_optimized(self):
"Test weights_optimized"
for n in range(1, 20):
w = Weight.setup_particle(IMP.Particle(self.m), n)
self.assertFalse(w.get_weights_are_optimized())
w.set_weights_are_optimized(True)
for k in range(n):
b = w.get_is_optimized(w.get_weight_key(k))
self.assertEqual(b, True)
self.assertTrue(w.get_weights_are_optimized())
w.set_weights_are_optimized(False)
for k in range(n):
b = w.get_is_optimized(w.get_weight_key(k))
self.assertEqual(b, False)
self.assertFalse(w.get_weights_are_optimized())
def test_set_weight_lazy(self):
for n in range(1, 20):
p = IMP.Particle(self.m)
w = Weight.setup_particle(p, n)
ws = np.random.uniform(size=n)
for k in range(n):
w.set_weight_lazy(k, ws[k])
self.assertAlmostEqual(w.get_weight(k), ws[k], delta=1e-6)
self.m.update()
self.assertSequenceAlmostEqual(
w.get_weights(),
IMP.algebra.get_projected(
w.get_unit_simplex(), IMP.algebra.VectorKD(ws)
),
)
def test_get_weight_keys(self):
for n in range(1, 20):
w = Weight.setup_particle(IMP.Particle(self.m), n)
wks = [w.get_weight_key(i) for i in range(n)]
self.assertSequenceEqual(wks, list(w.get_weight_keys()))
def setUp(self):
IMP.test.TestCase.setUp(self)
# IMP.set_log_level(IMP.MEMORY)
IMP.set_log_level(0)
self.m = IMP.Model()
self.w = Weight.setup_particle(IMP.Particle(self.m))
