val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create : ?sigma_0:float -> ?sigma_0_bounds:Py.Object.t -> unit -> tDot-Product kernel.
The DotProduct kernel is non-stationary and can be obtained from linear regression by putting N(0, 1) priors on the coefficients of x_d (d = 1, . . . , D) and a prior of N(0, \sigma_0^2) on the bias. The DotProduct kernel is invariant to a rotation of the coordinates about the origin, but not translations. It is parameterized by a parameter sigma_0^2. For sigma_0^2 =0, the kernel is called the homogeneous linear kernel, otherwise it is inhomogeneous. The kernel is given by
k(x_i, x_j) = sigma_0 ^ 2 + x_i \cdot x_j
The DotProduct kernel is commonly combined with exponentiation.
.. versionadded:: 0.18
Parameters ---------- sigma_0 : float >= 0, default: 1.0 Parameter controlling the inhomogenity of the kernel. If sigma_0=0, the kernel is homogenous.
sigma_0_bounds : pair of floats >= 0, default: (1e-5, 1e5) The lower and upper bound on l
val clone_with_theta : theta:Ndarray.t -> t -> Py.Object.tReturns a clone of self with given hyperparameters theta.
Parameters ---------- theta : array, shape (n_dims,) The hyperparameters
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, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y)
Returns ------- K_diag : array, shape (n_samples_X,) Diagonal of kernel k(X, X)
val get_params : ?deep:bool -> t -> Py.Object.tGet parameters of this kernel.
Parameters ---------- deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns ------- params : mapping of string to any Parameter names mapped to their values.
val is_stationary : t -> Py.Object.tReturns whether the kernel is stationary.
val set_params : ?params:(string * Py.Object.t) list -> t -> tSet 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
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.