


'''simple docstring'''
from __future__ import annotations

import sys






class 					lowerCAmelCase_     :






       def __init__(   self ,    _UpperCamelCase ,    _UpperCamelCase      )->			Optional[int]:
            _A				=       letter
            _A				=       freq
            _A				=       {}






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






class 					lowerCAmelCase_     :






       def __init__(   self ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )->			Optional[Any]:
            _A				=       freq
            _A				=       left
            _A				=       right






def 				lowerCamelCase_   (   __UpperCamelCase				:					str		)				->  list[Letter]:







     """simple docstring"""
     _A				=       {}
     with open(__UpperCamelCase		) as f:
          while True:
               _A				=       f.read(1		)
               if not c:
                    break
               _A				=       chars[c] + 1 if c in chars else 1
     return sorted((Letter(__UpperCamelCase       ,						__UpperCamelCase		) for c, f in chars.items())       ,						key=lambda __UpperCamelCase		: x.freq		)

def 				lowerCamelCase_   (   __UpperCamelCase				:					list[Letter]		)				->  Letter | TreeNode:







     """simple docstring"""
     _A				=       letters  # type: ignore
     while len(__UpperCamelCase		) > 1:
          _A				=       response.pop(0		)
          _A				=       response.pop(0		)
          _A				=       left.freq + right.freq
          _A				=       TreeNode(__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase		)
          response.append(__UpperCamelCase		)
          response.sort(key=lambda __UpperCamelCase		: x.freq		)
     return response[0]

def 				lowerCamelCase_   (   __UpperCamelCase				:					Letter | TreeNode       ,						__UpperCamelCase				:					str		)				->  list[Letter]:







     """simple docstring"""
     if isinstance(__UpperCamelCase       ,						__UpperCamelCase		):
          _A				=       bitstring
          return [root]
     _A				=       root  # type: ignore
     _A				=       []
     letters += traverse_tree(treenode.left       ,						bitstring + '0'		)
     letters += traverse_tree(treenode.right       ,						bitstring + '1'		)
     return letters

def 				lowerCamelCase_   (   __UpperCamelCase				:					str		)				->  None:







     """simple docstring"""
     _A				=       parse_file(__UpperCamelCase		)
     _A				=       build_tree(__UpperCamelCase		)
     _A				=       {
         k: v for letter in traverse_tree(__UpperCamelCase       ,						''		) for k, v in letter.bitstring.items()
     }
     print(F'Huffman Coding  of {file_path}: '		)
     with open(__UpperCamelCase		) as f:
          while True:
               _A				=       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])









'''simple docstring'''
import numpy as np
import requests

def 				lowerCamelCase_   (   )				->  int:







     """simple docstring"""
     _A				=       requests.get(
         'https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'
         'master/Week1/ADRvsRating.csv'		)
     _A				=       response.text.splitlines()
     _A				=       []
     for item in lines:
          _A				=       item.split(','		)
          data.append(__UpperCamelCase		)
     data.pop(0		)  # This is for removing the labels from the list
     _A				=       np.matrix(__UpperCamelCase		)
     return dataset

def 				lowerCamelCase_   (   __UpperCamelCase				:					Union[str, Any]       ,						__UpperCamelCase				:					Tuple       ,						__UpperCamelCase				:					int       ,						__UpperCamelCase				:					Tuple       ,						__UpperCamelCase				:					Dict		)				->  int:







     """simple docstring"""
     _A				=       len_data

     _A				=       np.dot(__UpperCamelCase       ,						data_x.transpose()		)
     prod -= data_y.transpose()
     _A				=       np.dot(__UpperCamelCase       ,						__UpperCamelCase		)
     _A				=       theta - (alpha / n) * sum_grad
     return theta

def 				lowerCamelCase_   (   __UpperCamelCase				:					Optional[int]       ,						__UpperCamelCase				:					Any       ,						__UpperCamelCase				:					List[str]       ,						__UpperCamelCase				:					Optional[int]		)				->  Dict:







     """simple docstring"""
     _A				=       np.dot(__UpperCamelCase       ,						data_x.transpose()		)
     prod -= data_y.transpose()
     _A				=       np.sum(np.square(__UpperCamelCase		)		)
     _A				=       sum_elem / (2 * len_data)
     return error

def 				lowerCamelCase_   (   __UpperCamelCase				:					Dict       ,						__UpperCamelCase				:					List[str]		)				->  int:







     """simple docstring"""
     _A				=       1_0_0_0_0_0
     _A				=       0.0_00_15_50

     _A				=       data_x.shape[1]
     _A				=       data_x.shape[0] - 1

     _A				=       np.zeros((1, no_features)		)

     for i in range(0       ,						__UpperCamelCase		):
          _A				=       run_steep_gradient_descent(__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase		)
          _A				=       sum_of_square_error(__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase		)
          print(F'At Iteration {i + 1} - Error is {error:.5f}'		)

     return theta

def 				lowerCamelCase_   (   __UpperCamelCase				:					Dict       ,						__UpperCamelCase				:					Tuple		)				->  Union[str, Any]:







     """simple docstring"""
     _A				=       sum(abs(y - predicted_y[i]		) for i, y in enumerate(__UpperCamelCase		)		)
     return total / len(__UpperCamelCase		)

def 				lowerCamelCase_   (   )				->  Optional[Any]:







     """simple docstring"""
     _A				=       collect_dataset()

     _A				=       data.shape[0]
     _A				=       np.c_[np.ones(__UpperCamelCase		), data[:, :-1]].astype(__UpperCamelCase		)
     _A				=       data[:, -1].astype(__UpperCamelCase		)

     _A				=       run_linear_regression(__UpperCamelCase       ,						__UpperCamelCase		)
     _A				=       theta.shape[1]
     print('Resultant Feature vector : '		)
     for i in range(0       ,						__UpperCamelCase		):
          print(F'{theta[0, i]:.5f}'		)


if __name__ == "__main__":
    main()





