

import numpy as np







class  __lowerCamelCase				:







      def __init__(     self							,  lowerCamelCase=5							,  lowerCamelCase=5   )						->       Any:
        snake_case_       						=      depth
        snake_case_       						=      0
        snake_case_       						=      None
        snake_case_       						=      None
        snake_case_       						=      min_leaf_size
        snake_case_       						=      None







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase   )						->       str:
        if labels.ndim != 1:
          print("""Error: Input labels must be one dimensional"""   )

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







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase   )						->       int:
        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:
          snake_case_       						=      np.mean(lowerCamelCase   )
          return

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

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

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

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

        return







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase   )						->       Optional[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(lowerCamelCase   )
          else:
            return self.left.predict(lowerCamelCase   )
        else:
          print("""Error: Decision tree not yet trained"""   )
          return None







class  __lowerCamelCase				:







      @staticmethod
      def 	lowerCAmelCase_    (     lowerCamelCase							,  lowerCamelCase   )						->       str:
        snake_case_       						=      float(0   )
        for label in labels:
          squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size   )



def    UpperCamelCase(					)	->				int:





  '''simple docstring'''





  snake_case_       						=      np.arange(-1.0			,					1.0			,					0.0_05							)
  snake_case_       						=      np.sin(lowercase_							)

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

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





'''simple docstring'''



import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
     from .testing_utils import (
         floats_tensor,
         load_hf_numpy,
         load_image,
         load_numpy,
         load_pt,
         nightly,
         parse_flag_from_env,
         print_tensor_test,
         require_torch_a,
         require_torch_gpu,
         skip_mps,
         slow,
         torch_all_close,
         torch_device,
     )
     from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


UpperCAmelCase_				:     int     			=						get_logger(__name__)







def 	_UpperCamelCase       (_lowerCamelCase							:   Union[str, Any]		)->      Tuple:




     '''simple docstring'''





     if version.parse(_lowerCamelCase		) < version.parse(_lowerCamelCase		):
          if "dev" in min_version:
               __snake_case			    =     (
                   '''This example requires a source install from HuggingFace diffusers (see '''
                   '''`https://huggingface.co/docs/diffusers/installation#install-from-source`),'''
               )
          else:
               __snake_case			    =     f'''This example requires a minimum version of {min_version},'''
          error_message += f''' but the version found is {__version__}.\n'''
          raise ImportError(_lowerCamelCase		)






