class_docstr: Draw random samples from time series.
  
  `transform` takes a time series :math:`X={x_1, x_2, ... , x_m}` with :math:`m`
  elements and returns :math:`X_t={x_i, x_{i+1}, ... , x_n}`, where
  :math:`{x_i, x_{i+1}, ... , x_n}` are :math:`n`=``n`` random samples drawn
  from :math:`X` (with or `without_replacement`).
  
  Parameters
  ----------
  n: int or float, optional (default = 1.0)
          To specify an exact number of samples to draw, set `n` to an int value.
          Number of samples to draw.
          To specify the returned samples as a proportion of the given times series
          set `n` to a float value :math:`n \in [0, 1]`.
          By default, the same number of samples is returned as given by the input
          time series.
  without_replacement: bool, optional (default = True)
          Whether to draw without replacement. If True, every sample of the input
          times series `X` will appear at most once in `Xt`.
  random_state: None or int or ``np.random.RandomState`` instance, optional
          "If int or RandomState, use it for drawing the random variates.
          If None, rely on ``self.random_state``.
          Default is None." [1]
  
  References and Footnotes
  ----------
  
      [1]: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_continuous.random_state.html # noqa
code: def __init__(
      self,
      n=1.0,
      without_replacement=True,
      random_state=42,
  ):
      if isinstance(n, float):
          if n <= 0.0 or not np.isfinite(n):
              raise ValueError("n must be a positive, finite number.")
      elif isinstance(n, int):
          if n < 1 or not np.isfinite(n):
              raise ValueError("n must be a finite number >= 1.")
      else:
          raise ValueError(
              "n must be int or float, not " + str(type(n))
          ) + "."
      self.n = n
      self.without_replacement = without_replacement
      self.random_state = random_state
      super().__init__()
  
imports: import numpy
  import pandas
  from sklearn.utils import check_random_state
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 1 to create a new RandomSamplesAugmenter object from inherited base classes: _AugmenterTags, BaseTransformer with example: obj = RandomSamplesAugmenter(n, without_replacement, random_state)