Python sklearn.gaussian_process.kernels.Kernel() Examples

The following are 2 code examples of sklearn.gaussian_process.kernels.Kernel(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module sklearn.gaussian_process.kernels , or try the search function .
Example #1
Source File: pairwise.py    From dask-ml with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def pairwise_kernels(
    X: ArrayLike,
    Y: Optional[ArrayLike] = None,
    metric: Union[str, Callable[[ArrayLike, ArrayLike], float]] = "linear",
    filter_params: bool = False,
    n_jobs: Optional[int] = 1,
    **kwds
):
    from sklearn.gaussian_process.kernels import Kernel as GPKernel

    if metric == "precomputed":
        X, _ = check_pairwise_arrays(X, Y, precomputed=True)
        return X
    elif isinstance(metric, GPKernel):
        raise NotImplementedError()
    elif metric in PAIRWISE_KERNEL_FUNCTIONS:
        if filter_params:
            kwds = dict((k, kwds[k]) for k in kwds if k in KERNEL_PARAMS[metric])
        assert isinstance(metric, str)
        func = PAIRWISE_KERNEL_FUNCTIONS[metric]
    elif callable(metric):
        raise NotImplementedError()
    else:
        raise ValueError("Unknown kernel %r" % metric)

    return func(X, Y, **kwds)
Example #2
Source File: utils.py    From sk-dist with Apache License 2.0 5 votes vote down vote up 
def _safe_split(estimator, X, y, indices, train_indices=None):
    """Create subset of dataset and properly handle kernels"""
    from sklearn.gaussian_process.kernels import Kernel as GPKernel

    if (hasattr(estimator, 'kernel') and callable(estimator.kernel) and
            not isinstance(estimator.kernel, GPKernel)):
        # cannot compute the kernel values with custom function
        raise ValueError("Cannot use a custom kernel function. "
                         "Precompute the kernel matrix instead.")

    if not hasattr(X, "shape"):
        if getattr(estimator, "_pairwise", False):
            raise ValueError("Precomputed kernels or affinity matrices have "
                             "to be passed as arrays or sparse matrices.")
        X_subset = [X[index] for index in indices]
    else:
        if getattr(estimator, "_pairwise", False):
            # X is a precomputed square kernel matrix
            if X.shape[0] != X.shape[1]:
                raise ValueError("X should be a square kernel matrix")
            if train_indices is None:
                X_subset = X[np.ix_(indices, indices)]
            else:
                X_subset = X[np.ix_(indices, train_indices)]
        else:
            X_subset = _safe_indexing(X, indices)

    if y is not None:
        y_subset = _safe_indexing(y, indices)
    else:
        y_subset = None
    return X_subset, y_subset