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.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import math

from numba import jit

import numpy as np
from flax import struct

from netket.operator import AbstractOperator

from .base import MetropolisRule

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

class HamiltonianRuleNumpy(MetropolisRule):
    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: AbstractOperator = struct.field(pytree_node=False)
    """The (hermitian) operator giving the transition amplitudes."""

    def __post_init__(self):
        # Raise errors if hilbert is not an Hilbert
        if not isinstance(self.operator, AbstractOperator):
            raise TypeError(
                "Argument to HamiltonianRule must be a valid operator, "
                f"but operator is a {type(self.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