






"""simple docstring"""

import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX






def _lowerCAmelCase	(  __lowerCamelCase:list ,						__lowerCamelCase:list ,						__lowerCamelCase:list ,						__lowerCamelCase:list ,						__lowerCamelCase:list   ):

						'''simple docstring'''






						__magic_name__            =     np.array([[1, item, train_mtch[i]] for i, item in enumerate(__lowerCamelCase   )]   )
						__magic_name__            =     np.array(__lowerCamelCase   )
						__magic_name__            =     np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() ,						__lowerCamelCase   )   ) ,						x.transpose()   ) ,						__lowerCamelCase   )
						return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]   )






def _lowerCAmelCase	(  __lowerCamelCase:list ,						__lowerCamelCase:list ,						__lowerCamelCase:list   ):

						'''simple docstring'''






						__magic_name__            =     (1, 2, 1)
						__magic_name__            =     (1, 1, 0, 7)
						__magic_name__            =     SARIMAX(
						    __lowerCamelCase ,						exog=__lowerCamelCase ,						order=__lowerCamelCase ,						seasonal_order=__lowerCamelCase   )
						__magic_name__            =     model.fit(disp=__lowerCamelCase ,						maxiter=6_0_0 ,						method="nm"   )
						__magic_name__            =     model_fit.predict(1 ,						len(__lowerCamelCase   ) ,						exog=[test_match]   )
						return result[0]






def _lowerCAmelCase	(  __lowerCamelCase:list ,						__lowerCamelCase:list ,						__lowerCamelCase:list   ):

						'''simple docstring'''






						__magic_name__            =     SVR(kernel="rbf" ,						C=1 ,						gamma=0.1 ,						epsilon=0.1   )
						regressor.fit(__lowerCamelCase ,						__lowerCamelCase   )
						__magic_name__            =     regressor.predict(__lowerCamelCase   )
						return y_pred[0]






def _lowerCAmelCase	(  __lowerCamelCase:list   ):

						'''simple docstring'''






						train_user.sort()
						__magic_name__            =     np.percentile(__lowerCamelCase ,						2_5   )
						__magic_name__            =     np.percentile(__lowerCamelCase ,						7_5   )
						__magic_name__            =     qa - qa
						__magic_name__            =     qa - (iqr * 0.1)
						return low_lim






def _lowerCAmelCase	(  __lowerCamelCase:list ,						__lowerCamelCase:float   ):

						'''simple docstring'''






						__magic_name__            =     0
						__magic_name__            =     0
						for i in list_vote:
												if i > actual_result:
																		__magic_name__            =     not_safe + 1
												else:
																		if abs(abs(__lowerCamelCase   ) - abs(__lowerCamelCase   )   ) <= 0.1:
																								safe += 1
																		else:
																								not_safe += 1
						return safe > not_safe


if __name__ == "__main__":
			# data_input_df = pd.read_csv("ex_data.csv", header=None)
			lowercase    			=       [[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
			lowercase    			=       pd.DataFrame(
			    data_input, columns=['''total_user''', '''total_even''', '''days''']
			)
			lowercase    			=       Normalizer().fit_transform(data_input_df.values)
			# split data
			lowercase    			=       normalize_df[:, 2].tolist()
			lowercase    			=       normalize_df[:, 0].tolist()
			lowercase    			=       normalize_df[:, 1].tolist()

			# for svr (input variable = total date and total match)
			lowercase    			=       normalize_df[:, [1, 2]].tolist()
			lowercase    			=       x[: len(x) - 1]
			lowercase    			=       x[len(x) - 1 :]

			# for linear regression & sarimax
			lowercase    			=       total_date[: len(total_date) - 1]
			lowercase    			=       total_user[: len(total_user) - 1]
			lowercase    			=       total_match[: len(total_match) - 1]

			lowercase    			=       total_date[len(total_date) - 1 :]
			lowercase    			=       total_user[len(total_user) - 1 :]
			lowercase    			=       total_match[len(total_match) - 1 :]

			# voting system with forecasting
			lowercase    			=       [
			    linear_regression_prediction(
			        trn_date, trn_user, trn_match, tst_date, tst_match
			    ),
			    sarimax_predictor(trn_user, trn_match, tst_match),
			    support_vector_regressor(x_train, x_test, trn_user),
			]

			# check the safety of today's data
			lowercase    			=       '''''' if data_safety_checker(res_vote, tst_user) else '''not '''
			print('''Today\'s data is {not_str}safe.''')











"""simple docstring"""

import colorsys

from PIL import Image  # type: ignore






def _lowerCAmelCase	(  __lowerCamelCase:float ,						__lowerCamelCase:float ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     x
						__magic_name__            =     y
						for step in range(__lowerCamelCase   ):  # noqa: B007
												__magic_name__            =     a * a - b * b + x
												__magic_name__            =     2 * a * b + y
												__magic_name__            =     a_new

												# divergence happens for all complex number with an absolute value
												# greater than 4
												if a * a + b * b > 4:
																		break
						return step / (max_step - 1)






def _lowerCAmelCase	(  __lowerCamelCase:float   ):

						'''simple docstring'''






						if distance == 1:
												return (0, 0, 0)
						else:
												return (2_5_5, 2_5_5, 2_5_5)






def _lowerCAmelCase	(  __lowerCamelCase:float   ):

						'''simple docstring'''






						if distance == 1:
												return (0, 0, 0)
						else:
												return tuple(round(i * 2_5_5   ) for i in colorsys.hsv_to_rgb(__lowerCamelCase ,						1 ,						1   )   )






def _lowerCAmelCase	(  __lowerCamelCase:int = 8_0_0 ,						__lowerCamelCase:int = 6_0_0 ,						__lowerCamelCase:float = -0.6 ,						__lowerCamelCase:float = 0 ,						__lowerCamelCase:float = 3.2 ,						__lowerCamelCase:int = 5_0 ,						__lowerCamelCase:bool = True ,						):

						'''simple docstring'''






						__magic_name__            =     Image.new("RGB" ,						(image_width, image_height)   )
						__magic_name__            =     img.load()

						# loop through the image-coordinates
						for image_x in range(__lowerCamelCase   ):
												for image_y in range(__lowerCamelCase   ):
																		# determine the figure-coordinates based on the image-coordinates
																		__magic_name__            =     figure_width / image_width * image_height
																		__magic_name__            =     figure_center_x + (image_x / image_width - 0.5) * figure_width
																		__magic_name__            =     figure_center_y + (image_y / image_height - 0.5) * figure_height

																		__magic_name__            =     get_distance(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   )

																		# color the corresponding pixel based on the selected coloring-function
																		if use_distance_color_coding:
																								__magic_name__            =     get_color_coded_rgb(__lowerCamelCase   )
																		else:
																								__magic_name__            =     get_black_and_white_rgb(__lowerCamelCase   )

						return img


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			# colored version, full figure
			lowercase    			=       get_image()

			# uncomment for colored version, different section, zoomed in
			# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
			# figure_width = 0.8)

			# uncomment for black and white version, full figure
			# img = get_image(use_distance_color_coding = False)

			# uncomment to save the image
			# img.save("mandelbrot.png")

			img.show()



