class_docstr: A data interface for private data with meta information.
class_docstr_tok: ["A","data","interface","for","private","data","with","meta","information","."]
docstr: Gets the min/max value of features in normalized or de-normalized form.
docstr_tok: ["Gets","the","min\/max","value","of","features","in","normalized","or","de-normalized","form","."]
code: def get_minx_maxx(self, normalized=True):
      """Gets the min/max value of features in normalized or de-normalized form."""
      minx = np.array([[0.0] * len(self.ohe_encoded_feature_names)])
      maxx = np.array([[1.0] * len(self.ohe_encoded_feature_names)])
  
      if normalized:
          return minx, maxx
      else:
          for idx, feature_name in enumerate(
              self.continuous_feature_names
          ):
              minx[0][idx] = self.permitted_range[feature_name][0]
              maxx[0][idx] = self.permitted_range[feature_name][1]
          return minx, maxx
  
imports: import collections
  import logging
  import sys
  from collections import defaultdict
  import numpy
  import pandas
  from sklearn.preprocessing import LabelEncoder
  from dice_ml.data_interfaces.base_data_interface import _BaseData
label_desc: Use image node_id 7 for calling the PrivateData obj's underlying member method code with example usage: obj.get_minx_maxx(normalized) and returns: minx,  maxx, minx,  maxx