如何在Matlab中显示具有多种特征的SVM图

% 步骤1：导入库和数据
% 假设您已经导入了特征矩阵X和标签向量Y

% 步骤2：训练SVM分类器
svmModel = fitcsvm(X, Y, 'KernelFunction', 'linear');

% 步骤3：绘制决策边界
figure;
gscatter(X(:,1), X(:,2), Y, 'rgb', 'osd');
hold on;
x1Range = min(X(:,1)):0.01:max(X(:,1));
x2Range = min(X(:,2)):0.01:max(X(:,2));
[x1Grid, x2Grid] = meshgrid(x1Range, x2Range);
xGrid = [x1Grid(:), x2Grid(:)];
[~, scores] = predict(svmModel, xGrid);
contour(x1Grid, x2Grid, reshape(scores(:,2), size(x1Grid)), [0 0], 'k');
hold off;

% 步骤4：绘制支持向量
hold on;
plot(svmModel.SupportVectors(:,1), svmModel.SupportVectors(:,2), 'ko', 'MarkerSize', 10);
hold off;

% 步骤5：添加其他特征（可选）

% 步骤6：添加图例和标签
legend('Class 1', 'Class 2', 'Class 3', 'Decision boundary', 'Support vectors');
xlabel('Feature 1');
ylabel('Feature 2');