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extend_params fix after n_particles removed #62

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extend_params fix after n_particles removed #62

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54287de
fix extend_params
andrewdipper Jun 16, 2024
1cb037d
adjust max_num_doubings
andrewdipper Jun 16, 2024
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8 changes: 4 additions & 4 deletions book/algorithms/TemperedSMC.md
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@ jupytext:
extension: .md
format_name: myst
format_version: 0.13
jupytext_version: 1.15.2
jupytext_version: 1.16.2
kernelspec:
display_name: Python 3 (ipykernel)
language: python
Expand Down Expand Up @@ -158,7 +158,7 @@ We now use a NUTS kernel.
```{code-cell} ipython3
%%time

nuts_parameters = dict(step_size=1e-4, inverse_mass_matrix=inv_mass_matrix)
nuts_parameters = dict(step_size=1e-4, inverse_mass_matrix=inv_mass_matrix, max_num_doublings=6)

nuts = blackjax.nuts(full_logdensity, **nuts_parameters)
nuts_state = nuts.init(jnp.ones((1,)))
Expand Down Expand Up @@ -219,7 +219,7 @@ tempered = blackjax.adaptive_tempered_smc(
loglikelihood,
blackjax.hmc.build_kernel(),
blackjax.hmc.init,
extend_params(n_samples, hmc_parameters),
extend_params(hmc_parameters),
resampling.systematic,
0.5,
num_mcmc_steps=1,
Expand Down Expand Up @@ -367,7 +367,7 @@ tempered = blackjax.adaptive_tempered_smc(
loglikelihood,
blackjax.hmc.build_kernel(),
blackjax.hmc.init,
extend_params(n_samples, hmc_parameters),
extend_params(hmc_parameters),
resampling.systematic,
0.75,
num_mcmc_steps=1,
Expand Down
15 changes: 7 additions & 8 deletions book/algorithms/TemperedSMCWithOptimizedInnerKernel.md
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@ jupytext:
extension: .md
format_name: myst
format_version: 0.13
jupytext_version: 1.15.2
jupytext_version: 1.16.2
kernelspec:
display_name: Python 3 (ipykernel)
language: python
Expand Down Expand Up @@ -158,7 +158,7 @@ Although the proposal distribution is always normal, the mean and diagonal of th
the particles outcome of the $i-th$ step, in order to mutate them in the step $i+1$

```{code-cell} ipython3
from blackjax.smc.inner_kernel_tuning import inner_kernel_tuning
from blackjax import inner_kernel_tuning
from blackjax.smc.tuning.from_particles import (
particles_covariance_matrix,
particles_stds,
Expand Down Expand Up @@ -212,10 +212,9 @@ def tuned_irmh_experiment(dimensions, target_ess, num_mcmc_steps):
mcmc_init_fn=irmh.init,
resampling_fn=resampling.systematic,
smc_algorithm=adaptive_tempered_smc,
mcmc_parameter_update_fn=lambda state, info: extend_params(n_particles,
{"means":particles_means(state.particles),
"stds":particles_stds(state.particles)}),
initial_parameter_value=extend_params(n_particles, {"means":jnp.zeros(dimensions), "stds":jnp.ones(dimensions) * 2}),
mcmc_parameter_update_fn=lambda state, info: extend_params({"means":particles_means(state.particles),
"stds":particles_stds(state.particles)}),
initial_parameter_value=extend_params({"means":jnp.zeros(dimensions), "stds":jnp.ones(dimensions) * 2}),
target_ess=target_ess,
num_mcmc_steps=num_mcmc_steps,
)
Expand Down Expand Up @@ -248,7 +247,7 @@ def irmh_full_cov_experiment(dimensions, target_ess, num_mcmc_steps):

def mcmc_parameter_update_fn(state, info):
covariance = jnp.atleast_2d(particles_covariance_matrix(state.particles))
return extend_params(n_particles, {"means":particles_means(state.particles), "cov":covariance})
return extend_params({"means":particles_means(state.particles), "cov":covariance})

kernel_tuned_proposal = inner_kernel_tuning(
logprior_fn=prior_log_prob,
Expand All @@ -258,7 +257,7 @@ def irmh_full_cov_experiment(dimensions, target_ess, num_mcmc_steps):
resampling_fn=resampling.systematic,
smc_algorithm=adaptive_tempered_smc,
mcmc_parameter_update_fn=mcmc_parameter_update_fn,
initial_parameter_value=extend_params(n_particles, {"means":jnp.zeros(dimensions), "cov":jnp.eye(dimensions) * 2}),
initial_parameter_value=extend_params({"means":jnp.zeros(dimensions), "cov":jnp.eye(dimensions) * 2}),
target_ess=target_ess,
num_mcmc_steps=num_mcmc_steps,
)
Expand Down