




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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					Tuple:
					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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					Optional[Any]:
					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=6_00		, method="nm")
					a     =				model_fit.predict(1		, len(UpperCamelCase__)		, exog=[test_match])
					return result[0]

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					Tuple:
					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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Any:
					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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					Tuple:
					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)
			lowercase__				:      Any	   = [[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 1.0, 4]]
			lowercase__				:      Dict	   = pd.DataFrame(
			    data_input, columns=["total_user", "total_even", "days"]
			)
			lowercase__				:      Tuple	   = Normalizer().fit_transform(data_input_df.values)
			# split data
			lowercase__				:      Any	   = normalize_df[:, 2].tolist()
			lowercase__				:      Tuple	   = normalize_df[:, 0].tolist()
			lowercase__				:      Tuple	   = normalize_df[:, 1].tolist()

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

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

			lowercase__				:      List[Any]	   = total_date[len(total_date) - 1 :]
			lowercase__				:      Dict	   = total_user[len(total_user) - 1 :]
			lowercase__				:      int	   = total_match[len(total_match) - 1 :]

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







lowercase__				:      str	   = "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowercase__				:      Any	   = [{"type": "code", "content": INSTALL_CONTENT}]
lowercase__				:      Any	   = {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}

