def test_monitor():
exponential = lambda u, t: diff(u, t) - u
init_val_ex = IVP(t_0=0.0, u_0=1.0)
solution_ex, _ = solve(ode=exponential, condition=init_val_ex,
t_min=0.0, t_max=2.0,
max_epochs=3,
monitor=Monitor1D(t_min=0.0, t_max=2.0, check_every=1))
with warns(DeprecationWarning):
solution_ex, _ = solve(ode=exponential, condition=init_val_ex,
t_min=0.0, t_max=2.0,
max_epochs=3,
monitor=Monitor(t_min=0.0, t_max=2.0, check_every=1))
def test_monitor():
exponential = lambda x, t: diff(x, t) - x
init_val_ex = IVP(t_0=0.0, x_0=1.0)
solution_ex, _ = solve(ode=exponential, condition=init_val_ex,
t_min=0.0, t_max=2.0,
max_epochs=3,
monitor=Monitor(t_min=0.0, t_max=2.0, check_every=1))
print('Monitor test passed.')
def test_ode_bvp():
oscillator = lambda x, t: diff(x, t, order=2) + x
bound_val_ho = DirichletBVP(t_0=0.0, x_0=0.0, t_1=1.5*np.pi, x_1=-1.0)
solution_ho, _ = solve(ode=oscillator, condition=bound_val_ho,
max_epochs=3000,
t_min=0.0, t_max=1.5*np.pi)
ts = np.linspace(0, 1.5*np.pi, 100)
x_net = solution_ho(ts, as_type='np')
x_ana = np.sin(ts)
assert isclose(x_net, x_ana, atol=0.1).all()
print('BVP basic test passed.')
def test_ode_ivp():
oscillator = lambda x, t: diff(x, t, order=2) + x
init_val_ho = IVP(t_0=0.0, x_0=0.0, x_0_prime=1.0)
solution_ho, _ = solve(ode=oscillator, condition=init_val_ho,
max_epochs=3000,
t_min=0.0, t_max=2*np.pi)
ts = np.linspace(0, 2*np.pi, 100)
x_net = solution_ho(ts, as_type='np')
x_ana = np.sin(ts)
assert isclose(x_net, x_ana, atol=0.1).all()
print('IVP basic test passed.')
def test_ode():
def mse(x, t):
true_x = torch.sin(t)
return torch.mean((x - true_x) ** 2)
exponential = lambda x, t: diff(x, t) - x
init_val_ex = IVP(t_0=0.0, x_0=1.0)
solution_ex, _ = solve(ode=exponential, condition=init_val_ex,
t_min=0.0, t_max=2.0, shuffle=False,
max_epochs=2000, return_best=True, metrics={'mse': mse})
ts = np.linspace(0, 2.0, 100)
x_net = solution_ex(ts, as_type='np')
x_ana = np.exp(ts)
assert isclose(x_net, x_ana, atol=0.1).all()
print('solve basic test passed.')
def test_ode():
def mse(u, t):
true_u = torch.sin(t)
return torch.mean((u - true_u) ** 2)
exponential = lambda u, t: diff(u, t) - u
init_val_ex = IVP(t_0=0.0, u_0=1.0)
solution_ex, loss_history = solve(ode=exponential, condition=init_val_ex,
t_min=0.0, t_max=2.0, shuffle=False,
max_epochs=10, return_best=True, metrics={'mse': mse})
assert isinstance(solution_ex, Solution1D)
assert isinstance(loss_history, dict)
keys = ['train_loss', 'valid_loss']
for key in keys:
assert key in loss_history
assert isinstance(loss_history[key], list)
assert len(loss_history[keys[0]]) == len(loss_history[keys[1]])
def test_train_generator():
exponential = lambda x, t: diff(x, t) - x
init_val_ex = IVP(t_0=0.0, x_0=1.0)
train_gen = Generator1D(size=32, t_min=0.0, t_max=2.0, method='uniform')
solution_ex, _ = solve(ode=exponential, condition=init_val_ex,
t_min=0.0, t_max=2.0,
train_generator=train_gen,
max_epochs=3)
train_gen = Generator1D(size=32, t_min=0.0, t_max=2.0, method='equally-spaced')
solution_ex, _ = solve(ode=exponential, condition=init_val_ex,
t_min=0.0, t_max=2.0,
train_generator=train_gen,
max_epochs=3)
train_gen = Generator1D(size=32, t_min=0.0, t_max=2.0, method='equally-spaced-noisy')
solution_ex, _ = solve(ode=exponential, condition=init_val_ex,
t_min=0.0, t_max=2.0,
train_generator=train_gen,
max_epochs=3)
train_gen = Generator1D(size=32, t_min=0.0, t_max=2.0, method='equally-spaced-noisy', noise_std=0.01)
solution_ex, _ = solve(ode=exponential, condition=init_val_ex,
t_min=0.0, t_max=2.0,
train_generator=train_gen,
max_epochs=3)
train_gen = Generator1D(size=32, t_min=np.log10(0.1), t_max=np.log10(2.0), method='log-spaced')
solution_ex, _ = solve(ode=exponential, condition=init_val_ex,
t_min=0.1, t_max=2.0,
train_generator=train_gen,
max_epochs=3)
train_gen = Generator1D(size=32, t_min=np.log10(0.1), t_max=np.log10(2.0), method='log-spaced-noisy')
solution_ex, _ = solve(ode=exponential, condition=init_val_ex,
t_min=0.1, t_max=2.0,
train_generator=train_gen,
max_epochs=3)
train_gen = Generator1D(size=32, t_min=np.log10(0.1), t_max=np.log10(2.0), method='log-spaced-noisy', noise_std=0.01)
solution_ex, _ = solve(ode=exponential, condition=init_val_ex,
t_min=0.1, t_max=2.0,
train_generator=train_gen,
max_epochs=3)
with raises(ValueError):
train_gen = Generator1D(size=32, t_min=0.0, t_max=2.0, method='magic')
print('ExampleGenerator test passed.')
