class_docstr: Class to run multiple iterative algorithms and compare the results.
docstr: Initialize the problem.
  
  lr = an instance of MyLogisticRegression
  dataset = dataset in the format of (features,label)
  optimal_w = optimal minimizer of logistic loss on dataset without privacy
  pb = hyperparameters
docstr_tok: ["Initialize","the","problem",".","lr","=","an","instance","of","MyLogisticRegression","dataset","=","dataset","in","the","format","of","(","features",",","label",")","optimal_w","=","optimal","minimizer","of","logistic","loss","on","dataset","without","privacy","pb","=","hyperparameters"]
code: def __init__(self, lrp, optimal_w, hyper_dict):
      """Initialize the problem.
  
      lr = an instance of MyLogisticRegression
      dataset = dataset in the format of (features,label)
      optimal_w = optimal minimizer of logistic loss on dataset without privacy
      pb = hyperparameters
      """
      self.w_opt = optimal_w
      self.lrp = lrp
      self.iters = hyper_dict["num_iteration"]
      self.hyper_params = hyper_dict
      self.clock_time = []
      self.params = []
      self.names = []
  
imports: import time
  from my_logistic_regression import MyLogisticRegression
  import numpy
label_desc: Use image node_id 1 to create a new CompareAlgs object  with example: obj = CompareAlgs(lrp, optimal_w, hyper_dict)