docstr: Update latent factors according to the stochastic gradient descent update rule
  
  :param user: User
  :type user: int
  
  :param u: User ID from self.users
  :type u: int
  
  :return: error and count
docstr_tok: ["Update","latent","factors","according","to","the","stochastic","gradient","descent","update","rule",":","param","user",":","User",":","type","user",":","int",":","param","u",":","User","ID","from","self.users",":","type","u",":","int",":","return",":","error","and","count"]
code: def update_factors(self, user, u):
      """
      Update latent factors according to the stochastic gradient descent update rule
  
      :param user: User
      :type user: int
  
      :param u: User ID from self.users
      :type u: int
  
      :return: error and count
  
      """
      c, e = 0, 0
      for item in self.train_set["items_seen_by_user"].get(user, []):
          i = self.item_to_item_id[item]
  
          rui = self._predict(u, i)
          error = self.train_set["feedback"][user][item] - rui
          b = np.array(self.q[i])
  
          # update factors
          self.p[u] += self.learn_rate * (
              error * b - self.delta * self.p[u]
          )
          self.q[i] += self.learn_rate * (
              error * self.p[u] - self.delta * self.q[i]
          )
          self.b[u] += self.learn_rate * (
              error - self.delta * self.b[u]
          )
          self.c[i] += self.learn_rate * (
              error - self.delta * self.c[i]
          )
          c += 1
          e += error**2
  
      return c, e
  
file_path: CaseRecommender/caserec/recommenders/rating_prediction/user_nsvd1.py
imports: import numpy
  from caserec.recommenders.rating_prediction.base_nsvd1 import BaseNSVD1
  from caserec.utils.extra_functions import timed
  from caserec.utils.process_data import ReadFile
label_desc: Use image node_id 4 for calling the UserNSVD1 obj's underlying member method code with example usage: obj.update_factors(user, u) and returns: c,  e