sklearn.metrics.pairwise.pairwise_kernels()

X : array [n_samples_a, n_samples_a] if metric == ?precomputed?, or, [n_samples_a, n_features] otherwise

Array of pairwise kernels between samples, or a feature array.

Y : array [n_samples_b, n_features]

A second feature array only if X has shape [n_samples_a, n_features].

metric : string, or callable

The metric to use when calculating kernel between instances in a feature array. If metric is a string, it must be one of the metrics in pairwise.PAIRWISE_KERNEL_FUNCTIONS. If metric is ?precomputed?, X is assumed to be a kernel matrix. Alternatively, if metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays from X as input and return a value indicating the distance between them.

n_jobs : int

The number of jobs to use for the computation. This works by breaking down the pairwise matrix into n_jobs even slices and computing them in parallel.

If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used.

filter_params : boolean

Whether to filter invalid parameters or not.

`**kwds` : optional keyword parameters

Any further parameters are passed directly to the kernel function.