class_docstr: Class for event segmentation of continuous fMRI data
  
  Parameters
  ----------
  n_events: int
      Number of segments to learn
  
  step_var: Callable[[int], float] : default 4 * (0.98 ** (step - 1))
      The Gaussian variance to use during fitting, as a function of the
      number of steps. Should decrease slowly over time.
  
  n_iter: int, default: 500
      Maximum number of steps to run during fitting
  
  event_chains: ndarray with length = n_events
      Array with unique value for each separate chain of events, each linked
      in the order they appear in the array
  
  split_merge: bool, default: False
      Determines whether merge/split proposals are used during fitting with
      fit(). This can improve fitting performance when events are highly
      uneven in size, but requires additional time
  
  split_merge_proposals: int, default: 1
      Number of merges and splits to consider at each step. Computation time
      scales as O(proposals^2) so this should usually be a small value
  
  Attributes
  ----------
  p_start, p_end: length n_events+1 ndarray
      initial and final prior distributions over events
  
  P: n_events+1 by n_events+1 ndarray
      HMM transition matrix
  
  ll_ : ndarray with length = number of training datasets
      Log-likelihood for training datasets over the course of training
  
  segments_:  list of (time by event) ndarrays
      Learned (soft) segmentation for training datasets
  
  event_var_ : float
      Gaussian variance at the end of learning
  
  event_pat_ : voxel by event ndarray
      Learned mean patterns for each event
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docstr: Set HMM event patterns manually
  
  Rather than fitting the event patterns automatically using fit(), this
  function allows them to be set explicitly. They can then be used to
  find corresponding events in a new dataset, using find_events().
  
  Parameters
  ----------
  event_pat: voxel by event ndarray
docstr_tok: ["Set","HMM","event","patterns","manually","Rather","than","fitting","the","event","patterns","automatically","using","fit","(",")",",","this","function","allows","them","to","be","set","explicitly",".","They","can","then","be","used","to","find","corresponding","events","in","a","new","dataset",",","using","find_events","(",")",".","Parameters","--","--","--","--","--","event_pat",":","voxel","by","event","ndarray"]
code: def set_event_patterns(self, event_pat):
      """Set HMM event patterns manually
  
      Rather than fitting the event patterns automatically using fit(), this
      function allows them to be set explicitly. They can then be used to
      find corresponding events in a new dataset, using find_events().
  
      Parameters
      ----------
      event_pat: voxel by event ndarray
      """
      if event_pat.shape[1] != self.n_events:
          raise ValueError(
              (
                  "Number of columns of event_pat must match "
                  "number of events"
              )
          )
      self.event_pat_ = event_pat.copy()
  
imports: import numpy
  from scipy import stats
  import logging
  import copy
  from sklearn.base import BaseEstimator
  from sklearn.utils.validation import check_is_fitted, check_array
  from sklearn.exceptions import NotFittedError
  import itertools
  from .None import _utils
label_desc: Use image node_id 8 for calling the EventSegment obj's underlying member method code with example usage: obj.set_event_patterns(event_pat) without return types