class_docstr: Projects a vector on n random hyperplane normals and assigns
  a binary value to each projection depending on the sign. This
  divides the data set by each hyperplane and generates a binary
  hash value in string form, which is being used as a bucket key
  for storage.
class_docstr_tok: ["Projects","a","vector","on","n","random","hyperplane","normals","and","assigns","a","binary","value","to","each","projection","depending","on","the","sign",".","This","divides","the","data","set","by","each","hyperplane","and","generates","a","binary","hash","value","in","string","form",",","which","is","being","used","as","a","bucket","key","for","storage","."]
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,
          "projection_count": self.projection_count,
          "normals": self.normals,
      }
  
file_path: NearPy/nearpy/hashes/randombinaryprojections.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 RandomBinaryProjections obj's underlying member method code with example usage: obj.get_config() and returns: dict