Pdist
Arguments
- self
NA input tensor of shape \(N \times M\).
- p
NA p value for the p-norm distance to calculate between each vector pair \(\in [0, \infty]\).
pdist(input, p=2) -> Tensor
Computes the p-norm distance between every pair of row vectors in the input.
This is identical to the upper triangular portion, excluding the diagonal, of
torch_norm(input[:, NULL] - input, dim=2, p=p). This function will be faster
if the rows are contiguous.
If input has shape \(N \times M\) then the output will have shape \(\frac{1}{2} N (N - 1)\).
This function is equivalent to scipy.spatial.distance.pdist(input, 'minkowski', p=p) if \(p \in (0, \infty)\). When \(p = 0\) it is
equivalent to scipy.spatial.distance.pdist(input, 'hamming') * M.
When \(p = \infty\), the closest scipy function is
scipy.spatial.distance.pdist(xn, lambda x, y: np.abs(x - y).max()).