day9_1

# In this challenge, we practice using multiple linear regression
# Credit for the solution goes to "pmtatar" on the hackerrank forums  

import numpy as np

def solve(y, x, x_pred):
  # x transpose
  x_dash  = x.T
  # product of x_dash and x
  X       = x_dash.dot(x)
  # inverse of X
  X_inv   = np.linalg.inv(X)
  # producet of X_inv and x_dash
  X_final = X_inv.dot(x_dash)
  # product of X_final and y i.e B
  B       = X_final.dot(y)
  # calculate the y_pred
  y_pred  = x_pred.dot(B)
  return y_pred

def main():
  m, n = map(int, input().strip().split())
  y = []; x = []; x_pred = []
  for _ in range(n):
    *features, y_val = map(float, input().strip().split())
    x.append([1] + features)
    y.append(y_val)

  for _ in range(int(input())):
    features = list(map(float, input().strip().split()))
    x_pred.append([1] + features)

  y = np.array(y)
  x = np.array(x)
  x_pred = np.array(x_pred)
  answer = solve(y, x, x_pred)
  
  for num in answer:
    print(round(num, 2))

if __name__ == "__main__":
  main()