


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     UpperCamelCase_      (   A__ :       list							,   A__ :       list							,   A__ :       list							,   A__ :       list							,   A__ :       list ):
  '''simple docstring'''





  lowerCAmelCase_      :				Any      =       np.array([[1, item, train_mtch[i]] for i, item in enumerate(A__ )] )
  lowerCAmelCase_      :				Optional[Any]      =       np.array(A__ )
  lowerCAmelCase_      :				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     UpperCamelCase_      (   A__ :       list							,   A__ :       list							,   A__ :       list ):
  '''simple docstring'''





  lowerCAmelCase_      :				Tuple      =       (1, 2, 1)
  lowerCAmelCase_      :				Optional[Any]      =       (1, 1, 0, 7)
  lowerCAmelCase_      :				List[Any]      =       SARIMAX(
      A__							,   exog=A__							,   order=A__							,   seasonal_order=A__ )
  lowerCAmelCase_      :				Union[str, Any]      =       model.fit(disp=A__							,   maxiter=6_00							,   method="""nm""" )
  lowerCAmelCase_      :				Optional[int]      =       model_fit.predict(1							,   len(A__ )							,   exog=[test_match] )
  return result[0]







def     UpperCamelCase_      (   A__ :       list							,   A__ :       list							,   A__ :       list ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[str]      =       SVR(kernel="""rbf"""							,   C=1							,   gamma=0.1							,   epsilon=0.1 )
  regressor.fit(A__							,   A__ )
  lowerCAmelCase_      :				List[str]      =       regressor.predict(A__ )
  return y_pred[0]







def     UpperCamelCase_      (   A__ :       list ):
  '''simple docstring'''





  train_user.sort()
  lowerCAmelCase_      :				Dict      =       np.percentile(A__							,   25 )
  lowerCAmelCase_      :				Union[str, Any]      =       np.percentile(A__							,   75 )
  lowerCAmelCase_      :				Optional[int]      =       qa - qa
  lowerCAmelCase_      :				Optional[int]      =       qa - (iqr * 0.1)
  return low_lim







def     UpperCamelCase_      (   A__ :       list							,   A__ :       float ):
  '''simple docstring'''





  lowerCAmelCase_      :				Tuple      =       0
  lowerCAmelCase_      :				Optional[Any]      =       0
  for i in list_vote:
    if i > actual_result:
      lowerCAmelCase_      :				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)
  __A :  Union[str, Any]    							=				[[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
  __A :  str    							=				pd.DataFrame(
      data_input, columns=["total_user", "total_even", "days"]
  )
  __A :  Tuple    							=				Normalizer().fit_transform(data_input_df.values)
  # split data
  __A :  List[str]    							=				normalize_df[:, 2].tolist()
  __A :  Optional[int]    							=				normalize_df[:, 0].tolist()
  __A :  Optional[int]    							=				normalize_df[:, 1].tolist()

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

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

  __A :  Optional[int]    							=				total_date[len(total_date) - 1 :]
  __A :  Tuple    							=				total_user[len(total_user) - 1 :]
  __A :  Optional[int]    							=				total_match[len(total_match) - 1 :]

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







'''simple docstring'''






from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt







def     UpperCamelCase_      (   A__ :       np.ndarray							,   A__ :       np.ndarray							,   A__ :       np.ndarray							,   A__ :       int							,   A__ :       int ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[Any]      =       cva.getAffineTransform(A__							,   A__ )
  return cva.warpAffine(A__							,   A__							,   (rows, cols) )


if __name__ == "__main__":
  # read original image
  __A :  Dict    							=				cva.imread(
      str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
  )
  # turn image in gray scale value
  __A :  List[Any]    							=				cva.cvtColor(image, cva.COLOR_BGR2GRAY)
  # get image shape
  __A							,			__A :  Dict    							=				gray_img.shape

  # set different points to rotate image
  __A :  List[str]    							=				np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
  __A :  Tuple    							=				np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
  __A :  List[Any]    							=				np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
  __A :  Optional[Any]    							=				np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

  # add all rotated images in a list
  __A :  Optional[Any]    							=				[
      gray_img,
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
  ]

  # plot different image rotations
  __A :  Dict    							=				plt.figure(1)
  __A :  Optional[Any]    							=				["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
  for i, image in enumerate(images):
    plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
    plt.title(titles[i])
    plt.axis("off")
    plt.subplots_adjust(left=0.0, bottom=0.0_5, right=1.0, top=0.9_5)
  plt.show()



