class sklearn.gaussian_process.kernels.RBF(length_scale=1.0, length_scale_bounds=(1e-05, 100000.0)) [source] Radial-basis function kernel (aka squared-exponential kernel). The RBF kernel is a stationary kernel. It is also known as the ?squared exponential? kernel. It is parameterized by a length-scale parameter length_scale>0, which can either be a scalar (isotropic variant of the kernel) or a vector with the same number of dimensions as the inputs X (anisotropic variant of the kernel).

class sklearn.gaussian_process.kernels.RationalQuadratic(length_scale=1.0, alpha=1.0, length_scale_bounds=(1e-05, 100000.0), alpha_bounds=(1e-05, 100000.0)) [source] Rational Quadratic kernel. The RationalQuadratic kernel can be seen as a scale mixture (an infinite sum) of RBF kernels with different characteristic length-scales. It is parameterized by a length-scale parameter length_scale>0 and a scale mixture parameter alpha>0. Only the isotropic variant where length_scale is a scala

class sklearn.gaussian_process.kernels.Product(k1, k2) [source] Product-kernel k1 * k2 of two kernels k1 and k2. The resulting kernel is defined as k_prod(X, Y) = k1(X, Y) * k2(X, Y) New in version 0.18. Parameters: k1 : Kernel object The first base-kernel of the product-kernel k2 : Kernel object The second base-kernel of the product-kernel Methods clone_with_theta(theta) Returns a clone of self with given hyperparameters theta. diag(X) Returns the diagonal of the kernel k(X, X).

class sklearn.gaussian_process.kernels.PairwiseKernel(gamma=1.0, gamma_bounds=(1e-05, 100000.0), metric='linear', pairwise_kernels_kwargs=None) [source] Wrapper for kernels in sklearn.metrics.pairwise. A thin wrapper around the functionality of the kernels in sklearn.metrics.pairwise. Note: Evaluation of eval_gradient is not analytic but numeric and all kernels support only isotropic distances. The parameter gamma is considered to be a hyperparameter and may be optimized. The other kernel p

class sklearn.gaussian_process.kernels.Matern(length_scale=1.0, length_scale_bounds=(1e-05, 100000.0), nu=1.5) [source] Matern kernel. The class of Matern kernels is a generalization of the RBF and the absolute exponential kernel parameterized by an additional parameter nu. The smaller nu, the less smooth the approximated function is. For nu=inf, the kernel becomes equivalent to the RBF kernel and for nu=0.5 to the absolute exponential kernel. Important intermediate values are nu=1.5 (once

class sklearn.gaussian_process.kernels.Kernel [source] Base class for all kernels. New in version 0.18. Methods 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. __init__() x.__init__(...) initializes x; see help(type(x)) for signature

class sklearn.gaussian_process.kernels.Hyperparameter [source] A kernel hyperparameter?s specification in form of a namedtuple. New in version 0.18. Attributes: name : string The name of the hyperparameter. Note that a kernel using a hyperparameter with name ?x? must have the attributes self.x and self.x_bounds value_type : string The type of the hyperparameter. Currently, only ?numeric? hyperparameters are supported. bounds : pair of floats >= 0 or ?fixed? The lower and upper bo

class sklearn.gaussian_process.kernels.ExpSineSquared(length_scale=1.0, periodicity=1.0, length_scale_bounds=(1e-05, 100000.0), periodicity_bounds=(1e-05, 100000.0)) [source] Exp-Sine-Squared kernel. The ExpSineSquared kernel allows modeling periodic functions. It is parameterized by a length-scale parameter length_scale>0 and a periodicity parameter periodicity>0. Only the isotropic variant where l is a scalar is supported at the moment. The kernel given by: k(x_i, x_j) = exp(-2 sin(

class sklearn.gaussian_process.kernels.Exponentiation(kernel, exponent) [source] Exponentiate kernel by given exponent. The resulting kernel is defined as k_exp(X, Y) = k(X, Y) ** exponent New in version 0.18. Parameters: kernel : Kernel object The base kernel exponent : float The exponent for the base kernel Methods 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 paramete

class sklearn.gaussian_process.kernels.DotProduct(sigma_0=1.0, sigma_0_bounds=(1e-05, 100000.0)) [source] Dot-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