



'''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  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		seed(1     )
		return [gauss(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ) for _ in range(SCREAMING_SNAKE_CASE     )]







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		return [k for k in range(SCREAMING_SNAKE_CASE     ) for _ in range(instance_count[k]     )]







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		return sum(SCREAMING_SNAKE_CASE     ) / instance_count







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		return instance_count / total_count







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Tuple										=      []  # An empty list to store all squared differences
		# iterate over number of elements in items
		for i in range(len(SCREAMING_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
		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      len(SCREAMING_SNAKE_CASE     )  # Number of classes in dataset
		return 1 / (total_count - n_classes) * sum(SCREAMING_SNAKE_CASE     )







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      []
		# for loop iterates over number of elements in list
		for i in range(len(SCREAMING_SNAKE_CASE     )     ):
				# for loop iterates over number of inner items of each element
				for j in range(len(x_items[i]     )     ):
						SCREAMING_SNAKE_CASE_						:int										=      []  # 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(SCREAMING_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(SCREAMING_SNAKE_CASE     )

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







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Any										=      sum(1 for i, j in zip(SCREAMING_SNAKE_CASE ,		SCREAMING_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(SCREAMING_SNAKE_CASE     )) * 100


SCREAMING_SNAKE_CASE__     : List[Any]				=   TypeVar("""num""")







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE = lambda SCREAMING_SNAKE_CASE     : True ,		SCREAMING_SNAKE_CASE = None ,		):
		while True:
				try:
						SCREAMING_SNAKE_CASE_						:List[Any]										=      input_type(input(SCREAMING_SNAKE_CASE     ).strip() or default     )
						if condition(SCREAMING_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  SCREAMING_SNAKE_CASE__ (				):
		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
				SCREAMING_SNAKE_CASE_						:Optional[Any]										=      valid_input(
				    input_type=SCREAMING_SNAKE_CASE ,		condition=lambda SCREAMING_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
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      valid_input(
				    input_type=SCREAMING_SNAKE_CASE ,		condition=lambda SCREAMING_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
				SCREAMING_SNAKE_CASE_						:Tuple										=      []  # An empty list to store instance counts of classes in dataset
				for i in range(SCREAMING_SNAKE_CASE     ):
						SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      valid_input(
						    input_type=SCREAMING_SNAKE_CASE ,		condition=lambda SCREAMING_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(SCREAMING_SNAKE_CASE     )
				print('-' * 100     )

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

				print('Standard deviation: ' ,		SCREAMING_SNAKE_CASE     )
				# print out the number of instances in classes in separated line
				for i, count in enumerate(SCREAMING_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(SCREAMING_SNAKE_CASE ,		1     ):
						print(F'Mean of class_{i} is: {user_mean}'     )
				print('-' * 100     )

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

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

				# Calculating the value of actual mean for each class
				SCREAMING_SNAKE_CASE_						:Dict										=      [calculate_mean(counts[k] ,		x[k]     ) for k in range(SCREAMING_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(SCREAMING_SNAKE_CASE ,		1     ):
						print(F'Actual(Real) mean of class_{i} is: {actual_mean}'     )
				print('-' * 100     )

				# Calculating the value of probabilities for each class
				SCREAMING_SNAKE_CASE_						:Optional[Any]										=      [
				    calculate_probabilities(counts[i] ,		sum(SCREAMING_SNAKE_CASE     )     ) for i in range(SCREAMING_SNAKE_CASE     )
				]

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

				# Calculating the values of variance for each class
				SCREAMING_SNAKE_CASE_						:List[str]										=      calculate_variance(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		sum(SCREAMING_SNAKE_CASE     )     )
				print('Variance: ' ,		SCREAMING_SNAKE_CASE     )
				print('-' * 100     )

				# Predicting Y values
				# storing predicted Y values in 'pre_indexes' variable
				SCREAMING_SNAKE_CASE_						:Any										=      predict_y_values(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )
				print('-' * 100     )

				# Calculating Accuracy of the model
				print(F'Accuracy: {accuracy(SCREAMING_SNAKE_CASE ,		SCREAMING_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()

"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase									=	{
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	["LayoutLMTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	[
				    "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "LayoutLMForMaskedLM",
				    "LayoutLMForSequenceClassification",
				    "LayoutLMForTokenClassification",
				    "LayoutLMForQuestionAnswering",
				    "LayoutLMModel",
				    "LayoutLMPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	[
				    "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFLayoutLMForMaskedLM",
				    "TFLayoutLMForSequenceClassification",
				    "TFLayoutLMForTokenClassification",
				    "TFLayoutLMForQuestionAnswering",
				    "TFLayoutLMMainLayer",
				    "TFLayoutLMModel",
				    "TFLayoutLMPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
				from .tokenization_layoutlm import LayoutLMTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_layoutlm import (
								    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    LayoutLMForMaskedLM,
								    LayoutLMForQuestionAnswering,
								    LayoutLMForSequenceClassification,
								    LayoutLMForTokenClassification,
								    LayoutLMModel,
								    LayoutLMPreTrainedModel,
								)
				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_layoutlm import (
								    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFLayoutLMForMaskedLM,
								    TFLayoutLMForQuestionAnswering,
								    TFLayoutLMForSequenceClassification,
								    TFLayoutLMForTokenClassification,
								    TFLayoutLMMainLayer,
								    TFLayoutLMModel,
								    TFLayoutLMPreTrainedModel,
								)

else:
				import sys

				__lowerCamelCase									=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





