np.arange(y_min-0.5, y_max+0.5, h)) def plot_svm(kernel, df_input, y, C, gamma, coef): svc_model = svm.SVC(kernel=kernel, C=C, gamma=gamma, coef0=coef, random_state=11, probability=True).fit(df_input, y) Z = svc_model.predict_proba(np.c_[xx.ravel(), yy.ravel()])...
采用线性核函数进行分类 kernel可用参数: "linear": 线性核函数 "poly":多项式核函数"rbf" : 径像核函数/高斯核函数 "sigmoid":核矩阵 ''' model = svm.SVC(kernel='linear', C=2.0) model.fit(train_X, train_y) y_pred = model.predict(test_X) print(accuracy_score(test_y, y_pred)) 更多精...
def plot_svm(kernel, df_input, y, C, gamma, coef): svc_model = svm.SVC(kernel=kernel, C=C, gamma=gamma, coef0=coef, random_state=11, probability=True).fit(df_input, y) Z = svc_model.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 0] Z = Z.reshape(xx.shape) fig =...
model= svm.SVC(probability=True)#根据网格搜索选择最优模型#整理网格搜索所需要的超参数列表params = [{'kernel': ['linear'],'C': [1, 10, 100, 1000]}, {'kernel': ['poly'],'C': [1],'degree': [2, 3]}, {'kernel': ['rbf'],'C': [1, 10, 100, 1000],'gamma': [1, 0.1,...
"linear": 线性核函数 "poly": 多项式核函数 "rbf" : 径像核函数/高斯核函数 "sigmoid":核矩阵 ''' model = svm.SVC(kernel='linear', C=2.0) model.fit(train_X, train_y) y_pred = model.predict(test_X) print(accuracy_score(test_y, y_pred)) ...
svc_model = svm.SVC(kernel=kernel, C=C, gamma=gamma, coef0=coef, random_state=11, probability=True).fit(df_input, y) Z = svc_model.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 0] Z = Z.reshape(xx.shape) fig = px.scatter_3d(df, x='PCAz_1', y='PCAz_2', z=...
model = SVC(kernel='linear', C=1E10) model.fit(X, y) # 绘制数据点和决策边界 plt.scatter(X[:,0], X[:,1], c=y, s=50, cmap='autumn') ax = plt.gca() xlim = ax.get_xlim() ylim = ax.get_ylim() # 创建网格以绘制决策边界 ...
1、LinearSVC使用的是平方hinge loss,SVC使用的是绝对值hinge loss (我们知道,绝对值hinge loss是非凸的,因而你不能用GD去优化,而平方hinge loss可以) 2、LinearSVC使用的是One-vs-All(也成One-vs-Rest)的优化方法,而SVC使用的是One-vs-One (其实我也不明白,如果有人明白恳请指教。。。) ...
"""2. 通过网格搜索寻找最优参数"""parameters={'gamma':np.linspace(0.0001,0.1),'kernel':['linear','poly','rbf','sigmoid'],}model=svm.SVC()grid_model=GridSearchCV(model,parameters,cv=10,return_train_score=True)grid_model.fit(X_train,y_train)# 用测试集做预测 ...