

import matplotlib.pyplot as plt
import numpy as np


def 			_UpperCAmelCase							(		a__       ,					a__       ,					a__):

			'''simple docstring'''



			a_		:		Union[str, Any]				    =				len(a__)  # Number of training samples
			a_		:		Union[str, Any]				    =				np.eye(a__)  # Initialize weights as identity matrix
			for j in range(a__):
						a_		:		Dict				    =				point - x_train[j]
						a_		:		Union[str, Any]				    =				np.exp(diff @ diff.T / (-2.0 * tau**2))

			return weights


def 			_UpperCAmelCase							(		a__       ,					a__       ,					a__       ,					a__):

			'''simple docstring'''



			a_		:		int				    =				weight_matrix(a__       ,					a__       ,					a__)
			a_		:		str				    =				np.linalg.inv(x_train.T @ weight_mat @ x_train) @ (
			    x_train.T @ weight_mat @ y_train.T
			)

			return weight


def 			_UpperCAmelCase							(		a__       ,					a__       ,					a__):

			'''simple docstring'''



			a_		:		List[str]				    =				np.zeros(len(a__))  # Initialize array of predictions
			for i, item in enumerate(a__):
						a_		:		Dict				    =				item @ local_weight(a__       ,					a__       ,					a__       ,					a__)

			return y_pred


def 			_UpperCAmelCase							(		a__       ,					a__       ,					a__):

			'''simple docstring'''



			import seaborn as sns

			a_		:		str				    =				sns.load_dataset(a__)
			a_		:		List[Any]				    =				np.array(data[x_name])
			a_		:		List[Any]				    =				np.array(data[y_name])

			a_		:		Tuple				    =				np.ones(len(a__))

			# pairing elements of one and x_data
			a_		:		Union[str, Any]				    =				np.column_stack((one, x_data))

			return x_train, x_data, y_data


def 			_UpperCAmelCase							(		a__       ,					a__       ,					a__       ,					a__       ,					a__       ,					a__       ,					):

			'''simple docstring'''



			a_		:		List[Any]				    =				np.sort(a__       ,					axis=0)
			plt.scatter(a__       ,					a__       ,					color="""blue""")
			plt.plot(
			    x_train_sorted[:, 1]       ,					preds[x_train[:, 1].argsort(0)]       ,					color="""yellow"""       ,					linewidth=5       ,					)
			plt.title("""Local Weighted Regression""")
			plt.xlabel(a__)
			plt.ylabel(a__)
			plt.show()


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	# Demo with a dataset from the seaborn module
	__snake_case						,				__snake_case						,				__snake_case							:     Dict 						=	load_data("""tips""", """total_bill""", """tip""")
	__snake_case							:     Optional[Any] 						=	local_weight_regression(training_data_x, tip, 5)
	plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")





from sklearn.metrics import mean_absolute_error

import datasets


__snake_case							:     List[Any] 						=	"""\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
"""

__snake_case							:     List[Any] 						=	"""\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


__snake_case							:     str 						=	"""
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 \"raw_values\" : Returns a full set of errors in case of multioutput input.

                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,       _KWARGS_DESCRIPTION    )
class    A__(datasets.Metric    ):
						"""simple docstring"""






						def 	UpperCamelCase__			(  self  )      ->				List[str]:
									return datasets.MetricInfo(
									    description=_DESCRIPTION       ,     citation=_CITATION       ,     inputs_description=_KWARGS_DESCRIPTION       ,     features=datasets.Features(self._get_feature_types()  )       ,     reference_urls=[
									        """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html"""
									    ]       ,     )





						def 	UpperCamelCase__			(  self  )      ->				Union[str, Any]:
									if self.config_name == "multilist":
												return {
												    "predictions": datasets.Sequence(datasets.Value("""float"""  )  ),
												    "references": datasets.Sequence(datasets.Value("""float"""  )  ),
												}
									else:
												return {
												    "predictions": datasets.Value("""float"""  ),
												    "references": datasets.Value("""float"""  ),
												}





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase=None       ,     _lowercase="uniform_average"  )      ->				Union[str, Any]:
									a_		:		Any				    =				mean_absolute_error(_lowercase       ,     _lowercase       ,     sample_weight=_lowercase       ,     multioutput=_lowercase  )

									return {"mae": mae_score}


