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 __init__(self, n_iter=10, shuffle=True, random_state=None):
      self.n_iter = n_iter
      self.shuffle = shuffle
      self.random_state = random_state
  
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 1 to create a new PRank object from inherited base classes: _BasePRank with example: obj = PRank(n_iter, shuffle, random_state)