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property requires_vector_input
Whether the kernel works only on fixed-length feature vectors. | sklearn.modules.generated.sklearn.gaussian_process.kernels.constantkernel#sklearn.gaussian_process.kernels.ConstantKernel.requires_vector_input |
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. Returns
self | sklearn.modules.generated.sklearn.gaussian_process.kernels.constantkernel#sklearn.gaussian_process.kernels.ConstantKernel.set_params |
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 l... | sklearn.modules.generated.sklearn.gaussian_process.kernels.constantkernel#sklearn.gaussian_process.kernels.ConstantKernel.theta |
__call__(X, Y=None, eval_gradient=False) [source]
Return the kernel k(X, Y) and optionally its gradient. Parameters
Xarray-like of shape (n_samples_X, n_features) or list of object
Left argument of the returned kernel k(X, Y)
Yarray-like of shape (n_samples_X, n_features) or list of object, default=None
Rig... | sklearn.modules.generated.sklearn.gaussian_process.kernels.constantkernel#sklearn.gaussian_process.kernels.ConstantKernel.__call__ |
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^... | sklearn.modules.generated.sklearn.gaussian_process.kernels.dotproduct#sklearn.gaussian_process.kernels.DotProduct |
sklearn.gaussian_process.kernels.DotProduct
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... | sklearn.modules.generated.sklearn.gaussian_process.kernels.dotproduct |
property bounds
Returns the log-transformed bounds on the theta. Returns
boundsndarray of shape (n_dims, 2)
The log-transformed bounds on the kernel’s hyperparameters theta | sklearn.modules.generated.sklearn.gaussian_process.kernels.dotproduct#sklearn.gaussian_process.kernels.DotProduct.bounds |
clone_with_theta(theta) [source]
Returns a clone of self with given hyperparameters theta. Parameters
thetandarray of shape (n_dims,)
The hyperparameters | sklearn.modules.generated.sklearn.gaussian_process.kernels.dotproduct#sklearn.gaussian_process.kernels.DotProduct.clone_with_theta |
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. Parameters
Xndarray of shape (n_samples_X, n_features)
Left argument of the returned kernel k(X, Y). Ret... | sklearn.modules.generated.sklearn.gaussian_process.kernels.dotproduct#sklearn.gaussian_process.kernels.DotProduct.diag |
get_params(deep=True) [source]
Get parameters of this kernel. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.gaussian_process.kernels.dotproduct#sklearn.gaussian_process.kernels.DotProduct.get_params |
property hyperparameters
Returns a list of all hyperparameter specifications. | sklearn.modules.generated.sklearn.gaussian_process.kernels.dotproduct#sklearn.gaussian_process.kernels.DotProduct.hyperparameters |
is_stationary() [source]
Returns whether the kernel is stationary. | sklearn.modules.generated.sklearn.gaussian_process.kernels.dotproduct#sklearn.gaussian_process.kernels.DotProduct.is_stationary |
property n_dims
Returns the number of non-fixed hyperparameters of the kernel. | sklearn.modules.generated.sklearn.gaussian_process.kernels.dotproduct#sklearn.gaussian_process.kernels.DotProduct.n_dims |
property requires_vector_input
Returns whether the kernel is defined on fixed-length feature vectors or generic objects. Defaults to True for backward compatibility. | sklearn.modules.generated.sklearn.gaussian_process.kernels.dotproduct#sklearn.gaussian_process.kernels.DotProduct.requires_vector_input |
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. Returns
self | sklearn.modules.generated.sklearn.gaussian_process.kernels.dotproduct#sklearn.gaussian_process.kernels.DotProduct.set_params |
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 l... | sklearn.modules.generated.sklearn.gaussian_process.kernels.dotproduct#sklearn.gaussian_process.kernels.DotProduct.theta |
__call__(X, Y=None, eval_gradient=False) [source]
Return the kernel k(X, Y) and optionally its gradient. Parameters
Xndarray of shape (n_samples_X, n_features)
Left argument of the returned kernel k(X, Y)
Yndarray of shape (n_samples_Y, n_features), default=None
Right argument of the returned kernel k(X, Y)... | sklearn.modules.generated.sklearn.gaussian_process.kernels.dotproduct#sklearn.gaussian_process.kernels.DotProduct.__call__ |
class sklearn.gaussian_process.kernels.Exponentiation(kernel, exponent) [source]
The Exponentiation kernel takes one base kernel and a scalar parameter \(p\) and combines them via \[k_{exp}(X, Y) = k(X, Y) ^p\] Note that the __pow__ magic method is overridden, so Exponentiation(RBF(), 2) is equivalent to using the *... | sklearn.modules.generated.sklearn.gaussian_process.kernels.exponentiation#sklearn.gaussian_process.kernels.Exponentiation |
sklearn.gaussian_process.kernels.Exponentiation
class sklearn.gaussian_process.kernels.Exponentiation(kernel, exponent) [source]
The Exponentiation kernel takes one base kernel and a scalar parameter \(p\) and combines them via \[k_{exp}(X, Y) = k(X, Y) ^p\] Note that the __pow__ magic method is overridden, so Exp... | sklearn.modules.generated.sklearn.gaussian_process.kernels.exponentiation |
property bounds
Returns the log-transformed bounds on the theta. Returns
boundsndarray of shape (n_dims, 2)
The log-transformed bounds on the kernel’s hyperparameters theta | sklearn.modules.generated.sklearn.gaussian_process.kernels.exponentiation#sklearn.gaussian_process.kernels.Exponentiation.bounds |
clone_with_theta(theta) [source]
Returns a clone of self with given hyperparameters theta. Parameters
thetandarray of shape (n_dims,)
The hyperparameters | sklearn.modules.generated.sklearn.gaussian_process.kernels.exponentiation#sklearn.gaussian_process.kernels.Exponentiation.clone_with_theta |
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. Parameters
Xarray-like of shape (n_samples_X, n_features) or list of object
Argument to the kernel. Retu... | sklearn.modules.generated.sklearn.gaussian_process.kernels.exponentiation#sklearn.gaussian_process.kernels.Exponentiation.diag |
get_params(deep=True) [source]
Get parameters of this kernel. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.gaussian_process.kernels.exponentiation#sklearn.gaussian_process.kernels.Exponentiation.get_params |
property hyperparameters
Returns a list of all hyperparameter. | sklearn.modules.generated.sklearn.gaussian_process.kernels.exponentiation#sklearn.gaussian_process.kernels.Exponentiation.hyperparameters |
is_stationary() [source]
Returns whether the kernel is stationary. | sklearn.modules.generated.sklearn.gaussian_process.kernels.exponentiation#sklearn.gaussian_process.kernels.Exponentiation.is_stationary |
property n_dims
Returns the number of non-fixed hyperparameters of the kernel. | sklearn.modules.generated.sklearn.gaussian_process.kernels.exponentiation#sklearn.gaussian_process.kernels.Exponentiation.n_dims |
property requires_vector_input
Returns whether the kernel is defined on discrete structures. | sklearn.modules.generated.sklearn.gaussian_process.kernels.exponentiation#sklearn.gaussian_process.kernels.Exponentiation.requires_vector_input |
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. Returns
self | sklearn.modules.generated.sklearn.gaussian_process.kernels.exponentiation#sklearn.gaussian_process.kernels.Exponentiation.set_params |
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 l... | sklearn.modules.generated.sklearn.gaussian_process.kernels.exponentiation#sklearn.gaussian_process.kernels.Exponentiation.theta |
__call__(X, Y=None, eval_gradient=False) [source]
Return the kernel k(X, Y) and optionally its gradient. Parameters
Xarray-like of shape (n_samples_X, n_features) or list of object
Left argument of the returned kernel k(X, Y)
Yarray-like of shape (n_samples_Y, n_features) or list of object, default=None
Rig... | sklearn.modules.generated.sklearn.gaussian_process.kernels.exponentiation#sklearn.gaussian_process.kernels.Exponentiation.__call__ |
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 (aka periodic kernel). The ExpSineSquared kernel allows one to model functions which repeat themselves exactly. It is param... | sklearn.modules.generated.sklearn.gaussian_process.kernels.expsinesquared#sklearn.gaussian_process.kernels.ExpSineSquared |
sklearn.gaussian_process.kernels.ExpSineSquared
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 (aka periodic kernel). The ExpSineSquared kernel allows one to model func... | sklearn.modules.generated.sklearn.gaussian_process.kernels.expsinesquared |
property bounds
Returns the log-transformed bounds on the theta. Returns
boundsndarray of shape (n_dims, 2)
The log-transformed bounds on the kernel’s hyperparameters theta | sklearn.modules.generated.sklearn.gaussian_process.kernels.expsinesquared#sklearn.gaussian_process.kernels.ExpSineSquared.bounds |
clone_with_theta(theta) [source]
Returns a clone of self with given hyperparameters theta. Parameters
thetandarray of shape (n_dims,)
The hyperparameters | sklearn.modules.generated.sklearn.gaussian_process.kernels.expsinesquared#sklearn.gaussian_process.kernels.ExpSineSquared.clone_with_theta |
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. Parameters
Xndarray of shape (n_samples_X, n_features)
Left argument of the returned kernel k(X, Y) Retu... | sklearn.modules.generated.sklearn.gaussian_process.kernels.expsinesquared#sklearn.gaussian_process.kernels.ExpSineSquared.diag |
get_params(deep=True) [source]
Get parameters of this kernel. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.gaussian_process.kernels.expsinesquared#sklearn.gaussian_process.kernels.ExpSineSquared.get_params |
property hyperparameters
Returns a list of all hyperparameter specifications. | sklearn.modules.generated.sklearn.gaussian_process.kernels.expsinesquared#sklearn.gaussian_process.kernels.ExpSineSquared.hyperparameters |
property hyperparameter_length_scale
Returns the length scale | sklearn.modules.generated.sklearn.gaussian_process.kernels.expsinesquared#sklearn.gaussian_process.kernels.ExpSineSquared.hyperparameter_length_scale |
is_stationary() [source]
Returns whether the kernel is stationary. | sklearn.modules.generated.sklearn.gaussian_process.kernels.expsinesquared#sklearn.gaussian_process.kernels.ExpSineSquared.is_stationary |
property n_dims
Returns the number of non-fixed hyperparameters of the kernel. | sklearn.modules.generated.sklearn.gaussian_process.kernels.expsinesquared#sklearn.gaussian_process.kernels.ExpSineSquared.n_dims |
property requires_vector_input
Returns whether the kernel is defined on fixed-length feature vectors or generic objects. Defaults to True for backward compatibility. | sklearn.modules.generated.sklearn.gaussian_process.kernels.expsinesquared#sklearn.gaussian_process.kernels.ExpSineSquared.requires_vector_input |
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. Returns
self | sklearn.modules.generated.sklearn.gaussian_process.kernels.expsinesquared#sklearn.gaussian_process.kernels.ExpSineSquared.set_params |
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 l... | sklearn.modules.generated.sklearn.gaussian_process.kernels.expsinesquared#sklearn.gaussian_process.kernels.ExpSineSquared.theta |
__call__(X, Y=None, eval_gradient=False) [source]
Return the kernel k(X, Y) and optionally its gradient. Parameters
Xndarray of shape (n_samples_X, n_features)
Left argument of the returned kernel k(X, Y)
Yndarray of shape (n_samples_Y, n_features), default=None
Right argument of the returned kernel k(X, Y)... | sklearn.modules.generated.sklearn.gaussian_process.kernels.expsinesquared#sklearn.gaussian_process.kernels.ExpSineSquared.__call__ |
class sklearn.gaussian_process.kernels.Hyperparameter(name, value_type, bounds, n_elements=1, fixed=None) [source]
A kernel hyperparameter’s specification in form of a namedtuple. New in version 0.18. Attributes
namestr
The name of the hyperparameter. Note that a kernel using a hyperparameter with name “x” mu... | sklearn.modules.generated.sklearn.gaussian_process.kernels.hyperparameter#sklearn.gaussian_process.kernels.Hyperparameter |
sklearn.gaussian_process.kernels.Hyperparameter
class sklearn.gaussian_process.kernels.Hyperparameter(name, value_type, bounds, n_elements=1, fixed=None) [source]
A kernel hyperparameter’s specification in form of a namedtuple. New in version 0.18. Attributes
namestr
The name of the hyperparameter. Note tha... | sklearn.modules.generated.sklearn.gaussian_process.kernels.hyperparameter |
bounds
Alias for field number 2 | sklearn.modules.generated.sklearn.gaussian_process.kernels.hyperparameter#sklearn.gaussian_process.kernels.Hyperparameter.bounds |
count(value, /)
Return number of occurrences of value. | sklearn.modules.generated.sklearn.gaussian_process.kernels.hyperparameter#sklearn.gaussian_process.kernels.Hyperparameter.count |
fixed
Alias for field number 4 | sklearn.modules.generated.sklearn.gaussian_process.kernels.hyperparameter#sklearn.gaussian_process.kernels.Hyperparameter.fixed |
index(value, start=0, stop=sys.maxsize, /)
Return first index of value. Raises ValueError if the value is not present. | sklearn.modules.generated.sklearn.gaussian_process.kernels.hyperparameter#sklearn.gaussian_process.kernels.Hyperparameter.index |
name
Alias for field number 0 | sklearn.modules.generated.sklearn.gaussian_process.kernels.hyperparameter#sklearn.gaussian_process.kernels.Hyperparameter.name |
n_elements
Alias for field number 3 | sklearn.modules.generated.sklearn.gaussian_process.kernels.hyperparameter#sklearn.gaussian_process.kernels.Hyperparameter.n_elements |
value_type
Alias for field number 1 | sklearn.modules.generated.sklearn.gaussian_process.kernels.hyperparameter#sklearn.gaussian_process.kernels.Hyperparameter.value_type |
__call__(*args, **kwargs)
Call self as a function. | sklearn.modules.generated.sklearn.gaussian_process.kernels.hyperparameter#sklearn.gaussian_process.kernels.Hyperparameter.__call__ |
sklearn.gaussian_process.kernels.Kernel
class sklearn.gaussian_process.kernels.Kernel [source]
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 n... | sklearn.modules.generated.sklearn.gaussian_process.kernels.kernel |
class sklearn.gaussian_process.kernels.Kernel [source]
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 ... | sklearn.modules.generated.sklearn.gaussian_process.kernels.kernel#sklearn.gaussian_process.kernels.Kernel |
property bounds
Returns the log-transformed bounds on the theta. Returns
boundsndarray of shape (n_dims, 2)
The log-transformed bounds on the kernel’s hyperparameters theta | sklearn.modules.generated.sklearn.gaussian_process.kernels.kernel#sklearn.gaussian_process.kernels.Kernel.bounds |
clone_with_theta(theta) [source]
Returns a clone of self with given hyperparameters theta. Parameters
thetandarray of shape (n_dims,)
The hyperparameters | sklearn.modules.generated.sklearn.gaussian_process.kernels.kernel#sklearn.gaussian_process.kernels.Kernel.clone_with_theta |
abstract 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. Parameters
Xarray-like of shape (n_samples,)
Left argument of the returned kernel k(X, Y) Retur... | sklearn.modules.generated.sklearn.gaussian_process.kernels.kernel#sklearn.gaussian_process.kernels.Kernel.diag |
get_params(deep=True) [source]
Get parameters of this kernel. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.gaussian_process.kernels.kernel#sklearn.gaussian_process.kernels.Kernel.get_params |
property hyperparameters
Returns a list of all hyperparameter specifications. | sklearn.modules.generated.sklearn.gaussian_process.kernels.kernel#sklearn.gaussian_process.kernels.Kernel.hyperparameters |
abstract is_stationary() [source]
Returns whether the kernel is stationary. | sklearn.modules.generated.sklearn.gaussian_process.kernels.kernel#sklearn.gaussian_process.kernels.Kernel.is_stationary |
property n_dims
Returns the number of non-fixed hyperparameters of the kernel. | sklearn.modules.generated.sklearn.gaussian_process.kernels.kernel#sklearn.gaussian_process.kernels.Kernel.n_dims |
property requires_vector_input
Returns whether the kernel is defined on fixed-length feature vectors or generic objects. Defaults to True for backward compatibility. | sklearn.modules.generated.sklearn.gaussian_process.kernels.kernel#sklearn.gaussian_process.kernels.Kernel.requires_vector_input |
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. Returns
self | sklearn.modules.generated.sklearn.gaussian_process.kernels.kernel#sklearn.gaussian_process.kernels.Kernel.set_params |
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 l... | sklearn.modules.generated.sklearn.gaussian_process.kernels.kernel#sklearn.gaussian_process.kernels.Kernel.theta |
abstract __call__(X, Y=None, eval_gradient=False) [source]
Evaluate the kernel. | sklearn.modules.generated.sklearn.gaussian_process.kernels.kernel#sklearn.gaussian_process.kernels.Kernel.__call__ |
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. It has an additional parameter \(\nu\) which controls the smoothness of the resulting function. The smaller \(\nu\), the less... | sklearn.modules.generated.sklearn.gaussian_process.kernels.matern#sklearn.gaussian_process.kernels.Matern |
sklearn.gaussian_process.kernels.Matern
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. It has an additional parameter \(\nu\) which controls the smoothness of the resulti... | sklearn.modules.generated.sklearn.gaussian_process.kernels.matern |
property bounds
Returns the log-transformed bounds on the theta. Returns
boundsndarray of shape (n_dims, 2)
The log-transformed bounds on the kernel’s hyperparameters theta | sklearn.modules.generated.sklearn.gaussian_process.kernels.matern#sklearn.gaussian_process.kernels.Matern.bounds |
clone_with_theta(theta) [source]
Returns a clone of self with given hyperparameters theta. Parameters
thetandarray of shape (n_dims,)
The hyperparameters | sklearn.modules.generated.sklearn.gaussian_process.kernels.matern#sklearn.gaussian_process.kernels.Matern.clone_with_theta |
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. Parameters
Xndarray of shape (n_samples_X, n_features)
Left argument of the returned kernel k(X, Y) Retu... | sklearn.modules.generated.sklearn.gaussian_process.kernels.matern#sklearn.gaussian_process.kernels.Matern.diag |
get_params(deep=True) [source]
Get parameters of this kernel. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.gaussian_process.kernels.matern#sklearn.gaussian_process.kernels.Matern.get_params |
property hyperparameters
Returns a list of all hyperparameter specifications. | sklearn.modules.generated.sklearn.gaussian_process.kernels.matern#sklearn.gaussian_process.kernels.Matern.hyperparameters |
is_stationary() [source]
Returns whether the kernel is stationary. | sklearn.modules.generated.sklearn.gaussian_process.kernels.matern#sklearn.gaussian_process.kernels.Matern.is_stationary |
property n_dims
Returns the number of non-fixed hyperparameters of the kernel. | sklearn.modules.generated.sklearn.gaussian_process.kernels.matern#sklearn.gaussian_process.kernels.Matern.n_dims |
property requires_vector_input
Returns whether the kernel is defined on fixed-length feature vectors or generic objects. Defaults to True for backward compatibility. | sklearn.modules.generated.sklearn.gaussian_process.kernels.matern#sklearn.gaussian_process.kernels.Matern.requires_vector_input |
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. Returns
self | sklearn.modules.generated.sklearn.gaussian_process.kernels.matern#sklearn.gaussian_process.kernels.Matern.set_params |
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 l... | sklearn.modules.generated.sklearn.gaussian_process.kernels.matern#sklearn.gaussian_process.kernels.Matern.theta |
__call__(X, Y=None, eval_gradient=False) [source]
Return the kernel k(X, Y) and optionally its gradient. Parameters
Xndarray of shape (n_samples_X, n_features)
Left argument of the returned kernel k(X, Y)
Yndarray of shape (n_samples_Y, n_features), default=None
Right argument of the returned kernel k(X, Y)... | sklearn.modules.generated.sklearn.gaussian_process.kernels.matern#sklearn.gaussian_process.kernels.Matern.__call__ |
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... | sklearn.modules.generated.sklearn.gaussian_process.kernels.pairwisekernel#sklearn.gaussian_process.kernels.PairwiseKernel |
sklearn.gaussian_process.kernels.PairwiseKernel
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.me... | sklearn.modules.generated.sklearn.gaussian_process.kernels.pairwisekernel |
property bounds
Returns the log-transformed bounds on the theta. Returns
boundsndarray of shape (n_dims, 2)
The log-transformed bounds on the kernel’s hyperparameters theta | sklearn.modules.generated.sklearn.gaussian_process.kernels.pairwisekernel#sklearn.gaussian_process.kernels.PairwiseKernel.bounds |
clone_with_theta(theta) [source]
Returns a clone of self with given hyperparameters theta. Parameters
thetandarray of shape (n_dims,)
The hyperparameters | sklearn.modules.generated.sklearn.gaussian_process.kernels.pairwisekernel#sklearn.gaussian_process.kernels.PairwiseKernel.clone_with_theta |
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. Parameters
Xndarray of shape (n_samples_X, n_features)
Left argument of the returned kernel k(X, Y) Retu... | sklearn.modules.generated.sklearn.gaussian_process.kernels.pairwisekernel#sklearn.gaussian_process.kernels.PairwiseKernel.diag |
get_params(deep=True) [source]
Get parameters of this kernel. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.gaussian_process.kernels.pairwisekernel#sklearn.gaussian_process.kernels.PairwiseKernel.get_params |
property hyperparameters
Returns a list of all hyperparameter specifications. | sklearn.modules.generated.sklearn.gaussian_process.kernels.pairwisekernel#sklearn.gaussian_process.kernels.PairwiseKernel.hyperparameters |
is_stationary() [source]
Returns whether the kernel is stationary. | sklearn.modules.generated.sklearn.gaussian_process.kernels.pairwisekernel#sklearn.gaussian_process.kernels.PairwiseKernel.is_stationary |
property n_dims
Returns the number of non-fixed hyperparameters of the kernel. | sklearn.modules.generated.sklearn.gaussian_process.kernels.pairwisekernel#sklearn.gaussian_process.kernels.PairwiseKernel.n_dims |
property requires_vector_input
Returns whether the kernel is defined on fixed-length feature vectors or generic objects. Defaults to True for backward compatibility. | sklearn.modules.generated.sklearn.gaussian_process.kernels.pairwisekernel#sklearn.gaussian_process.kernels.PairwiseKernel.requires_vector_input |
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. Returns
self | sklearn.modules.generated.sklearn.gaussian_process.kernels.pairwisekernel#sklearn.gaussian_process.kernels.PairwiseKernel.set_params |
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 l... | sklearn.modules.generated.sklearn.gaussian_process.kernels.pairwisekernel#sklearn.gaussian_process.kernels.PairwiseKernel.theta |
__call__(X, Y=None, eval_gradient=False) [source]
Return the kernel k(X, Y) and optionally its gradient. Parameters
Xndarray of shape (n_samples_X, n_features)
Left argument of the returned kernel k(X, Y)
Yndarray of shape (n_samples_Y, n_features), default=None
Right argument of the returned kernel k(X, Y)... | sklearn.modules.generated.sklearn.gaussian_process.kernels.pairwisekernel#sklearn.gaussian_process.kernels.PairwiseKernel.__call__ |
class sklearn.gaussian_process.kernels.Product(k1, k2) [source]
The Product kernel takes two kernels \(k_1\) and \(k_2\) and combines them via \[k_{prod}(X, Y) = k_1(X, Y) * k_2(X, Y)\] Note that the __mul__ magic method is overridden, so Product(RBF(), RBF()) is equivalent to using the * operator with RBF() * RBF()... | sklearn.modules.generated.sklearn.gaussian_process.kernels.product#sklearn.gaussian_process.kernels.Product |
sklearn.gaussian_process.kernels.Product
class sklearn.gaussian_process.kernels.Product(k1, k2) [source]
The Product kernel takes two kernels \(k_1\) and \(k_2\) and combines them via \[k_{prod}(X, Y) = k_1(X, Y) * k_2(X, Y)\] Note that the __mul__ magic method is overridden, so Product(RBF(), RBF()) is equivalent... | sklearn.modules.generated.sklearn.gaussian_process.kernels.product |
property bounds
Returns the log-transformed bounds on the theta. Returns
boundsndarray of shape (n_dims, 2)
The log-transformed bounds on the kernel’s hyperparameters theta | sklearn.modules.generated.sklearn.gaussian_process.kernels.product#sklearn.gaussian_process.kernels.Product.bounds |
clone_with_theta(theta) [source]
Returns a clone of self with given hyperparameters theta. Parameters
thetandarray of shape (n_dims,)
The hyperparameters | sklearn.modules.generated.sklearn.gaussian_process.kernels.product#sklearn.gaussian_process.kernels.Product.clone_with_theta |
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. Parameters
Xarray-like of shape (n_samples_X, n_features) or list of object
Argument to the kernel. Retu... | sklearn.modules.generated.sklearn.gaussian_process.kernels.product#sklearn.gaussian_process.kernels.Product.diag |
get_params(deep=True) [source]
Get parameters of this kernel. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.gaussian_process.kernels.product#sklearn.gaussian_process.kernels.Product.get_params |
property hyperparameters
Returns a list of all hyperparameter. | sklearn.modules.generated.sklearn.gaussian_process.kernels.product#sklearn.gaussian_process.kernels.Product.hyperparameters |
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