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sklearn.metrics.pairwise.pairwise_kernels(X, Y=None, metric='linear', filter_params=False, n_jobs=1, **kwds)[source]

Compute the kernel between arrays X and optional array Y.

This method takes either a vector array or a kernel matrix, and returns a kernel matrix. If the input is a vector array, the kernels are computed. If the input is a kernel matrix, it is returned instead.

This method provides a safe way to take a kernel matrix as input, while preserving compatibility with many other algorithms that take a vector array.

If Y is given (default is None), then the returned matrix is the pairwise kernel between the arrays from both X and Y.

Valid values for metric are::
[‘rbf’, ‘sigmoid’, ‘polynomial’, ‘poly’, ‘linear’, ‘cosine’]

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.


K : array [n_samples_a, n_samples_a] or [n_samples_a, n_samples_b]

A kernel matrix K such that K_{i, j} is the kernel between the ith and jth vectors of the given matrix X, if Y is None. If Y is not None, then K_{i, j} is the kernel between the ith array from X and the jth array from Y.


If metric is ‘precomputed’, Y is ignored and X is returned.