fig.colorbar(p32, ax=ax32)
x_min = a
y_min = a**2
scatter_31 = ax31.scatter(x_min, y_min, color='white')
scatter_32 = ax32.scatter(x_min, y_min, color='white')
####################################################################
# DISPLAY
plt.ion()
plt.show()
for i in range(200):
# Compute for Simple Convex
swarm_1.step()
next(GD_1.gradient_descent(lr=0.1))
for particle in swarm_1.get_particles():
ax11.scatter(*particle)
for particle in GD_1.particle_coordinates:
ax12.scatter(*particle)
# Compute for Ratrigin
swarm_2.step()
next(GD_2.gradient_descent(lr=0.1))
for particle in swarm_2.get_particles():
ax21.scatter(*particle)
for particle in GD_2.particle_coordinates:
sam = Sampler(omega, numerical_step_length, sys)
met = Metropolis(step_metropolis, step_importance, num_particles,
num_dimensions, sam, 0.0)
for i in range(monte_carlo_cycles):
new_energy, new_positions, count = met.metropolis(positions)
positions = new_positions
accumulate_energy += sam.local_energy(positions)
accumulate_psi_term += sys.derivative_psi_term(positions)
accumulate_both += sam.local_energy_times_wf(positions)
expec_val_energy = accumulate_energy / (monte_carlo_cycles * num_particles)
expec_val_psi = accumulate_psi_term / (monte_carlo_cycles * num_particles)
expec_val_both = accumulate_both / (monte_carlo_cycles * num_particles)
derivative_energy = 2 * (expec_val_both - expec_val_psi * expec_val_energy)
print('deri energy = ', derivative_energy)
print('counter (accepted moves in metropolis) = ', count)
return derivative_energy, new_energy
for i in range(gradient_iterations):
d_El, energy = run_vmc(parameter)
new_parameter = opt.gradient_descent(parameter, d_El)
parameter = new_parameter
print('new alpha = ', new_parameter)
print('----------------------------')
print('new energy = ', energy)
