netket.sampler.ExactSampler

class netket.sampler.ExactSampler(*args, __precompute_cached_properties=False, __skip_preprocess=False, **kwargs)

Bases: netket.sampler.Sampler

This sampler generates i.i.d. samples from \(|\Psi(\sigma)|^2\).

In order to perform exact sampling, \(|\Psi(\sigma)|^2\) is precomputed an all the possible values of the quantum numbers \(\sigma\). This sampler has thus an exponential cost with the number of degrees of freedom, and cannot be used for large systems, where Metropolis-based sampling are instead a viable option.

Inheritance
Inheritance diagram of netket.sampler.ExactSampler
Attributes
is_exact
machine_pow: int = 2
n_batches

The batch size of the configuration $sigma$ used by this sampler.

In general, it is equivalent to n_chains.

Return type

int

n_chains

The total number of chains across all MPI ranks.

If you are not using MPI, this is equal to n_chains_per_rank

Return type

int

n_chains_per_rank: int = None
Methods
init_state(machine, parameters, seed=None)

Creates the structure holding the state of the sampler.

If you want reproducible samples, you should specify seed, otherwise the state will be initialised randomly.

If running across several MPI processes, all sampler_states are guaranteed to be in a different (but deterministic) state. This is achieved by first reducing (summing) the seed provided to every MPI rank, then generating n_rank seeds starting from the reduced one, and every rank is initialized with one of those seeds.

The resulting state is guaranteed to be a frozen python dataclass (in particular, a flax’s dataclass), and it can be serialized using Flax serialization methods.

Parameters
  • machine (Union[Callable, Module]) – a Flax module or callable with the forward pass of the log-pdf.

  • parameters (Any) – The PyTree of parameters of the model.

  • seed (Union[int, Any, None]) – An optional seed or jax PRNGKey. If not specified, a random seed will be used.

Return type

SamplerState

Returns

The structure holding the state of the sampler. In general you should not expect it to be in a valid state, and should reset it before use.

log_pdf(model)

Returns a closure with the log_pdf function encoded by this sampler.

Note: the result is returned as an HashablePartial so that the closure does not trigger recompilation.

Parameters

model (Union[Callable, Module]) – The machine, or apply_fun

Return type

Callable

Returns

the log probability density function

replace(**updates)

Returns a new object replacing the specified fields with new values.

reset(machine, parameters, state=None)

Resets the state of the sampler. To be used every time the parameters are changed.

Parameters
  • machine (Union[Callable, Module]) – a Flax module or callable with the forward pass of the log-pdf.

  • parameters (Any) – The PyTree of parameters of the model.

  • state (Optional[SamplerState]) – The current state of the sampler. If it’s not provided, it will be constructed by calling sampler.init_state(machine, parameters) with a random seed.

Return type

SamplerState

Returns

A valid sampler state.

sample(machine, parameters, *, state=None, chain_length=1)

Samples chain_length elements along the chains.

Parameters
  • sampler – The Monte Carlo sampler.

  • machine (Union[Callable, Module]) – The model or callable to sample from (if it’s a function it should have the signature f(parameters, σ) -> jnp.ndarray).

  • parameters (Any) – The PyTree of parameters of the model.

  • state (Optional[SamplerState]) – current state of the sampler. If None, then initialises it.

  • chain_length (int) – (default=1), the length of the chains.

Returns

The next batch of samples. state: The new state of the sampler

Return type

σ

sample_next(machine, parameters, state=None)

Samples the next state in the markov chain.

Parameters
  • machine (Union[Callable, Module]) – a Flax module or callable apply function with the forward pass of the log-pdf.

  • parameters (Any) – The PyTree of parameters of the model.

  • state (Optional[SamplerState]) – The current state of the sampler. If it’s not provided, it will be constructed by calling sampler.reset(machine, parameters) with a random seed.

Returns

The new state of the sampler σ: The next batch of samples.

Return type

state