def fit(n, m, X, y):
  theta = np.zeros(n+1)
  for iter in range(iterations):
    for i in range(len(X)):
      h = sum([X[i][j] * theta[j] for j in X[i]])
      # konstantny clen
      h += theta[n];
      for tr  in X[i]:
          theta[tr] = theta[tr] - alpha * (y[i] - h) * X[i][tr];
      #konstantny clen
      theta[n] = theta[n] - alpha * (y[i] - h);

  train_error = 0
  for i in range(len(X)):
    h = sum([X[i][j] * theta[j] for j in X[i]])
    #konstantny clen
    h=+ theta[n];
    train_error += (h - y[i])**2
  train_error/=(2*m)

  return theta,train_error

n, m, X, y = load_data(sys.argv[1])
theta, train_error = fit(n, m, X, y)

print "Training error", train_error