



'''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_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      float:
		_a     :      List[str]        =							np.array([[1, item, train_mtch[i]] for i, item in enumerate(lowerCAmelCase_  )]  )
		_a     :      Optional[int]        =							np.array(lowerCAmelCase_  )
		_a     :      Optional[int]        =							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_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      float:
		_a     :      Any        =							(1, 2, 1)
		_a     :      List[Any]        =							(1, 1, 0, 7)
		_a     :      int        =							SARIMAX(
		    lowerCAmelCase_						,			exog=lowerCAmelCase_						,			order=lowerCAmelCase_						,			seasonal_order=lowerCAmelCase_  )
		_a     :      Optional[int]        =							model.fit(disp=lowerCAmelCase_						,			maxiter=600						,			method='nm'  )
		_a     :      Union[str, Any]        =							model_fit.predict(1						,			len(lowerCAmelCase_  )						,			exog=[test_match]  )
		return result[0]



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



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



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      bool:
		_a     :      str        =							0
		_a     :      List[Any]        =							0
		for i in list_vote:
				if i > actual_result:
						_a     :      Optional[int]        =							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)
							__lowerCAmelCase									=   [[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 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 collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean



def A_	(		A__				)  ->		Tuple:

       # A local function to see if a dot lands in the circle.
       def is_in_circle(A__		,      A__				) -> bool:
              a__						:	List[str] =				sqrt((x**2) + (y**2)				)
              # Our circle has a radius of 1, so a distance
              # greater than 1 would land outside the circle.
              return distance_from_centre <= 1

       # The proportion of guesses that landed in the circle
       a__						:	List[str] =				mean(
           int(is_in_circle(uniform(-1.0		,      1.0				)		,      uniform(-1.0		,      1.0				)				)				)
           for _ in range(A__				)				)
       # The ratio of the area for circle to square is pi/4.
       a__						:	Optional[Any] =				proportion * 4
       print(F'The estimated value of pi is {pi_estimate}'				)
       print(F'The numpy value of pi is {pi}'				)
       print(F'The total error is {abs(pi - pi_estimate				)}'				)



def A_	(		A__		,      A__		,      A__ = 0.0		,      A__ = 1.0		,      )  ->		float:
       return mean(
           function_to_integrate(uniform(A__		,      A__				)				) for _ in range(A__				)				) * (max_value - min_value)



def A_	(		A__		,      A__ = 0.0		,      A__ = 1.0				)  ->		None:

       def identity_function(A__				) -> float:
              return x

       a__						:	List[Any] =				area_under_curve_estimator(
           A__		,      A__		,      A__		,      A__				)
       a__						:	Union[str, Any] =				(max_value * max_value - min_value * min_value) / 2

       print('******************'				)
       print(F'Estimating area under y=x where x varies from {min_value} to {max_value}'				)
       print(F'Estimated value is {estimated_value}'				)
       print(F'Expected value is {expected_value}'				)
       print(F'Total error is {abs(estimated_value - expected_value				)}'				)
       print('******************'				)



def A_	(		A__				)  ->		None:

       def function_to_integrate(A__				) -> float:
              return sqrt(4.0 - x * x				)

       a__						:	Dict =				area_under_curve_estimator(
           A__		,      A__		,      0.0		,      2.0				)

       print('******************'				)
       print('Estimating pi using area_under_curve_estimator'				)
       print(F'Estimated value is {estimated_value}'				)
       print(F'Expected value is {pi}'				)
       print(F'Total error is {abs(estimated_value - pi				)}'				)
       print('******************'				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


