Source code for netket.sampler.rules.hamiltonian_numpy

# Copyright 2021 The NetKet Authors - All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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import math

from numba import jit

import numpy as np

from netket.operator import DiscreteOperator
from netket.utils import struct

from .base import MetropolisRule

class HamiltonianRuleState:
    sections: np.ndarray
    """Preallocated array for sections"""

[docs] class HamiltonianRuleNumpy(MetropolisRule): r""" Rule for Numpy sampler backend proposing moves according to the terms in an operator. In this case, the transition matrix is taken to be: .. math:: T( \mathbf{s} \rightarrow \mathbf{s}^\prime) = \frac{1}{\mathcal{N}(\mathbf{s})}\theta(|H_{\mathbf{s},\mathbf{s}^\prime}|), """ operator: DiscreteOperator = struct.field(pytree_node=False) """The (hermitian) operator giving the transition amplitudes."""
[docs] def __init__(self, operator: DiscreteOperator): """ Constructs the Hamiltonian sampling rule for the :class:`netket.sampler.MetropolisNumpy` sampler. If you are using the standard jax sampler, look for :class:`netket.sampler.rules.HamiltonianRule` . Args: operator: The hermitian operator to be used to generate configurations. """ if not isinstance(operator, DiscreteOperator): raise TypeError( "Argument to HamiltonianRule must be a valid operator, " f"but operator is a {type(operator)}." ) self.operator = operator
[docs] def init_state(rule, sampler, machine, params, key): if sampler.hilbert != rule.operator.hilbert: raise ValueError( f""" The hilbert space of the sampler ({sampler.hilbert}) and the hilbert space of the operator ({rule.operator.hilbert}) for HamiltonianRule must be the same. """ ) return HamiltonianRuleState( sections=np.empty(sampler.n_batches, dtype=np.int32) )
[docs] def transition(rule, sampler, machine, parameters, state, rng, σ): σ = state.σ σ1 = state.σ1 log_prob_corr = state.log_prob_corr sections = state.rule_state.sections σp = rule.operator.get_conn_flattened(σ, sections)[0] rand_vec = rng.uniform(0, 1, size=σ.shape[0]) _choose(σp, sections, σ1, log_prob_corr, rand_vec) rule.operator.n_conn(σ1, sections) log_prob_corr -= np.log(sections)
def __repr__(self): return f"HamiltonianRuleNumpy({self.operator})"
@jit(nopython=True) def _choose(states, sections, out, w, rand_vec): low_range = 0 for i, s in enumerate(sections): n_rand = low_range + int(np.floor(rand_vec[i] * (s - low_range))) out[i] = states[n_rand] w[i] = math.log(s - low_range) low_range = s