text: import numpy as np
  import pandas as pd
  import argparse
  import mmd
  from experiments import experiments
  import dp_penalty
  import dp_penalty_minibatch
  import hmc
  import dp_barker
  import params
  
  parser = argparse.ArgumentParser()
  parser.add_argument("algorithm", type=str)
  parser.add_argument("params", type=str)
  parser.add_argument("experiment", type=str)
  parser.add_argument("epsilon", type=float)
  parser.add_argument("index", type=int)
  parser.add_argument("output", type=str)
  args = parser.parse_args()
  
  algorithms = {
      "dpps": dp_penalty.dp_penalty,
      "dppa": dp_penalty.dp_penalty,
      "mdpps": dp_penalty_minibatch.dp_penalty_minibatch,
      "mdppa": dp_penalty_minibatch.dp_penalty_minibatch,
      "barker": dp_barker.dp_barker,
      "hmc": hmc.hmc
  }
  
  exp = experiments[args.experiment]
  problem = exp.get_problem()
  
  # Set the seed for the algorithm to be different for each algorithm
  np.random.seed(
      (int((
          args.algorithm + args.experiment + str(args.epsilon)
      ).encode("utf8").hex(), 16) + args.index) % 2**32
  )
  
  posterior = problem.true_posterior
  dim = problem.dim
  n, data_dim = problem.data.shape
  epsilon = args.epsilon
  delta = 0.1 / n
  
  par = params.__dict__[args.params].params
  res = algorithms[args.algorithm](
      problem, problem.get_start_point(args.index),
      epsilon, delta, par, verbose=False
  )
  
  result = pd.DataFrame({
      "epsilon": [epsilon],
      "delta": [delta],
      "experiment": [args.experiment],
      "dim": [dim],
      "tempering": [problem.temp_scale != 1],
      "i": [args.index],
      "algo": [args.algorithm],
      "acceptance": [res.acceptance],
      "r_clipping": [res.clipped_r],
      "grad_clipping": [res.clipped_grad],
      "mmd": [res.mmd],
      "mean error": [res.mean_error],
      "cov error": [res.cov_error]
  })
  result.to_csv(args.output, header=False, index=False)
  