

'''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							(		lowercase__  :					float,   lowercase__  :					float,   lowercase__  :					int	):






     '''simple docstring'''





     seed(1	)
     return [gauss(lowercase__,   lowercase__	) for _ in range(lowercase__	)]

def 			__UpperCamelCase							(		lowercase__  :					int,   lowercase__  :					list	):






     '''simple docstring'''





     return [k for k in range(lowercase__	) for _ in range(instance_count[k]	)]

def 			__UpperCamelCase							(		lowercase__  :					int,   lowercase__  :					list	):






     '''simple docstring'''





     return sum(lowercase__	) / instance_count

def 			__UpperCamelCase							(		lowercase__  :					int,   lowercase__  :					int	):






     '''simple docstring'''





     return instance_count / total_count

def 			__UpperCamelCase							(		lowercase__  :					list,   lowercase__  :					list,   lowercase__  :					int	):






     '''simple docstring'''





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

def 			__UpperCamelCase							(		lowercase__  :					list,   lowercase__  :					list,   lowercase__  :					float,   lowercase__  :					list	):






     '''simple docstring'''





     __lowercase     						=[]
     # for loop iterates over number of elements in list
     for i in range(len(lowercase__	)	):
          # for loop iterates over number of inner items of each element
          for j in range(len(x_items[i]	)	):
               __lowercase     						=[]  # 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(lowercase__	)	):
                    # 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(lowercase__	)

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

def 			__UpperCamelCase							(		lowercase__  :					list,   lowercase__  :					list	):






     '''simple docstring'''





     __lowercase     						=sum(1 for i, j in zip(lowercase__,   lowercase__	) 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(lowercase__	)) * 1_00


UpperCAmelCase							=					TypeVar('''num''')

def 			__UpperCamelCase							(		lowercase__  :					Callable[[object], num],   lowercase__  :					str,   lowercase__  :					str,   lowercase__  :					Callable[[num], bool] = lambda lowercase__	: True,   lowercase__  :					str | None = None,   ):






     '''simple docstring'''





     while True:
          try:
               __lowercase     						=input_type(input(lowercase__	).strip() or default	)
               if condition(lowercase__	):
                    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							(		):






     '''simple docstring'''





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

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

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

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

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

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

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

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

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

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

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





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase							=					{'''configuration_vit_hybrid''': ['''VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTHybridConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase							=					[
           '''VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''ViTHybridForImageClassification''',
           '''ViTHybridModel''',
           '''ViTHybridPreTrainedModel''',
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase							=					['''ViTHybridImageProcessor''']


if TYPE_CHECKING:
       from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vit_hybrid import (
                  VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ViTHybridForImageClassification,
                  ViTHybridModel,
                  ViTHybridPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
       import sys

       UpperCAmelCase							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


