Quellcode für qiskit.algorithms.optimizers.optimizer_utils.learning_rate

# This code is part of Qiskit.
# (C) Copyright IBM 2021, 2022.
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.

"""A class to represent the Learning Rate."""
from __future__ import annotations

from collections.abc import Generator, Callable
from itertools import tee
import numpy as np

[Doku]class LearningRate(Generator): """Represents a Learning Rate. Will be an attribute of :class:`~.GradientDescentState`. Note that :class:`~.GradientDescent` also has a learning rate. That learning rate can be a float, a list, an array, a function returning a generator and will be used to create a generator to be used during the optimization process. This class wraps ``Generator`` so that we can also access the last yielded value. """ def __init__( self, learning_rate: float | list[float] | np.ndarray | Callable[[], Generator[float, None, None]], ): """ Args: learning_rate: Used to create a generator to iterate on. """ if isinstance(learning_rate, (float, int)): self._gen = constant(learning_rate) elif isinstance(learning_rate, Generator): learning_rate, self._gen = tee(learning_rate) elif isinstance(learning_rate, (list, np.ndarray)): self._gen = (eta for eta in learning_rate) else: self._gen = learning_rate() self._current: float | None = None
[Doku] def send(self, value): """Send a value into the generator. Return next yielded value or raise StopIteration. """ self._current = next(self._gen) return self.current
[Doku] def throw(self, typ, val=None, tb=None): """Raise an exception in the generator. Return next yielded value or raise StopIteration. """ if val is None: if tb is None: raise typ val = typ() if tb is not None: val = val.with_traceback(tb) raise val
@property def current(self): """Returns the current value of the learning rate.""" return self._current
def constant(learning_rate: float = 0.01) -> Generator[float, None, None]: """Returns a python generator that always yields the same value. Args: learning_rate: The value to yield. Yields: The learning rate for the next iteration. """ while True: yield learning_rate