self.Proto[i, :] = self.momentum * feature + self.Proto[i, :] * (1 - self.momentum)

This line of code updates the i-th row of the self.Proto matrix with a weighted average of the previous value and the current features vector.

The momentum parameter controls the weight given to the previous value, while (1 - momentum) controls the weight given to the current features vector.

This update rule is commonly used in stochastic gradient descent optimization algorithms to smooth out the updates and prevent oscillations in the training process. By gradually incorporating the new information while retaining some of the previous values, the algorithm can converge to a more stable solution.