



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    ):





					"""simple docstring"""

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


def 	_UpperCAmelCase       (   snake_case			,		snake_case    ):





					"""simple docstring"""

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


def 	_UpperCAmelCase       (   snake_case			,		snake_case    ):





					"""simple docstring"""

					return sum(snake_case    ) / instance_count


def 	_UpperCAmelCase       (   snake_case			,		snake_case    ):





					"""simple docstring"""

					return instance_count / total_count


def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					[]  # 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
					_lowerCAmelCase      			=					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    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					[]
					# 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]    )    ):
															_lowerCAmelCase      			=					[]  # 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    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					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    )) * 1_00


A__			   = TypeVar("""num""")


def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case			,		snake_case = lambda snake_case    : True			,		snake_case = None			,		):





					"""simple docstring"""

					while True:
										try:
															_lowerCAmelCase      			=					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       (   ):





					"""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
										_lowerCAmelCase      			=					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("""-""" * 1_00    )

										# Trying to get the value of standard deviation
										_lowerCAmelCase      			=					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("""-""" * 1_00    )

										# Trying to get number of instances in classes and theirs means to generate
										# dataset
										_lowerCAmelCase      			=					[]  # An empty list to store instance counts of classes in dataset
										for i in range(snake_case    ):
															_lowerCAmelCase      			=					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("""-""" * 1_00    )

										# An empty list to store values of user-entered means of classes
										_lowerCAmelCase      			=					[]
										for a in range(snake_case    ):
															_lowerCAmelCase      			=					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("""-""" * 1_00    )

										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("""-""" * 1_00    )

										# 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("""-""" * 1_00    )

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

										# Generating Ys to detecting corresponding classes
										_lowerCAmelCase      			=					y_generator(snake_case			,		snake_case    )
										print("""Generated Corresponding Ys: \n"""			,		snake_case    )
										print("""-""" * 1_00    )

										# Calculating the value of actual mean for each class
										_lowerCAmelCase      			=					[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("""-""" * 1_00    )

										# Calculating the value of probabilities for each class
										_lowerCAmelCase      			=					[
										    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("""-""" * 1_00    )

										# Calculating the values of variance for each class
										_lowerCAmelCase      			=					calculate_variance(snake_case			,		snake_case			,		sum(snake_case    )    )
										print("""Variance: """			,		snake_case    )
										print("""-""" * 1_00    )

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

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








from typing import TYPE_CHECKING

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


A__			   = {
    """configuration_videomae""": ["""VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VideoMAEConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A__			   = [
	    """VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """VideoMAEForPreTraining""",
	    """VideoMAEModel""",
	    """VideoMAEPreTrainedModel""",
	    """VideoMAEForVideoClassification""",
	]

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A__			   = ["""VideoMAEFeatureExtractor"""]
	A__			   = ["""VideoMAEImageProcessor"""]

if TYPE_CHECKING:
	from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_videomae import (
		    VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    VideoMAEForPreTraining,
		    VideoMAEForVideoClassification,
		    VideoMAEModel,
		    VideoMAEPreTrainedModel,
		)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_videomae import VideoMAEFeatureExtractor
		from .image_processing_videomae import VideoMAEImageProcessor

else:
	import sys

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



