

'''simple docstring'''







import matplotlib.pyplot as plt
import numpy as np






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

	return weights






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_							,       snake_case_	):
	_lowercase							=     weight_matrix(snake_case_							,       snake_case_							,       snake_case_	)
	_lowercase							=     np.linalg.inv(x_train.T @ weight_mat @ x_train	) @ (
	    x_train.T @ weight_mat @ y_train.T
	)

	return weight






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_	):
	_lowercase							=     np.zeros(len(snake_case_	)	)  # Initialize array of predictions
	for i, item in enumerate(snake_case_	):
		_lowercase							=     item @ local_weight(snake_case_							,       snake_case_							,       snake_case_							,       snake_case_	)

	return y_pred






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_	):
	import seaborn as sns

	_lowercase							=     sns.load_dataset(snake_case_	)
	_lowercase							=     np.array(data[x_name]	)
	_lowercase							=     np.array(data[y_name]	)

	_lowercase							=     np.ones(len(snake_case_	)	)

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

	return x_train, x_data, y_data






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       ):
	_lowercase							=     np.sort(snake_case_							,       axis=0	)
	plt.scatter(snake_case_							,       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(snake_case_	)
	plt.ylabel(snake_case_	)
	plt.show()


if __name__ == "__main__":
							import doctest

							doctest.testmod()

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







'''simple docstring'''







from collections import namedtuple

_lowerCamelCase    =  namedtuple('from_to', 'from_ to')

_lowerCamelCase    =  {
    'millimeter': 'mm',
    'centimeter': 'cm',
    'meter': 'm',
    'kilometer': 'km',
    'inch': 'in',
    'inche': 'in',  # Trailing 's' has been stripped off
    'feet': 'ft',
    'foot': 'ft',
    'yard': 'yd',
    'mile': 'mi',
}

_lowerCamelCase    =  {
    'mm': from_to(0.001, 1_0_0_0),
    'cm': from_to(0.01, 1_0_0),
    'm': from_to(1, 1),
    'km': from_to(1_0_0_0, 0.001),
    'in': from_to(0.0254, 39.3701),
    'ft': from_to(0.3048, 3.2_8084),
    'yd': from_to(0.9144, 1.0_9361),
    'mi': from_to(1609.34, 0.0_0062_1371),
}






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_	):
	_lowercase							=     from_type.lower().rstrip("""s"""	)
	_lowercase							=     TYPE_CONVERSION.get(snake_case_							,       snake_case_	)
	_lowercase							=     to_type.lower().rstrip("""s"""	)
	_lowercase							=     TYPE_CONVERSION.get(snake_case_							,       snake_case_	)
	if new_from not in METRIC_CONVERSION:
		_lowercase							=     (
		    F"""Invalid 'from_type' value: {from_type!r}.\n"""
		    F"""Conversion abbreviations are: {", ".join(snake_case_	)}"""
		)
		raise ValueError(snake_case_	)
	if new_to not in METRIC_CONVERSION:
		_lowercase							=     (
		    F"""Invalid 'to_type' value: {to_type!r}.\n"""
		    F"""Conversion abbreviations are: {", ".join(snake_case_	)}"""
		)
		raise ValueError(snake_case_	)
	return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
							import doctest

							doctest.testmod()




