Source code for netket.operator._sumoperators

# 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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from typing import Union, List, Optional, Callable

from netket.jax.utils import is_scalar
from netket.utils.types import DType, PyTree, Array

import functools

from netket.operator import ContinuousOperator

import jax.numpy as jnp

class SumOperator(ContinuousOperator):
    r"""This class implements the action of the _expect_kernel()-method of
    ContinuousOperator for a sum of ContinuousOperator objects.

[docs] def __init__( self, *operators: List, coefficients: Union[float, List[float]] = 1.0, dtype: Optional[DType] = None, ): r""" Returns the action of a sum of local operators. Args: operators: A list of ContinuousOperator objects coefficients: A coefficient for each ContinuousOperator object dtype: Data type of the matrix elements. Defaults to `np.float64` """ hil = [op.hilbert for op in operators] if not all(_ == hil[0] for _ in hil): raise NotImplementedError( "Cannot add operators on different hilbert spaces" ) if is_scalar(coefficients): coefficients = [coefficients for _ in operators] if len(operators) != len(coefficients): raise AssertionError("Each operator needs a coefficient") new_operators = [] new_coeffs = [] for op, c in zip(operators, coefficients): if isinstance(op, SumOperator): new_operators = new_operators + op._ops new_coeffs = new_coeffs + list(c * op._coeff) else: new_operators.append(op) new_coeffs.append(c) operators = new_operators coefficients = jnp.asarray(new_coeffs, dtype=dtype) self._ops = operators self._coeff = coefficients if dtype is None: dtype = functools.reduce( lambda dt, op: jnp.promote_types(dt, op.dtype), operators, float ) self._dtype = dtype super().__init__(hil[0], self._dtype) self._is_hermitian = all([op.is_hermitian for op in operators])
@property def is_hermitian(self): return self._is_hermitian def _expect_kernel( self, logpsi: Callable, params: PyTree, x: Array, data: Optional[PyTree] ): term_coefficients, term_datas = data result = [ term_coefficients[i] * op._expect_kernel(logpsi, params, x, term_datas[i]) for i, op in enumerate(self._ops) ] return sum(result) def _pack_arguments(self): return self._coeff, [op._pack_arguments() for op in self._ops] def __repr__(self): return f"SumOperator(coefficients={self._coeff})"