



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__	(							__snake_case  ,			__snake_case  ,			__snake_case							)				->	list:
  seed(1							)
  return [gauss(__snake_case  ,			__snake_case							) for _ in range(__snake_case							)]




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




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case							)				->	float:
  return sum(__snake_case							) / instance_count




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case							)				->	float:
  return instance_count / total_count




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




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case  ,			__snake_case  ,			__snake_case							)				->	list:
  _A          =      []
  # for loop iterates over number of elements in list
  for i in range(len(__snake_case							)							):
    # 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(__snake_case							)							):
        # 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(__snake_case							)

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




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case							)				->	float:
  _A          =      sum(1 for i, j in zip(__snake_case  ,			__snake_case							) 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(__snake_case							)) * 100


__lowerCamelCase								=   TypeVar("""num""")




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case  ,			__snake_case  ,			__snake_case = lambda __snake_case							: True  ,			__snake_case = None  ,			)				->	num:
  while True:
    try:
      _A          =      input_type(input(__snake_case							).strip() or default							)
      if condition(__snake_case							):
        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__	(							)				->	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=__snake_case  ,			condition=lambda __snake_case							: 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=__snake_case  ,			condition=lambda __snake_case							: 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(__snake_case							):
      _A          =      valid_input(
          input_type=__snake_case  ,			condition=lambda __snake_case							: x > 0  ,			input_msg=(F'''Enter The number of instances for class_{i+1}: ''')  ,			err_msg='''Number of instances should be positive!'''  ,			)
      counts.append(__snake_case							)
    print('''-''' * 100							)

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

    print('''Standard deviation: '''  ,			__snake_case							)
    # print out the number of instances in classes in separated line
    for i, count in enumerate(__snake_case  ,			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(__snake_case  ,			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]  ,			__snake_case  ,			counts[j]							)
        for j in range(__snake_case							)
    ]
    print('''Generated Normal Distribution: \n'''  ,			__snake_case							)
    print('''-''' * 100							)

    # Generating Ys to detecting corresponding classes
    _A          =      y_generator(__snake_case  ,			__snake_case							)
    print('''Generated Corresponding Ys: \n'''  ,			__snake_case							)
    print('''-''' * 100							)

    # Calculating the value of actual mean for each class
    _A          =      [calculate_mean(counts[k]  ,			x[k]							) for k in range(__snake_case							)]
    # for loop iterates over number of elements in 'actual_means' list and print
    # out them in separated line
    for i, actual_mean in enumerate(__snake_case  ,			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(__snake_case							)							) for i in range(__snake_case							)
    ]

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

    # Calculating the values of variance for each class
    _A          =      calculate_variance(__snake_case  ,			__snake_case  ,			sum(__snake_case							)							)
    print('''Variance: '''  ,			__snake_case							)
    print('''-''' * 100							)

    # Predicting Y values
    # storing predicted Y values in 'pre_indexes' variable
    _A          =      predict_y_values(__snake_case  ,			__snake_case  ,			__snake_case  ,			__snake_case							)
    print('''-''' * 100							)

    # Calculating Accuracy of the model
    print(F'''Accuracy: {accuracy(__snake_case  ,			__snake_case							)}'''							)
    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()




from __future__ import annotations




def       UpperCAmelCase__	(							__snake_case							)				->	list[int]:
  _A          =      [0] * len(__snake_case							)
  for i in range(1  ,			len(__snake_case							)							):
    _A          =      duration_times[i - 1] + waiting_times[i - 1]
  return waiting_times




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case							)				->	list[int]:
  return [
      duration_time + waiting_times[i]
      for i, duration_time in enumerate(__snake_case							)
  ]




def       UpperCAmelCase__	(							__snake_case							)				->	float:
  return sum(__snake_case							) / len(__snake_case							)




def       UpperCAmelCase__	(							__snake_case							)				->	float:
  return sum(__snake_case							) / len(__snake_case							)


if __name__ == "__main__":
    # process id's
    __lowerCamelCase								=   [1, 2, 3]

    # ensure that we actually have processes
    if len(processes) == 0:
        print("""Zero amount of processes""")
        raise SystemExit(0)

    # duration time of all processes
    __lowerCamelCase								=   [1_9, 8, 9]

    # ensure we can match each id to a duration time
    if len(duration_times) != len(processes):
        print("""Unable to match all id's with their duration time""")
        raise SystemExit(0)

    # get the waiting times and the turnaround times
    __lowerCamelCase								=   calculate_waiting_times(duration_times)
    __lowerCamelCase								=   calculate_turnaround_times(duration_times, waiting_times)

    # get the average times
    __lowerCamelCase								=   calculate_average_waiting_time(waiting_times)
    __lowerCamelCase								=   calculate_average_turnaround_time(turnaround_times)

    # print all the results
    print("""Process ID\tDuration Time\tWaiting Time\tTurnaround Time""")
    for i, process in enumerate(processes):
        print(
            f'''{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t'''
            f'''{turnaround_times[i]}'''
        )
    print(f'''Average waiting time = {average_waiting_time}''')
    print(f'''Average turn around time = {average_turnaround_time}''')