class_docstr: Laplace distribution.
  
  Parameters
  ----------
  mean : float or array of float (1D or 2D)
      mean of the distribution
  scale : float or array of float (1D or 2D), must be positive
      scale parameter of the distribution, same as standard deviation / sqrt(2)
  index : pd.Index, optional, default = RangeIndex
  columns : pd.Index, optional, default = RangeIndex
  
  Example
  -------
  >>> from sktime.proba.laplace import Laplace
  
  >>> n = Laplace(mu=[[0, 1], [2, 3], [4, 5]], scale=1)
class_docstr_tok: ["Laplace","distribution",".","Parameters","--","--","--","--","--","mean",":","float","or","array","of","float","(","1D","or","2D",")","mean","of","the","distribution","scale",":","float","or","array","of","float","(","1D","or","2D",")",",","must","be","positive","scale","parameter","of","the","distribution",",","same","as","standard","deviation","\/","sqrt","(","2",")","index",":","pd.Index",",","optional",",","default","=","RangeIndex","columns",":","pd.Index",",","optional",",","default","=","RangeIndex","Example","--","--","--","-",">",">",">","from","sktime.proba.laplace","import","Laplace",">",">",">","n","=","Laplace","(","mu=","[","[","0",",","1","]",",","[","2",",","3","]",",","[","4",",","5","]","]",",","scale=1",")"]
docstr: Quantile function = percent point function = inverse cdf.
docstr_tok: ["Quantile","function","=","percent","point","function","=","inverse","cdf","."]
code: def ppf(self, p):
      """Quantile function = percent point function = inverse cdf."""
      d = self.loc[p.index, p.columns]
      sgn_arr = np.sign(p.values - 0.5)
      icdf_arr = d.mu - d.scale * sgn_arr * np.log(
          1 - 2 * np.abs(p.values - 0.5)
      )
      return pd.DataFrame(icdf_arr, index=p.index, columns=p.columns)
  
file_path: sktime/sktime/proba/laplace.py
imports: import numpy
  import pandas
  from sktime.proba.base import BaseDistribution
label_desc: Use image node_id 8 for calling the Laplace obj's underlying member method code with example usage: obj.ppf(p) and returns: pd