sklearn.metrics.pairwise
.pairwise_kernels¶
- sklearn.metrics.pairwise.pairwise_kernels(X, Y=None, metric='linear', *, filter_params=False, n_jobs=None, **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:
[‘additive_chi2’, ‘chi2’, ‘linear’, ‘poly’, ‘polynomial’, ‘rbf’, ‘laplacian’, ‘sigmoid’, ‘cosine’]
Read more in the User Guide.
- Parameters:
- X{array-like, sparse matrix} of shape (n_samples_X, n_samples_X) or (n_samples_X, n_features)
Array of pairwise kernels between samples, or a feature array. The shape of the array should be (n_samples_X, n_samples_X) if metric == “precomputed” and (n_samples_X, n_features) otherwise.
- Y{array-like, sparse matrix} of shape (n_samples_Y, n_features), default=None
A second feature array only if X has shape (n_samples_X, n_features).
- metricstr or callable, default=”linear”
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 rows from X as input and return the corresponding kernel value as a single number. This means that callables fromsklearn.metrics.pairwise
are not allowed, as they operate on matrices, not single samples. Use the string identifying the kernel instead.- filter_paramsbool, default=False
Whether to filter invalid parameters or not.
- n_jobsint, default=None
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.
None
means 1 unless in ajoblib.parallel_backend
context.-1
means using all processors. See Glossary for more details.- **kwdsoptional keyword parameters
Any further parameters are passed directly to the kernel function.
- Returns:
- Kndarray of shape (n_samples_X, n_samples_X) or (n_samples_X, n_samples_Y)
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.
Notes
If metric is ‘precomputed’, Y is ignored and X is returned.
Examples
>>> from sklearn.metrics.pairwise import pairwise_kernels >>> X = [[0, 0, 0], [1, 1, 1]] >>> Y = [[1, 0, 0], [1, 1, 0]] >>> pairwise_kernels(X, Y, metric='linear') array([[0., 0.], [1., 2.]])