



"""simple docstring"""


import numpy as np







class  A_     :



						"""simple docstring"""






						def __init__(   self					:Optional[Any]  ,    lowercase_					:Dict=5  ,    lowercase_					:Tuple=5   )							->     str:
												UpperCAmelCase			 =					depth
												UpperCAmelCase			 =					0
												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					min_leaf_size
												UpperCAmelCase			 =					None






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:str  ,    lowercase_					:List[Any]   )							->     Any:
												if labels.ndim != 1:
																		print('Error: Input labels must be one dimensional'   )

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






						def       UpperCAmelCase__	(   self					:List[str]  ,    lowercase_					:str  ,    lowercase_					:Union[str, Any]   )							->     Optional[Any]:
												if x.ndim != 1:
																		print('Error: Input data set must be one dimensional'   )
																		return
												if len(lowercase_   ) != len(lowercase_   ):
																		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(lowercase_   ) < 2 * self.min_leaf_size:
																		UpperCAmelCase			 =					np.mean(lowercase_   )
																		return

												if self.depth == 1:
																		UpperCAmelCase			 =					np.mean(lowercase_   )
																		return

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

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

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

												return






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:str   )							->     int:
												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(lowercase_   )
																		else:
																								return self.left.predict(lowercase_   )
												else:
																		print('Error: Decision tree not yet trained'   )
																		return None







class  A_     :



						"""simple docstring"""






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:int  ,    lowercase_					:List[Any]   )							->     Any:
												UpperCAmelCase			 =					float(0   )
												for label in labels:
																		squared_error_sum += (label - prediction) ** 2

												return float(squared_error_sum / labels.size   )


def 					_lowerCAmelCase (  ):
						UpperCAmelCase			 =					np.arange(-1.0						,      1.0						,      0.0_0_5		)
						UpperCAmelCase			 =					np.sin(lowercase_		)

						UpperCAmelCase			 =					DecisionTree(depth=10						,      min_leaf_size=10		)
						tree.train(lowercase_						,      lowercase_		)

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

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


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

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











class 			_lowerCamelCase    :

      """simple docstring"""

      def __init__(  self       :			Optional[Any]			,							snake_case       :			int					):
          __UpperCamelCase			=    data
          __UpperCamelCase			=    42
          __UpperCamelCase			=    42







def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						None:

    """simple docstring"""


    __UpperCamelCase			=    0
    __UpperCamelCase			=    x




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						None:

    """simple docstring"""


    __UpperCamelCase		,   __UpperCamelCase			=    find_set(lowercase_   ), find_set(lowercase_   )
    if x == y:
        return

    elif x.rank > y.rank:
        __UpperCamelCase			=    x
    else:
        __UpperCamelCase			=    y
        if x.rank == y.rank:
            y.rank += 1




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						Node:

    """simple docstring"""


    if x != x.parent:
        __UpperCamelCase			=    find_set(x.parent   )
    return x.parent




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						set:

    """simple docstring"""


    __UpperCamelCase			=    ({0, 1, 2}, {3, 4, 5})
    for s in sets:
        if node.data in s:
            return s
    __UpperCamelCase			=    F"{node.data} is not in {sets}"
    raise ValueError(lowercase_   )




def        __SCREAMING_SNAKE_CASE (	)							->						None:

    """simple docstring"""


    __UpperCamelCase			=    [Node(lowercase_   ) for i in range(6   )]
    for v in vertex:
        make_set(lowercase_   )

    union_set(vertex[0]       ,  vertex[1]   )
    union_set(vertex[1]       ,  vertex[2]   )
    union_set(vertex[3]       ,  vertex[4]   )
    union_set(vertex[3]       ,  vertex[5]   )

    for nodea in vertex:
        for nodea in vertex:
            if find_python_set(lowercase_   ).isdisjoint(find_python_set(lowercase_   )   ):
                assert find_set(lowercase_   ) != find_set(lowercase_   )
            else:
                assert find_set(lowercase_   ) == find_set(lowercase_   )


if __name__ == "__main__":
 test_disjoint_set()






