netket.sampler.rules.MultipleRules

class netket.sampler.rules.MultipleRules

Bases: MetropolisRule

A Metropolis sampling rule that can be used to pick a rule from a list of rules with a given probability.

Each rule[i] will be selected with a probability probabilities[i].

Inheritance

__init__(rules, probabilities)

A Metropolis sampling rule that can be used to pick a rule from a list of rules with a given probability.

Each rule[i] will be selected with a probability probabilities[i].

Parameters:

• rules (tuple[MetropolisRule, ...]) – A list of rules, one for each subspace of the tensor hilbert space.

• probabilities (Union[ndarray, Array]) – A list of probabilities, one for each rule.

Return type:

MetropolisRule

Attributes

rules: tuple[MetropolisRule, ...]

List of rules to be selected from.

probabilities: Array

Corresponding list of probabilities with which every rule can be picked.

Methods

init_state(sampler, machine, params, key)

Initialises the optional internal state of the Metropolis sampler transition rule.

The provided key is unique and does not need to be split.

It should return an immutable data structure.

Parameters:

• sampler (MetropolisSampler) – The Metropolis sampler.

• machine (Module) – A Flax module with the forward pass of the log-pdf.

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

• key (Any) – A Jax PRNGKey.

Return type:

Optional[Any]

Returns:

An optional state.

random_state(sampler, machine, params, sampler_state, key)

Generates a random state compatible with this rule.

By default this calls netket.hilbert.random.random_state().

Parameters:

• sampler (MetropolisSampler) – The Metropolis sampler.

• machine (Module) – A Flax module with the forward pass of the log-pdf.

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

• sampler_state (SamplerState) – The current state of the sampler. Should not modify it.

• key (Any) – The PRNGKey to use to generate the random state.

replace(**kwargs)

Replace the values of the fields of the object with the values of the keyword arguments. If the object is a dataclass, dataclasses.replace will be used. Otherwise, a new object will be created with the same type as the original object.

Return type:

TypeVar(P, bound= Pytree)

Parameters:

• self (P) –

• kwargs (Any) –

reset(sampler, machine, params, sampler_state)

Resets the internal state of the Metropolis Sampler Transition Rule.

The default implementation returns the current rule_state without modifying it.

Parameters:

• sampler (MetropolisSampler) – The Metropolis sampler.

• machine (Module) – A Flax module with the forward pass of the log-pdf.

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

• sampler_state (SamplerState) – The current state of the sampler. Should not modify it.

Return type:

Optional[Any]

Returns:

A reset state of the rule. This returns the same type of rule_state() and might be None.

transition(sampler, machine, parameters, state, key, σ)

Proposes a new configuration set of configurations $sigma’$ starting from the current chain configurations \(\sigma\).

The new configurations \(\sigma'\) should be a matrix with the same dimension as \(\sigma\).

This function should return a tuple. where the first element are the new configurations $sigma’$ and the second element is either None or an array of length σ.shape[0] containing an optional log-correction factor. The correction factor should be non-zero when the transition rule is non-symmetrical.

Parameters:

• sampler – The Metropolis sampler.

• machine – A Flax module with the forward pass of the log-pdf.

• params – The PyTree of parameters of the model.

• sampler_state – The current state of the sampler. Should not modify it.

• key – A Jax PRNGKey to use to generate new random configurations.

• σ – The current configurations stored in a 2D matrix.

Returns:

A tuple containing the new configurations \(\sigma'\) and the optional vector of log corrections to the transition probability.