





'''simple docstring'''







from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      torch.Tensor,      SCREAMING_SNAKE_CASE__   :      torch.Tensor,      SCREAMING_SNAKE_CASE__   :      bool )			->						torch.Tensor:
						# The first operation in a checkpoint can't be in-place, but it's
						# nice to have in-place addition during inference. Thus...
						if not inplace:
												UpperCAmelCase_      :   str      =	ma + ma
						else:
												ma += ma

						return ma





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      torch.Tensor,      SCREAMING_SNAKE_CASE__   :      List[int] )			->						torch.Tensor:
						UpperCAmelCase_      :   Optional[Any]      =	-1 * len(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   Optional[Any]      =	list(range(len(tensor.shape[:zero_index] ) ) )
						return tensor.permute(first_inds + [zero_index + i for i in inds] )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      torch.Tensor,      SCREAMING_SNAKE_CASE__   :      int )			->						torch.Tensor:
						return t.reshape(t.shape[:-no_dims] + (-1,) )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      torch.Tensor,      SCREAMING_SNAKE_CASE__   :      torch.Tensor,      SCREAMING_SNAKE_CASE__   :      int,      SCREAMING_SNAKE_CASE__   :      float = 1E-4 )			->						torch.Tensor:
						UpperCAmelCase_      :   Union[str, Any]      =	mask.expand(*value.shape )
						return torch.sum(mask * value,      dim=SCREAMING_SNAKE_CASE__ ) / (eps + torch.sum(SCREAMING_SNAKE_CASE__,      dim=SCREAMING_SNAKE_CASE__ ))





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      torch.Tensor,      SCREAMING_SNAKE_CASE__   :      torch.types.Number = 2.31_25,      SCREAMING_SNAKE_CASE__   :      torch.types.Number = 21.68_75,      SCREAMING_SNAKE_CASE__   :      int = 64 )			->						torch.Tensor:
						UpperCAmelCase_      :   Optional[int]      =	torch.linspace(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      no_bins - 1,      device=pts.device )
						UpperCAmelCase_      :   Union[str, Any]      =	torch.sqrt(torch.sum((pts.unsqueeze(-2 ) - pts.unsqueeze(-3 )) ** 2,      dim=-1 ) )
						return torch.bucketize(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Callable[[list], Any],      SCREAMING_SNAKE_CASE__   :      List[dict] )			->						dict:
						UpperCAmelCase_      :   List[Any]      =	dicts[0]
						UpperCAmelCase_      :   Optional[Any]      =	{}
						for k, v in first.items():
												UpperCAmelCase_      :   List[Any]      =	[d[k] for d in dicts]
												if isinstance(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ ):
																		UpperCAmelCase_      :   List[Any]      =	dict_multimap(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
												else:
																		UpperCAmelCase_      :   int      =	fn(SCREAMING_SNAKE_CASE__ )

						return new_dict





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      torch.Tensor,      SCREAMING_SNAKE_CASE__   :      torch.Tensor )			->						torch.Tensor:
						UpperCAmelCase_      :   Optional[int]      =	v_bins.view(((1,) * len(x.shape )) + (len(SCREAMING_SNAKE_CASE__ ),) )
						UpperCAmelCase_      :   List[str]      =	x[..., None] - reshaped_bins
						UpperCAmelCase_      :   Union[str, Any]      =	torch.argmin(torch.abs(SCREAMING_SNAKE_CASE__ ),      dim=-1 )
						return nn.functional.one_hot(SCREAMING_SNAKE_CASE__,      num_classes=len(SCREAMING_SNAKE_CASE__ ) ).float()





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      torch.Tensor,      SCREAMING_SNAKE_CASE__   :      torch.Tensor,      SCREAMING_SNAKE_CASE__   :      int = 0,      SCREAMING_SNAKE_CASE__   :      int = 0 )			->						torch.Tensor:
						UpperCAmelCase_      :   List[Union[slice, torch.Tensor]]      =	[]
						for i, s in enumerate(data.shape[:no_batch_dims] ):
												UpperCAmelCase_      :   Union[str, Any]      =	torch.arange(SCREAMING_SNAKE_CASE__ )
												UpperCAmelCase_      :   Union[str, Any]      =	r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape ) - i - 1))) )
												ranges.append(SCREAMING_SNAKE_CASE__ )

						UpperCAmelCase_      :   List[Union[slice, torch.Tensor]]      =	[slice(SCREAMING_SNAKE_CASE__ ) for _ in range(len(data.shape ) - no_batch_dims )]
						UpperCAmelCase_      :   Optional[Any]      =	inds
						ranges.extend(SCREAMING_SNAKE_CASE__ )
						# Matt note: Editing this to get around the behaviour of using a list as an array index changing
						# in recent Numpy versions
						return data[tuple(SCREAMING_SNAKE_CASE__ )]


snake_case_  :							List[str]            =	TypeVar("T")





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Callable[[T], Any],      SCREAMING_SNAKE_CASE__   :      Dict[Any, Union[dict, list, tuple, T]],      SCREAMING_SNAKE_CASE__   :      Type[T] )			->						Dict[Any, Union[dict, list, tuple, Any]]:
						UpperCAmelCase_      :   Dict[Any, Union[dict, list, tuple, Any]]      =	{}
						for k, v in dic.items():
												if isinstance(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ ):
																		UpperCAmelCase_      :   Union[str, Any]      =	dict_map(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
												else:
																		UpperCAmelCase_      :   int      =	tree_map(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )

						return new_dict





@overload
def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Callable[[T], Any],      SCREAMING_SNAKE_CASE__   :      T,      SCREAMING_SNAKE_CASE__   :      Type[T] )			->						Any:
						...





@overload
def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Callable[[T], Any],      SCREAMING_SNAKE_CASE__   :      dict,      SCREAMING_SNAKE_CASE__   :      Type[T] )			->						dict:
						...





@overload
def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Callable[[T], Any],      SCREAMING_SNAKE_CASE__   :      list,      SCREAMING_SNAKE_CASE__   :      Type[T] )			->						list:
						...





@overload
def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Callable[[T], Any],      SCREAMING_SNAKE_CASE__   :      tuple,      SCREAMING_SNAKE_CASE__   :      Type[T] )			->						tuple:
						...





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Dict,      SCREAMING_SNAKE_CASE__   :      Union[str, Any],      SCREAMING_SNAKE_CASE__   :      str )			->						Union[str, Any]:
						if isinstance(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ ):
												return dict_map(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
						elif isinstance(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ ):
												return [tree_map(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ ) for x in tree]
						elif isinstance(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ ):
												return tuple(tree_map(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ ) for x in tree )
						elif isinstance(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ ):
												return fn(SCREAMING_SNAKE_CASE__ )
						else:
												print(type(SCREAMING_SNAKE_CASE__ ) )
												raise ValueError('''Not supported''' )
snake_case_  :							str            =	partial(tree_map, leaf_type=torch.Tensor)













import numpy as np


class      __SCREAMING_SNAKE_CASE       :






						def __init__(						self  ,    _UpperCamelCase=5  ,    _UpperCamelCase=5 ):






												"""simple docstring"""
												lowerCAmelCase__         =    depth
												lowerCAmelCase__         =    0
												lowerCAmelCase__         =    None
												lowerCAmelCase__         =    None
												lowerCAmelCase__         =    min_leaf_size
												lowerCAmelCase__         =    None






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase ):






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

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






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												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:
																		lowerCAmelCase__         =    np.mean(_UpperCamelCase )
																		return

												if self.depth == 1:
																		lowerCAmelCase__         =    np.mean(_UpperCamelCase )
																		return

												lowerCAmelCase__         =    0
												lowerCAmelCase__         =    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:
																								lowerCAmelCase__         =    self.mean_squared_error(x[:i]  ,    np.mean(y[:i] ) )
																								lowerCAmelCase__         =    self.mean_squared_error(x[i:]  ,    np.mean(y[i:] ) )
																								lowerCAmelCase__         =    error_left + error_right
																								if error < min_error:
																														lowerCAmelCase__         =    i
																														lowerCAmelCase__         =    error

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

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

												return







						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






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




class      __SCREAMING_SNAKE_CASE       :






						@staticmethod
						def    UpperCamelCase__   (						_UpperCamelCase  ,    _UpperCamelCase ):






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

												return float(squared_error_sum / labels.size )







def 						_UpperCamelCase (     )	-> List[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    np.arange(-1.0     ,							1.0     ,							0.005		)
						lowerCAmelCase__         =    np.sin(UpperCamelCase_		)

						lowerCAmelCase__         =    DecisionTree(depth=10     ,							min_leaf_size=10		)
						tree.train(UpperCamelCase_     ,							UpperCamelCase_		)

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

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


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

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





