netket.sampler.rules.TensorRule#
- class netket.sampler.rules.TensorRule[source]#
Bases:
MetropolisRuleA Metropolis sampling rule that can be used to combine different rules acting on different subspaces of the same tensor-hilbert space.
- Inheritance
- __init__(hilbert, rules)[source]#
Construct the composition of rules.
It should be constructed by passing a TensorHilbert space as a first argument and a list of rules as a second argument. Each rule[i] will be used to generate a transition for the i-th subspace of the tensor hilbert space.
- Parameters:
hilbert (
TensorHilbert) – The tensor hilbert space on which the rule acts.rules (
tuple[MetropolisRule,...]) – A list of rules, one for each subspace of the tensor hilbert space.
- Return type:
- Attributes
-
hilbert:
TensorHilbert# The hilbert space upon which this rule is defined. This must be a
nk.hilbert.TensorHilbertwith the same size as the expected input samples.
-
rules:
tuple[MetropolisRule,...]# Tuple of rules to be used on every partition of the hilbert space.
-
hilbert:
- 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 split.
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
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.
- reset(sampler, machine, params, sampler_state)[source]#
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:
- 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)[source]#
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.