



"""simple docstring"""







import matplotlib.pyplot as plt
import numpy as np

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       np.ndarray    ,		lowerCamelCase_:       np.ndarray    ,		lowerCamelCase_:       float   ):
						"""simple docstring"""






						snake_case      : int							=   len(lowerCamelCase_   )  # Number of training samples
						snake_case      : Optional[Any]							=   np.eye(lowerCamelCase_   )  # Initialize weights as identity matrix
						for j in range(lowerCamelCase_   ):
												snake_case      : Any							=   point - x_train[j]
												snake_case      : int							=   np.exp(diff @ diff.T / (-2.0 * tau**2)   )

						return weights

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       np.ndarray    ,		lowerCamelCase_:       np.ndarray    ,		lowerCamelCase_:       np.ndarray    ,		lowerCamelCase_:       float   ):
						"""simple docstring"""






						snake_case      : Optional[int]							=   weight_matrix(lowerCamelCase_    ,		lowerCamelCase_    ,		lowerCamelCase_   )
						snake_case      : str							=   np.linalg.inv(x_train.T @ weight_mat @ x_train   ) @ (
						    x_train.T @ weight_mat @ y_train.T
						)

						return weight

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       np.ndarray    ,		lowerCamelCase_:       np.ndarray    ,		lowerCamelCase_:       float   ):
						"""simple docstring"""






						snake_case      : Tuple							=   np.zeros(len(lowerCamelCase_   )   )  # Initialize array of predictions
						for i, item in enumerate(lowerCamelCase_   ):
												snake_case      : Tuple							=   item @ local_weight(lowerCamelCase_    ,		lowerCamelCase_    ,		lowerCamelCase_    ,		lowerCamelCase_   )

						return y_pred

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       str    ,		lowerCamelCase_:       str    ,		lowerCamelCase_:       str   ):
						"""simple docstring"""






						import seaborn as sns

						snake_case      : int							=   sns.load_dataset(lowerCamelCase_   )
						snake_case      : Union[str, Any]							=   np.array(data[x_name]   )
						snake_case      : Union[str, Any]							=   np.array(data[y_name]   )

						snake_case      : List[str]							=   np.ones(len(lowerCamelCase_   )   )

						# pairing elements of one and x_data
						snake_case      : Dict							=   np.column_stack((one, x_data)   )

						return x_train, x_data, y_data

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       np.ndarray    ,		lowerCamelCase_:       np.ndarray    ,		lowerCamelCase_:       np.ndarray    ,		lowerCamelCase_:       np.ndarray    ,		lowerCamelCase_:       str    ,		lowerCamelCase_:       str    ,		):
						"""simple docstring"""






						snake_case      : Dict							=   np.sort(lowerCamelCase_    ,		axis=0   )
						plt.scatter(lowerCamelCase_    ,		lowerCamelCase_    ,		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(lowerCamelCase_   )
						plt.ylabel(lowerCamelCase_   )
						plt.show()


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			# Demo with a dataset from the seaborn module
			A  ,  A  ,  A    =	load_data('tips', 'total_bill', 'tip')
			A    =	local_weight_regression(training_data_x, tip, 5)
			plot_preds(training_data_x, predictions, total_bill, tip, 'total_bill', 'tip')








"""simple docstring"""







from sklearn.metrics import accuracy_score

import datasets


A    =	'\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n'


A    =	'\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {\'accuracy\': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {\'accuracy\': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {\'accuracy\': 0.8778625954198473}\n'


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

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,  _KWARGS_DESCRIPTION)
class 			_a     (   datasets.Metric):



			def 							__lowercase				( self       :							List[Any]		)       ->				List[str]:
									return datasets.MetricInfo(
									    description=_DESCRIPTION      ,  citation=_CITATION      ,  inputs_description=_KWARGS_DESCRIPTION      ,  features=datasets.Features(
									        {
									            "predictions": datasets.Sequence(datasets.Value("int32"		)		),
									            "references": datasets.Sequence(datasets.Value("int32"		)		),
									        }
									        if self.config_name == "multilabel"
									        else {
									            "predictions": datasets.Value("int32"		),
									            "references": datasets.Value("int32"		),
									        }		)      ,  reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"]      ,  )



			def 							__lowercase				( self       :							str      ,  _lowercase       :							Optional[int]      ,  _lowercase       :							Tuple      ,  _lowercase       :							Union[str, Any]=True      ,  _lowercase       :							int=None		)       ->				Any:
									return {
									    "accuracy": float(
									        accuracy_score(_lowercase      ,  _lowercase      ,  normalize=_lowercase      ,  sample_weight=_lowercase		)		)
									}



