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torch_pdist(self, p = 2L)



NA input tensor of shape \(N \times M\).


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()).