Comments (2)
Oh I think I see the problem. That function has functions within functions:
def DifferentialMove():
"""
The `Karamanis & Beutler (2020) <https://arxiv.org/abs/2002.06212>`_ "Differential Move" with parallelization.
When this Move is used the walkers move along directions defined by random pairs of walkers sampled (with no
replacement) from the complementary ensemble. This is the default choice and performs well along a wide range
of target distributions.
"""
def make_differential_move(n_chains):
PAIRS = get_nondiagonal_indices(n_chains // 2)
def differential_move(rng_key, inactive, mu):
n_active_chains, n_params = inactive.shape
selected_pairs = random.choice(rng_key, PAIRS, shape=(n_active_chains,))
diffs = jnp.diff(inactive[selected_pairs], axis=1).squeeze(
axis=1
) # get the pairwise difference of each vector
return 2.0 * mu * diffs
return differential_move
return make_differential_move
from numpyro.
Ok. I can pickle if I use dill, instead of pickle
from numpyro.
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from numpyro.