



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_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ) ->       float:
						UpperCAmelCase         =     np.array([[1, item, train_mtch[i]] for i, item in enumerate(lowerCamelCase_    )]    )
						UpperCAmelCase         =     np.array(lowerCamelCase_    )
						UpperCAmelCase         =     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    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ) ->       float:
						UpperCAmelCase         =     (1, 2, 1)
						UpperCAmelCase         =     (1, 1, 0, 7)
						UpperCAmelCase         =     SARIMAX(
						    lowerCamelCase_    ,   exog=lowerCamelCase_    ,   order=lowerCamelCase_    ,   seasonal_order=lowerCamelCase_    )
						UpperCAmelCase         =     model.fit(disp=lowerCamelCase_    ,   maxiter=600    ,   method="nm"    )
						UpperCAmelCase         =     model_fit.predict(1    ,   len(lowerCamelCase_    )    ,   exog=[test_match]    )
						return result[0]

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ) ->       float:
						UpperCAmelCase         =     SVR(kernel="rbf"    ,   C=1    ,   gamma=0.1    ,   epsilon=0.1    )
						regressor.fit(lowerCamelCase_    ,   lowerCamelCase_    )
						UpperCAmelCase         =     regressor.predict(lowerCamelCase_    )
						return y_pred[0]

def    lowerCamelCase_(lowerCamelCase_    ) ->       float:
						train_user.sort()
						UpperCAmelCase         =     np.percentile(lowerCamelCase_    ,   25    )
						UpperCAmelCase         =     np.percentile(lowerCamelCase_    ,   75    )
						UpperCAmelCase         =     qa - qa
						UpperCAmelCase         =     qa - (iqr * 0.1)
						return low_lim

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       bool:
						UpperCAmelCase         =     0
						UpperCAmelCase         =     0
						for i in list_vote:
												if i > actual_result:
																		UpperCAmelCase         =     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)
		__lowerCamelCase     :							Optional[Any]											=				[[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 1.0, 4]]
		__lowerCamelCase     :							Tuple											=				pd.DataFrame(
		    data_input, columns=["total_user", "total_even", "days"]
		)
		__lowerCamelCase     :							List[Any]											=				Normalizer().fit_transform(data_input_df.values)
		# split data
		__lowerCamelCase     :							int											=				normalize_df[:, 2].tolist()
		__lowerCamelCase     :							Optional[int]											=				normalize_df[:, 0].tolist()
		__lowerCamelCase     :							Dict											=				normalize_df[:, 1].tolist()

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

		# for linear regression & sarimax
		__lowerCamelCase     :							Union[str, Any]											=				total_date[: len(total_date) - 1]
		__lowerCamelCase     :							List[str]											=				total_user[: len(total_user) - 1]
		__lowerCamelCase     :							Any											=				total_match[: len(total_match) - 1]

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

		# voting system with forecasting
		__lowerCamelCase     :							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
		__lowerCamelCase     :							str											=				"" 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_tokenizers_available, is_torch_available


__lowerCamelCase     :							Optional[int]  							=				{
    "configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
    "tokenization_roc_bert": ["RoCBertTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase     :							List[Any]  							=				[
		    "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "RoCBertForCausalLM",
		    "RoCBertForMaskedLM",
		    "RoCBertForMultipleChoice",
		    "RoCBertForPreTraining",
		    "RoCBertForQuestionAnswering",
		    "RoCBertForSequenceClassification",
		    "RoCBertForTokenClassification",
		    "RoCBertLayer",
		    "RoCBertModel",
		    "RoCBertPreTrainedModel",
		    "load_tf_weights_in_roc_bert",
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

		__lowerCamelCase     :							Optional[int]  							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



