import numpy as np
import hmc
params = hmc.HMCParams(
tau=0.1,
tau_g=0.4,
L=10,
eta=0.0005,
mass=1,
r_clip=2,
grad_clip=1.0,
)
import hmc
np.random.seed(46327)
problem = circle.problem()
n, data_dim = problem.data.shape
dim = 2
epsilon = 1
delta = 0.1 / n
params = hmc.HMCParams(
tau=0.6,
tau_g=1.9,
L=4 * 12,
eta=0.07,
mass=np.array((1, 1)),
r_clip=0.001,
grad_clip=0.0015,
)
res = hmc.hmc(problem, np.array((0, 1)), epsilon, delta, params)
samples = res.chain
n_samples = samples.shape[0]
proposals = res.leapfrog_chain
props_per_sample = int(proposals.shape[0] / (samples.shape[0] - 1))
points = np.zeros((props_per_sample + 1, samples.shape[0], dim))
for i in range(samples.shape[0]):
points[0, i, :] = samples[i, :]
if i < samples.shape[0] - 1:
import numpy as np
import hmc
params = hmc.HMCParams(
tau=0.05,
tau_g=0.20,
L=5,
eta=0.00004,
mass=1,
r_clip=20,
grad_clip=25.0,
)
import numpy as np
import hmc
params = hmc.HMCParams(
tau=0.08,
tau_g=0.20,
L=10,
eta=0.015,
mass=1,
r_clip=2.5,
grad_clip=3.5,
)
import numpy as np
import hmc
params = hmc.HMCParams(
tau=0.2,
tau_g=0.6,
L=10,
eta=0.01,
mass=1,
r_clip=2.5,
grad_clip=2.0,
)
import numpy as np
import hmc
params = hmc.HMCParams(
tau = 0.05,
tau_g = 0.2,
L = 10,
eta = 0.00015,
mass = 1,
r_clip = 2.5,
grad_clip = 3.0,
)
import numpy as np
import hmc
params = hmc.HMCParams(
tau = 0.05,
tau_g = 0.10,
L = 5,
eta = 0.00025,
mass = 1,#np.hstack((np.array((0.1, 1)), np.repeat(2, 28))),
r_clip = 2.5,
grad_clip = 6.0,
# tau = 0.05,
# tau_g = 0.10,
# L = 5,
# eta = 0.0002,
# mass = np.hstack((np.array((0.1, 1)), np.repeat(2, 28))),
# r_clip = 3.1,
# grad_clip = 6.0,
)
import numpy as np
import hmc
params = hmc.HMCParams(
tau = 0.15,
tau_g = 0.25,
L = 5,
eta = 0.00045,
mass = 1,
r_clip = 5.0,
grad_clip = 2.8,
)
import numpy as np
import hmc
params = hmc.HMCParams(
tau=0.05,
tau_g=0.20,
L=8,
eta=0.00007,
mass=1,
r_clip=30,
grad_clip=29.0,
)
n, data_dim = problem.data.shape
posterior = problem.true_posterior
epsilon = 4
delta = 0.1 / n
start_point = 2
params = hmc.HMCParams(
tau=0.15,
tau_g=0.25,
L=5,
eta=0.00045,
mass=1,
r_clip=4.0,
grad_clip=2.8,
# tau = 0.05,
# tau_g = 0.20,
# L = 8,
# eta = 0.00007,
# mass = 1,#np.hstack((np.array((0.1, 1)), np.repeat(2, 28))),
# r_clip = 30,
# grad_clip = 29.0,
)
result = hmc.hmc(problem, problem.get_start_point(start_point), epsilon, delta,
params)
final_chain = result.final_chain
print("Acceptance: {}".format(result.acceptance))
print("Clipping: {}".format(result.clipped_r))