compute_kern, y, metric='gaussian', workers=1, **kwargs)

Kernel similarity matrices for the inputs.

  • x,y (ndarray) -- Input data matrices. x and y 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 and y can be kernel similarity matrices, where the shapes must both be (n, n).

  • metric (str, callable, or None, default: "gaussian") -- A function that computes the kernel similarity among the samples within each data matrix. Valid strings for metric are, as defined in sklearn.metrics.pairwise.pairwise_kernels,

    ['additive_chi2', 'chi2', 'linear', 'poly', 'polynomial', 'rbf', 'laplacian', 'sigmoid', 'cosine']

    Note 'rbf' and 'gaussian' are the same metric. Set to None or 'precomputed' if x and y are already similarity matrices. To call a custom function, either create the distance matrix before-hand or create a function of the form metric(x, **kwargs) where x 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 p-value computation over. Supply -1 to use all cores available to the Process.

  • **kwargs -- Arbitrary keyword arguments provided to sklearn.metrics.pairwise.pairwise_kernels or a custom kernel function.


simx, simy (ndarray) -- Similarity matrices based on the metric provided by the user.