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# `sklearn.gaussian_process.kernels`.Kernel

class sklearn.gaussian_process.kernels.Kernel

  Base class for all kernels.

  New in version 0.18.

  Attributes:
    `bounds`

      Returns the log-transformed bounds on the theta.

    `hyperparameters`

      Returns a list of all hyperparameter specifications.

    `n_dims`

      Returns the number of non-fixed hyperparameters of the kernel.

    `requires_vector_input`

      Returns whether the kernel is defined on fixed-length feature vectors or generic objects.

    `theta`

      Returns the (flattened, log-transformed) non-fixed hyperparameters.

  Methods

  `__call__`(X[, Y, eval_gradient]) | Evaluate the kernel.
  `clone_with_theta`(theta) | Returns a clone of self with given hyperparameters theta.
  `diag`(X) | Returns the diagonal of the kernel k(X, X).
  `get_params`([deep]) | Get parameters of this kernel.
  `is_stationary`() | Returns whether the kernel is stationary.
  `set_params`(**params) | Set the parameters of this kernel.

  abstract __call__(X, Y=None, eval_gradient=False)

    Evaluate the kernel.

  property bounds

    Returns the log-transformed bounds on the theta.

    Returns:
      bounds ndarray of shape (n_dims, 2)

        The log-transformed bounds on the kernel’s hyperparameters theta

  clone_with_theta(theta)

    Returns a clone of self with given hyperparameters theta.

    Parameters:
      theta ndarray of shape (n_dims,)

        The hyperparameters

  abstract diag(X)

    Returns the diagonal of the kernel k(X, X).

    The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated.

    Parameters:
      X array-like of shape (n_samples,)

        Left argument of the returned kernel k(X, Y)

    Returns:
      K_diag ndarray of shape (n_samples_X,)

        Diagonal of kernel k(X, X)

  get_params(deep=True)

    Get parameters of this kernel.

    Parameters:
      deep bool, default=True

        If True, will return the parameters for this estimator and contained subobjects that are estimators.

    Returns:
      params dict

        Parameter names mapped to their values.

  property hyperparameters

    Returns a list of all hyperparameter specifications.

  abstract is_stationary()

    Returns whether the kernel is stationary.

  property n_dims

    Returns the number of non-fixed hyperparameters of the kernel.

  property requires_vector_input

    Returns whether the kernel is defined on fixed-length feature vectors or generic objects. Defaults to True for backward compatibility.

  set_params(**params)

    Set the parameters of this kernel.

    The method works on simple kernels as well as on nested kernels. The latter have parameters of the form `<component>__<parameter>` so that it’s possible to update each component of a nested object.

    Returns:
      self
  property theta

    Returns the (flattened, log-transformed) non-fixed hyperparameters.

    Note that theta are typically the log-transformed values of the kernel’s hyperparameters as this representation of the search space is more amenable for hyperparameter search, as hyperparameters like length-scales naturally live on a log-scale.

    Returns:
      theta ndarray of shape (n_dims,)

        The non-fixed, log-transformed hyperparameters of the kernel

## Examples using `sklearn.gaussian_process.kernels.Kernel`

Gaussian processes on discrete data structures

Gaussian processes on discrete data structures