





'''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(	UpperCamelCase__     :			float     ,							UpperCamelCase__     :			float     ,							UpperCamelCase__     :			int		)		->	list:
    seed(1		)
    return [gauss(UpperCamelCase__     ,							UpperCamelCase__		) for _ in range(UpperCamelCase__		)]
def 				_lowerCamelCase(	UpperCamelCase__     :			int     ,							UpperCamelCase__     :			list		)		->	list:
    return [k for k in range(UpperCamelCase__		) for _ in range(instance_count[k]		)]
def 				_lowerCamelCase(	UpperCamelCase__     :			int     ,							UpperCamelCase__     :			list		)		->	float:
    return sum(UpperCamelCase__		) / instance_count
def 				_lowerCamelCase(	UpperCamelCase__     :			int     ,							UpperCamelCase__     :			int		)		->	float:
    return instance_count / total_count
def 				_lowerCamelCase(	UpperCamelCase__     :			list     ,							UpperCamelCase__     :			list     ,							UpperCamelCase__     :			int		)		->	float:
    A							:							Optional[int]				=    []  # An empty list to store all squared differences
    # iterate over number of elements in items
    for i in range(len(UpperCamelCase__		)		):
        # 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
    A							:							List[Any]				=    len(UpperCamelCase__		)  # Number of classes in dataset
    return 1 / (total_count - n_classes) * sum(UpperCamelCase__		)
def 				_lowerCamelCase(	UpperCamelCase__     :			list     ,							UpperCamelCase__     :			list     ,							UpperCamelCase__     :			float     ,							UpperCamelCase__     :			list		)		->	list:
    A							:							Optional[Any]				=    []
    # for loop iterates over number of elements in list
    for i in range(len(UpperCamelCase__		)		):
        # for loop iterates over number of inner items of each element
        for j in range(len(x_items[i]		)		):
            A							:							Optional[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(UpperCamelCase__		)		):
                # 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(UpperCamelCase__		)

    return [result.index(max(UpperCamelCase__		)		) for result in results]
def 				_lowerCamelCase(	UpperCamelCase__     :			list     ,							UpperCamelCase__     :			list		)		->	float:
    A							:							Optional[int]				=    sum(1 for i, j in zip(UpperCamelCase__     ,							UpperCamelCase__		) 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(UpperCamelCase__		)) * 100


snake_case_     			=						TypeVar("""num""")
def 				_lowerCamelCase(	UpperCamelCase__     :			Callable[[object], num]     ,							UpperCamelCase__     :			str     ,							UpperCamelCase__     :			str     ,							UpperCamelCase__     :			Callable[[num], bool] = lambda UpperCamelCase__		: True     ,							UpperCamelCase__     :			str | None = None     ,							)		->	num:
    while True:
        try:
            A							:							Tuple				=    input_type(input(UpperCamelCase__		).strip() or default		)
            if condition(UpperCamelCase__		):
                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(	)		->	List[str]:
    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
        A							:							int				=    valid_input(
            input_type=UpperCamelCase__     ,							condition=lambda UpperCamelCase__		: 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
        A							:							str				=    valid_input(
            input_type=UpperCamelCase__     ,							condition=lambda UpperCamelCase__		: 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
        A							:							Any				=    []  # An empty list to store instance counts of classes in dataset
        for i in range(UpperCamelCase__		):
            A							:							Tuple				=    valid_input(
                input_type=UpperCamelCase__     ,							condition=lambda UpperCamelCase__		: x > 0     ,							input_msg=(F'''Enter The number of instances for class_{i+1}: ''')     ,							err_msg='''Number of instances should be positive!'''     ,							)
            counts.append(UpperCamelCase__		)
        print('''-''' * 100		)

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

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

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

        # Generating Ys to detecting corresponding classes
        A							:							Optional[int]				=    y_generator(UpperCamelCase__     ,							UpperCamelCase__		)
        print('''Generated Corresponding Ys: \n'''     ,							UpperCamelCase__		)
        print('''-''' * 100		)

        # Calculating the value of actual mean for each class
        A							:							List[Any]				=    [calculate_mean(counts[k]     ,							x[k]		) for k in range(UpperCamelCase__		)]
        # for loop iterates over number of elements in 'actual_means' list and print
        # out them in separated line
        for i, actual_mean in enumerate(UpperCamelCase__     ,							1		):
            print(F'''Actual(Real) mean of class_{i} is: {actual_mean}'''		)
        print('''-''' * 100		)

        # Calculating the value of probabilities for each class
        A							:							Union[str, Any]				=    [
            calculate_probabilities(counts[i]     ,							sum(UpperCamelCase__		)		) for i in range(UpperCamelCase__		)
        ]

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

        # Calculating the values of variance for each class
        A							:							Dict				=    calculate_variance(UpperCamelCase__     ,							UpperCamelCase__     ,							sum(UpperCamelCase__		)		)
        print('''Variance: '''     ,							UpperCamelCase__		)
        print('''-''' * 100		)

        # Predicting Y values
        # storing predicted Y values in 'pre_indexes' variable
        A							:							Optional[Any]				=    predict_y_values(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__		)
        print('''-''' * 100		)

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




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import MobileNetVaImageProcessor



class 							a       (      unittest.TestCase ):





     def __init__(     self       :					Optional[int]     ,						SCREAMING_SNAKE_CASE_       :					str     ,						SCREAMING_SNAKE_CASE_       :					List[str]=7     ,						SCREAMING_SNAKE_CASE_       :					Union[str, Any]=3     ,						SCREAMING_SNAKE_CASE_       :					List[Any]=18     ,						SCREAMING_SNAKE_CASE_       :					List[str]=30     ,						SCREAMING_SNAKE_CASE_       :					List[Any]=400     ,						SCREAMING_SNAKE_CASE_       :					Dict=True     ,						SCREAMING_SNAKE_CASE_       :					Any=None     ,						SCREAMING_SNAKE_CASE_       :					List[str]=True     ,						SCREAMING_SNAKE_CASE_       :					Any=None     ,						):
        __lowerCamelCase:					List[Any]	 =						size if size is not None else {"""shortest_edge""": 20}
        __lowerCamelCase:					Tuple	 =						crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
        __lowerCamelCase:					List[Any]	 =						parent
        __lowerCamelCase:					Optional[int]	 =						batch_size
        __lowerCamelCase:					str	 =						num_channels
        __lowerCamelCase:					Dict	 =						image_size
        __lowerCamelCase:					Tuple	 =						min_resolution
        __lowerCamelCase:					Union[str, Any]	 =						max_resolution
        __lowerCamelCase:					Tuple	 =						do_resize
        __lowerCamelCase:					List[str]	 =						size
        __lowerCamelCase:					int	 =						do_center_crop
        __lowerCamelCase:					Tuple	 =						crop_size







     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[Any]				):
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
        }



@require_torch
@require_vision
class 							a       (      _UpperCAmelCase  ,unittest.TestCase ):
     UpperCAmelCase__			:	int       =						MobileNetVaImageProcessor if is_vision_available() else None





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]				):
        __lowerCamelCase:					Tuple	 =						MobileNetVaImageProcessingTester(self				)





     @property
     def 			SCREAMING_SNAKE_CASE__							(     self       :					str				):
        return self.image_processor_tester.prepare_image_processor_dict()





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]				):
        __lowerCamelCase:					List[Any]	 =						self.image_processing_class(**self.image_processor_dict				)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""do_resize"""				)				)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""size"""				)				)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""do_center_crop"""				)				)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""center_crop"""				)				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Any				):
        __lowerCamelCase:					str	 =						self.image_processing_class.from_dict(self.image_processor_dict				)
        self.assertEqual(image_processor.size     ,						{"""shortest_edge""": 20}				)
        self.assertEqual(image_processor.crop_size     ,						{"""height""": 18, """width""": 18}				)

        __lowerCamelCase:					Optional[int]	 =						self.image_processing_class.from_dict(self.image_processor_dict     ,						size=42     ,						crop_size=84				)
        self.assertEqual(image_processor.size     ,						{"""shortest_edge""": 42}				)
        self.assertEqual(image_processor.crop_size     ,						{"""height""": 84, """width""": 84}				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[Any]				):
        pass





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]				):
        # Initialize image_processing
        __lowerCamelCase:					List[str]	 =						self.image_processing_class(**self.image_processor_dict				)
        # create random PIL images
        __lowerCamelCase:					str	 =						prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=SCREAMING_SNAKE_CASE_				)
        for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,						Image.Image				)

        # Test not batched input
        __lowerCamelCase:					Optional[Any]	 =						image_processing(image_inputs[0]     ,						return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )     ,						)

        # Test batched
        __lowerCamelCase:					Tuple	 =						image_processing(SCREAMING_SNAKE_CASE_     ,						return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )     ,						)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]				):
        # Initialize image_processing
        __lowerCamelCase:					Union[str, Any]	 =						self.image_processing_class(**self.image_processor_dict				)
        # create random numpy tensors
        __lowerCamelCase:					Tuple	 =						prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=SCREAMING_SNAKE_CASE_     ,						numpify=SCREAMING_SNAKE_CASE_				)
        for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,						np.ndarray				)

        # Test not batched input
        __lowerCamelCase:					List[Any]	 =						image_processing(image_inputs[0]     ,						return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )     ,						)

        # Test batched
        __lowerCamelCase:					Optional[Any]	 =						image_processing(SCREAMING_SNAKE_CASE_     ,						return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )     ,						)







     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]				):
        # Initialize image_processing
        __lowerCamelCase:					str	 =						self.image_processing_class(**self.image_processor_dict				)
        # create random PyTorch tensors
        __lowerCamelCase:					List[str]	 =						prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=SCREAMING_SNAKE_CASE_     ,						torchify=SCREAMING_SNAKE_CASE_				)
        for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,						torch.Tensor				)

        # Test not batched input
        __lowerCamelCase:					Union[str, Any]	 =						image_processing(image_inputs[0]     ,						return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )     ,						)

        # Test batched
        __lowerCamelCase:					Optional[int]	 =						image_processing(SCREAMING_SNAKE_CASE_     ,						return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )     ,						)


