






import numpy as np
from PIL import Image






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__			,						lowercase__			,						lowercase__       			=rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
				return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				return (gray > 127) & (gray <= 255)






def   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=np.zeros_like(lowerCamelCase_    )
				lowercase__       			=np.zeros(
				    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)    )

				# Copy image to padded image
				lowercase__       			=image

				# Iterate over image & apply kernel
				for x in range(image.shape[1]    ):
								for y in range(image.shape[0]    ):
												lowercase__       			=(
												    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
												).sum()
												lowercase__       			=int(summation == 5    )
				return output


# kernel to be applied
A__     :      str             =			np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
							# read original image
							A__     :      str             =			np.array(Image.open(r'..\image_data\lena.jpg'))
							# Apply erosion operation to a binary image
							A__     :      Any             =			erosion(grayabinary(rgbagray(image)), structuring_element)
							# Save the output image
							A__     :      Any             =			Image.fromarray(output).convert('RGB')
							pil_img.save('result_erosion.png')












import numpy as np
class 				_UpperCAmelCase :

				"""simple docstring"""




				def __init__(					self						:   Union[str, Any],  lowerCamelCase						:   int=5,  lowerCamelCase						:   Optional[Any]=5  ):

								'''simple docstring'''
								lowercase__       			=      depth
								lowercase__       			=      0
								lowercase__       			=      None
								lowercase__       			=      None
								lowercase__       			=      min_leaf_size
								lowercase__       			=      None




				def     lowercase__       (					self						:   Union[str, Any],  lowerCamelCase						:   Tuple,  lowerCamelCase						:   Dict  ):

								'''simple docstring'''
								if labels.ndim != 1:
												print('''Error: Input labels must be one dimensional'''  )

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




				def     lowercase__       (					self						:   Tuple,  lowerCamelCase						:   List[str],  lowerCamelCase						:   Tuple  ):

								'''simple docstring'''
								if x.ndim != 1:
												print('''Error: Input data set must be one dimensional'''  )
												return
								if len(lowerCamelCase  ) != len(lowerCamelCase  ):
												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(lowerCamelCase  ) < 2 * self.min_leaf_size:
												lowercase__       			=      np.mean(lowerCamelCase  )
												return

								if self.depth == 1:
												lowercase__       			=      np.mean(lowerCamelCase  )
												return

								lowercase__       			=      0
								lowercase__       			=      self.mean_squared_error(lowerCamelCase,  np.mean(lowerCamelCase  )  ) * 2
								for i in range(len(lowerCamelCase  )  ):
												if len(x[:i]  ) < self.min_leaf_size:
																continue
												elif len(x[i:]  ) < self.min_leaf_size:
																continue
												else:
																lowercase__       			=      self.mean_squared_error(x[:i],  np.mean(y[:i]  )  )
																lowercase__       			=      self.mean_squared_error(x[i:],  np.mean(y[i:]  )  )
																lowercase__       			=      error_left + error_right
																if error < min_error:
																				lowercase__       			=      i
																				lowercase__       			=      error

								if best_split != 0:
												lowercase__       			=      x[:best_split]
												lowercase__       			=      y[:best_split]
												lowercase__       			=      x[best_split:]
												lowercase__       			=      y[best_split:]

												lowercase__       			=      x[best_split]
												lowercase__       			=      DecisionTree(
												    depth=self.depth - 1,  min_leaf_size=self.min_leaf_size  )
												lowercase__       			=      DecisionTree(
												    depth=self.depth - 1,  min_leaf_size=self.min_leaf_size  )
												self.left.train(lowerCamelCase,  lowerCamelCase  )
												self.right.train(lowerCamelCase,  lowerCamelCase  )
								else:
												lowercase__       			=      np.mean(lowerCamelCase  )

								return




				def     lowercase__       (					self						:   Optional[Any],  lowerCamelCase						:   Tuple  ):

								'''simple docstring'''
								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(lowerCamelCase  )
												else:
																return self.left.predict(lowerCamelCase  )
								else:
												print('''Error: Decision tree not yet trained'''  )
												return None



class 				_UpperCAmelCase :

				"""simple docstring"""




				@staticmethod
				def     lowercase__       (					lowerCamelCase						:   int,  lowerCamelCase						:   str  ):

								'''simple docstring'''
								lowercase__       			=      float(0  )
								for label in labels:
												squared_error_sum += (label - prediction) ** 2

								return float(squared_error_sum / labels.size  )






def   a      (						):






				'''simple docstring'''
				lowercase__       			=      np.arange(-1.0		,       1.0		,       0.0_05    )
				lowercase__       			=      np.sin(lowerCamelCase_    )

				lowercase__       			=      DecisionTree(depth=10		,       min_leaf_size=10    )
				tree.train(lowerCamelCase_		,       lowerCamelCase_    )

				lowercase__       			=      (np.random.rand(10    ) * 2) - 1
				lowercase__       			=      np.array([tree.predict(lowerCamelCase_    ) for x in test_cases]    )
				lowercase__       			=      np.mean((predictions - test_cases) ** 2    )

				print('''Test values: ''' + str(lowerCamelCase_    )    )
				print('''Predictions: ''' + str(lowerCamelCase_    )    )
				print('''Average error: ''' + str(lowerCamelCase_    )    )


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

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




