
import numpy as np


class 							lowerCamelCase       :



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase=5						,    __UpperCamelCase=5					):
      A_								=					depth
      A_								=					0
      A_								=					None
      A_								=					None
      A_								=					min_leaf_size
      A_								=					None

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase					):




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

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

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase					):




      if x.ndim != 1:
          print("Error: Input data set must be one dimensional"					)
          return
      if len(_UpperCAmelCase					) != len(_UpperCAmelCase					):
          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(_UpperCAmelCase					) < 2 * self.min_leaf_size:
          A_								=					np.mean(_UpperCAmelCase					)
          return

      if self.depth == 1:
          A_								=					np.mean(_UpperCAmelCase					)
          return

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

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

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

      return

  def 					lowercase_						(					self						,    __UpperCamelCase					):




      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(_UpperCAmelCase					)
          else:
              return self.left.predict(_UpperCAmelCase					)
      else:
          print("Error: Decision tree not yet trained"					)
          return None



class 							lowerCamelCase       :



  """simple docstring"""





  @staticmethod
  def 					lowercase_						(					__UpperCamelCase						,    __UpperCamelCase					):
      A_								=					float(0					)
      for label in labels:
          squared_error_sum += (label - prediction) ** 2

      return float(squared_error_sum / labels.size					)





def        lowerCAmelCase       (				)->			Union[str, Any]:
    A_								=					np.arange(-1.0				,					1.0				,					0.0_0_5 )
    A_								=					np.sin(lowercase__ )

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

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










from __future__ import annotations

from typing import Any
def 			__SCREAMING_SNAKE_CASE							(					*lowercase__	:list[list[int]]					)							->	list[list[int]]:
						if all(_check_not_integer(lowercase__					) for m in matrix_s					):
												for i in matrix_s[1:]:
																		_verify_matrix_sizes(matrix_s[0]   ,    lowercase__					)
												return [[sum(lowercase__					) for t in zip(*lowercase__					)] for m in zip(*lowercase__					)]
						raise TypeError('Expected a matrix, got int/list instead'					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]   ,    lowercase__	:list[list[int]]					)							->	list[list[int]]:
						if (
						    _check_not_integer(lowercase__					)
						    and _check_not_integer(lowercase__					)
						    and _verify_matrix_sizes(lowercase__   ,    lowercase__					)
						):
												return [[i - j for i, j in zip(*lowercase__					)] for m in zip(lowercase__   ,    lowercase__					)]
						raise TypeError('Expected a matrix, got int/list instead'					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]   ,    lowercase__	:int | float					)							->	list[list[float]]:
						return [[x * n for x in row] for row in matrix]
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]   ,    lowercase__	:list[list[int]]					)							->	list[list[int]]:
						if _check_not_integer(lowercase__					) and _check_not_integer(lowercase__					):
												__magic_name__		,			__magic_name__					:				str				=   _verify_matrix_sizes(lowercase__   ,    lowercase__					)

						if cols[0] != rows[1]:
												__magic_name__					:				Union[str, Any]				=   (
												    'Cannot multiply matrix of dimensions '
												    f"({rows[0]},{cols[0]}) and ({rows[1]},{cols[1]})"
												)
												raise ValueError(lowercase__					)
						return [
						    [sum(m * n for m, n in zip(lowercase__   ,    lowercase__					)					) for j in zip(*lowercase__					)] for i in matrix_a
						]
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:int					)							->	list[list[int]]:
						__magic_name__					:				Tuple				=   int(lowercase__					)
						return [[int(row == column					) for column in range(lowercase__					)] for row in range(lowercase__					)]
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]   ,    lowercase__	:bool = True					)							->	list[list[int]] | map[list[int]]:
						if _check_not_integer(lowercase__					):
												if return_map:
																		return map(lowercase__   ,    zip(*lowercase__					)					)
												else:
																		return list(map(lowercase__   ,    zip(*lowercase__					)					)					)
						raise TypeError('Expected a matrix, got int/list instead'					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]   ,    lowercase__	:int   ,    lowercase__	:int					)							->	list[list[int]]:
						__magic_name__					:				int				=   matrix[:row] + matrix[row + 1 :]
						return [row[:column] + row[column + 1 :] for row in minor]
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]					)							->	Any:
						if len(lowercase__					) == 1:
												return matrix[0][0]

						return sum(
						    x * determinant(minor(lowercase__   ,    0   ,    lowercase__					)					) * (-1) ** i
						    for i, x in enumerate(matrix[0]					)					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]					)							->	list[list[float]] | None:
						__magic_name__					:				Tuple				=   determinant(lowercase__					)
						if det == 0:
												return None

						__magic_name__					:				Optional[int]				=   [
						    [determinant(minor(lowercase__   ,    lowercase__   ,    lowercase__					)					) for j in range(len(lowercase__					)					)]
						    for i in range(len(lowercase__					)					)
						]

						__magic_name__					:				Optional[Any]				=   [
						    [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row]					)]
						    for row in range(len(lowercase__					)					)
						]
						__magic_name__					:				Optional[Any]				=   list(transpose(lowercase__					)					)
						return scalar_multiply(lowercase__   ,    1 / det					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]					)							->	bool:
						return not isinstance(lowercase__   ,    lowercase__					) and not isinstance(matrix[0]   ,    lowercase__					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]					)							->	tuple[int, int]:
						return len(lowercase__					), len(matrix[0]					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]   ,    lowercase__	:list[list[int]]					)							->	tuple[tuple[int, int], tuple[int, int]]:
						__magic_name__					:				str				=   _shape(lowercase__					) + _shape(lowercase__					)
						if shape[0] != shape[3] or shape[1] != shape[2]:
												__magic_name__					:				Optional[Any]				=   (
												    'operands could not be broadcast together with shape '
												    f"({shape[0], shape[1]}), ({shape[2], shape[3]})"
												)
												raise ValueError(lowercase__					)
						return (shape[0], shape[2]), (shape[1], shape[3])
def 			__SCREAMING_SNAKE_CASE							(					)							->	None:
						__magic_name__					:				Dict				=   [[1_2, 1_0], [3, 9]]
						__magic_name__					:				Optional[int]				=   [[3, 4], [7, 4]]
						__magic_name__					:				Optional[Any]				=   [[1_1, 1_2, 1_3, 1_4], [2_1, 2_2, 2_3, 2_4], [3_1, 3_2, 3_3, 3_4], [4_1, 4_2, 4_3, 4_4]]
						__magic_name__					:				List[str]				=   [[3, 0, 2], [2, 0, -2], [0, 1, 1]]
						print(f"Add Operation, {add(lowercase__   ,    lowercase__					) = } \n"					)
						print(
						    f"Multiply Operation, {multiply(lowercase__   ,    lowercase__					) = } \n"   ,    )
						print(f"Identity: {identity(5					)}\n"					)
						print(f"Minor of {matrix_c} = {minor(lowercase__   ,    1   ,    2					)} \n"					)
						print(f"Determinant of {matrix_b} = {determinant(lowercase__					)} \n"					)
						print(f"Inverse of {matrix_d} = {inverse(lowercase__					)}\n"					)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()


