本文整理汇总了Python中sklearn.gaussian_process.kernels.Kernel方法的典型用法代码示例。如果您正苦于以下问题：Python kernels.Kernel方法的具体用法？Python kernels.Kernel怎么用？Python kernels.Kernel使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.gaussian_process.kernels的用法示例。
在下文中一共展示了kernels.Kernel方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: pairwise_kernels
# 需要导入模块: from sklearn.gaussian_process import kernels [as 别名]
# 或者: from sklearn.gaussian_process.kernels import Kernel [as 别名]
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) 示例2: _safe_split
# 需要导入模块: from sklearn.gaussian_process import kernels [as 别名]
# 或者: from sklearn.gaussian_process.kernels import Kernel [as 别名]
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