compute_kern¶

hyppo.tools.
compute_kern
(x, y, metric='gaussian', workers=1, **kwargs)¶ Kernel similarity 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 kernel similarity matrices, where the shapes must both be(n, n)
.metric (
str
,callable
, orNone
, default:"gaussian"
)  A function that computes the kernel similarity among the samples within each data matrix. Valid strings formetric
are, as defined insklearn.metrics.pairwise.pairwise_kernels
,[
"additive_chi2"
,"chi2"
,"linear"
,"poly"
,"polynomial"
,"rbf"
,"laplacian"
,"sigmoid"
,"cosine"
]Note
"rbf"
and"gaussian"
are the same metric. Set toNone
or"precomputed"
ifx
andy
are already similarity matrices. To call a custom function, either create the similarity matrix beforehand or create a function of the formmetric(x, **kwargs)
wherex
is the data matrix for which pairwise kernel similarity matrices 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.pairwise_kernels
or a custom kernel function.
 Returns
simx, simy (
ndarray
)  Similarity matrices based on the metric provided by the user.