def test_lotka_volterra():
alpha, beta, delta, gamma = 1, 1, 1, 1
lotka_volterra = lambda x, y, t : [diff(x, t) - (alpha*x - beta*x*y),
diff(y, t) - (delta*x*y - gamma*y)]
init_vals_lv = [
IVP(t_0=0.0, x_0=1.5),
IVP(t_0=0.0, x_0=1.0)
]
nets_lv = [
FCNN(hidden_units=(32, 32), actv=SinActv),
FCNN(hidden_units=(32, 32), actv=SinActv),
]
solution_lv, _ = solve_system(ode_system=lotka_volterra, conditions=init_vals_lv,
t_min=0.0, t_max=12, nets=nets_lv, max_epochs=12000,
monitor=Monitor(t_min=0.0, t_max=12, check_every=100))
ts = np.linspace(0, 12, 100)
prey_net, pred_net = solution_lv(ts, as_type='np')
def dPdt(P, t):
return [P[0]*alpha - beta*P[0]*P[1], delta*P[0]*P[1] - gamma*P[1]]
P0 = [1.5, 1.0]
Ps = odeint(dPdt, P0, ts)
prey_num = Ps[:,0]
pred_num = Ps[:,1]
assert isclose(prey_net, prey_num, atol=0.1).all()
assert isclose(pred_net, pred_num, atol=0.1).all()
print('Lotka Volterra test passed.')
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_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=Monitor(t_min=0.0, t_max=2.0,
check_every=1))