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 parameters are set directly at initialization and are kept fixed.    New in version 0.18.   Parameters 
 
gammafloat, default=1.0 

Parameter gamma of the pairwise kernel specified by metric. It should be positive.  
gamma_boundspair of floats >= 0 or “fixed”, default=(1e-5, 1e5) 

The lower and upper bound on ‘gamma’. If set to “fixed”, ‘gamma’ cannot be changed during hyperparameter tuning.  
metric{“linear”, “additive_chi2”, “chi2”, “poly”, “polynomial”, “rbf”, “laplacian”, “sigmoid”, “cosine”} or callable, default=”linear” 

The metric to use when calculating kernel between instances in a feature array. If metric is a string, it must be one of the metrics in pairwise.PAIRWISE_KERNEL_FUNCTIONS. If metric is “precomputed”, X is assumed to be a kernel matrix. Alternatively, if metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays from X as input and return a value indicating the distance between them.  
pairwise_kernels_kwargsdict, default=None 

All entries of this dict (if any) are passed as keyword arguments to the pairwise kernel function.    Attributes 
 
bounds


Returns the log-transformed bounds on the theta.  hyperparameter_gamma

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.     Examples >>> from sklearn.datasets import load_iris
>>> from sklearn.gaussian_process import GaussianProcessClassifier
>>> from sklearn.gaussian_process.kernels import PairwiseKernel
>>> X, y = load_iris(return_X_y=True)
>>> kernel = PairwiseKernel(metric='rbf')
>>> gpc = GaussianProcessClassifier(kernel=kernel,
...         random_state=0).fit(X, y)
>>> gpc.score(X, y)
0.9733...
>>> gpc.predict_proba(X[:2,:])
array([[0.8880..., 0.05663..., 0.05532...],
       [0.8676..., 0.07073..., 0.06165...]])
 Methods  
__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.    
__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). If None, k(X, X) if evaluated instead.  
eval_gradientbool, default=False 

Determines whether the gradient with respect to the log of the kernel hyperparameter is computed. Only supported when Y is None.    Returns 
 
Kndarray of shape (n_samples_X, n_samples_Y) 

Kernel k(X, Y)  
K_gradientndarray of shape (n_samples_X, n_samples_X, n_dims), optional 

The gradient of the kernel k(X, X) with respect to the log of the hyperparameter of the kernel. Only returned when eval_gradient is True.     
  
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     
  
clone_with_theta(theta) [source]
 
Returns a clone of self with given hyperparameters theta.  Parameters 
 
thetandarray of shape (n_dims,) 

The hyperparameters     
  
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)    Returns 
 
K_diagndarray of shape (n_samples_X,) 

Diagonal of kernel k(X, X)     
  
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.     
  
property hyperparameters  
Returns a list of all hyperparameter specifications. 
  
is_stationary() [source]
 
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) [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
   
  
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 
 
thetandarray of shape (n_dims,) 

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