Исходный код qiskit_machine_learning.algorithms.regressors.neural_network_regressor

# 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.
"""An implementation of quantum neural network regressor."""

from typing import Optional

import numpy as np
from qiskit_algorithms.optimizers import OptimizerResult
from sklearn.base import RegressorMixin

from ..objective_functions import (
from ..trainable_model import TrainableModel

[документация]class NeuralNetworkRegressor(TrainableModel, RegressorMixin): """Implements a basic quantum neural network regressor. Implements Scikit-Learn compatible methods for regression and extends ``RegressorMixin``. See `Scikit-Learn <https://scikit-learn.org>`__ for more details. """ def _fit_internal( self, X: np.ndarray, y: np.ndarray ) -> OptimizerResult: # pylint: disable=invalid-name # mypy definition function: ObjectiveFunction = None if self._neural_network.output_shape == (1,): function = BinaryObjectiveFunction(X, y, self._neural_network, self._loss) else: function = MultiClassObjectiveFunction(X, y, self._neural_network, self._loss) return self._minimize(function)
[документация] def predict(self, X: np.ndarray) -> np.ndarray: # pylint: disable=invalid-name self._check_fitted() return self._neural_network.forward(X, self._fit_result.x)
[документация] def score( self, X: np.ndarray, y: np.ndarray, sample_weight: Optional[np.ndarray] = None ) -> float: return RegressorMixin.score(self, X, y, sample_weight)