'''simple docstring'''




from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar





def      _UpperCAmelCase    (							_UpperCamelCase    :	float,   _UpperCamelCase    :	float,   _UpperCamelCase    :	int   )	->      list:
    seed(1   )
    return [gauss(_UpperCamelCase,   _UpperCamelCase   ) for _ in range(_UpperCamelCase   )]





def      _UpperCAmelCase    (							_UpperCamelCase    :	int,   _UpperCamelCase    :	list   )	->      list:
    return [k for k in range(_UpperCamelCase   ) for _ in range(instance_count[k]   )]





def      _UpperCAmelCase    (							_UpperCamelCase    :	int,   _UpperCamelCase    :	list   )	->      float:
    return sum(_UpperCamelCase   ) / instance_count





def      _UpperCAmelCase    (							_UpperCamelCase    :	int,   _UpperCamelCase    :	int   )	->      float:
    return instance_count / total_count





def      _UpperCAmelCase    (							_UpperCamelCase    :	list,   _UpperCamelCase    :	list,   _UpperCamelCase    :	int   )	->      float:
    A_     =   []  # An empty list to store all squared differences
    # iterate over number of elements in items
    for i in range(len(_UpperCamelCase   )   ):
        # for loop iterates over number of elements in inner layer of items
        for j in range(len(items[i]   )   ):
            # appending squared differences to 'squared_diff' list
            squared_diff.append((items[i][j] - means[i]) ** 2   )

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
    A_     =   len(_UpperCamelCase   )  # Number of classes in dataset
    return 1 / (total_count - n_classes) * sum(_UpperCamelCase   )





def      _UpperCAmelCase    (							_UpperCamelCase    :	list,   _UpperCamelCase    :	list,   _UpperCamelCase    :	float,   _UpperCamelCase    :	list   )	->      list:
    A_     =   []
    # for loop iterates over number of elements in list
    for i in range(len(_UpperCamelCase   )   ):
        # for loop iterates over number of inner items of each element
        for j in range(len(x_items[i]   )   ):
            A_     =   []  # to store all discriminant values of each item as a list
            # for loop iterates over number of classes we have in our dataset
            for k in range(len(_UpperCamelCase   )   ):
                # appending values of discriminants for each class to 'temp' list
                temp.append(
                    x_items[i][j] * (means[k] / variance)
                    - (means[k] ** 2 / (2 * variance))
                    + log(probabilities[k]   )   )
            # appending discriminant values of each item to 'results' list
            results.append(_UpperCamelCase   )

    return [result.index(max(_UpperCamelCase   )   ) for result in results]





def      _UpperCAmelCase    (							_UpperCamelCase    :	list,   _UpperCamelCase    :	list   )	->      float:
    A_     =   sum(1 for i, j in zip(_UpperCamelCase,   _UpperCamelCase   ) if i == j   )
    # percentage of accuracy equals to number of correct predictions divided by number
    # of all data and multiplied by 100
    return (correct / len(_UpperCamelCase   )) * 1_00


__snake_case   :      str						    =  TypeVar('num')





def      _UpperCAmelCase    (							_UpperCamelCase    :	Callable[[object], num],   _UpperCamelCase    :	str,   _UpperCamelCase    :	str,   _UpperCamelCase    :	Callable[[num], bool] = lambda _UpperCamelCase   : True,   _UpperCamelCase    :	str | None = None,   )	->      num:




    while True:
        try:
            A_     =   input_type(input(_UpperCamelCase   ).strip() or default   )
            if condition(_UpperCamelCase   ):
                return user_input
            else:
                print(F'''{user_input}: {err_msg}'''   )
                continue
        except ValueError:
            print(
                F'''{user_input}: Incorrect input type, expected {input_type.__name__!r}'''   )





def      _UpperCAmelCase    (							)	->      str:




    while True:
        print(''' Linear Discriminant Analysis '''.center(50,   '''*'''   )   )
        print('''*''' * 50,   '''\n'''   )
        print('''First of all we should specify the number of classes that'''   )
        print('''we want to generate as training dataset'''   )
        # Trying to get number of classes
        A_     =   valid_input(
            input_type=_UpperCamelCase,   condition=lambda _UpperCamelCase   : x > 0,   input_msg='''Enter the number of classes (Data Groupings): ''',   err_msg='''Number of classes should be positive!''',   )

        print('''-''' * 1_00   )

        # Trying to get the value of standard deviation
        A_     =   valid_input(
            input_type=_UpperCamelCase,   condition=lambda _UpperCamelCase   : x >= 0,   input_msg=(
                '''Enter the value of standard deviation'''
                '''(Default value is 1.0 for all classes): '''
            ),   err_msg='''Standard deviation should not be negative!''',   default='''1.0''',   )

        print('''-''' * 1_00   )

        # Trying to get number of instances in classes and theirs means to generate
        # dataset
        A_     =   []  # An empty list to store instance counts of classes in dataset
        for i in range(_UpperCamelCase   ):
            A_     =   valid_input(
                input_type=_UpperCamelCase,   condition=lambda _UpperCamelCase   : x > 0,   input_msg=(F'''Enter The number of instances for class_{i+1}: '''),   err_msg='''Number of instances should be positive!''',   )
            counts.append(_UpperCamelCase   )
        print('''-''' * 1_00   )

        # An empty list to store values of user-entered means of classes
        A_     =   []
        for a in range(_UpperCamelCase   ):
            A_     =   valid_input(
                input_type=_UpperCamelCase,   input_msg=(F'''Enter the value of mean for class_{a+1}: '''),   err_msg='''This is an invalid value.''',   )
            user_means.append(_UpperCamelCase   )
        print('''-''' * 1_00   )

        print('''Standard deviation: ''',   _UpperCamelCase   )
        # print out the number of instances in classes in separated line
        for i, count in enumerate(_UpperCamelCase,   1   ):
            print(F'''Number of instances in class_{i} is: {count}'''   )
        print('''-''' * 1_00   )

        # print out mean values of classes separated line
        for i, user_mean in enumerate(_UpperCamelCase,   1   ):
            print(F'''Mean of class_{i} is: {user_mean}'''   )
        print('''-''' * 1_00   )

        # Generating training dataset drawn from gaussian distribution
        A_     =   [
            gaussian_distribution(user_means[j],   _UpperCamelCase,   counts[j]   )
            for j in range(_UpperCamelCase   )
        ]
        print('''Generated Normal Distribution: \n''',   _UpperCamelCase   )
        print('''-''' * 1_00   )

        # Generating Ys to detecting corresponding classes
        A_     =   y_generator(_UpperCamelCase,   _UpperCamelCase   )
        print('''Generated Corresponding Ys: \n''',   _UpperCamelCase   )
        print('''-''' * 1_00   )

        # Calculating the value of actual mean for each class
        A_     =   [calculate_mean(counts[k],   x[k]   ) for k in range(_UpperCamelCase   )]
        # for loop iterates over number of elements in 'actual_means' list and print
        # out them in separated line
        for i, actual_mean in enumerate(_UpperCamelCase,   1   ):
            print(F'''Actual(Real) mean of class_{i} is: {actual_mean}'''   )
        print('''-''' * 1_00   )

        # Calculating the value of probabilities for each class
        A_     =   [
            calculate_probabilities(counts[i],   sum(_UpperCamelCase   )   ) for i in range(_UpperCamelCase   )
        ]

        # for loop iterates over number of elements in 'probabilities' list and print
        # out them in separated line
        for i, probability in enumerate(_UpperCamelCase,   1   ):
            print(F'''Probability of class_{i} is: {probability}'''   )
        print('''-''' * 1_00   )

        # Calculating the values of variance for each class
        A_     =   calculate_variance(_UpperCamelCase,   _UpperCamelCase,   sum(_UpperCamelCase   )   )
        print('''Variance: ''',   _UpperCamelCase   )
        print('''-''' * 1_00   )

        # Predicting Y values
        # storing predicted Y values in 'pre_indexes' variable
        A_     =   predict_y_values(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )
        print('''-''' * 1_00   )

        # Calculating Accuracy of the model
        print(F'''Accuracy: {accuracy(_UpperCamelCase,   _UpperCamelCase   )}'''   )
        print('''-''' * 1_00   )
        print(''' DONE '''.center(1_00,   '''+'''   )   )

        if input('''Press any key to restart or \'q\' for quit: '''   ).strip().lower() == "q":
            print('''\n''' + '''GoodBye!'''.center(1_00,   '''-'''   ) + '''\n'''   )
            break
        system('''cls''' if name == '''nt''' else '''clear'''   )  # noqa: S605


if __name__ == "__main__":
  main()












from __future__ import annotations

import sys






class 	A	:
     """simple docstring"""

     def __init__(			self     :      str		,     A__     :      str		,     A__     :      int	) ->				str:
           lowerCamelCase__					     = letter
           lowerCamelCase__					     = freq
           lowerCamelCase__					     = {}




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






class 	A	:
     """simple docstring"""

     def __init__(			self     :      Optional[Any]		,     A__     :      int		,     A__     :      Letter | TreeNode		,     A__     :      Letter | TreeNode	) ->				Any:
           lowerCamelCase__					     = freq
           lowerCamelCase__					     = left
           lowerCamelCase__					     = right

def 	lowerCAmelCase_	(     lowerCamelCase:				str  )    ->	list[Letter]:


      '''simple docstring'''




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

def 	lowerCAmelCase_	(     lowerCamelCase:				list[Letter]  )    ->	Letter | TreeNode:


      '''simple docstring'''




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

def 	lowerCAmelCase_	(     lowerCamelCase:				Letter | TreeNode   , lowerCamelCase:				str  )    ->	list[Letter]:


      '''simple docstring'''




      if isinstance(lowerCamelCase   , lowerCamelCase  ):
            lowerCamelCase__					     = bitstring
            return [root]
      lowerCamelCase__					     = root  # type: ignore
      lowerCamelCase__					     = []
      letters += traverse_tree(treenode.left   , bitstring + '0'  )
      letters += traverse_tree(treenode.right   , bitstring + '1'  )
      return letters

def 	lowerCAmelCase_	(     lowerCamelCase:				str  )    ->	None:


      '''simple docstring'''




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