Calculating Euclidian Norm in Pytorch.. Trouble understanding an implementation
I've seen another StackOverflow thread talking about the various implementations for calculating the Euclidian norm and I'm having trouble seeing why/how a particular implementation works.

The code is found in an implementation of the MMD metric: https://github.com/josipd/torch-two-sample/blob/master/torch_two_sample/statistics_diff.py

Here is some beginning boilerplate:

import torch
sample_1, sample_2 = torch.ones((10,2)), torch.zeros((10,2))


Then the next part is where we pick up from the code above.. I'm unsure why the samples are being concatenated together..

sample_12 = torch.cat((sample_1, sample_2), 0)
distances = pdist(sample_12, sample_12, norm=2)


and are then passed to the pdist function:

def pdist(sample_1, sample_2, norm=2, eps=1e-5):
    r"""Compute the matrix of all squared pairwise distances.
    Arguments
    ---------
    sample_1 : torch.Tensor or Variable
        The first sample, should be of shape ``(n_1, d)``.
    sample_2 : torch.Tensor or Variable
        The second sample, should be of shape ``(n_2, d)``.
    norm : float
        The l_p norm to be used.
    Returns
    -------
    torch.Tensor or Variable
        Matrix of shape (n_1, n_2). The [i, j]-th entry is equal to
        ``|| sample_1[i, :] - sample_2[j, :] ||_p``."""


here we get to the meat of the calculation

    n_1, n_2 = sample_1.size(0), sample_2.size(0)
    norm = float(norm)
    if norm == 2.:
        norms_1 = torch.sum(sample_1**2, dim=1, keepdim=True)
        norms_2 = torch.sum(sample_2**2, dim=1, keepdim=True)
        norms = (norms_1.expand(n_1, n_2) +
             norms_2.transpose(0, 1).expand(n_1, n_2))
        distances_squared = norms - 2 * sample_1.mm(sample_2.t())
        return torch.sqrt(eps + torch.abs(distances_squared))


I am at a loss for why the euclidian norm would be calculated this way. Any insight would be greatly appreciated

Let's walk through this block of code step by step. The definition of Euclidean distance, i.e., L2 norm is 



Let's consider the simplest case. We have two samples, 



Sample a has two vectors [a00, a01] and [a10, a11]. Same for sample b. Let first calculate the norm

n1, n2 = a.size(0), b.size(0)  # here both n1 and n2 have the value 2
norm1 = torch.sum(a**2, dim=1)
norm2 = torch.sum(b**2, dim=1)


Now we get



Next, we have norms_1.expand(n_1, n_2) and norms_2.transpose(0, 1).expand(n_1, n_2)



Note that b is transposed. The sum of the two gives norm



sample_1.mm(sample_2.t()), that's the multiplication of the two matrix. 



Therefore, after the operation

distances_squared = norms - 2 * sample_1.mm(sample_2.t())


you get



In the end, the last step is taking the square root of every element in the matrix.
