docstr: Learn model from training data.
  
  Parameters
  ----------
  X : {array-like, sparse matrix}, shape = [n_samples, n_features]
      Training vectors, where n_samples is the number of samples and
      n_features is the number of features.
  init_params : bool (default: True)
      Re-initializes model parameters prior to fitting.
      Set False to continue training with weights from
      a previous model fitting.
  
  Returns
  -------
  self : object
docstr_tok: ["Learn","model","from","training","data",".","Parameters","--","--","--","--","--","X",":","{","array-like",",","sparse","matrix","}",",","shape","=","[","n_samples",",","n_features","]","Training","vectors",",","where","n_samples","is","the","number","of","samples","and","n_features","is","the","number","of","features",".","init_params",":","bool","(","default",":","True",")","Re-initializes","model","parameters","prior","to","fitting",".","Set","False","to","continue","training","with","weights","from","a","previous","model","fitting",".","Returns","--","--","--","-","self",":","object"]
code: def fit(self, X, init_params=True):
      """Learn model from training data.
  
      Parameters
      ----------
      X : {array-like, sparse matrix}, shape = [n_samples, n_features]
          Training vectors, where n_samples is the number of samples and
          n_features is the number of features.
      init_params : bool (default: True)
          Re-initializes model parameters prior to fitting.
          Set False to continue training with weights from
          a previous model fitting.
  
      Returns
      -------
      self : object
  
      """
      self._is_fitted = False
      self._check_arrays(X=X)
      if hasattr(self, "self.random_seed") and self.random_seed:
          self._rgen = np.random.RandomState(self.random_seed)
      self._init_time = time()
      self._fit(X=X, init_params=init_params)
      self._is_fitted = True
      return self
  
imports: from time import time
  import numpy
label_desc: Use image node_id 2 for calling the _Cluster obj's underlying member method code with example usage: obj.fit(X, init_params) and returns: self