
"""simple docstring"""




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 lowercase_  (   _snake_case   ,_snake_case   ,_snake_case   ,_snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       List[str]           =   np.array([[1, item, train_mtch[i]] for i, item in enumerate(__UpperCamelCase						)]						)
							SCREAMING_SNAKE_CASE__						:       int           =   np.array(__UpperCamelCase						)
							SCREAMING_SNAKE_CASE__						:       List[str]           =   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 lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   (1, 2, 1)
							SCREAMING_SNAKE_CASE__						:       List[str]           =   (1, 1, 0, 7)
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   SARIMAX(
							    __UpperCamelCase   ,exog=__UpperCamelCase   ,order=__UpperCamelCase   ,seasonal_order=__UpperCamelCase						)
							SCREAMING_SNAKE_CASE__						:       Any           =   model.fit(disp=__UpperCamelCase   ,maxiter=600   ,method="""nm"""						)
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   model_fit.predict(1   ,len(__UpperCamelCase						)   ,exog=[test_match]						)
							return result[0]

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       int           =   SVR(kernel="""rbf"""   ,C=1   ,gamma=0.1   ,epsilon=0.1						)
							regressor.fit(__UpperCamelCase   ,__UpperCamelCase						)
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   regressor.predict(__UpperCamelCase						)
							return y_pred[0]

def lowercase_  (   _snake_case						):
							train_user.sort()
							SCREAMING_SNAKE_CASE__						:       Any           =   np.percentile(__UpperCamelCase   ,25						)
							SCREAMING_SNAKE_CASE__						:       str           =   np.percentile(__UpperCamelCase   ,75						)
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   qa - qa
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   qa - (iqr * 0.1)
							return low_lim

def lowercase_  (   _snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       List[str]           =   0
							SCREAMING_SNAKE_CASE__						:       int           =   0
							for i in list_vote:
														if i > actual_result:
																					SCREAMING_SNAKE_CASE__						:       Optional[int]           =   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__      :    str    							= [[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]]
					UpperCAmelCase__      :    int    							= pd.DataFrame(
					    data_input, columns=['total_user', 'total_even', 'days']
					)
					UpperCAmelCase__      :    Tuple    							= Normalizer().fit_transform(data_input_df.values)
					# split data
					UpperCAmelCase__      :    Optional[Any]    							= normalize_df[:, 2].tolist()
					UpperCAmelCase__      :    Union[str, Any]    							= normalize_df[:, 0].tolist()
					UpperCAmelCase__      :    int    							= normalize_df[:, 1].tolist()

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

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

					UpperCAmelCase__      :    Dict    							= total_date[len(total_date) - 1 :]
					UpperCAmelCase__      :    str    							= total_user[len(total_user) - 1 :]
					UpperCAmelCase__      :    Optional[int]    							= total_match[len(total_match) - 1 :]

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












"""simple docstring"""


import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

lowercase_				     =     imread(R'digital_image_processing/image_data/lena_small.jpg')
lowercase_				     =     cvtColor(img, COLOR_BGR2GRAY)



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= cn.convert_to_negative(__UpperCamelCase )
  # assert negative_img array for at least one True
  assert negative_img.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  with Image.open('''digital_image_processing/image_data/lena_small.jpg''' ) as img:
    # Work around assertion for response
    assert str(cc.change_contrast(__UpperCamelCase		,       1_1_0 ) ).startswith(
        '''<PIL.Image.Image image mode=RGB size=100x100 at''' )



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= canny.gen_gaussian_kernel(9		,       sigma=1.4 )
  # Assert ambiguous array
  assert resp.all()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= imread('''digital_image_processing/image_data/lena_small.jpg'''		,       0 )
  # assert ambiguous array for all == True
  assert canny_img.all()
  __A									= canny.canny(__UpperCamelCase )
  # assert canny array for at least one True
  assert canny_array.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  assert gg.gaussian_filter(__UpperCamelCase		,       5		,       sigma=0.9 ).all()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]] )
  __A									= conv.img_convolve(__UpperCamelCase		,       __UpperCamelCase ).astype(__UpperCamelCase )
  assert res.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  assert med.median_filter(__UpperCamelCase		,       3 ).any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A			,    __A									= sob.sobel_filter(__UpperCamelCase )
  assert grad.any() and theta.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= sp.make_sepia(__UpperCamelCase		,       2_0 )
  assert sepia.all()



def 					lowerCAmelCase				(    __UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg" ):




  """simple docstring"""







  __A									= bs.Burkes(imread(__UpperCamelCase		,       1 )		,       1_2_0 )
  burkes.process()
  assert burkes.output_img.any()



def 					lowerCAmelCase				(    __UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"		,       ):




  """simple docstring"""







  __A									= rs.NearestNeighbour(imread(__UpperCamelCase		,       1 )		,       4_0_0		,       2_0_0 )
  nn.process()
  assert nn.output.any()





def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= '''digital_image_processing/image_data/lena.jpg'''

  # Reading the image and converting it to grayscale.
  __A									= imread(__UpperCamelCase		,       0 )

  # Test for get_neighbors_pixel function() return not None
  __A									= 0
  __A									= 0
  __A									= image[x_coordinate][y_coordinate]

  __A									= lbp.get_neighbors_pixel(
      __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase )

  assert neighbors_pixels is not None

  # Test for local_binary_pattern function()
  # Create a numpy array as the same height and width of read image
  __A									= np.zeros((image.shape[0], image.shape[1]) )

  # Iterating through the image and calculating the local binary pattern value
  # for each pixel.
  for i in range(0		,       image.shape[0] ):
    for j in range(0		,       image.shape[1] ):
      __A									= lbp.local_binary_value(__UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase )

  assert lbp_image.any()




