class_docstr: Projects a vector on n first principal components and assigns
  a discrete value to each projection depending on the bin.
class_docstr_tok: ["Projects","a","vector","on","n","first","principal","components","and","assigns","a","discrete","value","to","each","projection","depending","on","the","bin","."]
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.bin_width = config["bin_width"]
      self.projection_count = config["projection_count"]
      self.components = config["components"]
  
file_path: NearPy/nearpy/hashes/pcadiscretizedprojections.py
imports: import numpy
  import scipy
  import scipy.sparse
  from nearpy.hashes.lshash import LSHash
  from nearpy.utils import numpy_array_from_list_or_numpy_array, perform_pca
label_desc: Use image node_id 5 for calling the PCADiscretizedProjections obj's underlying member method code with example usage: obj.apply_config(config) without return types