import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
X, y = datasets.make_moons()
X, y = datasets.make_moons(noise=0.15, random_state=666)
plt.scatter(X[y==0,0],X[y==0,1])
plt.scatter(X[y==1,0],X[y==1,1])
plt.show()
训练高斯核的SVM
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.pipeline import Pipeline
def RBFKernelSVC(gamma=1.0):
return Pipeline([
('std_scaler', StandardScaler()),
('rbfSVC', SVC(kernel='rbf', gamma= gamma))
])
svc = PolynomialKernelSVC()
svc.fit(X, y)
