class_docstr: Two class LogisticRegression with Prejudice Remover
  
  Parameters
  ----------
  C : float
      regularization parameter
  eta : float
      penalty parameter
  fit_intercept : bool
      use a constant term
  penalty : str
      fixed to 'l2'
  
  Attributes
  ----------
  minor_type : int
      type of likelihood fitting
  `coef_` : array, shape=(n_features)
      parameters for logistic regression model
  `mx_` : array-like, shape(n_sfv, n_nsf)
      mx_[si, :] is a mean rows of X whose corresponding sensitive
      feature is exactly si.
  `n_s_` : int
      the number of sensitive features
  `n_sfv_` : int
      the number of sensitive feature values.
  `c_s_` : ary, shape=(`n_sfv_`)
      the counts of each senstive values in training samples
  `n_features_` : int
      the number of non-sensitive features including a bias constant
  `n_samples_` : int
      the number of samples
  `f_loss_` : float
      the value of loss function after training
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docstr: predict classes
  
  Parameters
  ----------
  X : array, shape=(n_samples, n_features)
      feature vectors of samples
  
  Returns
  -------
  y : array, shape=(n_samples), dtype=int
      array of predicted class
docstr_tok: ["predict","classes","Parameters","--","--","--","--","--","X",":","array",",","shape=","(","n_samples",",","n_features",")","feature","vectors","of","samples","Returns","--","--","--","-","y",":","array",",","shape=","(","n_samples",")",",","dtype=int","array","of","predicted","class"]
code: def predict(self, X):
      """predict classes
  
      Parameters
      ----------
      X : array, shape=(n_samples, n_features)
          feature vectors of samples
  
      Returns
      -------
      y : array, shape=(n_samples), dtype=int
          array of predicted class
      """
  
      return np.argmax(self.predict_proba(X), 1)
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import unicode_literals
  import logging
  import numpy
  from scipy.optimize import fmin_cg
  from sklearn.linear_model import LogisticRegression
  from sklearn.base import BaseEstimator, ClassifierMixin
label_desc: Use image node_id 2 for calling the LRwPR obj's underlying member method code with example usage: obj.predict(X) and returns: np