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.
  
  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]>)) + (reg/2)||w||^2
code: def loss(self, param):
      """Computes the loss represented by this object at w.
  
      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]>)) + (reg/2)||w||^2
      """
      data_loss = np.mean(np.log1p(np.exp(-np.dot(self.data, param))))
      reg_loss = 0.5 * self.reg * np.linalg.norm(param) ** 2
      return data_loss + reg_loss
  
file_path: privacy/research/dp_newton/src/my_logistic_regression.py
imports: import numpy
label_desc: Use image node_id 2 for calling the MyLogisticRegression obj's underlying member method code with example usage: obj.loss(param) and returns: unknown