- 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.
- 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 data structure.
- random_state(sampler, machine, params, sampler_state, key)#
Generates a random state compatible with this rule.
By default this calls
sampler (MetropolisSampler) – The Metropolis sampler.
Module) – A Flax module with the forward pass of the log-pdf.
Any) – The PyTree of parameters of the model.
sampler_state (SamplerState) – The current state of the sampler. Should not modify it.
Any) – The PRNGKey to use to generate the random state.
“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.
The default implementation returns the current rule_state without modofying it.
- Return type
A resetted, 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 containing an optional log-correction factor. The correction factor should be non-zero when the transition rule is non-symmetrical.
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.
A tuple containing the new configurations \(\sigma'\) and the optional vector of log corrections to the transition probability.