parameters: (self, X)
return_statement: return X
docstring: Apply the inverse power transformation using the fitted lambdas.
  
          The inverse of the Box-Cox transformation is given by::
  
              if lambda_ == 0:
                  X = exp(X_trans)
              else:
                  X = (X_trans * lambda_ + 1) ** (1 / lambda_)
  
          The inverse of the Yeo-Johnson transformation is given by::
  
              if X >= 0 and lambda_ == 0:
                  X = exp(X_trans) - 1
              elif X >= 0 and lambda_ != 0:
                  X = (X_trans * lambda_ + 1) ** (1 / lambda_) - 1
              elif X < 0 and lambda_ != 2:
                  X = 1 - (-(2 - lambda_) * X_trans + 1) ** (1 / (2 - lambda_))
              elif X < 0 and lambda_ == 2:
                  X = 1 - exp(-X_trans)
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
              The transformed data.
  
          Returns
          -------
          X : array-like, shape (n_samples, n_features)
              The original data
docstring_summary: Apply the inverse power transformation using the fitted lambdas.
docstring_tokens: Apply the inverse power transformation using the fitted lambdas .
function: def inverse_transform(self, X):
          """Apply the inverse power transformation using the fitted lambdas.
  
          The inverse of the Box-Cox transformation is given by::
  
              if lambda_ == 0:
                  X = exp(X_trans)
              else:
                  X = (X_trans * lambda_ + 1) ** (1 / lambda_)
  
          The inverse of the Yeo-Johnson transformation is given by::
  
              if X >= 0 and lambda_ == 0:
                  X = exp(X_trans) - 1
              elif X >= 0 and lambda_ != 0:
                  X = (X_trans * lambda_ + 1) ** (1 / lambda_) - 1
              elif X < 0 and lambda_ != 2:
                  X = 1 - (-(2 - lambda_) * X_trans + 1) ** (1 / (2 - lambda_))
              elif X < 0 and lambda_ == 2:
                  X = 1 - exp(-X_trans)
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
              The transformed data.
  
          Returns
          -------
          X : array-like, shape (n_samples, n_features)
              The original data
          """
          check_is_fitted(self)
          X = self._check_input(X, check_shape=True)
  
          if self.standardize:
              X = self._scaler.inverse_transform(X)
  
          inv_fun = {'box-cox': self._box_cox_inverse_tranform,
                     'yeo-johnson': self._yeo_johnson_inverse_transform
                     }[self.method]
          for i, lmbda in enumerate(self.lambdas_):
              with np.errstate(invalid='ignore'):  # hide NaN warnings
                  X[:, i] = inv_fun(X[:, i], lmbda)
  
          return X
function_tokens: def inverse_transform ( self , X ) : check_is_fitted ( self ) X = self . _check_input ( X , check_shape = True ) if self . standardize : X = self . _scaler . inverse_transform ( X ) inv_fun = { 'box-cox' : self . _box_cox_inverse_tranform , 'yeo-johnson' : self . _yeo_johnson_inverse_transform } [ self . method ] for i , lmbda in enumerate ( self . lambdas_ ) : with np . errstate ( invalid = 'ignore' ) : # hide NaN warnings X [ : , i ] = inv_fun ( X [ : , i ] , lmbda ) return X