





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_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						float:






				"""simple docstring"""


				snake_case_						:   str           =  np.array([[1, item, train_mtch[i]] for i, item in enumerate(_UpperCamelCase			)]			)
				snake_case_						:   List[str]           =  np.array(_UpperCamelCase			)
				snake_case_						:   Union[str, Any]           =  np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()			,    _UpperCamelCase			)			)			,    x.transpose()			)			,    _UpperCamelCase			)
				return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]			)






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						float:






				"""simple docstring"""


				snake_case_						:   List[str]           =  (1, 2, 1)
				snake_case_						:   List[Any]           =  (1, 1, 0, 7)
				snake_case_						:   Any           =  SARIMAX(
				    _UpperCamelCase			,    exog=_UpperCamelCase			,    order=_UpperCamelCase			,    seasonal_order=_UpperCamelCase			)
				snake_case_						:   Dict           =  model.fit(disp=_UpperCamelCase			,    maxiter=600			,    method='''nm'''			)
				snake_case_						:   str           =  model_fit.predict(1			,    len(_UpperCamelCase			)			,    exog=[test_match]			)
				return result[0]






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						float:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  SVR(kernel='''rbf'''			,    C=1			,    gamma=0.1			,    epsilon=0.1			)
				regressor.fit(_UpperCamelCase			,    _UpperCamelCase			)
				snake_case_						:   str           =  regressor.predict(_UpperCamelCase			)
				return y_pred[0]






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						float:






				"""simple docstring"""


				train_user.sort()
				snake_case_						:   Optional[Any]           =  np.percentile(_UpperCamelCase			,    25			)
				snake_case_						:   List[str]           =  np.percentile(_UpperCamelCase			,    75			)
				snake_case_						:   int           =  qa - qa
				snake_case_						:   List[str]           =  qa - (iqr * 0.1)
				return low_lim






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						bool:






				"""simple docstring"""


				snake_case_						:   Union[str, Any]           =  0
				snake_case_						:   Tuple           =  0
				for i in list_vote:
								if i > actual_result:
												snake_case_						:   Dict           =  not_safe + 1
								else:
												if abs(abs(_UpperCamelCase			) - abs(_UpperCamelCase			)			) <= 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)
	lowerCAmelCase_    =    [[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]]
	lowerCAmelCase_    =    pd.DataFrame(
	    data_input, columns=['''total_user''', '''total_even''', '''days''']
	)
	lowerCAmelCase_    =    Normalizer().fit_transform(data_input_df.values)
	# split data
	lowerCAmelCase_    =    normalize_df[:, 2].tolist()
	lowerCAmelCase_    =    normalize_df[:, 0].tolist()
	lowerCAmelCase_    =    normalize_df[:, 1].tolist()

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

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

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

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







from math import isclose, sqrt






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						tuple[float, float, float]:






				"""simple docstring"""


				snake_case_						:   Dict           =  point_y / 4 / point_x
				snake_case_						:   List[str]           =  2 * normal_gradient / (1 + normal_gradient * normal_gradient)
				snake_case_						:   Union[str, Any]           =  (1 - normal_gradient * normal_gradient) / (
				    1 + normal_gradient * normal_gradient
				)
				snake_case_						:   Tuple           =  (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

				# to find the next point, solve the simultaeneous equations:
				# y^2 + 4x^2 = 100
				# y - b = m * (x - a)
				# ==> A x^2 + B x + C = 0
				snake_case_						:   Union[str, Any]           =  outgoing_gradient**2 + 4
				snake_case_						:   Tuple           =  2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
				snake_case_						:   Optional[Any]           =  (point_y - outgoing_gradient * point_x) ** 2 - 100

				snake_case_						:   Dict           =  (
				    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term			)
				) / (2 * quadratic_term)
				snake_case_						:   Optional[int]           =  (
				    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term			)
				) / (2 * quadratic_term)

				# two solutions, one of which is our input point
				snake_case_						:   Any           =  x_minus if isclose(_UpperCamelCase			,    _UpperCamelCase			) else x_plus
				snake_case_						:   int           =  point_y + outgoing_gradient * (next_x - point_x)

				return next_x, next_y, outgoing_gradient






def 					lowerCamelCase_						(    _UpperCamelCase = 1.4			,    _UpperCamelCase = -9.6			)		->						int:






				"""simple docstring"""


				snake_case_						:   int           =  0
				snake_case_						:   float           =  first_x_coord
				snake_case_						:   float           =  first_y_coord
				snake_case_						:   float           =  (10.1 - point_y) / (0.0 - point_x)

				while not (-0.01 <= point_x <= 0.01 and point_y > 0):
								snake_case_      ,							snake_case_      ,							snake_case_						:   str           =  next_point(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)
								num_reflections += 1

				return num_reflections


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