





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'''




					_snake_case									=				np.array([[1, item, train_mtch[i]] for i, item in enumerate(UpperCamelCase__						)]						)
					_snake_case									=				np.array(UpperCamelCase__						)
					_snake_case									=				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 						lowerCamelCase__     (     UpperCamelCase__			:       list       ,    UpperCamelCase__			:       list       ,    UpperCamelCase__			:       list						)			-> float:



					'''simple docstring'''




					_snake_case									=				(1, 2, 1)
					_snake_case									=				(1, 1, 0, 7)
					_snake_case									=				SARIMAX(
					    UpperCamelCase__       ,    exog=UpperCamelCase__       ,    order=UpperCamelCase__       ,    seasonal_order=UpperCamelCase__						)
					_snake_case									=				model.fit(disp=UpperCamelCase__       ,    maxiter=600       ,    method='nm'						)
					_snake_case									=				model_fit.predict(1       ,    len(UpperCamelCase__						)       ,    exog=[test_match]						)
					return result[0]





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



					'''simple docstring'''




					_snake_case									=				SVR(kernel='rbf'       ,    C=1       ,    gamma=0.1       ,    epsilon=0.1						)
					regressor.fit(UpperCamelCase__       ,    UpperCamelCase__						)
					_snake_case									=				regressor.predict(UpperCamelCase__						)
					return y_pred[0]





def 						lowerCamelCase__     (     UpperCamelCase__			:       list						)			-> float:



					'''simple docstring'''




					train_user.sort()
					_snake_case									=				np.percentile(UpperCamelCase__       ,    25						)
					_snake_case									=				np.percentile(UpperCamelCase__       ,    75						)
					_snake_case									=				qa - qa
					_snake_case									=				qa - (iqr * 0.1)
					return low_lim





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



					'''simple docstring'''




					_snake_case									=				0
					_snake_case									=				0
					for i in list_vote:
										if i > actual_result:
															_snake_case									=				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)
					UpperCAmelCase_      							=     [[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
					UpperCAmelCase_      							=     pd.DataFrame(
					    data_input, columns=["""total_user""", """total_even""", """days"""]
					)
					UpperCAmelCase_      							=     Normalizer().fit_transform(data_input_df.values)
					# split data
					UpperCAmelCase_      							=     normalize_df[:, 2].tolist()
					UpperCAmelCase_      							=     normalize_df[:, 0].tolist()
					UpperCAmelCase_      							=     normalize_df[:, 1].tolist()

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

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

					UpperCAmelCase_      							=     total_date[len(total_date) - 1 :]
					UpperCAmelCase_      							=     total_user[len(total_user) - 1 :]
					UpperCAmelCase_      							=     total_match[len(total_match) - 1 :]

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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_      							=     {
    """configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
    """tokenization_luke""": ["""LukeTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase_      							=     [
					    """LUKE_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LukeForEntityClassification""",
					    """LukeForEntityPairClassification""",
					    """LukeForEntitySpanClassification""",
					    """LukeForMultipleChoice""",
					    """LukeForQuestionAnswering""",
					    """LukeForSequenceClassification""",
					    """LukeForTokenClassification""",
					    """LukeForMaskedLM""",
					    """LukeModel""",
					    """LukePreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
					from .tokenization_luke import LukeTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_luke import (
										    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LukeForEntityClassification,
										    LukeForEntityPairClassification,
										    LukeForEntitySpanClassification,
										    LukeForMaskedLM,
										    LukeForMultipleChoice,
										    LukeForQuestionAnswering,
										    LukeForSequenceClassification,
										    LukeForTokenClassification,
										    LukeModel,
										    LukePreTrainedModel,
										)

else:
					import sys

					UpperCAmelCase_      							=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



