def derivative(self, x: np.ndarray, y: np.array, est: Predictor):
'''returns gradient (vector)'''
m = len(y)
updates = np.zeros(self.dim, dtype=float)
for j in range(len(updates)):
updates[j] = est.lr() * sum([(est.predict(x[i]) - y[i]) * x[i][j]
for i in range(m)]) / m
return updates
