


'''simple docstring'''

import matplotlib.pyplot as plt
import numpy as np



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					float )		->       np.ndarray:





   '''simple docstring'''







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

   return weights



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					float )		->       np.ndarray:





   '''simple docstring'''







   UpperCamelCase__			    =    weight_matrix(_UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase )
   UpperCamelCase__			    =    np.linalg.inv(x_train.T @ weight_mat @ x_train ) @ (
       x_train.T @ weight_mat @ y_train.T
   )

   return weight



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					float )		->       np.ndarray:





   '''simple docstring'''







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

   return y_pred



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					str     ,		_UpperCamelCase   :					str     ,		_UpperCamelCase   :					str )		->       tuple[np.ndarray, np.ndarray, np.ndarray]:





   '''simple docstring'''







   import seaborn as sns

   UpperCamelCase__			    =    sns.load_dataset(_UpperCamelCase )
   UpperCamelCase__			    =    np.array(data[x_name] )
   UpperCamelCase__			    =    np.array(data[y_name] )

   UpperCamelCase__			    =    np.ones(len(_UpperCamelCase ) )

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

   return x_train, x_data, y_data



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					str     ,		_UpperCamelCase   :					str     ,		)		->       None:





   '''simple docstring'''







   UpperCamelCase__			    =    np.sort(_UpperCamelCase     ,		axis=0 )
   plt.scatter(_UpperCamelCase     ,		_UpperCamelCase     ,		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(_UpperCamelCase )
   plt.ylabel(_UpperCamelCase )
   plt.show()


if __name__ == "__main__":
       import doctest

       doctest.testmod()

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

import sys
class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =letter
		_lowercase   =freq
		_lowercase   ={}


	def __repr__(   self       ):
		return F'''{self.letter}:{self.freq}'''

class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =freq
		_lowercase   =left
		_lowercase   =right

def 	__lowerCamelCase							(				__a    : str		)      ->       list[Letter]:
	_lowercase   ={}
	with open(__a		) as f:
		while True:
			_lowercase   =f.read(1		)
			if not c:
				break
			_lowercase   =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 	__lowerCamelCase							(				__a    : list[Letter]		)      ->       Letter | TreeNode:
	_lowercase   =letters  # type: ignore
	while len(__a		) > 1:
		_lowercase   =response.pop(0		)
		_lowercase   =response.pop(0		)
		_lowercase   =left.freq + right.freq
		_lowercase   =TreeNode(__a      ,							__a      ,							__a		)
		response.append(__a		)
		response.sort(key=lambda __a		: x.freq		)
	return response[0]

def 	__lowerCamelCase							(				__a    : Letter | TreeNode      ,							__a    : str		)      ->       list[Letter]:
	if isinstance(__a      ,							__a		):
		_lowercase   =bitstring
		return [root]
	_lowercase   =root  # type: ignore
	_lowercase   =[]
	letters += traverse_tree(treenode.left      ,							bitstring + "0"		)
	letters += traverse_tree(treenode.right      ,							bitstring + "1"		)
	return letters

def 	__lowerCamelCase							(				__a    : str		)      ->       None:
	_lowercase   =parse_file(__a		)
	_lowercase   =build_tree(__a		)
	_lowercase   ={
	    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:
			_lowercase   =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])




