new_contents: import numpy as np
  from sklearn.preprocessing import PolynomialFeatures
  from sklearn import linear_model
  
  if __name__ == "__main__":
      (f, n) = map(int, raw_input().split())
      x = []
      y = []
      poly = PolynomialFeatures(degree = 4)
      for i in range(n):
          v = map(float, raw_input().split())
          x.append(poly.fit_transform(v[:-1])[0])
          y.append(v[-1])
          
      clf = linear_model.BayesianRidge()
      clf.fit(x, y)
      
      tc = int(raw_input())
      for i in range(tc):
          v = map(float, raw_input().split())
          print clf.predict(poly.transform(v))[0]
      
subject: add predicting-office-space-price