




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							:   list						,							_lowerCAmelCase							:   list						,							_lowerCAmelCase							:   list						,							_lowerCAmelCase							:   list						,							_lowerCAmelCase							:   list	)     ->   Any:




 """simple docstring"""







 UpperCAmelCase__							 =						np.array([[1, item, train_mtch[i]] for i, item in enumerate(__UpperCamelCase	)]	)
 UpperCAmelCase__							 =						np.array(__UpperCamelCase	)
 UpperCAmelCase__							 =						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		(		_lowerCAmelCase							:   list						,							_lowerCAmelCase							:   list						,							_lowerCAmelCase							:   list	)     ->   str:




 """simple docstring"""







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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   list						,							_lowerCAmelCase							:   list						,							_lowerCAmelCase							:   list	)     ->   Any:




 """simple docstring"""







 UpperCAmelCase__							 =						SVR(kernel="rbf"						,							C=1						,							gamma=0.1						,							epsilon=0.1	)
 regressor.fit(__UpperCamelCase						,							__UpperCamelCase	)
 UpperCAmelCase__							 =						regressor.predict(__UpperCamelCase	)
 return y_pred[0]



def 						lowerCAmelCase		(		_lowerCAmelCase							:   list	)     ->   List[str]:




 """simple docstring"""







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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   list						,							_lowerCAmelCase							:   float	)     ->   Optional[Any]:




 """simple docstring"""







 UpperCAmelCase__							 =						0
 UpperCAmelCase__							 =						0
 for i in list_vote:
  if i > actual_result:
   UpperCAmelCase__							 =						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)
  _lowerCAmelCase						:   Optional[int]		=   [[1_8_2_3_1, 0.0, 1], [2_2_6_2_1, 1.0, 2], [1_5_6_7_5, 0.0, 3], [2_3_5_8_3, 1.0, 4]]
  _lowerCAmelCase						:   Any		=   pd.DataFrame(
      data_input, columns=["total_user", "total_even", "days"]
  )
  _lowerCAmelCase						:   Tuple		=   Normalizer().fit_transform(data_input_df.values)
  # split data
  _lowerCAmelCase						:   str		=   normalize_df[:, 2].tolist()
  _lowerCAmelCase						:   Union[str, Any]		=   normalize_df[:, 0].tolist()
  _lowerCAmelCase						:   Tuple		=   normalize_df[:, 1].tolist()

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

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

  _lowerCAmelCase						:   List[Any]		=   total_date[len(total_date) - 1 :]
  _lowerCAmelCase						:   str		=   total_user[len(total_user) - 1 :]
  _lowerCAmelCase						:   List[str]		=   total_match[len(total_match) - 1 :]

  # voting system with forecasting
  _lowerCAmelCase						:   Optional[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						:   Dict		=   '''''' 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, is_vision_available


_lowerCAmelCase						:   Optional[int]		=   {
    "configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
    "configuration_maskformer_swin": ["MaskFormerSwinConfig"],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Optional[int]		=   ["MaskFormerFeatureExtractor"]
  _lowerCAmelCase						:   Dict		=   ["MaskFormerImageProcessor"]


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   int		=   [
      "MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MaskFormerForInstanceSegmentation",
      "MaskFormerModel",
      "MaskFormerPreTrainedModel",
  ]
  _lowerCAmelCase						:   List[str]		=   [
      "MaskFormerSwinBackbone",
      "MaskFormerSwinModel",
      "MaskFormerSwinPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
  from .configuration_maskformer_swin import MaskFormerSwinConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_maskformer import MaskFormerFeatureExtractor
    from .image_processing_maskformer import MaskFormerImageProcessor
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_maskformer import (
        MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        MaskFormerForInstanceSegmentation,
        MaskFormerModel,
        MaskFormerPreTrainedModel,
    )
    from .modeling_maskformer_swin import (
        MaskFormerSwinBackbone,
        MaskFormerSwinModel,
        MaskFormerSwinPreTrainedModel,
    )


else:
  import sys

  _lowerCAmelCase						:   str		=   _LazyModule(__name__, globals()["__file__"], _import_structure)





