compute_dist¶

hyppo.tools.
compute_dist
(x, y, metric='euclidean', workers=1, **kwargs)¶ Distance matrices for the inputs.
 Parameters
x,y (
ndarray
)  Input data matrices.x
andy
must have the same number of samples. That is, the shapes must be(n, p)
and(n, q)
where n is the number of samples and p and q are the number of dimensions. Alternatively,x
andy
can be distance matrices, where the shapes must both be(n, n)
.metric (
str
,callable
, orNone
, default:"euclidean"
)  A function that computes the distance among the samples within each data matrix. Valid strings formetric
are, as defined insklearn.metrics.pairwise_distances
,From scikitlearn: [
"euclidean"
,"cityblock"
,"cosine"
,"l1"
,"l2"
,"manhattan"
] See the documentation forscipy.spatial.distance
for details on these metrics.From scipy.spatial.distance: [
"braycurtis"
,"canberra"
,"chebyshev"
,"correlation"
,"dice"
,"hamming"
,"jaccard"
,"kulsinski"
,"mahalanobis"
,"minkowski"
,"rogerstanimoto"
,"russellrao"
,"seuclidean"
,"sokalmichener"
,"sokalsneath"
,"sqeuclidean"
,"yule"
] See the documentation forscipy.spatial.distance
for details on these metrics.
Set to
None
or"precomputed"
ifx
andy
are already distance matrices. To call a custom function, either create the distance matrix beforehand or create a function of the formmetric(x, **kwargs)
wherex
is the data matrix for which pairwise distances are calculated and**kwargs
are extra arguements to send to your custom function.workers (
int
, default:1
)  The number of cores to parallelize the pvalue computation over. Supply1
to use all cores available to the Process.**kwargs  Arbitrary keyword arguments provided to
sklearn.metrics.pairwise_distances
or a custom distance function.
 Returns
distx, disty (
ndarray
)  Distance matrices based on the metric provided by the user.