





from __future__ import annotations

import sys







class 	_UpperCamelCase	:







					'''simple docstring'''

					def __init__(    self     :	Union[str, Any]			,       __lowercase     :	str			,       __lowercase     :	int		):






						'''simple docstring'''







						UpperCAmelCase_		    =  letter
						UpperCAmelCase_		    =  freq
						UpperCAmelCase_		    =  {}





					def __repr__(    self     :	Any		):






						'''simple docstring'''







						return F"""{self.letter}:{self.freq}"""







class 	_UpperCamelCase	:







					'''simple docstring'''

					def __init__(    self     :	str			,       __lowercase     :	int			,       __lowercase     :	Letter | TreeNode			,       __lowercase     :	Letter | TreeNode		):






						'''simple docstring'''







						UpperCAmelCase_		    =  freq
						UpperCAmelCase_		    =  left
						UpperCAmelCase_		    =  right




def  A_(      A ):
	UpperCAmelCase_		    =  {}
	with open(A ) as f:
		while True:
			UpperCAmelCase_		    =  f.read(1 )
			if not c:
				break
			UpperCAmelCase_		    =  chars[c] + 1 if c in chars else 1
	return sorted((Letter(A					,   A ) for c, f in chars.items())					,   key=lambda A : x.freq )




def  A_(      A ):
	UpperCAmelCase_		    =  letters  # type: ignore
	while len(A ) > 1:
		UpperCAmelCase_		    =  response.pop(0 )
		UpperCAmelCase_		    =  response.pop(0 )
		UpperCAmelCase_		    =  left.freq + right.freq
		UpperCAmelCase_		    =  TreeNode(A					,   A					,   A )
		response.append(A )
		response.sort(key=lambda A : x.freq )
	return response[0]




def  A_(      A					,   A ):
	if isinstance(A					,   A ):
		UpperCAmelCase_		    =  bitstring
		return [root]
	UpperCAmelCase_		    =  root  # type: ignore
	UpperCAmelCase_		    =  []
	letters += traverse_tree(treenode.left					,   bitstring + """0""" )
	letters += traverse_tree(treenode.right					,   bitstring + """1""" )
	return letters




def  A_(      A ):
	UpperCAmelCase_		    =  parse_file(A )
	UpperCAmelCase_		    =  build_tree(A )
	UpperCAmelCase_		    =  {
	    k: v for letter in traverse_tree(A					,   """""" ) for k, v in letter.bitstring.items()
	}
	print(f"""Huffman Coding  of {file_path}: """ )
	with open(A ) as f:
		while True:
			UpperCAmelCase_		    =  f.read(1 )
			if not c:
				break
			print(letters[c]					,   end=""" """ )
	print()


if __name__ == "__main__":
				# pass the file path to the huffman function
				huffman(sys.argv[1])









import matplotlib.pyplot as plt
import numpy as np




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

	return weights




def  A_(      A					,   A					,   A					,   A ):
	UpperCAmelCase_		    =  weight_matrix(A					,   A					,   A )
	UpperCAmelCase_		    =  np.linalg.inv(x_train.T @ weight_mat @ x_train ) @ (
	    x_train.T @ weight_mat @ y_train.T
	)

	return weight




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  np.zeros(len(A ) )  # Initialize array of predictions
	for i, item in enumerate(A ):
		UpperCAmelCase_		    =  item @ local_weight(A					,   A					,   A					,   A )

	return y_pred




def  A_(      A					,   A					,   A ):
	import seaborn as sns

	UpperCAmelCase_		    =  sns.load_dataset(A )
	UpperCAmelCase_		    =  np.array(data[x_name] )
	UpperCAmelCase_		    =  np.array(data[y_name] )

	UpperCAmelCase_		    =  np.ones(len(A ) )

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

	return x_train, x_data, y_data




def  A_(      A					,   A					,   A					,   A					,   A					,   A					,   ):
	UpperCAmelCase_		    =  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
				UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__ :			Tuple       	=			load_data("""tips""", """total_bill""", """tip""")
				UpperCamelCase__ :			Tuple       	=			local_weight_regression(training_data_x, tip, 5)
				plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")


