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def update(
adaptation_state: WindowAdaptationState,
adaptation_stage: tuple,
position: ArrayLikeTree,
acceptance_rate: float,
) -> WindowAdaptationState:
"""Update the adaptation state and parameter values.
Parameters
----------
adaptation_state
... | Update the adaptation state and parameter values.
Parameters
----------
adaptation_state
Current adptation state.
adaptation_stage
The current stage of the warmup: whether this is a slow window,
a fast window and if we are at the last step of a slow w... | update | python | blackjax-devs/blackjax | blackjax/adaptation/window_adaptation.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/adaptation/window_adaptation.py | Apache-2.0 |
def final(warmup_state: WindowAdaptationState) -> tuple[float, Array]:
"""Return the final values for the step size and mass matrix."""
step_size = jnp.exp(warmup_state.ss_state.log_step_size_avg)
inverse_mass_matrix = warmup_state.imm_state.inverse_mass_matrix
return step_size, inverse_... | Return the final values for the step size and mass matrix. | final | python | blackjax-devs/blackjax | blackjax/adaptation/window_adaptation.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/adaptation/window_adaptation.py | Apache-2.0 |
def window_adaptation(
algorithm,
logdensity_fn: Callable,
is_mass_matrix_diagonal: bool = True,
initial_step_size: float = 1.0,
target_acceptance_rate: float = 0.80,
progress_bar: bool = False,
adaptation_info_fn: Callable = return_all_adapt_info,
integrator=mcmc.integrators.velocity_ve... | Adapt the value of the inverse mass matrix and step size parameters of
algorithms in the HMC fmaily. See Blackjax.hmc_family
Algorithms in the HMC family on a euclidean manifold depend on the value of
at least two parameters: the step size, related to the trajectory
integrator, and the mass matrix, lin... | window_adaptation | python | blackjax-devs/blackjax | blackjax/adaptation/window_adaptation.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/adaptation/window_adaptation.py | Apache-2.0 |
def build_schedule(
num_steps: int,
initial_buffer_size: int = 75,
final_buffer_size: int = 50,
first_window_size: int = 25,
) -> list[tuple[int, bool]]:
"""Return the schedule for Stan's warmup.
The schedule below is intended to be as close as possible to Stan's :cite:p:`stan_hmc_param`.
T... | Return the schedule for Stan's warmup.
The schedule below is intended to be as close as possible to Stan's :cite:p:`stan_hmc_param`.
The warmup period is split into three stages:
1. An initial fast interval to reach the typical set. Only the step size is
adapted in this window.
2. "Slow" parameter... | build_schedule | python | blackjax-devs/blackjax | blackjax/adaptation/window_adaptation.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/adaptation/window_adaptation.py | Apache-2.0 |
def build_kernel(
logdensity_fn: Callable,
integrator: Callable = integrators.isokinetic_mclachlan,
divergence_threshold: float = 1000,
inverse_mass_matrix=1.0,
):
"""Build an MHMCHMC kernel where the number of integration steps is chosen randomly.
Parameters
----------
integrator
... | Build an MHMCHMC kernel where the number of integration steps is chosen randomly.
Parameters
----------
integrator
The integrator to use to integrate the Hamiltonian dynamics.
divergence_threshold
Value of the difference in energy above which we consider that the transition is divergent... | build_kernel | python | blackjax-devs/blackjax | blackjax/mcmc/adjusted_mclmc.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/adjusted_mclmc.py | Apache-2.0 |
def kernel(
rng_key: PRNGKey,
state: HMCState,
step_size: float,
num_integration_steps: int,
L_proposal_factor: float = jnp.inf,
) -> tuple[HMCState, HMCInfo]:
"""Generate a new sample with the MHMCHMC kernel."""
key_momentum, key_integrator = jax.random.spli... | Generate a new sample with the MHMCHMC kernel. | kernel | python | blackjax-devs/blackjax | blackjax/mcmc/adjusted_mclmc.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/adjusted_mclmc.py | Apache-2.0 |
def as_top_level_api(
logdensity_fn: Callable,
step_size: float,
L_proposal_factor: float = jnp.inf,
inverse_mass_matrix=1.0,
*,
divergence_threshold: int = 1000,
integrator: Callable = integrators.isokinetic_mclachlan,
num_integration_steps,
) -> SamplingAlgorithm:
"""Implements the... | Implements the (basic) user interface for the MHMCHMC kernel.
Parameters
----------
logdensity_fn
The log-density function we wish to draw samples from.
step_size
The value to use for the step size in the symplectic integrator.
divergence_threshold
The absolute value of the ... | as_top_level_api | python | blackjax-devs/blackjax | blackjax/mcmc/adjusted_mclmc.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/adjusted_mclmc.py | Apache-2.0 |
def adjusted_mclmc_proposal(
integrator: Callable,
step_size: Union[float, ArrayLikeTree],
L_proposal_factor: float,
num_integration_steps: int = 1,
divergence_threshold: float = 1000,
*,
sample_proposal: Callable = static_binomial_sampling,
) -> Callable:
"""Vanilla MHMCHMC algorithm.
... | Vanilla MHMCHMC algorithm.
The algorithm integrates the trajectory applying a integrator
`num_integration_steps` times in one direction to get a proposal and uses a
Metropolis-Hastings acceptance step to either reject or accept this
proposal. This is what people usually refer to when they talk about "t... | adjusted_mclmc_proposal | python | blackjax-devs/blackjax | blackjax/mcmc/adjusted_mclmc.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/adjusted_mclmc.py | Apache-2.0 |
def build_kernel(
integration_steps_fn,
integrator: Callable = integrators.isokinetic_mclachlan,
divergence_threshold: float = 1000,
next_random_arg_fn: Callable = lambda key: jax.random.split(key)[1],
inverse_mass_matrix=1.0,
):
"""Build a Dynamic MHMCHMC kernel where the number of integration ... | Build a Dynamic MHMCHMC kernel where the number of integration steps is chosen randomly.
Parameters
----------
integrator
The integrator to use to integrate the Hamiltonian dynamics.
divergence_threshold
Value of the difference in energy above which we consider that the transition is di... | build_kernel | python | blackjax-devs/blackjax | blackjax/mcmc/adjusted_mclmc_dynamic.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/adjusted_mclmc_dynamic.py | Apache-2.0 |
def kernel(
rng_key: PRNGKey,
state: DynamicHMCState,
logdensity_fn: Callable,
step_size: float,
L_proposal_factor: float = jnp.inf,
) -> tuple[DynamicHMCState, HMCInfo]:
"""Generate a new sample with the MHMCHMC kernel."""
num_integration_steps = integration... | Generate a new sample with the MHMCHMC kernel. | kernel | python | blackjax-devs/blackjax | blackjax/mcmc/adjusted_mclmc_dynamic.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/adjusted_mclmc_dynamic.py | Apache-2.0 |
def as_top_level_api(
logdensity_fn: Callable,
step_size: float,
L_proposal_factor: float = jnp.inf,
inverse_mass_matrix=1.0,
*,
divergence_threshold: int = 1000,
integrator: Callable = integrators.isokinetic_mclachlan,
next_random_arg_fn: Callable = lambda key: jax.random.split(key)[1],... | Implements the (basic) user interface for the dynamic MHMCHMC kernel.
Parameters
----------
logdensity_fn
The log-density function we wish to draw samples from.
step_size
The value to use for the step size in the symplectic integrator.
divergence_threshold
The absolute value... | as_top_level_api | python | blackjax-devs/blackjax | blackjax/mcmc/adjusted_mclmc_dynamic.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/adjusted_mclmc_dynamic.py | Apache-2.0 |
def adjusted_mclmc_proposal(
integrator: Callable,
step_size: Union[float, ArrayLikeTree],
L_proposal_factor: float,
num_integration_steps: int = 1,
divergence_threshold: float = 1000,
*,
sample_proposal: Callable = static_binomial_sampling,
) -> Callable:
"""Vanilla MHMCHMC algorithm.
... | Vanilla MHMCHMC algorithm.
The algorithm integrates the trajectory applying a integrator
`num_integration_steps` times in one direction to get a proposal and uses a
Metropolis-Hastings acceptance step to either reject or accept this
proposal. This is what people usually refer to when they talk about "t... | adjusted_mclmc_proposal | python | blackjax-devs/blackjax | blackjax/mcmc/adjusted_mclmc_dynamic.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/adjusted_mclmc_dynamic.py | Apache-2.0 |
def rescale(mu):
"""returns s, such that
round(U(0, 1) * s + 0.5)
has expected value mu.
"""
k = jnp.floor(2 * mu - 1)
x = k * (mu - 0.5 * (k + 1)) / (k + 1 - mu)
return k + x | returns s, such that
round(U(0, 1) * s + 0.5)
has expected value mu.
| rescale | python | blackjax-devs/blackjax | blackjax/mcmc/adjusted_mclmc_dynamic.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/adjusted_mclmc_dynamic.py | Apache-2.0 |
def build_kernel():
"""Build a Barker's proposal kernel.
Returns
-------
A kernel that takes a rng_key and a Pytree that contains the current state
of the chain and that returns a new state of the chain along with
information about the transition.
"""
def _compute_acceptance_probabili... | Build a Barker's proposal kernel.
Returns
-------
A kernel that takes a rng_key and a Pytree that contains the current state
of the chain and that returns a new state of the chain along with
information about the transition.
| build_kernel | python | blackjax-devs/blackjax | blackjax/mcmc/barker.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/barker.py | Apache-2.0 |
def _compute_acceptance_probability(
state: BarkerState, proposal: BarkerState, metric: Metric
) -> Numeric:
"""Compute the acceptance probability of the Barker's proposal kernel."""
x = state.position
y = proposal.position
log_x = state.logdensity_grad
log_y = propo... | Compute the acceptance probability of the Barker's proposal kernel. | _compute_acceptance_probability | python | blackjax-devs/blackjax | blackjax/mcmc/barker.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/barker.py | Apache-2.0 |
def kernel(
rng_key: PRNGKey,
state: BarkerState,
logdensity_fn: Callable,
step_size: float,
inverse_mass_matrix: metrics.MetricTypes | None = None,
) -> tuple[BarkerState, BarkerInfo]:
"""Generate a new sample with the Barker kernel."""
if inverse_mass_matrix... | Generate a new sample with the Barker kernel. | kernel | python | blackjax-devs/blackjax | blackjax/mcmc/barker.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/barker.py | Apache-2.0 |
def as_top_level_api(
logdensity_fn: Callable,
step_size: float,
inverse_mass_matrix: metrics.MetricTypes | None = None,
) -> SamplingAlgorithm:
"""Implements the (basic) user interface for the Barker's proposal :cite:p:`Livingstone2022Barker` kernel with a
Gaussian base kernel.
The general Bar... | Implements the (basic) user interface for the Barker's proposal :cite:p:`Livingstone2022Barker` kernel with a
Gaussian base kernel.
The general Barker kernel builder (:meth:`blackjax.mcmc.barker.build_kernel`, alias `blackjax.barker.build_kernel`) can be
cumbersome to manipulate. Since most users only need... | as_top_level_api | python | blackjax-devs/blackjax | blackjax/mcmc/barker.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/barker.py | Apache-2.0 |
def _barker_sample(key, mean, a, scale, metric):
r"""
Sample from a multivariate Barker's proposal distribution for PyTrees.
Parameters
----------
key
A PRNG key.
mean
The mean of the normal distribution, a PyTree. This corresponds to :math:`\mu` in the equation above.
a
... |
Sample from a multivariate Barker's proposal distribution for PyTrees.
Parameters
----------
key
A PRNG key.
mean
The mean of the normal distribution, a PyTree. This corresponds to :math:`\mu` in the equation above.
a
The parameter :math:`a` in the equation above, the s... | _barker_sample | python | blackjax-devs/blackjax | blackjax/mcmc/barker.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/barker.py | Apache-2.0 |
def overdamped_langevin(logdensity_grad_fn):
"""Euler solver for overdamped Langevin diffusion."""
def one_step(rng_key, state: DiffusionState, step_size: float, batch: tuple = ()):
position, _, logdensity_grad = state
noise = generate_gaussian_noise(rng_key, position)
position = jax.tr... | Euler solver for overdamped Langevin diffusion. | overdamped_langevin | python | blackjax-devs/blackjax | blackjax/mcmc/diffusions.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/diffusions.py | Apache-2.0 |
def build_kernel(
integrator: Callable = integrators.velocity_verlet,
divergence_threshold: float = 1000,
next_random_arg_fn: Callable = lambda key: jax.random.split(key)[1],
integration_steps_fn: Callable = lambda key: jax.random.randint(key, (), 1, 10),
):
"""Build a Dynamic HMC kernel where the n... | Build a Dynamic HMC kernel where the number of integration steps is chosen randomly.
Parameters
----------
integrator
The symplectic integrator to use to integrate the Hamiltonian dynamics.
divergence_threshold
Value of the difference in energy above which we consider that the transitio... | build_kernel | python | blackjax-devs/blackjax | blackjax/mcmc/dynamic_hmc.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/dynamic_hmc.py | Apache-2.0 |
def kernel(
rng_key: PRNGKey,
state: DynamicHMCState,
logdensity_fn: Callable,
step_size: float,
inverse_mass_matrix: Array,
**integration_steps_kwargs,
) -> tuple[DynamicHMCState, HMCInfo]:
"""Generate a new sample with the HMC kernel."""
num_integrat... | Generate a new sample with the HMC kernel. | kernel | python | blackjax-devs/blackjax | blackjax/mcmc/dynamic_hmc.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/dynamic_hmc.py | Apache-2.0 |
def as_top_level_api(
logdensity_fn: Callable,
step_size: float,
inverse_mass_matrix: Array,
*,
divergence_threshold: int = 1000,
integrator: Callable = integrators.velocity_verlet,
next_random_arg_fn: Callable = lambda key: jax.random.split(key)[1],
integration_steps_fn: Callable = lamb... | Implements the (basic) user interface for the dynamic HMC kernel.
Parameters
----------
logdensity_fn
The log-density function we wish to draw samples from.
step_size
The value to use for the step size in the symplectic integrator.
inverse_mass_matrix
The value to use for th... | as_top_level_api | python | blackjax-devs/blackjax | blackjax/mcmc/dynamic_hmc.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/dynamic_hmc.py | Apache-2.0 |
def halton_trajectory_length(
i: Array, trajectory_length_adjustment: float, max_bits: int = 10
) -> int:
"""Generate a quasi-random number of integration steps."""
s = rescale(trajectory_length_adjustment)
return jnp.asarray(jnp.rint(0.5 + halton_sequence(i, max_bits) * s), dtype=int) | Generate a quasi-random number of integration steps. | halton_trajectory_length | python | blackjax-devs/blackjax | blackjax/mcmc/dynamic_hmc.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/dynamic_hmc.py | Apache-2.0 |
def build_kernel(cov_matrix: Array, mean: Array):
"""Build an Elliptical Slice sampling kernel :cite:p:`murray2010elliptical`.
Parameters
----------
cov_matrix
The value of the covariance matrix of the gaussian prior distribution from
the posterior we wish to sample.
Returns
--... | Build an Elliptical Slice sampling kernel :cite:p:`murray2010elliptical`.
Parameters
----------
cov_matrix
The value of the covariance matrix of the gaussian prior distribution from
the posterior we wish to sample.
Returns
-------
A kernel that takes a rng_key and a Pytree that... | build_kernel | python | blackjax-devs/blackjax | blackjax/mcmc/elliptical_slice.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/elliptical_slice.py | Apache-2.0 |
def as_top_level_api(
loglikelihood_fn: Callable,
*,
mean: Array,
cov: Array,
) -> SamplingAlgorithm:
"""Implements the (basic) user interface for the Elliptical Slice sampling kernel.
Examples
--------
A new Elliptical Slice sampling kernel can be initialized and used with the followi... | Implements the (basic) user interface for the Elliptical Slice sampling kernel.
Examples
--------
A new Elliptical Slice sampling kernel can be initialized and used with the following code:
.. code::
ellip_slice = blackjax.elliptical_slice(loglikelihood_fn, cov_matrix)
state = ellip_... | as_top_level_api | python | blackjax-devs/blackjax | blackjax/mcmc/elliptical_slice.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/elliptical_slice.py | Apache-2.0 |
def elliptical_proposal(
logdensity_fn: Callable,
momentum_generator: Callable,
mean: Array,
) -> Callable:
"""Build an Ellitpical slice sampling kernel.
The algorithm samples a latent parameter, traces an ellipse connecting the
initial position and the latent parameter and does slice sampling ... | Build an Ellitpical slice sampling kernel.
The algorithm samples a latent parameter, traces an ellipse connecting the
initial position and the latent parameter and does slice sampling on this
ellipse to output a new sample from the posterior distribution.
Parameters
----------
logdensity_fn
... | elliptical_proposal | python | blackjax-devs/blackjax | blackjax/mcmc/elliptical_slice.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/elliptical_slice.py | Apache-2.0 |
def slice_fn(vals):
"""Perform slice sampling around the ellipsis.
Checks if the proposed position's likelihood is larger than the slice
variable. Returns the position if True, shrinks the bracket for sampling
`theta` and samples a new proposal if False.
As ... | Perform slice sampling around the ellipsis.
Checks if the proposed position's likelihood is larger than the slice
variable. Returns the position if True, shrinks the bracket for sampling
`theta` and samples a new proposal if False.
As the bracket `[theta_min, theta_max]... | slice_fn | python | blackjax-devs/blackjax | blackjax/mcmc/elliptical_slice.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/elliptical_slice.py | Apache-2.0 |
def ellipsis(position, momentum, theta, mean):
"""Generate proposal from the ellipsis.
Given a scalar theta indicating a point on the circumference of the ellipsis
and the shared mean vector for both position and momentum variables,
generate proposed position and momentum to later accept or reject
... | Generate proposal from the ellipsis.
Given a scalar theta indicating a point on the circumference of the ellipsis
and the shared mean vector for both position and momentum variables,
generate proposed position and momentum to later accept or reject
depending on the slice variable.
| ellipsis | python | blackjax-devs/blackjax | blackjax/mcmc/elliptical_slice.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/elliptical_slice.py | Apache-2.0 |
def build_kernel(
noise_fn: Callable = lambda _: 0.0,
divergence_threshold: float = 1000,
):
"""Build a Generalized HMC kernel.
The Generalized HMC kernel performs a similar procedure to the standard HMC
kernel with the difference of a persistent momentum variable and a non-reversible
Metropoli... | Build a Generalized HMC kernel.
The Generalized HMC kernel performs a similar procedure to the standard HMC
kernel with the difference of a persistent momentum variable and a non-reversible
Metropolis-Hastings step instead of the standard Metropolis-Hastings acceptance
step. This means that; apart from... | build_kernel | python | blackjax-devs/blackjax | blackjax/mcmc/ghmc.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/ghmc.py | Apache-2.0 |
def kernel(
rng_key: PRNGKey,
state: GHMCState,
logdensity_fn: Callable,
step_size: float,
momentum_inverse_scale: ArrayLikeTree,
alpha: float,
delta: float,
) -> tuple[GHMCState, hmc.HMCInfo]:
"""Generate new sample with the Generalized HMC kernel.
... | Generate new sample with the Generalized HMC kernel.
Parameters
----------
rng_key
JAX's pseudo random number generating key.
state
Current state of the chain.
logdensity_fn
(Unnormalized) Log density function being targeted.
step_size... | kernel | python | blackjax-devs/blackjax | blackjax/mcmc/ghmc.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/ghmc.py | Apache-2.0 |
def update_momentum(rng_key, state, alpha, momentum_generator):
"""Persistent update of the momentum variable.
Performs a persistent update of the momentum, taking as input the previous
momentum, a random number generating key, the parameter alpha and the
momentum generator function. Outputs
an upd... | Persistent update of the momentum variable.
Performs a persistent update of the momentum, taking as input the previous
momentum, a random number generating key, the parameter alpha and the
momentum generator function. Outputs
an updated momentum that is a mixture of the previous momentum a new sample
... | update_momentum | python | blackjax-devs/blackjax | blackjax/mcmc/ghmc.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/ghmc.py | Apache-2.0 |
def as_top_level_api(
logdensity_fn: Callable,
step_size: float,
momentum_inverse_scale: ArrayLikeTree,
alpha: float,
delta: float,
*,
divergence_threshold: int = 1000,
noise_gn: Callable = lambda _: 0.0,
) -> SamplingAlgorithm:
"""Implements the (basic) user interface for the Genera... | Implements the (basic) user interface for the Generalized HMC kernel.
The Generalized HMC kernel performs a similar procedure to the standard HMC
kernel with the difference of a persistent momentum variable and a non-reversible
Metropolis-Hastings step instead of the standard Metropolis-Hastings acceptance... | as_top_level_api | python | blackjax-devs/blackjax | blackjax/mcmc/ghmc.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/ghmc.py | Apache-2.0 |
def build_kernel(
integrator: Callable = integrators.velocity_verlet,
divergence_threshold: float = 1000,
):
"""Build a HMC kernel.
Parameters
----------
integrator
The symplectic integrator to use to integrate the Hamiltonian dynamics.
divergence_threshold
Value of the diff... | Build a HMC kernel.
Parameters
----------
integrator
The symplectic integrator to use to integrate the Hamiltonian dynamics.
divergence_threshold
Value of the difference in energy above which we consider that the transition is
divergent.
Returns
-------
A kernel tha... | build_kernel | python | blackjax-devs/blackjax | blackjax/mcmc/hmc.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/hmc.py | Apache-2.0 |
def kernel(
rng_key: PRNGKey,
state: HMCState,
logdensity_fn: Callable,
step_size: float,
inverse_mass_matrix: metrics.MetricTypes,
num_integration_steps: int,
) -> tuple[HMCState, HMCInfo]:
"""Generate a new sample with the HMC kernel."""
metric = me... | Generate a new sample with the HMC kernel. | kernel | python | blackjax-devs/blackjax | blackjax/mcmc/hmc.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/hmc.py | Apache-2.0 |
def as_top_level_api(
logdensity_fn: Callable,
step_size: float,
inverse_mass_matrix: metrics.MetricTypes,
num_integration_steps: int,
*,
divergence_threshold: int = 1000,
integrator: Callable = integrators.velocity_verlet,
) -> SamplingAlgorithm:
"""Implements the (basic) user interface... | Implements the (basic) user interface for the HMC kernel.
The general hmc kernel builder (:meth:`blackjax.mcmc.hmc.build_kernel`, alias
`blackjax.hmc.build_kernel`) can be cumbersome to manipulate. Since most users only
need to specify the kernel parameters at initialization time, we provide a helper
f... | as_top_level_api | python | blackjax-devs/blackjax | blackjax/mcmc/hmc.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/hmc.py | Apache-2.0 |
def hmc_proposal(
integrator: Callable,
kinetic_energy: metrics.KineticEnergy,
step_size: Union[float, ArrayLikeTree],
num_integration_steps: int = 1,
divergence_threshold: float = 1000,
*,
sample_proposal: Callable = static_binomial_sampling,
) -> Callable:
"""Vanilla HMC algorithm.
... | Vanilla HMC algorithm.
The algorithm integrates the trajectory applying a symplectic integrator
`num_integration_steps` times in one direction to get a proposal and uses a
Metropolis-Hastings acceptance step to either reject or accept this
proposal. This is what people usually refer to when they talk a... | hmc_proposal | python | blackjax-devs/blackjax | blackjax/mcmc/hmc.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/hmc.py | Apache-2.0 |
def flip_momentum(
state: integrators.IntegratorState,
) -> integrators.IntegratorState:
"""Flip the momentum at the end of the trajectory.
To guarantee time-reversibility (hence detailed balance) we
need to flip the last state's momentum. If we run the hamiltonian
dynamics starting from the last s... | Flip the momentum at the end of the trajectory.
To guarantee time-reversibility (hence detailed balance) we
need to flip the last state's momentum. If we run the hamiltonian
dynamics starting from the last state with flipped momentum we
should indeed retrieve the initial state (with flipped momentum).
... | flip_momentum | python | blackjax-devs/blackjax | blackjax/mcmc/hmc.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/hmc.py | Apache-2.0 |
def generalized_two_stage_integrator(
operator1: Callable,
operator2: Callable,
coefficients: list[float],
format_output_fn: Callable = lambda x: x,
):
"""Generalized numerical integrator for solving ODEs.
The generalized integrator performs numerical integration of a ODE system by
alernati... | Generalized numerical integrator for solving ODEs.
The generalized integrator performs numerical integration of a ODE system by
alernating between stage 1 and stage 2 updates.
The update scheme is decided by the coefficients, The scheme should be palindromic,
i.e. the coefficients of the update scheme ... | generalized_two_stage_integrator | python | blackjax-devs/blackjax | blackjax/mcmc/integrators.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/integrators.py | Apache-2.0 |
def generate_euclidean_integrator(coefficients):
"""Generate symplectic integrator for solving a Hamiltonian system.
The resulting integrator is volume-preserve and preserves the symplectic structure
of phase space.
"""
def euclidean_integrator(
logdensity_fn: Callable, kinetic_energy_fn: ... | Generate symplectic integrator for solving a Hamiltonian system.
The resulting integrator is volume-preserve and preserves the symplectic structure
of phase space.
| generate_euclidean_integrator | python | blackjax-devs/blackjax | blackjax/mcmc/integrators.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/integrators.py | Apache-2.0 |
def update(
momentum: ArrayTree,
logdensity_grad: ArrayTree,
step_size: float,
coef: float,
previous_kinetic_energy_change=None,
is_last_call=False,
):
"""Momentum update based on Esh dynamics.
The momentum updating map of the esh dynamics as derived ... | Momentum update based on Esh dynamics.
The momentum updating map of the esh dynamics as derived in :cite:p:`steeg2021hamiltonian`
There are no exponentials e^delta, which prevents overflows when the gradient norm
is large.
| update | python | blackjax-devs/blackjax | blackjax/mcmc/integrators.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/integrators.py | Apache-2.0 |
def partially_refresh_momentum(momentum, rng_key, step_size, L):
"""Adds a small noise to momentum and normalizes.
Parameters
----------
rng_key
The pseudo-random number generator key used to generate random numbers.
momentum
PyTree that the structure the output should to match.
... | Adds a small noise to momentum and normalizes.
Parameters
----------
rng_key
The pseudo-random number generator key used to generate random numbers.
momentum
PyTree that the structure the output should to match.
step_size
Step size
L
controls rate of momentum cha... | partially_refresh_momentum | python | blackjax-devs/blackjax | blackjax/mcmc/integrators.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/integrators.py | Apache-2.0 |
def solve_fixed_point_iteration(
func: Callable[[ArrayTree], Tuple[ArrayTree, ArrayTree]],
x0: ArrayTree,
*,
convergence_tol: float = 1e-6,
divergence_tol: float = 1e10,
max_iters: int = 100,
norm_fn: Callable[[ArrayTree], float] = lambda x: jnp.max(jnp.abs(x)),
) -> Tuple[ArrayTree, ArrayTr... | Solve for x = func(x) using a fixed point iteration | solve_fixed_point_iteration | python | blackjax-devs/blackjax | blackjax/mcmc/integrators.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/integrators.py | Apache-2.0 |
def implicit_midpoint(
logdensity_fn: Callable,
kinetic_energy_fn: KineticEnergy,
*,
solver: FixedPointSolver = solve_fixed_point_iteration,
**solver_kwargs: Any,
) -> Integrator:
"""The implicit midpoint integrator with support for non-stationary kinetic energy
This is an integrator based ... | The implicit midpoint integrator with support for non-stationary kinetic energy
This is an integrator based on :cite:t:`brofos2021evaluating`, which provides
support for kinetic energies that depend on position. This integrator requires that
the kinetic energy function takes two arguments: position and mom... | implicit_midpoint | python | blackjax-devs/blackjax | blackjax/mcmc/integrators.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/integrators.py | Apache-2.0 |
def build_kernel():
"""Build a MALA kernel.
Returns
-------
A kernel that takes a rng_key and a Pytree that contains the current state
of the chain and that returns a new state of the chain along with
information about the transition.
"""
def transition_energy(state, new_state, step_s... | Build a MALA kernel.
Returns
-------
A kernel that takes a rng_key and a Pytree that contains the current state
of the chain and that returns a new state of the chain along with
information about the transition.
| build_kernel | python | blackjax-devs/blackjax | blackjax/mcmc/mala.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/mala.py | Apache-2.0 |
def transition_energy(state, new_state, step_size):
"""Transition energy to go from `state` to `new_state`"""
theta = jax.tree_util.tree_map(
lambda x, new_x, g: x - new_x - step_size * g,
state.position,
new_state.position,
new_state.logdensity_grad,
... | Transition energy to go from `state` to `new_state` | transition_energy | python | blackjax-devs/blackjax | blackjax/mcmc/mala.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/mala.py | Apache-2.0 |
def kernel(
rng_key: PRNGKey, state: MALAState, logdensity_fn: Callable, step_size: float
) -> tuple[MALAState, MALAInfo]:
"""Generate a new sample with the MALA kernel."""
grad_fn = jax.value_and_grad(logdensity_fn)
integrator = diffusions.overdamped_langevin(grad_fn)
key_i... | Generate a new sample with the MALA kernel. | kernel | python | blackjax-devs/blackjax | blackjax/mcmc/mala.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/mala.py | Apache-2.0 |
def as_top_level_api(
logdensity_fn: Callable,
step_size: float,
) -> SamplingAlgorithm:
"""Implements the (basic) user interface for the MALA kernel.
The general mala kernel builder (:meth:`blackjax.mcmc.mala.build_kernel`, alias `blackjax.mala.build_kernel`) can be
cumbersome to manipulate. Since... | Implements the (basic) user interface for the MALA kernel.
The general mala kernel builder (:meth:`blackjax.mcmc.mala.build_kernel`, alias `blackjax.mala.build_kernel`) can be
cumbersome to manipulate. Since most users only need to specify the kernel
parameters at initialization time, we provide a helper f... | as_top_level_api | python | blackjax-devs/blackjax | blackjax/mcmc/mala.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/mala.py | Apache-2.0 |
def svd_from_covariance(covariance: Array) -> CovarianceSVD:
"""Compute the singular value decomposition of the covariance matrix.
Parameters
----------
covariance
The covariance matrix.
Returns
-------
A ``CovarianceSVD`` object.
"""
U, Gamma, U_t = jnp.linalg.svd(covaria... | Compute the singular value decomposition of the covariance matrix.
Parameters
----------
covariance
The covariance matrix.
Returns
-------
A ``CovarianceSVD`` object.
| svd_from_covariance | python | blackjax-devs/blackjax | blackjax/mcmc/marginal_latent_gaussian.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/marginal_latent_gaussian.py | Apache-2.0 |
def generate_mean_shifted_logprob(logdensity_fn, mean, covariance):
"""Generate a log-density function that is shifted by a constant
Parameters
----------
logdensity_fn
The original log-density function
mean
The mean of the prior Gaussian density
covariance
The covarianc... | Generate a log-density function that is shifted by a constant
Parameters
----------
logdensity_fn
The original log-density function
mean
The mean of the prior Gaussian density
covariance
The covariance of the prior Gaussian density.
Returns
-------
A log-density... | generate_mean_shifted_logprob | python | blackjax-devs/blackjax | blackjax/mcmc/marginal_latent_gaussian.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/marginal_latent_gaussian.py | Apache-2.0 |
def init(position, logdensity_fn, U_t):
"""Initialize the marginal version of the auxiliary gradient-based sampler.
Parameters
----------
position
The initial position of the chain.
logdensity_fn
The logarithm of the likelihood function for the latent Gaussian model.
U_t
... | Initialize the marginal version of the auxiliary gradient-based sampler.
Parameters
----------
position
The initial position of the chain.
logdensity_fn
The logarithm of the likelihood function for the latent Gaussian model.
U_t
The unitary array of the covariance matrix.
... | init | python | blackjax-devs/blackjax | blackjax/mcmc/marginal_latent_gaussian.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/marginal_latent_gaussian.py | Apache-2.0 |
def build_kernel(cov_svd: CovarianceSVD):
"""Build the marginal version of the auxiliary gradient-based sampler.
Parameters
----------
cov_svd
The singular value decomposition of the covariance matrix.
Returns
-------
A kernel that takes a rng_key and a Pytree that contains the cur... | Build the marginal version of the auxiliary gradient-based sampler.
Parameters
----------
cov_svd
The singular value decomposition of the covariance matrix.
Returns
-------
A kernel that takes a rng_key and a Pytree that contains the current state
of the chain and that returns a ne... | build_kernel | python | blackjax-devs/blackjax | blackjax/mcmc/marginal_latent_gaussian.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/marginal_latent_gaussian.py | Apache-2.0 |
def build_kernel(
logdensity_fn,
inverse_mass_matrix,
integrator,
desired_energy_var_max_ratio=jnp.inf,
desired_energy_var=5e-4,
):
"""Build a HMC kernel.
Parameters
----------
integrator
The symplectic integrator to use to integrate the Hamiltonian dynamics.
L
t... | Build a HMC kernel.
Parameters
----------
integrator
The symplectic integrator to use to integrate the Hamiltonian dynamics.
L
the momentum decoherence rate.
step_size
step size of the integrator.
Returns
-------
A kernel that takes a rng_key and a Pytree that c... | build_kernel | python | blackjax-devs/blackjax | blackjax/mcmc/mclmc.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/mclmc.py | Apache-2.0 |
def as_top_level_api(
logdensity_fn: Callable,
L,
step_size,
integrator=isokinetic_mclachlan,
inverse_mass_matrix=1.0,
desired_energy_var_max_ratio=jnp.inf,
) -> SamplingAlgorithm:
"""The general mclmc kernel builder (:meth:`blackjax.mcmc.mclmc.build_kernel`, alias `blackjax.mclmc.build_kern... | The general mclmc kernel builder (:meth:`blackjax.mcmc.mclmc.build_kernel`, alias `blackjax.mclmc.build_kernel`) can be
cumbersome to manipulate. Since most users only need to specify the kernel
parameters at initialization time, we provide a helper function that
specializes the general kernel.
We also... | as_top_level_api | python | blackjax-devs/blackjax | blackjax/mcmc/mclmc.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/mclmc.py | Apache-2.0 |
def default_metric(metric: MetricTypes) -> Metric:
"""Convert an input metric into a ``Metric`` object following sensible default rules
The metric can be specified in three different ways:
- A ``Metric`` object that implements the full interface
- An ``Array`` which is assumed to specify the inverse m... | Convert an input metric into a ``Metric`` object following sensible default rules
The metric can be specified in three different ways:
- A ``Metric`` object that implements the full interface
- An ``Array`` which is assumed to specify the inverse mass matrix of a static
metric
- A function that ... | default_metric | python | blackjax-devs/blackjax | blackjax/mcmc/metrics.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/metrics.py | Apache-2.0 |
def gaussian_euclidean(
inverse_mass_matrix: Array,
) -> Metric:
r"""Hamiltonian dynamic on euclidean manifold with normally-distributed momentum
:cite:p:`betancourt2013general`.
The gaussian euclidean metric is a euclidean metric further characterized
by setting the conditional probability density... | Hamiltonian dynamic on euclidean manifold with normally-distributed momentum
:cite:p:`betancourt2013general`.
The gaussian euclidean metric is a euclidean metric further characterized
by setting the conditional probability density :math:`\pi(momentum|position)`
to follow a standard gaussian distributio... | gaussian_euclidean | python | blackjax-devs/blackjax | blackjax/mcmc/metrics.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/metrics.py | Apache-2.0 |
def is_turning(
momentum_left: ArrayLikeTree,
momentum_right: ArrayLikeTree,
momentum_sum: ArrayLikeTree,
position_left: Optional[ArrayLikeTree] = None,
position_right: Optional[ArrayLikeTree] = None,
) -> bool:
"""Generalized U-turn criterion :cite:p:`betancourt2013g... | Generalized U-turn criterion :cite:p:`betancourt2013generalizing,nuts_uturn`.
Parameters
----------
momentum_left
Momentum of the leftmost point of the trajectory.
momentum_right
Momentum of the rightmost point of the trajectory.
momentum_sum
... | is_turning | python | blackjax-devs/blackjax | blackjax/mcmc/metrics.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/metrics.py | Apache-2.0 |
def scale(
position: ArrayLikeTree,
element: ArrayLikeTree,
*,
inv: bool,
trans: bool,
) -> ArrayLikeTree:
"""Scale elements by the mass matrix.
Parameters
----------
position
The current position. Not used in this metric.
... | Scale elements by the mass matrix.
Parameters
----------
position
The current position. Not used in this metric.
elements
Elements to scale
inv
Whether to scale the elements by the inverse mass matrix or the mass matrix.
If True, t... | scale | python | blackjax-devs/blackjax | blackjax/mcmc/metrics.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/metrics.py | Apache-2.0 |
def scale(
position: ArrayLikeTree,
element: ArrayLikeTree,
*,
inv: bool,
trans: bool,
) -> ArrayLikeTree:
"""Scale elements by the mass matrix.
Parameters
----------
position
The current position.
Returns
-------
... | Scale elements by the mass matrix.
Parameters
----------
position
The current position.
Returns
-------
scaled_elements
The scaled elements.
| scale | python | blackjax-devs/blackjax | blackjax/mcmc/metrics.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/metrics.py | Apache-2.0 |
def build_kernel(
integrator: Callable = integrators.velocity_verlet,
divergence_threshold: int = 1000,
):
"""Build an iterative NUTS kernel.
This algorithm is an iteration on the original NUTS algorithm :cite:p:`hoffman2014no`
with two major differences:
- We do not use slice samplig but mult... | Build an iterative NUTS kernel.
This algorithm is an iteration on the original NUTS algorithm :cite:p:`hoffman2014no`
with two major differences:
- We do not use slice samplig but multinomial sampling for the proposal
:cite:p:`betancourt2017conceptual`;
- The trajectory expansion is not recursiv... | build_kernel | python | blackjax-devs/blackjax | blackjax/mcmc/nuts.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/nuts.py | Apache-2.0 |
def kernel(
rng_key: PRNGKey,
state: hmc.HMCState,
logdensity_fn: Callable,
step_size: float,
inverse_mass_matrix: metrics.MetricTypes,
max_num_doublings: int = 10,
) -> tuple[hmc.HMCState, NUTSInfo]:
"""Generate a new sample with the NUTS kernel."""
... | Generate a new sample with the NUTS kernel. | kernel | python | blackjax-devs/blackjax | blackjax/mcmc/nuts.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/nuts.py | Apache-2.0 |
def as_top_level_api(
logdensity_fn: Callable,
step_size: float,
inverse_mass_matrix: metrics.MetricTypes,
*,
max_num_doublings: int = 10,
divergence_threshold: int = 1000,
integrator: Callable = integrators.velocity_verlet,
) -> SamplingAlgorithm:
"""Implements the (basic) user interfac... | Implements the (basic) user interface for the nuts kernel.
Examples
--------
A new NUTS kernel can be initialized and used with the following code:
.. code::
nuts = blackjax.nuts(logdensity_fn, step_size, inverse_mass_matrix)
state = nuts.init(position)
new_state, info = nuts... | as_top_level_api | python | blackjax-devs/blackjax | blackjax/mcmc/nuts.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/nuts.py | Apache-2.0 |
def iterative_nuts_proposal(
integrator: Callable,
kinetic_energy: metrics.KineticEnergy,
uturn_check_fn: metrics.CheckTurning,
max_num_expansions: int = 10,
divergence_threshold: float = 1000,
) -> Callable:
"""Iterative NUTS proposal.
Parameters
----------
integrator
Sympl... | Iterative NUTS proposal.
Parameters
----------
integrator
Symplectic integrator used to build the trajectory step by step.
kinetic_energy
Function that computes the kinetic energy.
uturn_check_fn:
Function that determines whether the trajectory is turning on itself
(... | iterative_nuts_proposal | python | blackjax-devs/blackjax | blackjax/mcmc/nuts.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/nuts.py | Apache-2.0 |
def init(
position: ArrayLikeTree, logdensity_fn: Callable, period: int
) -> PeriodicOrbitalState:
"""Create a periodic orbital state from a position.
Parameters
----------
position
the current values of the random variables whose posterior we want to
sample from. Can be anything fr... | Create a periodic orbital state from a position.
Parameters
----------
position
the current values of the random variables whose posterior we want to
sample from. Can be anything from a list, a (named) tuple or a dict of
arrays. The arrays can either be Numpy or JAX arrays.
logd... | init | python | blackjax-devs/blackjax | blackjax/mcmc/periodic_orbital.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/periodic_orbital.py | Apache-2.0 |
def build_kernel(
bijection: Callable = integrators.velocity_verlet,
):
"""Build a Periodic Orbital kernel :cite:p:`neklyudov2022orbital`.
Parameters
----------
bijection
transformation used to build the orbit (given a step size).
Returns
-------
A kernel that takes a rng_key a... | Build a Periodic Orbital kernel :cite:p:`neklyudov2022orbital`.
Parameters
----------
bijection
transformation used to build the orbit (given a step size).
Returns
-------
A kernel that takes a rng_key and a Pytree that contains the current state
of the chain and that returns a new... | build_kernel | python | blackjax-devs/blackjax | blackjax/mcmc/periodic_orbital.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/periodic_orbital.py | Apache-2.0 |
def kernel(
rng_key: PRNGKey,
state: PeriodicOrbitalState,
logdensity_fn: Callable,
step_size: float,
inverse_mass_matrix: Array,
period: int,
) -> tuple[PeriodicOrbitalState, PeriodicOrbitalInfo]:
"""Generate a new orbit with the Periodic Orbital kernel.
... | Generate a new orbit with the Periodic Orbital kernel.
Choose a step from the orbit with probability proportional to its weights.
Then shift the direction (or alternatively sample a new direction randomly),
in order to make the algorithm irreversible, and compute a new orbit from
the se... | kernel | python | blackjax-devs/blackjax | blackjax/mcmc/periodic_orbital.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/periodic_orbital.py | Apache-2.0 |
def as_top_level_api(
logdensity_fn: Callable,
step_size: float,
inverse_mass_matrix: Array, # assume momentum is always Gaussian
period: int,
*,
bijection: Callable = integrators.velocity_verlet,
) -> SamplingAlgorithm:
"""Implements the (basic) user interface for the Periodic orbital MCMC... | Implements the (basic) user interface for the Periodic orbital MCMC kernel.
Each iteration of the periodic orbital MCMC outputs ``period`` weighted samples from
a single Hamiltonian orbit connecting the previous sample and momentum (latent) variable
with precision matrix ``inverse_mass_matrix``, evaluated ... | as_top_level_api | python | blackjax-devs/blackjax | blackjax/mcmc/periodic_orbital.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/periodic_orbital.py | Apache-2.0 |
def periodic_orbital_proposal(
bijection: Callable,
kinetic_energy_fn: Callable,
period: int,
step_size: float,
) -> Callable:
"""Periodic Orbital algorithm.
The algorithm builds and orbit and computes the weights for each of its steps
by applying a bijection `period` times, both forwards a... | Periodic Orbital algorithm.
The algorithm builds and orbit and computes the weights for each of its steps
by applying a bijection `period` times, both forwards and backwards depending
on the direction of the initial state.
Parameters
----------
bijection
continuous, differentialble and... | periodic_orbital_proposal | python | blackjax-devs/blackjax | blackjax/mcmc/periodic_orbital.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/periodic_orbital.py | Apache-2.0 |
def generate(
direction: int, init_state: integrators.IntegratorState
) -> tuple[PeriodicOrbitalState, PeriodicOrbitalInfo]:
"""Generate orbit by applying bijection forwards and backwards on period.
As described in algorithm 2 of :cite:p:`neklyudov2022orbital`, each iteration of the periodi... | Generate orbit by applying bijection forwards and backwards on period.
As described in algorithm 2 of :cite:p:`neklyudov2022orbital`, each iteration of the periodic orbital
MCMC takes a position and its direction, i.e. its step in the orbit, then
it runs the bijection backwards until it reaches... | generate | python | blackjax-devs/blackjax | blackjax/mcmc/periodic_orbital.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/periodic_orbital.py | Apache-2.0 |
def proposal_generator(energy_fn: Callable) -> tuple[Callable, Callable]:
"""
Parameters
----------
energy_fn
A function that computes the energy associated to a given state
Returns
-------
Two functions, one to generate an initial proposal when no step has been taken,
another ... |
Parameters
----------
energy_fn
A function that computes the energy associated to a given state
Returns
-------
Two functions, one to generate an initial proposal when no step has been taken,
another to generate proposals after each step.
| proposal_generator | python | blackjax-devs/blackjax | blackjax/mcmc/proposal.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/proposal.py | Apache-2.0 |
def update(initial_energy: float, new_state: TrajectoryState) -> Proposal:
"""Generate a new proposal from a trajectory state.
The trajectory state records information about the position in the state
space and corresponding logdensity. A proposal also carries a
weight that is equal to t... | Generate a new proposal from a trajectory state.
The trajectory state records information about the position in the state
space and corresponding logdensity. A proposal also carries a
weight that is equal to the difference between the current energy and
the previous one. It thus carries... | update | python | blackjax-devs/blackjax | blackjax/mcmc/proposal.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/proposal.py | Apache-2.0 |
def progressive_biased_sampling(
rng_key: PRNGKey, proposal: Proposal, new_proposal: Proposal
) -> Proposal:
"""Baised proposal sampling :cite:p:`betancourt2017conceptual`.
Unlike uniform sampling, biased sampling favors new proposals. It thus
biases the transition away from the trajectory's initial st... | Baised proposal sampling :cite:p:`betancourt2017conceptual`.
Unlike uniform sampling, biased sampling favors new proposals. It thus
biases the transition away from the trajectory's initial state.
| progressive_biased_sampling | python | blackjax-devs/blackjax | blackjax/mcmc/proposal.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/proposal.py | Apache-2.0 |
def compute_asymmetric_acceptance_ratio(transition_energy_fn: Callable) -> Callable:
"""Generate a meta function to compute the transition between two states.
In particular, both states are used to compute the energies to consider in weighting
the proposal, to account for asymmetries.
Parameters
-... | Generate a meta function to compute the transition between two states.
In particular, both states are used to compute the energies to consider in weighting
the proposal, to account for asymmetries.
Parameters
----------
transition_energy_fn
A function that computes the energy of a transiti... | compute_asymmetric_acceptance_ratio | python | blackjax-devs/blackjax | blackjax/mcmc/proposal.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/proposal.py | Apache-2.0 |
def static_binomial_sampling(
rng_key: PRNGKey, log_p_accept: float, proposal, new_proposal
):
"""Accept or reject a proposal.
In the static setting, the probability with which the new proposal is
accepted is a function of the difference in energy between the previous and
the current states. If the... | Accept or reject a proposal.
In the static setting, the probability with which the new proposal is
accepted is a function of the difference in energy between the previous and
the current states. If the current energy is lower than the previous one
then the new proposal is accepted with probability 1.
... | static_binomial_sampling | python | blackjax-devs/blackjax | blackjax/mcmc/proposal.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/proposal.py | Apache-2.0 |
def nonreversible_slice_sampling(
slice: Array, delta_energy: float, proposal, new_proposal
):
"""Slice sampling for non-reversible Metropolis-Hasting update.
Performs a non-reversible update of a uniform [0, 1] value
for Metropolis-Hastings accept/reject decisions :cite:p:`neal2020non`, in addition
... | Slice sampling for non-reversible Metropolis-Hasting update.
Performs a non-reversible update of a uniform [0, 1] value
for Metropolis-Hastings accept/reject decisions :cite:p:`neal2020non`, in addition
to the accept/reject step of a current state and new proposal.
| nonreversible_slice_sampling | python | blackjax-devs/blackjax | blackjax/mcmc/proposal.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/proposal.py | Apache-2.0 |
def normal(sigma: Array) -> Callable:
"""Normal Random Walk proposal.
Propose a new position such that its distance to the current position is
normally distributed. Suitable for continuous variables.
Parameter
---------
sigma:
vector or matrix that contains the standard deviation of th... | Normal Random Walk proposal.
Propose a new position such that its distance to the current position is
normally distributed. Suitable for continuous variables.
Parameter
---------
sigma:
vector or matrix that contains the standard deviation of the centered
normal distribution from w... | normal | python | blackjax-devs/blackjax | blackjax/mcmc/random_walk.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/random_walk.py | Apache-2.0 |
def build_additive_step():
"""Build a Random Walk Rosenbluth-Metropolis-Hastings kernel
Returns
-------
A kernel that takes a rng_key and a Pytree that contains the current state
of the chain and that returns a new state of the chain along with
information about the transition.
"""
def... | Build a Random Walk Rosenbluth-Metropolis-Hastings kernel
Returns
-------
A kernel that takes a rng_key and a Pytree that contains the current state
of the chain and that returns a new state of the chain along with
information about the transition.
| build_additive_step | python | blackjax-devs/blackjax | blackjax/mcmc/random_walk.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/random_walk.py | Apache-2.0 |
def additive_step_random_walk(
logdensity_fn: Callable, random_step: Callable
) -> SamplingAlgorithm:
"""Implements the user interface for the Additive Step RMH
Examples
--------
A new kernel can be initialized and used with the following code:
.. code::
rw = blackjax.additive_step_r... | Implements the user interface for the Additive Step RMH
Examples
--------
A new kernel can be initialized and used with the following code:
.. code::
rw = blackjax.additive_step_random_walk(logdensity_fn, random_step)
state = rw.init(position)
new_state, info = rw.step(rng_ke... | additive_step_random_walk | python | blackjax-devs/blackjax | blackjax/mcmc/random_walk.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/random_walk.py | Apache-2.0 |
def build_irmh() -> Callable:
"""
Build an Independent Random Walk Rosenbluth-Metropolis-Hastings kernel. This implies
that the proposal distribution does not depend on the particle being mutated :cite:p:`wang2022exact`.
Returns
-------
A kernel that takes a rng_key and a Pytree that contains t... |
Build an Independent Random Walk Rosenbluth-Metropolis-Hastings kernel. This implies
that the proposal distribution does not depend on the particle being mutated :cite:p:`wang2022exact`.
Returns
-------
A kernel that takes a rng_key and a Pytree that contains the current state
of the chain and... | build_irmh | python | blackjax-devs/blackjax | blackjax/mcmc/random_walk.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/random_walk.py | Apache-2.0 |
def kernel(
rng_key: PRNGKey,
state: RWState,
logdensity_fn: Callable,
proposal_distribution: Callable,
proposal_logdensity_fn: Optional[Callable] = None,
) -> tuple[RWState, RWInfo]:
"""
Parameters
----------
proposal_distribution
... |
Parameters
----------
proposal_distribution
A function that, given a PRNGKey, is able to produce a sample in the same
domain of the target distribution.
proposal_logdensity_fn:
For non-symmetric proposals, a function that returns the log-density
... | kernel | python | blackjax-devs/blackjax | blackjax/mcmc/random_walk.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/random_walk.py | Apache-2.0 |
def irmh_as_top_level_api(
logdensity_fn: Callable,
proposal_distribution: Callable,
proposal_logdensity_fn: Optional[Callable] = None,
) -> SamplingAlgorithm:
"""Implements the (basic) user interface for the independent RMH.
Examples
--------
A new kernel can be initialized and used with ... | Implements the (basic) user interface for the independent RMH.
Examples
--------
A new kernel can be initialized and used with the following code:
.. code::
rmh = blackjax.irmh(logdensity_fn, proposal_distribution)
state = rmh.init(position)
new_state, info = rmh.step(rng_key... | irmh_as_top_level_api | python | blackjax-devs/blackjax | blackjax/mcmc/random_walk.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/random_walk.py | Apache-2.0 |
def build_rmh():
"""Build a Rosenbluth-Metropolis-Hastings kernel.
Returns
-------
A kernel that takes a rng_key and a Pytree that contains the current state
of the chain and that returns a new state of the chain along with
information about the transition.
"""
def kernel(
rng... | Build a Rosenbluth-Metropolis-Hastings kernel.
Returns
-------
A kernel that takes a rng_key and a Pytree that contains the current state
of the chain and that returns a new state of the chain along with
information about the transition.
| build_rmh | python | blackjax-devs/blackjax | blackjax/mcmc/random_walk.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/random_walk.py | Apache-2.0 |
def kernel(
rng_key: PRNGKey,
state: RWState,
logdensity_fn: Callable,
transition_generator: Callable,
proposal_logdensity_fn: Optional[Callable] = None,
) -> tuple[RWState, RWInfo]:
"""Move the chain by one step using the Rosenbluth Metropolis Hastings
algori... | Move the chain by one step using the Rosenbluth Metropolis Hastings
algorithm.
Parameters
----------
rng_key:
The pseudo-random number generator key used to generate random
numbers.
logdensity_fn:
A function that returns the log-probability at a... | kernel | python | blackjax-devs/blackjax | blackjax/mcmc/random_walk.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/random_walk.py | Apache-2.0 |
def rmh_as_top_level_api(
logdensity_fn: Callable,
proposal_generator: Callable[[PRNGKey, ArrayLikeTree], ArrayTree],
proposal_logdensity_fn: Optional[Callable[[ArrayLikeTree], ArrayTree]] = None,
) -> SamplingAlgorithm:
"""Implements the user interface for the RMH.
Examples
--------
A new... | Implements the user interface for the RMH.
Examples
--------
A new kernel can be initialized and used with the following code:
.. code::
rmh = blackjax.rmh(logdensity_fn, proposal_generator)
state = rmh.init(position)
new_state, info = rmh.step(rng_key, state)
We can JIT... | rmh_as_top_level_api | python | blackjax-devs/blackjax | blackjax/mcmc/random_walk.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/random_walk.py | Apache-2.0 |
def as_top_level_api(
logdensity_fn: Callable,
step_size: float,
mass_matrix: Union[metrics.Metric, Callable],
num_integration_steps: int,
*,
divergence_threshold: int = 1000,
integrator: Callable = integrators.implicit_midpoint,
) -> SamplingAlgorithm:
"""A Riemannian Manifold Hamiltoni... | A Riemannian Manifold Hamiltonian Monte Carlo kernel
Of note, this kernel is simply an alias of the ``hmc`` kernel with a
different choice of default integrator (``implicit_midpoint`` instead of
``velocity_verlet``) since RMHMC is typically used for Hamiltonian systems
that are not separable.
Para... | as_top_level_api | python | blackjax-devs/blackjax | blackjax/mcmc/rmhmc.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/rmhmc.py | Apache-2.0 |
def _leaf_idx_to_ckpt_idxs(n):
"""Find the checkpoint id from a step number."""
# computes the number of non-zero bits except the last bit
# e.g. 6 -> 2, 7 -> 2, 13 -> 2
idx_max = jnp.bitwise_count(n >> 1).astype(jnp.int32)
# computes the number of contiguous last non-zero bits
... | Find the checkpoint id from a step number. | _leaf_idx_to_ckpt_idxs | python | blackjax-devs/blackjax | blackjax/mcmc/termination.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/termination.py | Apache-2.0 |
def _is_iterative_turning(checkpoints, momentum_sum, momentum):
"""Checks whether there is a U-turn in the iteratively built expanded trajectory.
These checks only need to be performed as specific points.
"""
r, _ = jax.flatten_util.ravel_pytree(momentum)
r_sum, _ = jax.flatten... | Checks whether there is a U-turn in the iteratively built expanded trajectory.
These checks only need to be performed as specific points.
| _is_iterative_turning | python | blackjax-devs/blackjax | blackjax/mcmc/termination.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/termination.py | Apache-2.0 |
def append_to_trajectory(trajectory: Trajectory, state: IntegratorState) -> Trajectory:
"""Append a state to the (right of the) trajectory to form a new trajectory."""
momentum_sum = jax.tree_util.tree_map(
jnp.add, trajectory.momentum_sum, state.momentum
)
return Trajectory(
trajectory.... | Append a state to the (right of the) trajectory to form a new trajectory. | append_to_trajectory | python | blackjax-devs/blackjax | blackjax/mcmc/trajectory.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/trajectory.py | Apache-2.0 |
def reorder_trajectories(
direction: int, trajectory: Trajectory, new_trajectory: Trajectory
) -> tuple[Trajectory, Trajectory]:
"""Order the two trajectories depending on the direction."""
return jax.lax.cond(
direction > 0,
lambda _: (
trajectory,
new_trajectory,
... | Order the two trajectories depending on the direction. | reorder_trajectories | python | blackjax-devs/blackjax | blackjax/mcmc/trajectory.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/trajectory.py | Apache-2.0 |
def static_integration(
integrator: Callable,
direction: int = 1,
) -> Callable:
"""Generate a trajectory by integrating several times in one direction."""
def integrate(
initial_state: IntegratorState, step_size, num_integration_steps
) -> IntegratorState:
directed_step_size = jax.... | Generate a trajectory by integrating several times in one direction. | static_integration | python | blackjax-devs/blackjax | blackjax/mcmc/trajectory.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/trajectory.py | Apache-2.0 |
def dynamic_progressive_integration(
integrator: Callable,
kinetic_energy: Callable,
update_termination_state: Callable,
is_criterion_met: Callable,
divergence_threshold: float,
):
"""Integrate a trajectory and update the proposal sequentially in one direction
until the termination criterion... | Integrate a trajectory and update the proposal sequentially in one direction
until the termination criterion is met.
Parameters
----------
integrator
The symplectic integrator used to integrate the hamiltonian trajectory.
kinetic_energy
Function to compute the current value of the k... | dynamic_progressive_integration | python | blackjax-devs/blackjax | blackjax/mcmc/trajectory.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/trajectory.py | Apache-2.0 |
def integrate(
rng_key: PRNGKey,
initial_state: IntegratorState,
direction: int,
termination_state,
max_num_steps: int,
step_size,
initial_energy,
):
"""Integrate the trajectory starting from `initial_state` and update the
proposal sequentially... | Integrate the trajectory starting from `initial_state` and update the
proposal sequentially (hence progressive) until the termination
criterion is met (hence dynamic).
Parameters
----------
rng_key
Key used by JAX's random number generator.
initial_state
... | integrate | python | blackjax-devs/blackjax | blackjax/mcmc/trajectory.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/trajectory.py | Apache-2.0 |
def do_keep_integrating(loop_state):
"""Decide whether we should continue integrating the trajectory"""
integration_state, (is_diverging, has_terminated) = loop_state
return (
(integration_state.step < max_num_steps)
& ~has_terminated
&... | Decide whether we should continue integrating the trajectory | do_keep_integrating | python | blackjax-devs/blackjax | blackjax/mcmc/trajectory.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/trajectory.py | Apache-2.0 |
def dynamic_recursive_integration(
integrator: Callable,
kinetic_energy: Callable,
uturn_check_fn: Callable,
divergence_threshold: float,
use_robust_uturn_check: bool = False,
):
"""Integrate a trajectory and update the proposal recursively in Python
until the termination criterion is met.
... | Integrate a trajectory and update the proposal recursively in Python
until the termination criterion is met.
This is the implementation of Algorithm 6 from :cite:p:`hoffman2014no` with
multinomial sampling. The implemenation here is mostly for validating the
progressive implementation to make sure the ... | dynamic_recursive_integration | python | blackjax-devs/blackjax | blackjax/mcmc/trajectory.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/trajectory.py | Apache-2.0 |
def buildtree_integrate(
rng_key: PRNGKey,
initial_state: IntegratorState,
direction: int,
tree_depth: int,
step_size,
initial_energy: float,
):
"""Integrate the trajectory starting from `initial_state` and update
the proposal recursively with tree dou... | Integrate the trajectory starting from `initial_state` and update
the proposal recursively with tree doubling until the termination criterion is met.
The function `buildtree_integrate` calls itself for tree_depth > 0, thus invokes
the recursive scheme that builds a trajectory by doubling a bina... | buildtree_integrate | python | blackjax-devs/blackjax | blackjax/mcmc/trajectory.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/trajectory.py | Apache-2.0 |
def dynamic_multiplicative_expansion(
trajectory_integrator: Callable,
uturn_check_fn: Callable,
max_num_expansions: int = 10,
rate: int = 2,
) -> Callable:
"""Sample a trajectory and update the proposal sequentially
until the termination criterion is met.
The trajectory is sampled with the... | Sample a trajectory and update the proposal sequentially
until the termination criterion is met.
The trajectory is sampled with the following procedure:
1. Pick a direction at random;
2. Integrate `num_step` steps in this direction;
3. If the integration has stopped prematurely, do not update the p... | dynamic_multiplicative_expansion | python | blackjax-devs/blackjax | blackjax/mcmc/trajectory.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/trajectory.py | Apache-2.0 |
def do_keep_expanding(loop_state) -> bool:
"""Determine whether we need to keep expanding the trajectory."""
expansion_state, (is_diverging, is_turning) = loop_state
return (
(expansion_state.step < max_num_expansions)
& ~is_diverging
&... | Determine whether we need to keep expanding the trajectory. | do_keep_expanding | python | blackjax-devs/blackjax | blackjax/mcmc/trajectory.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/trajectory.py | Apache-2.0 |
def expand_once(loop_state):
"""Expand the current trajectory.
At each step we draw a direction at random, build a subtrajectory
starting from the leftmost or rightmost point of the current
trajectory that is twice as long as the current trajectory.
Once tha... | Expand the current trajectory.
At each step we draw a direction at random, build a subtrajectory
starting from the leftmost or rightmost point of the current
trajectory that is twice as long as the current trajectory.
Once that is done, possibly update the current propo... | expand_once | python | blackjax-devs/blackjax | blackjax/mcmc/trajectory.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/mcmc/trajectory.py | Apache-2.0 |
def dual_averaging(
t0: int = 10, gamma: float = 0.05, kappa: float = 0.75
) -> tuple[Callable, Callable, Callable]:
"""Find the state that minimizes an objective function using a primal-dual
subgradient method.
See :cite:p:`nesterov2009primal` for a detailed explanation of the algorithm and its mathem... | Find the state that minimizes an objective function using a primal-dual
subgradient method.
See :cite:p:`nesterov2009primal` for a detailed explanation of the algorithm and its mathematical
properties.
Parameters
----------
t0: float >= 0
Free parameter that stabilizes the initial iter... | dual_averaging | python | blackjax-devs/blackjax | blackjax/optimizers/dual_averaging.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/optimizers/dual_averaging.py | Apache-2.0 |
def init(x_init: float) -> DualAveragingState:
"""Initialize the state of the dual averaging scheme.
The parameter :math:`\\mu` is set to :math:`\\log(10 \\x_init)`
where :math:`\\x_init` is the initial value of the state.
"""
mu: float = jnp.log(10 * x_init)
step = 1
... | Initialize the state of the dual averaging scheme.
The parameter :math:`\mu` is set to :math:`\log(10 \x_init)`
where :math:`\x_init` is the initial value of the state.
| init | python | blackjax-devs/blackjax | blackjax/optimizers/dual_averaging.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/optimizers/dual_averaging.py | Apache-2.0 |
def update(da_state: DualAveragingState, gradient) -> DualAveragingState:
"""Update the state of the Dual Averaging adaptive algorithm.
Parameters
----------
gradient:
The gradient of the function to optimize with respect to the state
`x`, computed at the current... | Update the state of the Dual Averaging adaptive algorithm.
Parameters
----------
gradient:
The gradient of the function to optimize with respect to the state
`x`, computed at the current value of `x`.
da_state:
The current state of the dual averaging ... | update | python | blackjax-devs/blackjax | blackjax/optimizers/dual_averaging.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/optimizers/dual_averaging.py | Apache-2.0 |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
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Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
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