code: def M_step(self):
  
          
  
          for k in range(self.K):
              self.rho[k] = self.type_prob[:, k].sum() / self.N
  
          for k in range(self.K):
              for m in range(self.M):
                  temp_prob = np.dot(self.type_prob[:, k],
                                     self.feature_counts[:, m])
                  if temp_prob < 1e-99:
                      temp_prob = 1e-99
                  self.mu[k, m] = temp_prob / np.dot(self.type_prob[:, k],
                                                     self.observations)
docs: generate new parameter estimates given updated type distribution