
"""simple docstring"""




import matplotlib.pyplot as plt
import numpy as np

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      len(SCREAMING_SNAKE_CASE_     )  # Number of training samples
					SCREAMING_SNAKE_CASE           =      np.eye(SCREAMING_SNAKE_CASE_     )  # Initialize weights as identity matrix
					for j in range(SCREAMING_SNAKE_CASE_     ):
										SCREAMING_SNAKE_CASE           =      point - x_train[j]
										SCREAMING_SNAKE_CASE           =      np.exp(diff @ diff.T / (-2.0 * tau**2)     )

					return weights

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      weight_matrix(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      np.linalg.inv(x_train.T @ weight_mat @ x_train     ) @ (
					    x_train.T @ weight_mat @ y_train.T
					)

					return weight

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      np.zeros(len(SCREAMING_SNAKE_CASE_     )     )  # Initialize array of predictions
					for i, item in enumerate(SCREAMING_SNAKE_CASE_     ):
										SCREAMING_SNAKE_CASE           =      item @ local_weight(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )

					return y_pred

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					import seaborn as sns

					SCREAMING_SNAKE_CASE           =      sns.load_dataset(SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      np.array(data[x_name]     )
					SCREAMING_SNAKE_CASE           =      np.array(data[y_name]     )

					SCREAMING_SNAKE_CASE           =      np.ones(len(SCREAMING_SNAKE_CASE_     )     )

					# pairing elements of one and x_data
					SCREAMING_SNAKE_CASE           =      np.column_stack((one, x_data)     )

					return x_train, x_data, y_data

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	):
					SCREAMING_SNAKE_CASE           =      np.sort(SCREAMING_SNAKE_CASE_,	axis=0     )
					plt.scatter(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	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(SCREAMING_SNAKE_CASE_     )
					plt.ylabel(SCREAMING_SNAKE_CASE_     )
					plt.show()


if __name__ == "__main__":
	import doctest

	doctest.testmod()

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


"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case					  =						{'configuration_qdqbert': ['QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'QDQBertConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case					  =						[
	    'QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'QDQBertForMaskedLM',
	    'QDQBertForMultipleChoice',
	    'QDQBertForNextSentencePrediction',
	    'QDQBertForQuestionAnswering',
	    'QDQBertForSequenceClassification',
	    'QDQBertForTokenClassification',
	    'QDQBertLayer',
	    'QDQBertLMHeadModel',
	    'QDQBertModel',
	    'QDQBertPreTrainedModel',
	    'load_tf_weights_in_qdqbert',
	]


if TYPE_CHECKING:
	from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_qdqbert import (
		    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    QDQBertForMaskedLM,
		    QDQBertForMultipleChoice,
		    QDQBertForNextSentencePrediction,
		    QDQBertForQuestionAnswering,
		    QDQBertForSequenceClassification,
		    QDQBertForTokenClassification,
		    QDQBertLayer,
		    QDQBertLMHeadModel,
		    QDQBertModel,
		    QDQBertPreTrainedModel,
		    load_tf_weights_in_qdqbert,
		)


else:
	import sys

	snake_case					  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
