'''simple docstring'''

import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey




class    lowerCamelCase__  :



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Optional[Any]:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =0
        # max greyscale value for #FFFFFF
        _UpperCamelCase  :str       =int(self.get_greyscale(255					,      255					,      255     )     )

        if not self.min_threshold < threshold < self.max_threshold:
             _UpperCamelCase  :Union[str, Any]       =f'''Factor value should be from 0 to {self.max_threshold}'''
             raise ValueError(lowerCAmelCase__     )

        _UpperCamelCase  :str       =input_img
        _UpperCamelCase  :Dict       =threshold
        _UpperCamelCase       ,      _UpperCamelCase  :Dict       =self.input_img.shape[1], self.input_img.shape[0]

        # error table size (+4 columns and +1 row) greater than input image because of
        # lack of if statements
        _UpperCamelCase  :List[str]       =[
            [0 for _ in range(self.height + 4     )] for __ in range(self.width + 1     )
        ]
        _UpperCamelCase  :Optional[Any]       =np.ones((self.width, self.height, 3)					,      np.uinta     ) * 255



   @classmethod
   def  _UpperCamelCase		(    cls					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     float:
        """simple docstring"""





        return 0.114 * blue + 0.587 * green + 0.2126 * red




   def  _UpperCamelCase		(    self     )				->     None:
        """simple docstring"""





        for y in range(self.height     ):
             for x in range(self.width     ):
                  _UpperCamelCase  :str       =int(self.get_greyscale(*self.input_img[y][x]     )     )
                  if self.threshold > greyscale + self.error_table[y][x]:
                       _UpperCamelCase  :Any       =(0, 0, 0)
                       _UpperCamelCase  :Tuple       =greyscale + self.error_table[x][y]
                  else:
                       _UpperCamelCase  :Optional[Any]       =(255, 255, 255)
                       _UpperCamelCase  :int       =greyscale + self.error_table[x][y] - 255
                  self.error_table[y][x + 1] += int(8 / 32 * current_error     )
                  self.error_table[y][x + 2] += int(4 / 32 * current_error     )
                  self.error_table[y + 1][x] += int(8 / 32 * current_error     )
                  self.error_table[y + 1][x + 1] += int(4 / 32 * current_error     )
                  self.error_table[y + 1][x + 2] += int(2 / 32 * current_error     )
                  self.error_table[y + 1][x - 1] += int(4 / 32 * current_error     )
                  self.error_table[y + 1][x - 2] += int(2 / 32 * current_error     )


if __name__ == "__main__":
      # create Burke's instances with original images in greyscale
      _lowerCamelCase :				Optional[Any]														= [
          Burkes(imread("""image_data/lena.jpg""", 1), threshold)
          for threshold in (1, 1_26, 1_30, 1_40)
      ]

      for burkes in burkes_instances:
            burkes.process()

      for burkes in burkes_instances:
            imshow(
                f"Original image with dithering threshold: {burkes.threshold}",
                burkes.output_img,
            )

      waitKey(0)
      destroyAllWindows()




import numpy as np


class 						lowerCamelCase__			:


						def __init__(			self    :	Tuple   ,							_a    :	List[str]=5   ,							_a    :	Any=5      ):
													a__:							int									=depth
													a__:							Dict									=0
													a__:							str									=None
													a__:							List[Any]									=None
													a__:							List[Any]									=min_leaf_size
													a__:							Dict									=None


						def 	_lowerCamelCase      (			self    :	Union[str, Any]   ,							_a    :	int   ,							_a    :	Optional[int]      ):
													if labels.ndim != 1:
																				print("Error: Input labels must be one dimensional"      )

													return np.mean((labels - prediction) ** 2      )


						def 	_lowerCamelCase      (			self    :	Any   ,							_a    :	List[Any]   ,							_a    :	Dict      ):
													if x.ndim != 1:
																				print("Error: Input data set must be one dimensional"      )
																				return
													if len(_a      ) != len(_a      ):
																				print("Error: X and y have different lengths"      )
																				return
													if y.ndim != 1:
																				print("Error: Data set labels must be one dimensional"      )
																				return

													if len(_a      ) < 2 * self.min_leaf_size:
																				a__:							List[str]									=np.mean(_a      )
																				return

													if self.depth == 1:
																				a__:							Dict									=np.mean(_a      )
																				return

													a__:							Any									=0
													a__:							List[Any]									=self.mean_squared_error(_a   ,							np.mean(_a      )      ) * 2
													for i in range(len(_a      )      ):
																				if len(x[:i]      ) < self.min_leaf_size:
																											continue
																				elif len(x[i:]      ) < self.min_leaf_size:
																											continue
																				else:
																											a__:							List[str]									=self.mean_squared_error(x[:i]   ,							np.mean(y[:i]      )      )
																											a__:							str									=self.mean_squared_error(x[i:]   ,							np.mean(y[i:]      )      )
																											a__:							str									=error_left + error_right
																											if error < min_error:
																																		a__:							Optional[int]									=i
																																		a__:							Tuple									=error

													if best_split != 0:
																				a__:							List[Any]									=x[:best_split]
																				a__:							Any									=y[:best_split]
																				a__:							List[str]									=x[best_split:]
																				a__:							List[str]									=y[best_split:]

																				a__:							List[Any]									=x[best_split]
																				a__:							Any									=DecisionTree(
																				    depth=self.depth - 1   ,							min_leaf_size=self.min_leaf_size      )
																				a__:							Any									=DecisionTree(
																				    depth=self.depth - 1   ,							min_leaf_size=self.min_leaf_size      )
																				self.left.train(_a   ,							_a      )
																				self.right.train(_a   ,							_a      )
													else:
																				a__:							Tuple									=np.mean(_a      )

													return







						def 	_lowerCamelCase      (			self    :	List[str]   ,							_a    :	Tuple      ):
													if self.prediction is not None:
																				return self.prediction
													elif self.left or self.right is not None:
																				if x >= self.decision_boundary:
																											return self.right.predict(_a      )
																				else:
																											return self.left.predict(_a      )
													else:
																				print("Error: Decision tree not yet trained"      )
																				return None


class 						lowerCamelCase__			:


						@staticmethod
						def 	_lowerCamelCase      (			_a    :	Optional[int]   ,							_a    :	Optional[int]      ):
													a__:							Optional[int]									=float(0      )
													for label in labels:
																				squared_error_sum += (label - prediction) ** 2

													return float(squared_error_sum / labels.size      )





def     __lowerCamelCase							(   ):
							a__:							Optional[int]									=np.arange(-1.0  ,			1.0  ,			0.005    )
							a__:							List[Any]									=np.sin(__magic_name__    )

							a__:							int									=DecisionTree(depth=10  ,			min_leaf_size=10    )
							tree.train(__magic_name__  ,			__magic_name__    )

							a__:							Union[str, Any]									=(np.random.rand(10    ) * 2) - 1
							a__:							Any									=np.array([tree.predict(__magic_name__    ) for x in test_cases]    )
							a__:							Dict									=np.mean((predictions - test_cases) ** 2    )

							print("Test values: " + str(__magic_name__    )    )
							print("Predictions: " + str(__magic_name__    )    )
							print("Average error: " + str(__magic_name__    )    )


if __name__ == "__main__":
		main()
		import doctest

		doctest.testmod(name='''mean_squarred_error''', verbose=True)





