netket.sampler.rules.CustomRuleNumpy¶
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class
netket.sampler.rules.
CustomRuleNumpy
(operator, weight_list=None)[source]¶ Bases:
netket.sampler.metropolis.MetropolisRule
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__init__
(operator, weight_list=None)¶ Initialize self. See help(type(self)) for accurate signature.
- Parameters
operator (Any) –
weight_list (Optional[Any]) –
- Return type
- Attributes
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weight_list
: Any = None¶
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- Methods
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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.
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random_state
(sampler, machine, parameters, state, key)¶ Generates a random state compatible with this rule.
By default this calls
netket.hilbert.random.random_state()
.
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replace
(**updates)¶ “Returns a new object replacing the specified fields with new values.
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reset
(sampler, machine, params, sampler_state)¶ Resets the internal state of the Metropolis Sampler Transition Rule.
- Parameters
sampler (
Sampler
) – The Metropolis samplermachine (
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
- Returns
A new, resetted, state of the rule. This returns the same type of
sampler_state.rule_state()
and might be None.
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