




import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


lowercase_		=							1000





class 		A__				:






	def __init__(	self				,     lowerCamelCase				,     lowerCamelCase=1.2				,     lowerCamelCase="g"				,     lowerCamelCase=0.5				,     lowerCamelCase="-"				,     lowerCamelCase="test_out.jpg"				,     lowerCamelCase=True				,     lowerCamelCase=False				,     lowerCamelCase=None				,     lowerCamelCase=None				,     lowerCamelCase=0.7				,     )      ->   Optional[Any]:




						"""simple docstring"""






						if isinstance(lowerCamelCase				,     torch.Tensor						):
											__magic_name__					:							int		   =							img.numpy().astype('''np.uint8'''						)
						if isinstance(lowerCamelCase				,     lowerCamelCase						):
											__magic_name__					:							Dict		   =							img_tensorize(lowerCamelCase						)
						assert isinstance(lowerCamelCase				,     np.ndarray						)

						__magic_name__   , __magic_name__					:							Optional[Any]		   =							img.shape[1], img.shape[0]
						__magic_name__					:							Tuple		   =							mplfigure.Figure(frameon=lowerCamelCase						)
						__magic_name__					:							List[Any]		   =							fig.get_dpi()
						__magic_name__					:							str		   =							(width * scale + 1e-2) / dpi
						__magic_name__					:							List[str]		   =							(height * scale + 1e-2) / dpi
						fig.set_size_inches(lowerCamelCase				,     lowerCamelCase						)
						__magic_name__					:							List[str]		   =							fig.add_axes([0.0, 0.0, 1.0, 1.0]						)
						ax.axis('''off'''						)
						ax.set_xlim(0.0				,     lowerCamelCase						)
						ax.set_ylim(lowerCamelCase						)

						__magic_name__					:							str		   =							saveas
						__magic_name__					:							Any		   =							rgb
						__magic_name__					:							Tuple		   =							pynb
						__magic_name__					:							Tuple		   =							img
						__magic_name__					:							Any		   =							edgecolor
						__magic_name__					:							Any		   =							0.5
						__magic_name__					:							Union[str, Any]		   =							linestyle
						__magic_name__					:							Any		   =							int(np.sqrt(min(lowerCamelCase				,     lowerCamelCase						)						) * scale // 3						)
						__magic_name__					:							Any		   =							width
						__magic_name__					:							str		   =							height
						__magic_name__					:							str		   =							scale
						__magic_name__					:							Optional[int]		   =							fig
						__magic_name__					:							List[Any]		   =							ax
						__magic_name__					:							int		   =							pad
						__magic_name__					:							int		   =							idaobj
						__magic_name__					:							Optional[Any]		   =							idaattr
						__magic_name__					:							List[Any]		   =							FigureCanvasAgg(lowerCamelCase						)






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase=None						)      ->   Any:




						"""simple docstring"""






						if color is None:
											__magic_name__					:							Dict		   =							self.edgecolor
						((__magic_name__)   , (__magic_name__)   , (__magic_name__)   , (__magic_name__))					:							Optional[int]		   =							box
						__magic_name__					:							Union[str, Any]		   =							xa - xa
						__magic_name__					:							Optional[int]		   =							ya - ya
						self.ax.add_patch(
						    mpl.patches.Rectangle(
						        (xa, ya)				,     lowerCamelCase				,     lowerCamelCase				,     fill=lowerCamelCase				,     edgecolor=lowerCamelCase				,     linewidth=self.font_size // 3				,     alpha=self.alpha				,     linestyle=self.linestyle				,     )						)






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase=None				,     lowerCamelCase=None				,     lowerCamelCase=None				,     lowerCamelCase=None						)      ->   str:




						"""simple docstring"""






						if len(boxes.shape						) > 2:
											__magic_name__					:							int		   =							boxes[0]
						if len(obj_ids.shape						) > 1:
											__magic_name__					:							Tuple		   =							obj_ids[0]
						if len(obj_scores.shape						) > 1:
											__magic_name__					:							List[str]		   =							obj_scores[0]
						if len(attr_ids.shape						) > 1:
											__magic_name__					:							Tuple		   =							attr_ids[0]
						if len(attr_scores.shape						) > 1:
											__magic_name__					:							int		   =							attr_scores[0]
						if isinstance(lowerCamelCase				,     torch.Tensor						):
											__magic_name__					:							Optional[int]		   =							boxes.numpy()
						if isinstance(lowerCamelCase				,     lowerCamelCase						):
											__magic_name__					:							List[Any]		   =							np.array(lowerCamelCase						)
						assert isinstance(lowerCamelCase				,     np.ndarray						)
						__magic_name__					:							Dict		   =							np.prod(boxes[:, 2:] - boxes[:, :2]				,     axis=1						)
						__magic_name__					:							Union[str, Any]		   =							np.argsort(-areas						).tolist()
						__magic_name__					:							int		   =							boxes[sorted_idxs] if boxes is not None else None
						__magic_name__					:							List[str]		   =							obj_ids[sorted_idxs] if obj_ids is not None else None
						__magic_name__					:							Optional[int]		   =							obj_scores[sorted_idxs] if obj_scores is not None else None
						__magic_name__					:							Tuple		   =							attr_ids[sorted_idxs] if attr_ids is not None else None
						__magic_name__					:							Dict		   =							attr_scores[sorted_idxs] if attr_scores is not None else None

						__magic_name__					:							str		   =							[self._random_color(maximum=1						) for _ in range(len(lowerCamelCase						)						)]
						__magic_name__					:							Tuple		   =							[assigned_colors[idx] for idx in sorted_idxs]
						if obj_ids is not None:
											__magic_name__					:							Union[str, Any]		   =							self._create_text_labels_attr(lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase						)
											for i in range(len(lowerCamelCase						)						):
																__magic_name__					:							Union[str, Any]		   =							assigned_colors[i]
																self.add_box(boxes[i]				,     lowerCamelCase						)
																self.draw_labels(labels[i]				,     boxes[i]				,     lowerCamelCase						)






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase						)      ->   int:




						"""simple docstring"""






						__magic_name__   , __magic_name__   , __magic_name__   , __magic_name__					:							Union[str, Any]		   =							box
						__magic_name__					:							Union[str, Any]		   =							(xa, ya)
						__magic_name__					:							int		   =							(ya - ya) * (xa - xa)
						__magic_name__					:							Tuple		   =							_SMALL_OBJ * self.scale
						if instance_area < small or ya - ya < 40 * self.scale:
											if ya >= self.height - 5:
																__magic_name__					:							Tuple		   =							(xa, ya)
											else:
																__magic_name__					:							List[Any]		   =							(xa, ya)

						__magic_name__					:							Optional[Any]		   =							(ya - ya) / np.sqrt(self.height * self.width						)
						__magic_name__					:							Optional[int]		   =							self._change_color_brightness(lowerCamelCase				,     brightness_factor=0.7						)
						__magic_name__					:							Union[str, Any]		   =							np.clip((height_ratio - 0.0_2) / 0.0_8 + 1				,     1.2				,     2						)
						font_size *= 0.7_5 * self.font_size

						self.draw_text(
						    text=lowerCamelCase				,     position=lowerCamelCase				,     color=lowerCamelCase				,     )






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase="g"				,     lowerCamelCase="left"				,     )      ->   Tuple:




						"""simple docstring"""






						__magic_name__					:							Any		   =							0
						__magic_name__					:							List[Any]		   =							self.font_size
						__magic_name__					:							List[Any]		   =							np.maximum(list(mplc.to_rgb(lowerCamelCase						)						)				,     0.2						)
						__magic_name__					:							Optional[Any]		   =							max(0.8				,     np.max(lowerCamelCase						)						)
						__magic_name__					:							Tuple		   =							{
						    '''facecolor''': '''black''',
						    '''alpha''': self.alpha,
						    '''pad''': self.pad,
						    '''edgecolor''': '''none''',
						}
						__magic_name__   , __magic_name__					:							str		   =							position
						self.ax.text(
						    lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     size=font_size * self.scale				,     family='''sans-serif'''				,     bbox=lowerCamelCase				,     verticalalignment='''top'''				,     horizontalalignment=lowerCamelCase				,     color=lowerCamelCase				,     zorder=10				,     rotation=lowerCamelCase				,     )






	def  lowercase      (	self				,     lowerCamelCase=None						)      ->   Optional[Any]:




						"""simple docstring"""






						if saveas is None:
											__magic_name__					:							Dict		   =							self.saveas
						if saveas.lower().endswith('''.jpg'''						) or saveas.lower().endswith('''.png'''						):
											cva.imwrite(
											    lowerCamelCase				,     self._get_buffer()[:, :, ::-1]				,     )
						else:
											self.fig.savefig(lowerCamelCase						)






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase						)      ->   Union[str, Any]:




						"""simple docstring"""






						__magic_name__					:							Dict		   =							[self.idaobj[i] for i in classes]
						__magic_name__					:							int		   =							[self.idaattr[i] for i in attr_classes]
						__magic_name__					:							List[str]		   =							[
						    F'''{label} {score:.2f} {attr} {attr_score:.2f}'''
						    for label, score, attr, attr_score in zip(lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase						)
						]
						return labels






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase						)      ->   Any:




						"""simple docstring"""






						__magic_name__					:							Dict		   =							[self.idaobj[i] for i in classes]
						if scores is not None:
											if labels is None:
																__magic_name__					:							List[Any]		   =							['''{:.0f}%'''.format(s * 100						) for s in scores]
											else:
																__magic_name__					:							Union[str, Any]		   =							['''{} {:.0f}%'''.format(lowerCamelCase				,     s * 100						) for li, s in zip(lowerCamelCase				,     lowerCamelCase						)]
						return labels






	def  lowercase      (	self				,     lowerCamelCase=255						)      ->   Tuple:




						"""simple docstring"""






						__magic_name__					:							Optional[Any]		   =							np.random.randint(0				,     len(_COLORS						)						)
						__magic_name__					:							int		   =							_COLORS[idx] * maximum
						if not self.rgb:
											__magic_name__					:							Optional[Any]		   =							ret[::-1]
						return ret






	def  lowercase      (	self						)      ->   List[Any]:




						"""simple docstring"""






						if not self.pynb:
											__magic_name__   , __magic_name__					:							int		   =							self.canvas.print_to_buffer()
											if (width, height) != (self.width, self.height):
																__magic_name__					:							List[Any]		   =							cva.resize(self.img				,     (width, height)						)
											else:
																__magic_name__					:							Union[str, Any]		   =							self.img
						else:
											__magic_name__					:							List[Any]		   =							io.BytesIO()  # works for cairo backend
											self.canvas.print_rgba(lowerCamelCase						)
											__magic_name__   , __magic_name__					:							List[str]		   =							self.width, self.height
											__magic_name__					:							int		   =							buf.getvalue()
											__magic_name__					:							Any		   =							self.img

						__magic_name__					:							Dict		   =							np.frombuffer(lowerCamelCase				,     dtype='''uint8'''						)
						__magic_name__					:							int		   =							buffer.reshape(lowerCamelCase				,     lowerCamelCase				,     4						)
						__magic_name__   , __magic_name__					:							Union[str, Any]		   =							np.split(lowerCamelCase				,     [3]				,     axis=2						)

						try:
											import numexpr as ne  # fuse them with numexpr

											__magic_name__					:							Union[str, Any]		   =							ne.evaluate('''img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'''						)
						except ImportError:
											__magic_name__					:							Optional[Any]		   =							alpha.astype('''float32'''						) / 2_5_5.0
											__magic_name__					:							Union[str, Any]		   =							img * (1 - alpha) + rgb * alpha

						return visualized_image.astype('''uint8'''						)






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase						)      ->   int:




						"""simple docstring"""






						assert brightness_factor >= -1.0 and brightness_factor <= 1.0
						__magic_name__					:							Union[str, Any]		   =							mplc.to_rgb(lowerCamelCase						)
						__magic_name__					:							Dict		   =							colorsys.rgb_to_hls(*mplc.to_rgb(lowerCamelCase						)						)
						__magic_name__					:							Optional[Any]		   =							polygon_color[1] + (brightness_factor * polygon_color[1])
						__magic_name__					:							str		   =							0.0 if modified_lightness < 0.0 else modified_lightness
						__magic_name__					:							List[str]		   =							1.0 if modified_lightness > 1.0 else modified_lightness
						__magic_name__					:							Tuple		   =							colorsys.hls_to_rgb(polygon_color[0]				,     lowerCamelCase				,     polygon_color[2]						)
						return modified_color

lowercase_		=							(
    np.array(
        [
            0.0_0_0,
            0.4_4_7,
            0.7_4_1,
            0.8_5_0,
            0.3_2_5,
            0.0_9_8,
            0.9_2_9,
            0.6_9_4,
            0.1_2_5,
            0.4_9_4,
            0.1_8_4,
            0.5_5_6,
            0.4_6_6,
            0.6_7_4,
            0.1_8_8,
            0.3_0_1,
            0.7_4_5,
            0.9_3_3,
            0.6_3_5,
            0.0_7_8,
            0.1_8_4,
            0.3_0_0,
            0.3_0_0,
            0.3_0_0,
            0.6_0_0,
            0.6_0_0,
            0.6_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.7_4_9,
            0.7_4_9,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            0.0_0_0,
            0.3_3_3,
            0.6_6_7,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.3_3_3,
            0.0_0_0,
            0.6_6_7,
            0.6_6_7,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.5_0_0,
            0.0_0_0,
            0.6_6_7,
            0.5_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.3_3_3,
            0.5_0_0,
            0.3_3_3,
            0.6_6_7,
            0.5_0_0,
            0.3_3_3,
            1.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.3_3_3,
            0.5_0_0,
            0.6_6_7,
            0.6_6_7,
            0.5_0_0,
            0.6_6_7,
            1.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.3_3_3,
            0.5_0_0,
            1.0_0_0,
            0.6_6_7,
            0.5_0_0,
            1.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            1.0_0_0,
            0.3_3_3,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.3_3_3,
            1.0_0_0,
            0.6_6_7,
            0.6_6_7,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_4_3,
            0.1_4_3,
            0.1_4_3,
            0.8_5_7,
            0.8_5_7,
            0.8_5_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)









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,	UpperCAmelCase,	UpperCAmelCase       )  ->list:






					"""simple docstring"""






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







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->list:






					"""simple docstring"""






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







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->float:






					"""simple docstring"""






					return sum(UpperCAmelCase       ) / instance_count







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->float:






					"""simple docstring"""






					return instance_count / total_count







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->float:






					"""simple docstring"""






					__magic_name__					:							Any		   =							[]  # 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
					__magic_name__					:							Optional[int]		   =							len(UpperCAmelCase       )  # Number of classes in dataset
					return 1 / (total_count - n_classes) * sum(UpperCAmelCase       )







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->list:






					"""simple docstring"""






					__magic_name__					:							int		   =							[]
					# 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]       )       ):
															__magic_name__					:							Dict		   =							[]  # 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,	UpperCAmelCase       )  ->float:






					"""simple docstring"""






					__magic_name__					:							Optional[Any]		   =							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


lowercase_		=							TypeVar('''num''')







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase = lambda UpperCAmelCase       : True,	UpperCAmelCase = None,	)  ->num:






					"""simple docstring"""






					while True:
										try:
															__magic_name__					:							List[Any]		   =							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  (	)  ->str:






					"""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
										__magic_name__					:							List[Any]		   =							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
										__magic_name__					:							Any		   =							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
										__magic_name__					:							Optional[Any]		   =							[]  # An empty list to store instance counts of classes in dataset
										for i in range(UpperCAmelCase       ):
															__magic_name__					:							Dict		   =							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
										__magic_name__					:							Dict		   =							[]
										for a in range(UpperCAmelCase       ):
															__magic_name__					:							Dict		   =							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
										__magic_name__					:							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
										__magic_name__					:							List[str]		   =							y_generator(UpperCAmelCase,	UpperCAmelCase       )
										print('''Generated Corresponding Ys: \n''',	UpperCAmelCase       )
										print('''-''' * 100       )

										# Calculating the value of actual mean for each class
										__magic_name__					:							Dict		   =							[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
										__magic_name__					:							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
										__magic_name__					:							List[Any]		   =							calculate_variance(UpperCAmelCase,	UpperCAmelCase,	sum(UpperCAmelCase       )       )
										print('''Variance: ''',	UpperCAmelCase       )
										print('''-''' * 100       )

										# Predicting Y values
										# storing predicted Y values in 'pre_indexes' variable
										__magic_name__					:							List[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()



