Source code for netket.operator._sumoperators

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#    http://www.apache.org/licenses/LICENSE-2.0
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from typing import Union, List, Optional, Callable

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" ) 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] ): result = [ op._expect_kernel(logpsi, params, x, data[i]) for i, op in enumerate(self._ops) ] return sum(result) def _expect_kernel_batched( self, logpsi: Callable, params: PyTree, x: Array, data: Optional[PyTree] ): result = [ op._expect_kernel_batched(logpsi, params, x, data[i]) for i, op in enumerate(self._ops) ] return sum(result) def _pack_arguments(self): return [self._coeff * jnp.array(op._pack_arguments()) for op in self._ops] def __repr__(self): return f"SumOperator(coefficients={self._pack_arguments()})"