Quantization error formula

[FlorentF9]

I see in the code that quantization error is defined using a L1 norm (mean absolute error), and not a euclidean distance :

def calculate_quantization_error(self):
        neuron_values = self.codebook.matrix[self.find_bmu(self._data)[0].astype(int)]
        quantization_error = np.mean(np.abs(neuron_values - self._data))
        return quantization_error

Traditionally, quantization error is defined by the mean euclidean distance between samples and BMUs (see papers or other SOM libraries such as Matlab SOM-Toolbox). Shouldn't it be:

def calculate_quantization_error(self):
        neuron_values = self.codebook.matrix[self.find_bmu(self._data)[0].astype(int)]
        quantization_error = np.mean(np.sqrt(np.sum(np.square(neuron_values - self._data), axis=1)))
        return quantization_error