class_docstr: Base class of Hawkes learners
  
  Parameters
  ----------
  tol : `float`, default=1e-5
      The tolerance of the solver (iterations stop when the stopping
      criterion is below it). If not reached the solver does ``max_iter``
      iterations
  
  max_iter : `int`, default=100
      Maximum number of iterations of the solver
  
  verbose : `bool`, default=False
      If `True`, we verbose things, otherwise the solver does not
      print anything (but records information in history anyway)
  
  print_every : `int`, default=10
      Print history information when ``n_iter`` (iteration number) is
      a multiple of ``print_every``
  
  record_every : `int`, default=10
      Record history information when ``n_iter`` (iteration number) is
      a multiple of ``record_every``
  
  Notes
  -----
  This class should be not used by end-users, it is intended for
  development only.
docstr: Set the corresponding realization(s) of the process.
  
  Parameters
  ----------
  events : `list` of `list` of `np.ndarray`
      List of Hawkes processes realizations.
      Each realization of the Hawkes process is a list of n_node for
      each component of the Hawkes. Namely `events[i][j]` contains a
      one-dimensional `numpy.array` of the events' timestamps of
      component j of realization i.
      If only one realization is given, it will be wrapped into a list
  
  end_times : `np.ndarray` or `float`, default = None
      List of end time of all hawkes processes that will be given to the
      model. If None, it will be set to each realization's latest time.
      If only one realization is provided, then a float can be given.
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code: def fit(self, events, end_times=None):
      """Set the corresponding realization(s) of the process.
  
      Parameters
      ----------
      events : `list` of `list` of `np.ndarray`
          List of Hawkes processes realizations.
          Each realization of the Hawkes process is a list of n_node for
          each component of the Hawkes. Namely `events[i][j]` contains a
          one-dimensional `numpy.array` of the events' timestamps of
          component j of realization i.
          If only one realization is given, it will be wrapped into a list
  
      end_times : `np.ndarray` or `float`, default = None
          List of end time of all hawkes processes that will be given to the
          model. If None, it will be set to each realization's latest time.
          If only one realization is provided, then a float can be given.
      """
      self._set("_fitted", True)
      self._set("_end_times", end_times)
      self._set_data(events)
      return self
  
file_path: tick/tick/hawkes/inference/base/learner_hawkes_noparam.py
imports: import numpy
  from tick.solver.base import Solver
label_desc: Use image node_id 3 for calling the LearnerHawkesNoParam obj's underlying member method code with example usage: obj.fit(events, end_times) and returns: self