



'''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 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase,				_lowercase,				_lowercase					) ->			float:
	_lowercase					:       Union[str, Any]       =		np.array([[1, item, train_mtch[i]] for i, item in enumerate(_lowercase					)]					)
	_lowercase					:       List[str]       =		np.array(_lowercase					)
	_lowercase					:       Dict       =		np.dot(np.dot(np.linalg.inv(np.dot(x.transpose(),				_lowercase					)					),				x.transpose()					),				_lowercase					)
	return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]					)







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			float:
	_lowercase					:       List[str]       =		(1, 2, 1)
	_lowercase					:       Dict       =		(1, 1, 0, 7)
	_lowercase					:       Union[str, Any]       =		SARIMAX(
	    _lowercase,				exog=_lowercase,				order=_lowercase,				seasonal_order=_lowercase					)
	_lowercase					:       str       =		model.fit(disp=_lowercase,				maxiter=600,				method='nm'					)
	_lowercase					:       int       =		model_fit.predict(1,				len(_lowercase					),				exog=[test_match]					)
	return result[0]







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			float:
	_lowercase					:       Any       =		SVR(kernel='rbf',				C=1,				gamma=0.1,				epsilon=0.1					)
	regressor.fit(_lowercase,				_lowercase					)
	_lowercase					:       int       =		regressor.predict(_lowercase					)
	return y_pred[0]







def 						__UpperCamelCase				(							_lowercase					) ->			float:
	train_user.sort()
	_lowercase					:       str       =		np.percentile(_lowercase,				25					)
	_lowercase					:       Union[str, Any]       =		np.percentile(_lowercase,				75					)
	_lowercase					:       Optional[int]       =		qa - qa
	_lowercase					:       Optional[Any]       =		qa - (iqr * 0.1)
	return low_lim







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			bool:
	_lowercase					:       Any       =		0
	_lowercase					:       List[str]       =		0
	for i in list_vote:
		if i > actual_result:
			_lowercase					:       Optional[Any]       =		not_safe + 1
		else:
			if abs(abs(_lowercase					) - abs(_lowercase					)					) <= 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)
		_A				:    int     						=[[1_8_2_3_1, 0.0, 1], [2_2_6_2_1, 1.0, 2], [1_5_6_7_5, 0.0, 3], [2_3_5_8_3, 1.0, 4]]
		_A				:    List[str]     						=pd.DataFrame(
		    data_input, columns=['''total_user''', '''total_even''', '''days''']
		)
		_A				:    Union[str, Any]     						=Normalizer().fit_transform(data_input_df.values)
		# split data
		_A				:    Any     						=normalize_df[:, 2].tolist()
		_A				:    Dict     						=normalize_df[:, 0].tolist()
		_A				:    List[Any]     						=normalize_df[:, 1].tolist()

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

		# for linear regression & sarimax
		_A				:    str     						=total_date[: len(total_date) - 1]
		_A				:    Tuple     						=total_user[: len(total_user) - 1]
		_A				:    Union[str, Any]     						=total_match[: len(total_match) - 1]

		_A				:    Optional[Any]     						=total_date[len(total_date) - 1 :]
		_A				:    Optional[int]     						=total_user[len(total_user) - 1 :]
		_A				:    Optional[Any]     						=total_match[len(total_match) - 1 :]

		# voting system with forecasting
		_A				:    Dict     						=[
		    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
		_A				:    Any     						='''''' if data_safety_checker(res_vote, tst_user) else '''not '''
		print('''Today\'s data is {not_str}safe.''')





'''simple docstring'''

def 						__UpperCamelCase				(							_lowercase					) ->			bool:
	return str(_lowercase					) == str(_lowercase					)[::-1]







def 						__UpperCamelCase				(							_lowercase					) ->			int:
	return int(_lowercase					) + int(str(_lowercase					)[::-1]					)







def 						__UpperCamelCase				(							_lowercase = 1_0000					) ->			int:
	_lowercase					:       List[str]       =		[]
	for num in range(1,				_lowercase					):
		_lowercase					:       Tuple       =		0
		_lowercase					:       Tuple       =		num
		while iterations < 50:
			_lowercase					:       Union[str, Any]       =		sum_reverse(_lowercase					)
			iterations += 1
			if is_palindrome(_lowercase					):
				break
		else:
			lychrel_nums.append(_lowercase					)
	return len(_lowercase					)


if __name__ == "__main__":
		print(F'''{solution() = }''')
