




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





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> list:
 seed(1					)
 return [gauss(__lowerCamelCase							,    __lowerCamelCase					) for _ in range(__lowerCamelCase					)]





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> list:
 return [k for k in range(__lowerCamelCase					) for _ in range(instance_count[k]					)]





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> float:
 return sum(__lowerCamelCase					) / instance_count





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> float:
 return instance_count / total_count





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> float:
 a		       =							[]  # An empty list to store all squared differences
 # iterate over number of elements in items
 for i in range(len(__lowerCamelCase					)					):
  # 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(__lowerCamelCase					)  # Number of classes in dataset
 return 1 / (total_count - n_classes) * sum(__lowerCamelCase					)





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> list:
 a		       =							[]
 # for loop iterates over number of elements in list
 for i in range(len(__lowerCamelCase					)					):
  # 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(__lowerCamelCase					)					):
    # 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(__lowerCamelCase					)

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





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> float:
 a		       =							sum(1 for i, j in zip(__lowerCamelCase							,    __lowerCamelCase					) 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(__lowerCamelCase					)) * 100


__UpperCamelCase      : str					      =   TypeVar("num")





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase = lambda __lowerCamelCase					: True							,    __lowerCamelCase = None							,    )   -> num:
 while True:
  try:
   a		       =							input_type(input(__lowerCamelCase					).strip() or default					)
   if condition(__lowerCamelCase					):
    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 	__A	(			)   -> Any:
 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=__lowerCamelCase							,    condition=lambda __lowerCamelCase					: x > 0							,    input_msg="""Enter the number of classes (Data Groupings): """							,    err_msg="""Number of classes should be positive!"""							,    )

  print("""-""" * 100					)

  # Trying to get the value of standard deviation
  a		       =							valid_input(
      input_type=__lowerCamelCase							,    condition=lambda __lowerCamelCase					: 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("""-""" * 100					)

  # 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(__lowerCamelCase					):
   a		       =							valid_input(
       input_type=__lowerCamelCase							,    condition=lambda __lowerCamelCase					: x > 0							,    input_msg=(f'Enter The number of instances for class_{i+1}: ')							,    err_msg="""Number of instances should be positive!"""							,    )
   counts.append(__lowerCamelCase					)
  print("""-""" * 100					)

  # An empty list to store values of user-entered means of classes
  a		       =							[]
  for a in range(__lowerCamelCase					):
   a		       =							valid_input(
       input_type=__lowerCamelCase							,    input_msg=(f'Enter the value of mean for class_{a+1}: ')							,    err_msg="""This is an invalid value."""							,    )
   user_means.append(__lowerCamelCase					)
  print("""-""" * 100					)

  print("""Standard deviation: """							,    __lowerCamelCase					)
  # print out the number of instances in classes in separated line
  for i, count in enumerate(__lowerCamelCase							,    1					):
   print(f'Number of instances in class_{i} is: {count}'					)
  print("""-""" * 100					)

  # print out mean values of classes separated line
  for i, user_mean in enumerate(__lowerCamelCase							,    1					):
   print(f'Mean of class_{i} is: {user_mean}'					)
  print("""-""" * 100					)

  # Generating training dataset drawn from gaussian distribution
  a		       =							[
      gaussian_distribution(user_means[j]							,    __lowerCamelCase							,    counts[j]					)
      for j in range(__lowerCamelCase					)
  ]
  print("""Generated Normal Distribution: \n"""							,    __lowerCamelCase					)
  print("""-""" * 100					)

  # Generating Ys to detecting corresponding classes
  a		       =							y_generator(__lowerCamelCase							,    __lowerCamelCase					)
  print("""Generated Corresponding Ys: \n"""							,    __lowerCamelCase					)
  print("""-""" * 100					)

  # Calculating the value of actual mean for each class
  a		       =							[calculate_mean(counts[k]							,    x[k]					) for k in range(__lowerCamelCase					)]
  # for loop iterates over number of elements in 'actual_means' list and print
  # out them in separated line
  for i, actual_mean in enumerate(__lowerCamelCase							,    1					):
   print(f'Actual(Real) mean of class_{i} is: {actual_mean}'					)
  print("""-""" * 100					)

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

  # for loop iterates over number of elements in 'probabilities' list and print
  # out them in separated line
  for i, probability in enumerate(__lowerCamelCase							,    1					):
   print(f'Probability of class_{i} is: {probability}'					)
  print("""-""" * 100					)

  # Calculating the values of variance for each class
  a		       =							calculate_variance(__lowerCamelCase							,    __lowerCamelCase							,    sum(__lowerCamelCase					)					)
  print("""Variance: """							,    __lowerCamelCase					)
  print("""-""" * 100					)

  # Predicting Y values
  # storing predicted Y values in 'pre_indexes' variable
  a		       =							predict_y_values(__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)
  print("""-""" * 100					)

  # Calculating Accuracy of the model
  print(f'Accuracy: {accuracy(__lowerCamelCase							,    __lowerCamelCase					)}'					)
  print("""-""" * 100					)
  print(""" DONE """.center(100							,    """+"""					)					)

  if input("""Press any key to restart or 'q' for quit: """					).strip().lower() == "q":
   print("""\n""" + """GoodBye!""".center(100							,    """-"""					) + """\n"""					)
   break
  system("""cls""" if name == """nt""" else """clear"""					)  # noqa: S605


if __name__ == "__main__":
  main()







import doctest

import projectq
from projectq.ops import H, Measure





def 	__A	(			__lowerCamelCase					)   -> int:
	a		       =							quantum_engine.allocate_qubit()
	H | qubit
	Measure | qubit
	return int(__lowerCamelCase					)


if __name__ == "__main__":
		doctest.testmod()

		# initialises a new quantum backend
		__UpperCamelCase      : List[Any]					      =   projectq.MainEngine()

		# Generate a list of 10 random numbers
		__UpperCamelCase      : Optional[int]					      =   [get_random_number(quantum_engine) for _ in range(10)]

		# Flushes the quantum engine from memory
		quantum_engine.flush()

		print("Random numbers", random_numbers_list)

