# This code is part of a Qiskit project.
#
# (C) Copyright IBM 2021, 2023.
#
# 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
[docs]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
[docs] 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
[docs] 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