class_docstr: Empirical distribution (sktime native).
  
  Parameters
  ----------
  spl : pd.DataFrame with pd.MultiIndex
      empirical sample
      last (highest) index is instance, first (lowest) index is sample
  weights : pd.Series, with same index and length as spl, optional, default=None
      if not passed, ``spl`` is assumed to be unweighted
  time_indep : bool, optional, default=True
      if True, ``sample`` will sample individual instance indices independently
      if False, ``sample`` will sample entire instances from ``spl``
  index : pd.Index, optional, default = RangeIndex
  columns : pd.Index, optional, default = RangeIndex
  
  Example
  -------
  >>> import pandas as pd
  >>> from sktime.proba.empirical import Empirical
  
  >>> spl_idx = pd.MultiIndex.from_product(
  ...     [[0, 1], [0, 1, 2]], names=["sample", "time"]
  ... )
  >>> spl = pd.DataFrame(
  ...     [[0, 1], [2, 3], [10, 11], [6, 7], [8, 9], [4, 5]],
  ...     index=spl_idx,
  ...     columns=["a", "b"],
  ... )
  >>> dist = Empirical(spl)
  >>> empirical_sample = dist.sample(3)
class_docstr_tok: ["Empirical","distribution","(","sktime","native",")",".","Parameters","--","--","--","--","--","spl",":","pd.DataFrame","with","pd.MultiIndex","empirical","sample","last","(","highest",")","index","is","instance",",","first","(","lowest",")","index","is","sample","weights",":","pd.Series",",","with","same","index","and","length","as","spl",",","optional",",","default=None","if","not","passed",",","``","spl","``","is","assumed","to","be","unweighted","time_indep",":","bool",",","optional",",","default=True","if","True",",","``","sample","``","will","sample","individual","instance","indices","independently","if","False",",","``","sample","``","will","sample","entire","instances","from","``","spl","``","index",":","pd.Index",",","optional",",","default","=","RangeIndex","columns",":","pd.Index",",","optional",",","default","=","RangeIndex","Example","--","--","--","-",">",">",">","import","pandas","as","pd",">",">",">","from","sktime.proba.empirical","import","Empirical",">",">",">","spl_idx","=","pd.MultiIndex.from_product","(","...","[","[","0",",","1","]",",","[","0",",","1",",","2","]","]",",","names=","[","``","sample","''",",","``","time","''","]","...",")",">",">",">","spl","=","pd.DataFrame","(","...","[","[","0",",","1","]",",","[","2",",","3","]",",","[","10",",","11","]",",","[","6",",","7","]",",","[","8",",","9","]",",","[","4",",","5","]","]",",","...","index=spl_idx",",","...","columns=","[","``","a","''",",","``","b","''","]",",","...",")",">",">",">","dist","=","Empirical","(","spl",")",">",">",">","empirical_sample","=","dist.sample","(","3",")"]
docstr: Return testing parameter settings for the estimator.
docstr_tok: ["Return","testing","parameter","settings","for","the","estimator","."]
code: def get_test_params(cls, parameter_set="default"):
      """Return testing parameter settings for the estimator."""
      # params1 is a DataFrame with simple row multiindex
      spl_idx = pd.MultiIndex.from_product(
          [[0, 1], [0, 1, 2]], names=["sample", "time"]
      )
      spl = pd.DataFrame(
          [[0, 1], [2, 3], [10, 11], [6, 7], [8, 9], [4, 5]],
          index=spl_idx,
          columns=["a", "b"],
      )
      params1 = {
          "spl": spl,
          "weights": None,
          "time_indep": True,
          "index": pd.RangeIndex(3),
          "columns": pd.Index(["a", "b"]),
      }
  
      # params2 is weighted
      params2 = {
          "spl": spl,
          "weights": pd.Series(
              [0.5, 0.5, 0.5, 1, 1, 1.1], index=spl_idx
          ),
          "time_indep": False,
          "index": pd.RangeIndex(3),
          "columns": pd.Index(["a", "b"]),
      }
      return [params1, params2]
  
file_path: sktime/sktime/proba/empirical.py
imports: import numpy
  import pandas
  from sktime.proba.base import BaseDistribution
label_desc: Use image node_id 11 for calling the Empirical obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list