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

sklearn.metrics.pairwise.sigmoid_kernel(X, Y=None, gamma=None, coef0=1)

  Compute the sigmoid kernel between X and Y.

  > K(X, Y) = tanh(gamma <X, Y> + coef0)

  Read more in the User Guide.

  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

      Coefficient of the vector inner product. If None, defaults to 1.0 / n_features.

    coef0 float, default=1

      Constant offset added to scaled inner product.

  Returns:
    Gram matrix ndarray of shape (n_samples_X, n_samples_Y)

      Sigmoid kernel between two arrays.