





'''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     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     float,     SCREAMING_SNAKE_CASE__ :     float,     SCREAMING_SNAKE_CASE__ :     int				)	->		list:
						seed(1				)
						return [gauss(SCREAMING_SNAKE_CASE__,     SCREAMING_SNAKE_CASE__				) for _ in range(SCREAMING_SNAKE_CASE__				)]





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     int,     SCREAMING_SNAKE_CASE__ :     list				)	->		list:
						return [k for k in range(SCREAMING_SNAKE_CASE__				) for _ in range(instance_count[k]				)]





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     int,     SCREAMING_SNAKE_CASE__ :     list				)	->		float:
						return sum(SCREAMING_SNAKE_CASE__				) / instance_count





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     int,     SCREAMING_SNAKE_CASE__ :     int				)	->		float:
						return instance_count / total_count





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     list,     SCREAMING_SNAKE_CASE__ :     list,     SCREAMING_SNAKE_CASE__ :     int				)	->		float:
						UpperCAmelCase_     : Union[str, Any]         =					[]  # 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
						UpperCAmelCase_     : List[Any]         =					len(SCREAMING_SNAKE_CASE__				)  # Number of classes in dataset
						return 1 / (total_count - n_classes) * sum(SCREAMING_SNAKE_CASE__				)





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     list,     SCREAMING_SNAKE_CASE__ :     list,     SCREAMING_SNAKE_CASE__ :     float,     SCREAMING_SNAKE_CASE__ :     list				)	->		list:
						UpperCAmelCase_     : Tuple         =					[]
						# 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]				)				):
																		UpperCAmelCase_     : List[Any]         =					[]  # 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     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     list,     SCREAMING_SNAKE_CASE__ :     list				)	->		float:
						UpperCAmelCase_     : List[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


snake_case_       :    str          =					TypeVar("num")





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     Callable[[object], num],     SCREAMING_SNAKE_CASE__ :     str,     SCREAMING_SNAKE_CASE__ :     str,     SCREAMING_SNAKE_CASE__ :     Callable[[num], bool] = lambda SCREAMING_SNAKE_CASE__				: True,     SCREAMING_SNAKE_CASE__ :     str | None = None,     )	->		num:
						while True:
												try:
																		UpperCAmelCase_     : str         =					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     lowerCamelCase_ (					)	->		int:
						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
												UpperCAmelCase_     : Optional[int]         =					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
												UpperCAmelCase_     : Tuple         =					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
												UpperCAmelCase_     : Union[str, Any]         =					[]  # An empty list to store instance counts of classes in dataset
												for i in range(SCREAMING_SNAKE_CASE__				):
																		UpperCAmelCase_     : Dict         =					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
												UpperCAmelCase_     : Any         =					[]
												for a in range(SCREAMING_SNAKE_CASE__				):
																		UpperCAmelCase_     : List[str]         =					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
												UpperCAmelCase_     : Optional[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
												UpperCAmelCase_     : Optional[Any]         =					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
												UpperCAmelCase_     : Optional[int]         =					[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
												UpperCAmelCase_     : Union[str, 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
												UpperCAmelCase_     : Union[str, Any]         =					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
												UpperCAmelCase_     : Optional[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'''







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

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


snake_case_       :    List[Any]          =					{
    "configuration_tvlt": ["TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP", "TvltConfig"],
    "feature_extraction_tvlt": ["TvltFeatureExtractor"],
    "processing_tvlt": ["TvltProcessor"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_       :    Optional[int]          =					[
					    "TVLT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TvltModel",
					    "TvltForPreTraining",
					    "TvltForAudioVisualClassification",
					    "TvltPreTrainedModel",
					]

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_       :    List[Any]          =					["TvltImageProcessor"]


if TYPE_CHECKING:
					from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
					from .processing_tvlt import TvltProcessor
					from .feature_extraction_tvlt import TvltFeatureExtractor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tvlt import (
										    TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TvltForAudioVisualClassification,
										    TvltForPreTraining,
										    TvltModel,
										    TvltPreTrainedModel,
										)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_tvlt import TvltImageProcessor


else:
					import sys

					snake_case_       :    Any          =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





