class_docstr: Simple Nested sampler for reference.
class_docstr_tok: ["Simple","Nested","sampler","for","reference","."]
docstr: Give summary of marginal likelihood and parameters.
docstr_tok: ["Give","summary","of","marginal","likelihood","and","parameters","."]
code: def print_results(self):
      """Give summary of marginal likelihood and parameters."""
      print()
      print("logZ = %(logz).3f +- %(logzerr).3f" % self.results)
  
      print()
      for i, p in enumerate(self.paramnames + self.derivedparamnames):
          v = self.results["samples"][:, i]
          sigma = v.std()
          med = v.mean()
          if sigma == 0:
              i = 3
          else:
              i = max(0, int(-np.floor(np.log10(sigma))) + 1)
          fmt = "%%.%df" % i
          fmts = "\t".join(["    %-20s" + fmt + " +- " + fmt])
          print(fmts % (p, med, sigma))
  
imports: from __future__ import print_function, division
  import os
  import sys
  import csv
  import json
  import operator
  import time
  import warnings
  from numpy import log, exp, logaddexp
  import numpy
  from .utils import create_logger, make_run_dir, resample_equal, vol_prefactor, vectorize, listify
  from .utils import is_affine_transform, normalised_kendall_tau_distance, distributed_work_chunk_size
  from ultranest.mlfriends import MLFriends, AffineLayer, ScalingLayer, find_nearby, WrappingEllipsoid, RobustEllipsoidRegion
  from .store import HDF5PointStore, TextPointStore, NullPointStore
  from .viz import get_default_viz_callback
  from .ordertest import UniformOrderAccumulator
  from .netiter import PointPile, SingleCounter, MultiCounter, BreadthFirstIterator, TreeNode, count_tree_between, find_nodes_before, logz_sequence
  from .netiter import dump_tree, combine_results
  from .hotstart import get_auxiliary_contbox_parameterization
label_desc: Use image node_id 3 for calling the NestedSampler obj's underlying member method code with example usage: obj.print_results() without return types