class_docstr: Projects a vector on n first principal components and assigns
  a binary value to each projection depending on the sign. This
  divides the data set by each principal component 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","first","principal","components","and","assigns","a","binary","value","to","each","projection","depending","on","the","sign",".","This","divides","the","data","set","by","each","principal","component","hyperplane","and","generates","a","binary","hash","value","in","string","form",",","which","is","being","used","as","a","bucket","key","for","storage","."]
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.components = config["components"]
  
file_path: NearPy/nearpy/hashes/pcabinaryprojections.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 PCABinaryProjections obj's underlying member method code with example usage: obj.apply_config(config) without return types