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: 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/randombinaryprojections.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 RandomBinaryProjections obj's underlying member method code with example usage: obj.reset(dim) without return types