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



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						SVR(kernel='''rbf''' ,			C=1 ,			gamma=0.1 ,			epsilon=0.1		)
					regressor.fit(lowerCAmelCase__ ,			lowerCAmelCase__		)
					lowercase							=						regressor.predict(lowerCAmelCase__		)
					return y_pred[0]



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					train_user.sort()
					lowercase							=						np.percentile(lowerCAmelCase__ ,			25		)
					lowercase							=						np.percentile(lowerCAmelCase__ ,			75		)
					lowercase							=						qa - qa
					lowercase							=						qa - (iqr * 0.1)
					return low_lim



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						0
					lowercase							=						0
					for i in list_vote:
										if i > actual_result:
															lowercase							=						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__		:int   =    [[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
							lowercase__		:Any   =    pd.DataFrame(
							    data_input, columns=["total_user", "total_even", "days"]
							)
							lowercase__		:Tuple   =    Normalizer().fit_transform(data_input_df.values)
							# split data
							lowercase__		:Union[str, Any]   =    normalize_df[:, 2].tolist()
							lowercase__		:Tuple   =    normalize_df[:, 0].tolist()
							lowercase__		:List[Any]   =    normalize_df[:, 1].tolist()

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

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

							lowercase__		:Optional[int]   =    total_date[len(total_date) - 1 :]
							lowercase__		:int   =    total_user[len(total_user) - 1 :]
							lowercase__		:Union[str, Any]   =    total_match[len(total_match) - 1 :]

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











def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						str:






				"""simple docstring"""


				return "\n".join(
				    f'''{number} * {i} = {number * i}''' for i in range(1 ,				number_of_terms + 1 ) )


if __name__ == "__main__":
	print(multiplication_table(number=5, number_of_terms=1_0))
