cr.sparse.pdist_sqr_l2_cw¶
- cr.sparse.pdist_sqr_l2_cw(A)[source]¶
Computes the pairwise squared distances between points in A where each point is a column vector
- Parameters
A (jax.numpy.ndarray) – A set of N K-dimensional points (column-wise)
- Returns
An NxN matrix D of squared euclidean distances between points in A
- Return type
Let the ambient space of points be \(\mathbb{F}^K\).
\(A\) contains the points \(a_i\) with \(1 \leq i \leq N\) and each point maps to a column of \(A\).
Then the distance matrix \(D\) is of size \(N \times N\) and consists of:
(1)¶\[d_{i, j} = \| a_i - a_j \|_2^2 = \langle a_i - a_j , a_i - a_j \rangle\]
