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: Resets / Initializes the hash for the specified dimension. 
docstr_tok: ["Resets","\/","Initializes","the","hash","for","the","specified","dimension","."]
code: def reset(self, dim):
      """Resets / Initializes the hash for the specified dimension."""
      if self.dim != dim:
          self.dim = dim
          self.normals = self.rand.randn(self.projection_count, dim)
  
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 2 for calling the RandomDiscretizedProjections obj's underlying member method code with example usage: obj.reset(dim) without return types