text: import numpy as np
  
  from predict import sigmoid
  
  
  def predictOneVsAll(all_theta, X):
      """ will return a vector of predictions
          for each example in the matrix X. Note that X contains the examples in
          rows. all_theta is a matrix where the i-th row is a trained logistic
          regression theta vector for the i-th class.
      """
  
      m = X.shape[0]
  
      p = np.zeros((m, 1))
      # Add ones to the X data matrix
      X = np.column_stack((np.ones((m, 1)), X))
      class_prediction = (all_theta @ X.T).T
  
      for i in range(m):
          p[i] = np.argmax(class_prediction[i])
  
      return p    # add 1 to offset index of maximum in A row
  