

UpperCamelCase__					: List[Any]								=      [0.33, 0.44, 0.55, 0.44, 0.33]
UpperCamelCase__					: Optional[int]								=      3
UpperCamelCase__					: List[str]								=      5

# Buffer Space (with Parameters Space)
UpperCamelCase__					: Optional[Any]								=      []
UpperCamelCase__					: Optional[Any]								=      []

# Machine Time
UpperCamelCase__					: int								=      0


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Optional[Any]    ):





    """simple docstring"""


    global buffer_space, params_space, machine_time, K, m, t

    # Choosing Dynamical Systems (All)
    for key, value in enumerate(SCREAMING_SNAKE_CASE_    ):
        # Evolution Parameter
        SCREAMING_SNAKE_CASE_													=  float(seed / value    )

        # Control Theory: Orbit Change
        SCREAMING_SNAKE_CASE_													=  (buffer_space[(key + 1) % m] + e) % 1

        # Control Theory: Trajectory Change
        SCREAMING_SNAKE_CASE_													=  (params_space[key] + e) % 1 + 3

        # Modification (Transition Function) - Jumps
        SCREAMING_SNAKE_CASE_													=  round(float(r * value * (1 - value)    )	,				10    )
        SCREAMING_SNAKE_CASE_													=  r  # Saving to Parameters Space

    # Logistic Map
    assert max(SCREAMING_SNAKE_CASE_    ) < 1
    assert max(SCREAMING_SNAKE_CASE_    ) < 4

    # Machine Time
    machine_time += 1


def    _UpperCAmelCase    (					):





    """simple docstring"""


    global buffer_space, params_space, machine_time, K, m, t

    # PRNG (Xorshift by George Marsaglia)
    def xorshift(_SCREAMING_SNAKE_CASE   :						Any	,				_SCREAMING_SNAKE_CASE   :						Optional[int]    ):
        x ^= y >> 13
        y ^= x << 17
        x ^= y >> 5
        return x

    # Choosing Dynamical Systems (Increment)
    SCREAMING_SNAKE_CASE_													=  machine_time % m

    # Evolution (Time Length)
    for _ in range(0	,				SCREAMING_SNAKE_CASE_    ):
        # Variables (Position + Parameters)
        SCREAMING_SNAKE_CASE_													=  params_space[key]
        SCREAMING_SNAKE_CASE_													=  buffer_space[key]

        # Modification (Transition Function) - Flow
        SCREAMING_SNAKE_CASE_													=  round(float(r * value * (1 - value)    )	,				10    )
        SCREAMING_SNAKE_CASE_													=  (machine_time * 0.01 + r * 1.01) % 1 + 3

    # Choosing Chaotic Data
    SCREAMING_SNAKE_CASE_													=  int(buffer_space[(key + 2) % m] * (10**10)    )
    SCREAMING_SNAKE_CASE_													=  int(buffer_space[(key - 2) % m] * (10**10)    )

    # Machine Time
    machine_time += 1

    return xorshift(SCREAMING_SNAKE_CASE_	,				SCREAMING_SNAKE_CASE_    ) % 0xff_fff_fff


def    _UpperCAmelCase    (					):





    """simple docstring"""


    global buffer_space, params_space, machine_time, K, m, t

    SCREAMING_SNAKE_CASE_													=  K
    SCREAMING_SNAKE_CASE_													=  [0] * m
    SCREAMING_SNAKE_CASE_													=  0


if __name__ == "__main__":
       # Initialization
       reset()

       # Pushing Data (Input)
       import random

       UpperCamelCase__					: Any								=      random.sample(range(0xFFFF_FFFF), 100)
       for chunk in message:
              push(chunk)

       # for controlling
       UpperCamelCase__					: str								=      ""

       # Pulling Data (Output)
       while inp in ("e", "E"):
              print(F'{format(pull(), "#04x")}')
              print(buffer_space)
              print(params_space)
              UpperCamelCase__					: Dict								=      input("(e)exit? ").strip()



from sklearn.metrics import mean_absolute_error

import datasets


UpperCamelCase__					: Union[str, Any]								=      "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"

UpperCamelCase__					: Optional[Any]								=      "\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n"


UpperCamelCase__					: Tuple								=      "\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n                 \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\nReturns:\n    mae : mean absolute error.\n        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric(\"mae\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mae': 0.5}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mae': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results)\n    {'mae': array([0.5, 1. ])}\n"



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,	_KWARGS_DESCRIPTION    )
class  __snake_case					(      datasets.Metric    ):

  def   lowerCAmelCase__       (   self):
      return datasets.MetricInfo(
          description=_DESCRIPTION  ,						citation=_CITATION  ,						inputs_description=_KWARGS_DESCRIPTION  ,						features=datasets.Features(self._get_feature_types())  ,						reference_urls=[
              'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html'
          ]  ,						)

  def   lowerCAmelCase__       (   self):
      if self.config_name == "multilist":
          return {
              "predictions": datasets.Sequence(datasets.Value('float')),
              "references": datasets.Sequence(datasets.Value('float')),
          }
      else:
          return {
              "predictions": datasets.Value('float'),
              "references": datasets.Value('float'),
          }


  def   lowerCAmelCase__       (   self  ,						_A  ,						_A  ,						_A=None  ,						_A="uniform_average"):
      SCREAMING_SNAKE_CASE_													=  mean_absolute_error(_A  ,						_A  ,						sample_weight=_A  ,						multioutput=_A)

      return {"mae": mae_score}


