class_docstr: Helpfer function to run different algorithms and store the results
class_docstr_tok: ["Helpfer","function","to","run","different","algorithms","and","store","the","results"]
docstr: method to run different algorithms and store different stats
docstr_tok: ["method","to","run","different","algorithms","and","store","different","stats"]
code: def run_algs(self):
      """method to run different algorithms and store different stats"""
      for rep in range(self.num_rep):
          for alg_name, alg_update_rule in self.algs_dict.items():
              self.compare_algs.add_algo(alg_update_rule, alg_name)
          losses_dict = self.compare_algs.loss_vals()
          gradnorm_dict = self.compare_algs.gradnorm_vals()
          accuracy_dict = self.compare_algs.accuracy_vals()
          wall_clock_dict = self.compare_algs.wall_clock_alg()
          if rep == 0:
              self.losses = losses_dict
              self.gradnorm = gradnorm_dict
              self.accuracy = accuracy_dict
              self.wall_clock = wall_clock_dict
          else:
              for alg in self.losses:
                  self.losses[alg].extend(losses_dict[alg])
                  self.gradnorm[alg].extend(gradnorm_dict[alg])
                  self.accuracy[alg].extend(accuracy_dict[alg])
                  self.wall_clock[alg].extend(wall_clock_dict[alg])
  
file_path: privacy/research/dp_newton/src/run.py
imports: import argparse
  import json
  from dataset_loader import Mydatasets
  from my_logistic_regression import MyLogisticRegression
  import numpy
  from opt_algs import CompareAlgs, DoubleNoiseMech, gd_priv, private_newton
  from scipy.optimize import fsolve
label_desc: Use image node_id 2 for calling the RunReleaseStats obj's underlying member method code with example usage: obj.run_algs() without return types