sklearn svm with kernel
code:kernel.py
from sklearn.metrics.pairwise import rbf_kernel
from sklearn.metrics.pairwise import pairwise_kernels
A=rbf_kernel(X,gamma=0.1)
svm = SVR(kernel="precomputed")
svm.fit(A, y,)
pred1 = svm.predict(A)
svm = SVR(kernel="rbf",gamma=0.1)
svm.fit(X, y)
pred2 = svm.predict(X)
# pred1==pred2