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, 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.
New in version 0.18.
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__call__ (X[, Y, eval_gradient]) | Return the kernel k(X, Y) and optionally its gradient. |
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__(sigma_0=1.0, sigma_0_bounds=(1e-05, 100000.0))
[source]
__call__(X, Y=None, eval_gradient=False)
[source]
Return the kernel k(X, Y) and optionally its gradient.
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bounds
Returns the log-transformed bounds on the theta.
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clone_with_theta(theta)
[source]
Returns a clone of self with given hyperparameters theta.
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diag(X)
[source]
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.
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get_params(deep=True)
[source]
Get parameters of this kernel.
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hyperparameters
Returns a list of all hyperparameter specifications.
is_stationary()
[source]
Returns whether the kernel is stationary.
n_dims
Returns the number of non-fixed hyperparameters of the kernel.
set_params(**params)
[source]
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.
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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.
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sklearn.gaussian_process.kernels.DotProduct
© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.DotProduct.html