squareform(X, force='no', checks=True)
v = squareform(X)
Given a square n-by-n symmetric distance matrix X
, v = squareform(X)
returns a n * (n-1) / 2
(i.e. binomial coefficient n choose 2) sized vector :None:None:`v` where $v[{n \choose 2} - {n-i \choose 2} + (j-i-1)]$
is the distance between distinct points i
and j
. If X
is non-square or asymmetric, an error is raised.
X = squareform(v)
Given a n * (n-1) / 2
sized vector v
for some integer n >= 1
encoding distances as described, X = squareform(v)
returns a n-by-n distance matrix X
. The X[i, j]
and X[j, i]
values are set to $v[{n \choose 2} - {n-i \choose 2} + (j-i-1)]$
and all diagonal elements are zero.
In SciPy 0.19.0, squareform
stopped casting all input types to float64, and started returning arrays of the same dtype as the input.
Either a condensed or redundant distance matrix.
As with MATLAB(TM), if force is equal to 'tovector'
or 'tomatrix'
, the input will be treated as a distance matrix or distance vector respectively.
If set to False, no checks will be made for matrix symmetry nor zero diagonals. This is useful if it is known that X - X.T1
is small and diag(X)
is close to zero. These values are ignored any way so they do not disrupt the squareform transformation.
If a condensed distance matrix is passed, a redundant one is returned, or if a redundant one is passed, a condensed distance matrix is returned.
Convert a vector-form distance vector to a square-form distance matrix, and vice-versa.
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.spatial.distance.pdist
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