netket.sampler.rules.LocalRule¶

class netket.sampler.rules.LocalRule

A transition rule acting on the local degree of freedom.

This transition acts locally only on one local degree of freedom $$s_i$$, and proposes a new state: $$s_1 \dots s^\prime_i \dots s_N$$, where $$s^\prime_i \neq s_i$$.

The transition probability associated to this sampler can be decomposed into two steps:

1. One of the site indices $$i = 1\dots N$$ is chosen with uniform probability. 2. Among all the possible ($$m$$) values that $$s_i$$ can take, one of them is chosen with uniform probability.

Inheritance
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 splitted. It should return an immutable datastructure.

Parameters
Return type
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
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
Return type
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, key, σ)[source]