



from math import isclose, sqrt





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    point_y / 4 / point_x
    __a							=    2 * normal_gradient / (1 + normal_gradient * normal_gradient)
    __a							=    (1 - normal_gradient * normal_gradient) / (
        1 + normal_gradient * normal_gradient
    )
    __a							=    (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
    __a							=    outgoing_gradient**2 + 4
    __a							=    2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
    __a							=    (point_y - outgoing_gradient * point_x) ** 2 - 100

    __a							=    (
        -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term    )
    ) / (2 * quadratic_term)
    __a							=    (
        -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term    )
    ) / (2 * quadratic_term)

    # two solutions, one of which is our input point
    __a							=    x_minus if isclose(_UpperCAmelCase       ,			_UpperCAmelCase    ) else x_plus
    __a							=    point_y + outgoing_gradient * (next_x - point_x)

    return next_x, next_y, outgoing_gradient





def 					__snake_case     (      _UpperCAmelCase = 1.4       ,			_UpperCAmelCase = -9.6    ):
    __a							=    0
    __a							=    first_x_coord
    __a							=    first_y_coord
    __a							=    (10.1 - point_y) / (0.0 - point_x)

    while not (-0.01 <= point_x <= 0.01 and point_y > 0):
        __a	,       __a	,       __a							=    next_point(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    )
        num_reflections += 1

    return num_reflections


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










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 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    np.array([[1, item, train_mtch[i]] for i, item in enumerate(_UpperCAmelCase    )]    )
    __a							=    np.array(_UpperCAmelCase    )
    __a							=    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 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    (1, 2, 1)
    __a							=    (1, 1, 0, 7)
    __a							=    SARIMAX(
        _UpperCAmelCase       ,			exog=_UpperCAmelCase       ,			order=_UpperCAmelCase       ,			seasonal_order=_UpperCAmelCase    )
    __a							=    model.fit(disp=_UpperCAmelCase       ,			maxiter=600       ,			method='''nm'''    )
    __a							=    model_fit.predict(1       ,			len(_UpperCAmelCase    )       ,			exog=[test_match]    )
    return result[0]





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    SVR(kernel='''rbf'''       ,			C=1       ,			gamma=0.1       ,			epsilon=0.1    )
    regressor.fit(_UpperCAmelCase       ,			_UpperCAmelCase    )
    __a							=    regressor.predict(_UpperCAmelCase    )
    return y_pred[0]





def 					__snake_case     (      _UpperCAmelCase    ):
    train_user.sort()
    __a							=    np.percentile(_UpperCAmelCase       ,			25    )
    __a							=    np.percentile(_UpperCAmelCase       ,			75    )
    __a							=    qa - qa
    __a							=    qa - (iqr * 0.1)
    return low_lim





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    0
    __a							=    0
    for i in list_vote:
        if i > actual_result:
            __a							=    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)
 __snake_case						:Any						=					[[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
 __snake_case						:List[Any]						=					pd.DataFrame(
     data_input, columns=['''total_user''', '''total_even''', '''days''']
 )
 __snake_case						:Dict						=					Normalizer().fit_transform(data_input_df.values)
 # split data
 __snake_case						:Any						=					normalize_df[:, 2].tolist()
 __snake_case						:Tuple						=					normalize_df[:, 0].tolist()
 __snake_case						:Union[str, Any]						=					normalize_df[:, 1].tolist()

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

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

 __snake_case						:List[Any]						=					total_date[len(total_date) - 1 :]
 __snake_case						:Tuple						=					total_user[len(total_user) - 1 :]
 __snake_case						:List[str]						=					total_match[len(total_match) - 1 :]

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





