class_docstr: Projects a vector on n random vectors and assigns
  a discrete value to each projection depending on the location on the
  random vector using a bin width.
class_docstr_tok: ["Projects","a","vector","on","n","random","vectors","and","assigns","a","discrete","value","to","each","projection","depending","on","the","location","on","the","random","vector","using","a","bin","width","."]
docstr: Returns pickle-serializable configuration struct for storage.
docstr_tok: ["Returns","pickle-serializable","configuration","struct","for","storage","."]
code: def get_config(self):
      """
      Returns pickle-serializable configuration struct for storage.
      """
      # Fill this dict with config data
      return {
          "hash_name": self.hash_name,
          "dim": self.dim,
          "bin_width": self.bin_width,
          "projection_count": self.projection_count,
          "normals": self.normals,
      }
  
file_path: NearPy/nearpy/hashes/randomdiscretizedprojections.py
imports: import numpy
  import scipy
  import scipy.sparse
  from nearpy.hashes.lshash import LSHash
label_desc: Use image node_id 4 for calling the RandomDiscretizedProjections obj's underlying member method code with example usage: obj.get_config() and returns: dict