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scikit-learn 1.3.2
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# `sklearn.metrics.pairwise`.laplacian_kernel

sklearn.metrics.pairwise.laplacian_kernel(X, Y=None, gamma=None)

  Compute the laplacian kernel between X and Y.

  The laplacian kernel is defined as:

      K(x, y) = exp(-gamma ||x-y||_1)

  for each pair of rows x in X and y in Y. Read more in the User Guide.

  New in version 0.17.

  Parameters:
    X{array-like, sparse matrix} of shape (n_samples_X, n_features)

      A feature array.

    Y{array-like, sparse matrix} of shape (n_samples_Y, n_features), default=None

      An optional second feature array. If `None`, uses `Y=X`.

    gamma float, default=None

      If None, defaults to 1.0 / n_features. Otherwise it should be strictly positive.

  Returns:
    kernel_matrix ndarray of shape (n_samples_X, n_samples_Y)

      The kernel matrix.