

"""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   (    a__: float ,	a__: float ,	a__: int			)				->			list:




					"""simple docstring"""



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





def 				__UpperCamelCase   (    a__: int ,	a__: list			)				->			list:




					"""simple docstring"""



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





def 				__UpperCamelCase   (    a__: int ,	a__: list			)				->			float:




					"""simple docstring"""



					return sum(a__			) / instance_count





def 				__UpperCamelCase   (    a__: int ,	a__: int			)				->			float:




					"""simple docstring"""



					return instance_count / total_count





def 				__UpperCamelCase   (    a__: list ,	a__: list ,	a__: int			)				->			float:




					"""simple docstring"""



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





def 				__UpperCamelCase   (    a__: list ,	a__: list ,	a__: float ,	a__: list			)				->			list:




					"""simple docstring"""



					__lowercase												=			[]
					# for loop iterates over number of elements in list
					for i in range(len(a__			)			):
										# 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(a__			)			):
																				# 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(a__			)

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





def 				__UpperCamelCase   (    a__: list ,	a__: list			)				->			float:




					"""simple docstring"""



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


a__ :	Tuple						= TypeVar("""num""")





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




					"""simple docstring"""



					while True:
										try:
															__lowercase												=			input_type(input(a__			).strip() or default			)
															if condition(a__			):
																				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   (    )				->			int:




					"""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=a__ ,	condition=lambda a__			: 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=a__ ,	condition=lambda a__			: 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(a__			):
															__lowercase												=			valid_input(
															    input_type=a__ ,	condition=lambda a__			: x > 0 ,	input_msg=(f'''Enter The number of instances for class_{i+1}: ''') ,	err_msg='''Number of instances should be positive!''' ,	)
															counts.append(a__			)
										print('''-''' * 1_00			)

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

										print('''Standard deviation: ''' ,	a__			)
										# print out the number of instances in classes in separated line
										for i, count in enumerate(a__ ,	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(a__ ,	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] ,	a__ ,	counts[j]			)
										    for j in range(a__			)
										]
										print('''Generated Normal Distribution: \n''' ,	a__			)
										print('''-''' * 1_00			)

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

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

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

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

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

										# Calculating Accuracy of the model
										print(f'''Accuracy: {accuracy(a__ ,	a__			)}'''			)
										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"""

import json
import logging
import os
import subprocess
from argparse import ArgumentParser


a__ :	List[str]						= logging.getLogger(__name__)





def 				__UpperCamelCase   (    )				->			Optional[Any]:




					"""simple docstring"""



					__lowercase												=			ArgumentParser()
					__lowercase						, __lowercase												=			parser.parse_known_args()
					for arg in unknown:
										if arg.startswith(('''-''', '''--''')			):
															parser.add_argument(arg.split('''='''			)[0]			)

					return parser.parse_args()





def 				__UpperCamelCase   (    )				->			int:




					"""simple docstring"""



					__lowercase												=			parse_args()
					__lowercase												=			88_88
					__lowercase												=			int(os.environ['''SM_NUM_GPUS''']			)
					__lowercase												=			json.loads(os.environ['''SM_HOSTS''']			)
					__lowercase												=			len(a__			)
					__lowercase												=			os.environ['''SM_CURRENT_HOST''']
					__lowercase												=			hosts.index(a__			)
					__lowercase												=			'''INFO'''

					if num_nodes > 1:
										__lowercase												=			f'''python -m torch.distributed.launch \
                --nnodes={num_nodes}  \
                --node_rank={rank}  \
                --nproc_per_node={num_gpus}  \
                --master_addr={hosts[0]}  \
                --master_port={port} \
                ./run_glue.py \
                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}'''
					else:
										__lowercase												=			f'''python -m torch.distributed.launch \
            --nproc_per_node={num_gpus}  \
            ./run_glue.py \
            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}'''
					try:
										subprocess.run(a__ ,	shell=a__			)
					except Exception as e:
										logger.info(a__			)


if __name__ == "__main__":
							main()


