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: Applies config
docstr_tok: ["Applies","config"]
code: def apply_config(self, config):
      """
      Applies config
      """
      self.hash_name = config["hash_name"]
      self.dim = config["dim"]
      self.bin_width = config["bin_width"]
      self.projection_count = config["projection_count"]
      self.normals = config["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 5 for calling the RandomDiscretizedProjections obj's underlying member method code with example usage: obj.apply_config(config) without return types