H.local_energy_array(psi_sorted_sampler, psi_sorted,
evaluation_points),
method="blocking",
),
]
old = psi_sorted.parameters
psi_sorted.parameters = psi.parameters
psi_sorted_sampler.thermalize(10000)
stats.append(
compute_statistics_for_series(
H.local_energy_array(psi_sorted_sampler, psi_sorted,
evaluation_points),
method="blocking",
))
labels = [
r"$\psi_{PJ}$", r"$\psi_{DNN}$", r"$\psi_{SDNN}$", r"$\hat{\psi}_{SDNN}$"
]
mpiprint(stats, pretty=True)
mpiprint(statistics_to_tex(stats, labels, filename=__file__ + ".table.tex"))
# mpiprint(psi.parameters)
if master_rank():
plot_training(
[psi_energies, psi_sorted_energies, psi_bench_energies],
[psi_parameters, psi_parameters],
psi_symmetries,
)
plt.show()
eta = timedelta(seconds=round(t1 / 500 * evaluation_points))
mpiprint(f"Calculating final energy - ETA {eta}")
labels = [
r"$\psi_{PJ}$", r"$\psi_{DNN}$", r"$\psi_{SDNN}$", r"$\hat{\psi}_{SDNN}$"
]
r2_stats = [
compute_statistics_for_series(H.mean_squared_radius_array(
s, evaluation_points),
method="blocking") for s in samplers
]
mpiprint(
statistics_to_tex(
r2_stats,
labels,
filename=__file__ + ".r2-table.tex",
quantity_name="$\\langle r^2\\rangle$",
))
r_stats = [
compute_statistics_for_series(H.mean_radius_array(s, evaluation_points),
method="blocking") for s in samplers
]
mpiprint(
statistics_to_tex(
r_stats,
labels,
filename=__file__ + ".r-table.tex",
quantity_name="$\\langle r\\rangle$",
))
rij_stats = [
compute_statistics_for_series(H.mean_distance_array(