netket.sampler.rules.CustomRuleNumpy¶

class netket.sampler.rules.CustomRuleNumpy(operator, weight_list=None)¶

Bases: netket.sampler.MetropolisRule

Inheritance
Inheritance diagram of netket.sampler.rules.CustomRuleNumpy
Attributes
weight_list: Any = None¶
Methods
init_state(sampler, machine, params, key)[source]¶

Initialises the optional internal state of the Metropolis Sampler Transition Rule.

The provided key is unique and does not need to be splitted. It should return an immutable datastructure.

Parameters

  • sampler – The Metropolis sampler

  • machine – The forward evaluation function of the model, accepting PyTrees of parameters and inputs.

  • params – The dict of variables needed to evaluate the model.

  • key – A Jax PRNGKey rng state.

Returns

An Optional State.

random_state(sampler, machine, parameters, state, key)¶

Generates a random state compatible with this rule.

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

Parameters

  • sampler (Sampler) – the sampler

  • machine (Callable) – the function to evaluate the model

  • parameters (Any) – the parameters of the model

  • state (SamplerState) – the current sampler state

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

replace(**updates)¶

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

reset(sampler, machine, params, sampler_state)¶

Resets the internal state of the Metropolis Sampler Transition Rule.

Parameters

  • sampler (Sampler) – The Metropolis sampler

  • machine (Callable) – The forward evaluation function of the model, accepting PyTrees of parameters and inputs.

  • params (Any) – The dict of variables needed to evaluate the model.

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

Return type

Optional[Any]

Returns

A new, resetted, state of the rule. This returns the same type of sampler_state.rule_state() and might be None.

transition(sampler, machine, parameters, state, rng, σ)[source]¶