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.
  
  Almost same as RandomBinaryProjections. Difference is that
  this hash constructs a binary tree in order to guarantee to
  always be able to retrieve N results. Use minimum_result_size
  to set this N.
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",".","Almost","same","as","RandomBinaryProjections",".","Difference","is","that","this","hash","constructs","a","binary","tree","in","order","to","guarantee","to","always","be","able","to","retrieve","N","results",".","Use","minimum_result_size","to","set","this","N","."]
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.projection_count = config["projection_count"]
      self.normals = config["normals"]
      self.tree_root = config["tree_root"]
      self.minimum_result_size = config["minimum_result_size"]
  
file_path: NearPy/nearpy/hashes/randombinaryprojectiontree.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 RandomBinaryProjectionTree obj's underlying member method code with example usage: obj.apply_config(config) without return types