def vmc_iterate(tf, num_iters=100):
opt = optim.SGD(tf.parameters(), lr=1e-2, momentum=0.9)
for i in range(num_iters):
results = metropolis_symmetric(tf,
normal_proposal,
num_walkers=1000,
num_steps=5000)
energy_minimize_step(tf, results, opt)
print(tf.alpha)
def harmonic_energy_alpha_values():
vals = np.arange(0.2, 1.5, 0.1)
means = []
for alpha_val in vals:
print(alpha_val)
tf = HarmonicTrialFunction(alpha_val)
samples = metropolis_symmetric(tf,
normal_proposal,
num_walkers=100,
num_steps=20000)
means.append(torch.mean(tf.local_energy(samples)).item())
return vals, means
def harmonic_energy_alpha_values():
vals = np.arange(0.2, 1.5, 0.1)
means = []
for alpha_val in vals:
print(alpha_val)
tf = HarmonicTrialFunction(torch.tensor(alpha_val))
init_config = 0.5 * torch.ones(100, 1)
samples = metropolis_symmetric(tf,
init_config,
normal_proposal,
num_steps=20000)
means.append(torch.mean(tf.local_energy(samples)).item())
return vals, means
def vmc_iterate(tf, init_config, num_iters=100):
opt = optim.SGD(tf.parameters(), lr=5e-2, momentum=0.3)
# propdist = NormalProposal(0.3)
propdist = ClipNormalProposal(0.1)
for i in range(num_iters):
print(i)
results = metropolis_symmetric(tf,
init_config,
propdist,
num_steps=1000)
energy_minimize_step(tf, results, opt)
print(tf.alpha)
print(results)
def hydrogen_energy_alpha_values():
vals = np.arange(0.2, 1.5, 0.1)
means = []
for alpha_val in vals:
print(alpha_val)
tf = HydrogenTrialWavefunction(alpha_val)
samples = metropolis_symmetric(tf,
clip_normal_proposal,
num_walkers=100,
num_steps=20000,
init_val=0.5)
means.append(torch.mean(tf.local_energy(samples)).item())
return vals, means
def hydrogen_energy_alpha_values():
vals = np.arange(0.2, 1.5, 0.1)
means = []
propdist = ClipNormalProposal(0.3, min_val=0.0)
for alpha_val in vals:
print(alpha_val)
tf = HydrogenTrialWavefunction(torch.tensor(alpha_val))
init_config = 0.5 * torch.ones(100, 1)
samples = metropolis_symmetric(tf,
init_config,
propdist,
num_steps=20000)
means.append(torch.mean(tf.local_energy(samples)).item())
return vals, means
def helium_energy_alpha_values():
vals = np.arange(1.2, 2.5, 0.1)
means = []
propdist = ClipNormalProposal(0.05, min_val=0.0)
for alpha_val in vals:
print(alpha_val)
tf = HeliumTrialWavefunction(torch.ones(1) * alpha_val)
init_config = 0.5 * torch.ones(100, 3)
samples = metropolis_symmetric(tf,
init_config,
propdist,
num_steps=20000)
means.append(torch.mean(tf.local_energy(samples)).item())
print(means[-1])
return vals, means
# %%
import torch
import matplotlib.pyplot as plt
from qmc.mcmc import metropolis_symmetric, clip_mvnormal_proposal
from qmc.wavefunction import HeliumTrialWavefunction
# %%
d = 3
tf = HeliumTrialWavefunction(torch.ones(1))
#
n_walkers = 5
init_config = torch.rand(n_walkers, 3)
results = metropolis_symmetric(tf,
init_config,
clip_mvnormal_proposal,
num_walkers=n_walkers,
num_steps=10000)
# %%
#results_numpy = results.view(-1,3).numpy()
# %%
#plt.scatter(results_numpy[:,0],results_numpy[:,1], s=1)
#plt.savefig("box.png")
# %%
print(torch.mean(tf.local_energy(results)))
a = torch.tensor(Tdat)
# a = torch.ones(2)
tf = twvan(a)
#
n_walkers = 10
#init_config = torch.ones(n_walkers,2)*0.5
#results = metropolis_symmetric(tf, init_config, ClipNormalProposal(sigma=0.001, min_val=0.1, max_val=0.45), num_walkers=n_walkers, num_steps=10000)
init_config = 0.1 * torch.ones(n_walkers, 2)
results = metropolis_symmetric(tf,
init_config,
ClipNormalProposal(sigma=0.01,
min_val=-1,
max_val=1),
num_walkers=n_walkers,
num_steps=10000)
#init_config = torch.rand(n_walkers,2)
#results = metropolis_symmetric(tf, init_config, ClipNormalProposal(sigma=0.05, num_walkers=n_walkers, num_steps=10000)
# %%
#results_numpy = results.view(-1,3).numpy()
# %%
# %%
#plt.scatter(results_numpy[:,0],results_numpy[:,1], s=1)
#plt.savefig("box.png")samples[:, 1000:, :]