Z=clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) Z=Z.reshape(xx.shape) no_weights= plt.contour(xx, yy, Z, levels=[0], linestyles=['solid'])## fit the weighted modelclf =linear_model.SGDClassifier(alpha=0.01, n_iter=100) clf.fit(X, y,sample_weight=sample_weight) Z=c...
例子: >>>importnumpyasnp>>>fromsklearnimportlinear_model>>>X = np.array([[-1,-1], [-2,-1], [1,1], [2,1]])>>>clf = linear_model.SGDOneClassSVM(random_state=42)>>>clf.fit(X)SGDOneClassSVM(random_state=42) >>>print(clf.predict([[4,4]])) [1]...
x2 = X2[i, j]#p = clf.decision_function([[x1, x2]])# 计算输出值,也就是到超平面的符号距离。(支持向量到最佳超平面的符号距离为-1和+1)Z[i, j] = p[0] levels = [-1.0,0.0,1.0]# 将输出值分为-1,0,1几个区间linestyles = ['dashed','solid','dashed'] plt.contour(X1, X2, ...