python实现主成分分析核方法（KPCA）还没写完 别看

主成分分析核方法（KPCA）

• 基本代码

from sklearn.decompositon import KernelPCA
clf_kpca = sklearn.decomposition.KernelPCA(n_components=None, kernel='linear', gamma=None, degree=3, coef0=1, kernel_params=None, alpha=1.0, fit_inverse_transform=False, eigen_solver='auto', tol=0, max_iter=None, remove_zero_eig=False, random_state=None, copy_X=True, n_jobs=None)

n_components： 主成分个数
[default] n_components=None
kernel='linear’
[default] kernel=‘linear’
可选：“linear” | “poly” | “rbf” | “sigmoid” | “cosine” | “precomputed”
gamma：kernel为rbf, poly and sigmoid时的核系数，kernel为其他时该参数无效
[default] gamma = None =1/n_features
degree： kernel为ploy时的degree，其他情况无效
[default] degree=3
coef0
[default] coef0 = 1
kernel_params :Parameters (keyword arguments) and values for kernel passed as callable object. Ignored by other kernels.
[default] kernel_params=None
alpha
[default] alpha=1.0
fit_inverse_transform
[default] fit_inverse_transform=False
eigen_solver
[default] eigen_solver=‘auto’
tol=0
[default] tol=0
max_iter=None
[default] max_iter=None
remove_zero_eig
[default] remove_zero_eig=False
random_state
[default] random_state=None
copy_X
[default] copy_X=True
n_jobs
[default] n_jobs=None)