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: Creates projection_count random vectors, that are used for projections.
  Each random vector will result in one discretized coordinate.
  
  So if you for example decide to use projection_count=3, the bucket
  keys will have 3 coordinates and look like '14_4_1' or '-4_18_-1'.
docstr_tok: ["Creates","projection_count","random","vectors",",","that","are","used","for","projections",".","Each","random","vector","will","result","in","one","discretized","coordinate",".","So","if","you","for","example","decide","to","use","projection_count=3",",","the","bucket","keys","will","have","3","coordinates","and","look","like","'14_4_1","'","or","'-4_18_-1","'","."]
code: def __init__(
      self, hash_name, projection_count, bin_width, rand_seed=None
  ):
      """
      Creates projection_count random vectors, that are used for projections.
      Each random vector will result in one discretized coordinate.
  
      So if you for example decide to use projection_count=3, the bucket
      keys will have 3 coordinates and look like '14_4_1' or '-4_18_-1'.
      """
      super(RandomDiscretizedProjections, self).__init__(hash_name)
      self.projection_count = projection_count
      self.dim = None
      self.normals = None
      self.bin_width = bin_width
      self.rand = numpy.random.RandomState(rand_seed)
      self.normals_csr = None
  
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 1 to create a new RandomDiscretizedProjections object from inherited base classes: LSHash with example: obj = RandomDiscretizedProjections(hash_name, projection_count, bin_width, rand_seed)