Parameters
----------
n_components : int, default=None
Number of components. If None, all non-zero components are kept.降维到的维度
kernel : "linear" | "poly" | "rbf" | "sigmoid" | "cosine" | "precomputed"
Kernel. Default="linear".降维的核的类型
gamma : float, default=1/n_features
Kernel coefficient for rbf, poly and sigmoid kernels. Ignored by other
kernels.非线性的系数
degree : int, default=3
Degree for poly kernels. Ignored by other kernels.
coef0 : float, default=1
Independent term in poly and sigmoid kernels.
Ignored by other kernels.
kernel_params : mapping of string to any, default=None
Parameters (keyword arguments) and values for kernel passed as
callable object. Ignored by other kernels.
alpha : int, default=1.0
Hyperparameter of the ridge regression that learns the
inverse transform (when fit_inverse_transform=True).
fit_inverse_transform : bool, default=False
Learn the inverse transform for non-precomputed kernels.
(i.e. learn to find the pre-image of a point)
eigen_solver : string ['auto'|'dense'|'arpack'], default='auto'
Select eigensolver to use. If n_components is much less than
the number of training samples, arpack may be more efficient
than the dense eigensolver.
tol : float, default=0
Convergence tolerance for arpack.
If 0, optimal value will be chosen by arpack.
max_iter : int, default=None
Maximum number of iterations for arpack.
If None, optimal value will be chosen by arpack.
remove_zero_eig : boolean, default=False
If True, then all components with zero eigenvalues are removed, so
that the number of components in the output may be < n_components
(and sometimes even zero due to numerical instability).
When n_components is None, this parameter is ignored and components
with zero eigenvalues are removed regardless.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`. Used when ``eigen_solver`` == 'arpack'.
.. versionadded:: 0.18
copy_X : boolean, default=True
If True, input X is copied and stored by the model in the `X_fit_`
attribute. If no further changes will be done to X, setting
`copy_X=False` saves memory by storing a reference.
.. versionadded:: 0.18
n_jobs : int, default=1
The number of parallel jobs to run.
If `-1`, then the number of jobs is set to the number of CPU cores.
.. versionadded:: 0.18
Attributes
----------
lambdas_ : array, (n_components,)
Eigenvalues of the centered kernel matrix in decreasing order.
If `n_components` and `remove_zero_eig` are not set,
then all values are stored.就是数据集XXT这个矩阵的特征值
alphas_ : array, (n_samples, n_components)
Eigenvectors of the centered kernel matrix. If `n_components` and
`remove_zero_eig` are not set, then all components are stored.特征向量
dual_coef_ : array, (n_samples, n_features)
Inverse transform matrix. Set if `fit_inverse_transform` is True.
X_transformed_fit_ : array, (n_samples, n_components)
Projection of the fitted data on the kernel principal components.
X_fit_ : (n_samples, n_features)
The data used to fit the model. If `copy_X=False`, then `X_fit_` is
a reference. This attribute is used for the calls to transform.
KPCA也可以计算各个特征方向上的方差，也就是所含有的信息多少。
1，先计算出经过非线性变换后的坐标
2，各个坐标方向，计算方差
3，求比例
kpca_transform = kpca.fit_transform(feature_vec) explained_variance = numpy.var(kpca_transform, axis=0) explained_variance_ratio = explained_variance / numpy.sum(explained_variance)stackoverflow相关问题： 点击打开链接