class_docstr: return a logistic regression problem
  
  There is a dataset consisting of features (vectors of norm <=1)
  and labels (+1,-1), represented as a numpy array.
  There is also an L2 regularizer.
docstr: Computes the loss represented by this object at w without regularizer.
  
  If X,y is the data and reg is the regularizer, then the loss is
  (1/n)sum_i^n log(1+exp(-<w,X[i,:]*y[i]>))
code: def loss_wor(self, param):
      """Computes the loss represented by this object at w without regularizer.
  
      If X,y is the data and reg is the regularizer, then the loss is
      (1/n)sum_i^n log(1+exp(-<w,X[i,:]*y[i]>))
      """
      data_loss = np.mean(np.log1p(np.exp(-np.dot(self.data, param))))
      return data_loss
  
file_path: privacy/research/dp_newton/src/my_logistic_regression.py
imports: import numpy
label_desc: Use image node_id 3 for calling the MyLogisticRegression obj's underlying member method code with example usage: obj.loss_wor(param) and returns: data_loss