python的一个SVM多分类例子

一个SVM的简单例子可以从中窥探奥秘，大家可以更改X的数值，更改Y 的标签名

import numpy as np
from sklearn.svm import SVC

X = np.array([[0, 1], [2, 3], [4, 5], [6, 7]])
print(X)
y = np.array([1, 2, 3, 4])
clf = SVC()
clf.fit(X, y)
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
    decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
    max_iter=-1, probability=False, random_state=None, shrinking=True,
    tol=0.001, verbose=False)
print(clf.predict([[2.1, 3.9]]))