val get_py : string -> Py.Object.tGet an attribute of this module as a Py.Object.t. This is useful to pass a Python function to another function.
module KernelRidge : sig ... endval check_is_fitted :
?attributes:
[ `S of string
| `StringList of string list
| `Arr of [> `ArrayLike ] Np.Obj.t ] ->
?msg:string ->
?all_or_any:[ `Callable of Py.Object.t | `PyObject of Py.Object.t ] ->
estimator:[> `BaseEstimator ] Np.Obj.t ->
unit ->
Py.Object.tPerform is_fitted validation for estimator.
Checks if the estimator is fitted by verifying the presence of fitted attributes (ending with a trailing underscore) and otherwise raises a NotFittedError with the given message.
This utility is meant to be used internally by estimators themselves, typically in their own predict / transform methods.
Parameters ---------- estimator : estimator instance. estimator instance for which the check is performed.
attributes : str, list or tuple of str, default=None Attribute name(s) given as string or a list/tuple of strings Eg.: ``'coef_', 'estimator_', ..., 'coef_'``
If `None`, `estimator` is considered fitted if there exist an attribute that ends with a underscore and does not start with double underscore.
msg : string The default error message is, 'This %(name)s instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.'
For custom messages if '%(name)s' is present in the message string, it is substituted for the estimator name.
Eg. : 'Estimator, %(name)s, must be fitted before sparsifying'.
all_or_any : callable, all, any, default all Specify whether all or any of the given attributes must exist.
Returns ------- None
Raises ------ NotFittedError If the attributes are not found.
val pairwise_kernels :
?y:[> `ArrayLike ] Np.Obj.t ->
?metric:[ `S of string | `Callable of Py.Object.t ] ->
?filter_params:bool ->
?n_jobs:int ->
?kwds:(string * Py.Object.t) list ->
x:[ `Otherwise of Py.Object.t | `Arr of [> `ArrayLike ] Np.Obj.t ] ->
unit ->
[> `ArrayLike ] Np.Obj.tCompute the kernel between arrays X and optional array Y.
This method takes either a vector array or a kernel matrix, and returns a kernel matrix. If the input is a vector array, the kernels are computed. If the input is a kernel matrix, it is returned instead.
This method provides a safe way to take a kernel matrix as input, while preserving compatibility with many other algorithms that take a vector array.
If Y is given (default is None), then the returned matrix is the pairwise kernel between the arrays from both X and Y.
Valid values for metric are: 'additive_chi2', 'chi2', 'linear', 'poly', 'polynomial', 'rbf',
'laplacian', 'sigmoid', 'cosine'
Read more in the :ref:`User Guide <metrics>`.
Parameters ---------- X : array n_samples_a, n_samples_a if metric == 'precomputed', or, n_samples_a, n_features otherwise Array of pairwise kernels between samples, or a feature array.
Y : array n_samples_b, n_features A second feature array only if X has shape n_samples_a, n_features.
metric : string, or callable 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 rows from X as input and return the corresponding kernel value as a single number. This means that callables from :mod:`sklearn.metrics.pairwise` are not allowed, as they operate on matrices, not single samples. Use the string identifying the kernel instead.
filter_params : boolean Whether to filter invalid parameters or not.
n_jobs : int or None, optional (default=None) The number of jobs to use for the computation. This works by breaking down the pairwise matrix into n_jobs even slices and computing them in parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details.
**kwds : optional keyword parameters Any further parameters are passed directly to the kernel function.
Returns ------- K : array n_samples_a, n_samples_a or n_samples_a, n_samples_b A kernel matrix K such that K_, j is the kernel between the ith and jth vectors of the given matrix X, if Y is None. If Y is not None, then K_, j is the kernel between the ith array from X and the jth array from Y.
Notes ----- If metric is 'precomputed', Y is ignored and X is returned.