class_docstr: Online algorithm for learning an ordinal regression model.
  
  Parameters
  ----------
  n_iter : int
      Number of iterations to run.
  
  shuffle : boolean
      Whether to shuffle data.
  
  random_state : RandomState or int
      The seed of the pseudo random number generator to use.
  
  Attributes
  ----------
  coef_ : array, shape=[n_features]
      Estimated weights.
  
  thresholds_ : array, shape=[n_classes]
      Estimated thresholds.
  
  References
  ----------
  Pranking with Ranking
  Koby Crammer, Yoram Singer
  NIPS 2001
code: def predict(self, X):
      n_samples = X.shape[0]
      dot = safe_sparse_dot(X, self.coef_)
      out = np.zeros(n_samples, dtype=np.int32)
      _prank_predict(dot, self.thresholds_, len(self.classes_), out)
      return self._label_encoder.inverse_transform(out)
  
imports: import numpy
  from sklearn.preprocessing import LabelEncoder
  from sklearn.utils.extmath import safe_sparse_dot
  from sklearn.metrics.pairwise import pairwise_kernels
  from .base import BaseEstimator
  from .dataset_fast import get_dataset
  from .prank_fast import _prank_fit
  from .prank_fast import _prank_fit_kernel
  from .prank_fast import _prank_predict
label_desc: Use image node_id 3 for calling the PRank obj's underlying member method code with example usage: obj.predict(X) and returns: self