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.
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docstr: Creates projection_count random vectors, that are used for projections
  thus working as normals of random hyperplanes. Each random vector /
  hyperplane will result in one bit of hash.
  
  So if you for example decide to use projection_count=10, the bucket
  keys will have 10 digits and will look like '1010110011'.
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code: def __init__(self, hash_name, projection_count, rand_seed=None):
      """
      Creates projection_count random vectors, that are used for projections
      thus working as normals of random hyperplanes. Each random vector /
      hyperplane will result in one bit of hash.
  
      So if you for example decide to use projection_count=10, the bucket
      keys will have 10 digits and will look like '1010110011'.
      """
      super(RandomBinaryProjections, self).__init__(hash_name)
      self.projection_count = projection_count
      self.dim = None
      self.normals = None
      self.rand = numpy.random.RandomState(rand_seed)
      self.normals_csr = None
  
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 1 to create a new RandomBinaryProjections object from inherited base classes: LSHash with example: obj = RandomBinaryProjections(hash_name, projection_count, rand_seed)