





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__   :   list       ,			UpperCamelCase__   :   list       ,			UpperCamelCase__   :   list       ,			UpperCamelCase__   :   list       ,			UpperCamelCase__   :   list					)      ->    float:

						"""simple docstring"""





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






def 		lowerCAmelCase  (	UpperCamelCase__   :   list       ,			UpperCamelCase__   :   list       ,			UpperCamelCase__   :   list					)      ->    float:

						"""simple docstring"""





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






def 		lowerCAmelCase  (	UpperCamelCase__   :   list       ,			UpperCamelCase__   :   list       ,			UpperCamelCase__   :   list					)      ->    float:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				List[Any]           =   SVR(kernel='''rbf'''       ,			C=1       ,			gamma=0.1       ,			epsilon=0.1					)
						regressor.fit(_A       ,			_A					)
						__SCREAMING_SNAKE_CASE:				Dict           =   regressor.predict(_A					)
						return y_pred[0]






def 		lowerCAmelCase  (	UpperCamelCase__   :   list					)      ->    float:

						"""simple docstring"""





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






def 		lowerCAmelCase  (	UpperCamelCase__   :   list       ,			UpperCamelCase__   :   float					)      ->    bool:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   0
						__SCREAMING_SNAKE_CASE:				List[Any]           =   0
						for i in list_vote:
												if i > actual_result:
																		__SCREAMING_SNAKE_CASE:				Any           =   not_safe + 1
												else:
																		if abs(abs(_A					) - abs(_A					)					) <= 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							:       List[Any]					 =	[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
							lowerCAmelCase							:       str					 =	pd.DataFrame(
							    data_input, columns=["""total_user""", """total_even""", """days"""]
							)
							lowerCAmelCase							:       str					 =	Normalizer().fit_transform(data_input_df.values)
							# split data
							lowerCAmelCase							:       List[str]					 =	normalize_df[:, 2].tolist()
							lowerCAmelCase							:       List[str]					 =	normalize_df[:, 0].tolist()
							lowerCAmelCase							:       int					 =	normalize_df[:, 1].tolist()

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

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

							lowerCAmelCase							:       Any					 =	total_date[len(total_date) - 1 :]
							lowerCAmelCase							:       Dict					 =	total_user[len(total_user) - 1 :]
							lowerCAmelCase							:       Optional[int]					 =	total_match[len(total_match) - 1 :]

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




'''simple docstring'''




import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate






@dataclass(frozen=lowerCAmelCase__      )
class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	str						=			field(default='''automatic-speech-recognition'''					,		metadata={'''include_in_asdict_even_if_is_default''': True}      )
			UpperCAmelCase_     :	ClassVar[Features]						=			Features({'''audio''': Audio()}      )
			UpperCAmelCase_     :	ClassVar[Features]						=			Features({'''transcription''': Value('''string'''      )}      )
			UpperCAmelCase_     :	str						=			"audio"
			UpperCAmelCase_     :	str						=			"transcription"



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					if self.audio_column not in features:
							raise ValueError(f"Column {self.audio_column} is not present in features.")
					if not isinstance(features[self.audio_column]			,       __lowerCAmelCase):
							raise ValueError(f"Column {self.audio_column} is not an Audio type.")
					lowerCAmelCase     =    copy.deepcopy(self)
					lowerCAmelCase     =    self.input_schema.copy()
					lowerCAmelCase     =    features[self.audio_column]
					lowerCAmelCase     =    input_schema
					return task_template







			@property
			def 				a_			(  self):




					"""simple docstring"""


					return {self.audio_column: "audio", self.transcription_column: "transcription"}




