def step(self,t,u,h): system = self.system noise = np.random.normal(size=[len(system.noise(t,u).T)]) def residual(v): return (system.mass(t+h,v) - system.mass(t,u))/h - system.deterministic(t+h,v) - np.sqrt(h)/h*np.dot(system.noise(t,u),noise) N = Newton(residual) ## N.xtol = min(N.xtol, h*1e-4) result = N.run(u) return t+h, result
def step(self, t, u, h):
system = self.system
noise = np.random.normal(size=[len(system.noise(t, u).T)])
def residual(v):
return (
(system.mass(t + h, v) - system.mass(t, u)) / h
- system.deterministic(t + h, v)
- np.sqrt(h) / h * np.dot(system.noise(t, u), noise)
)
N = Newton(residual)
## N.xtol = min(N.xtol, h*1e-4)
result = N.run(u)
return t + h, result
opt = Newton(obj_func=obj_func,
gradient_func=gradient,
reg_inv_hessian=reg_inv_hessian)
opt.log.setLevel(logging.INFO)
x_rng = [-2, 2]
y_rng = [-1, 1]
fig_dir = "figures"
Path(fig_dir).mkdir(parents=True, exist_ok=True)
# params_init = get_random_uniform_in_range(x_rng, y_rng)
# params_init = np.array([-1.55994695, -0.31833122])
params_init = np.array([-1.1, -0.5])
converged, no_opt_steps, final_update, traj, line_search_factors = opt.run(
no_steps=10, params_init=params_init, tol=1e-8, store_traj=True)
print("Converged: %s" % converged)
print(traj)
print(line_search_factors)
trajs = {}
trajs[0] = traj
endpoints, counts = plot.get_endpoints_and_counts(trajs)
for i in range(0, len(endpoints)):
print(endpoints[i], " : ", counts[i])
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# plot.plot_3d_endpoint_lines(ax, trajs)
