clear; clc; addpath('libsvm-3.25/matlab'); load DATA_PCA_2445M_100.mat; num_DJI_Mavic_2_ZOOM=100; num_DJI_Phantom_4Pro=100; num_Parrot_ANAFI=100; num_XIRO=100; features=data_PCA; %特征 class=[ones(num_DJI_Mavic_2_ZOOM,1);ones(num_DJI_Phantom_4Pro,1)+1;ones(num_Parrot_ANAFI,1)+2;ones(num_XIRO,1)+3]; %类型 这是我准备在matlab中使用的SVM模型去给数据DATA_PCA_2445M_100.mat进行分类操作的,画出训练集和测试集在不同情况下的总体识别率的箱线图,并给出平均识别率。请给出我完整的代码
时间: 2023-09-12 20:05:29 浏览: 60
你可以使用以下代码来计算在不同情况下训练集和测试集的总体识别率,并绘制箱线图和计算平均识别率:
```matlab
clear;
clc;
addpath('libsvm-3.25/matlab');
load DATA_PCA_2445M_100.mat;
num_DJI_Mavic_2_ZOOM = 100;
num_DJI_Phantom_4Pro = 100;
num_Parrot_ANAFI = 100;
num_XIRO = 100;
features = data_PCA; % 特征
class = [ones(num_DJI_Mavic_2_ZOOM,1); ones(num_DJI_Phantom_4Pro,1)+1; ones(num_Parrot_ANAFI,1)+2; ones(num_XIRO,1)+3]; % 类型
train_ratio = 0.7; % 训练集比例
num_folds = 10; % 交叉验证折数
accuracy_list = zeros(num_folds, 1); % 存储每一折的准确度
for fold = 1:num_folds
% 划分训练集和测试集
[train_indices, test_indices] = crossvalind('Kfold', class, num_folds);
train_features = features(train_indices,:);
train_class = class(train_indices);
test_features = features(test_indices,:);
test_class = class(test_indices);
% 训练SVM模型
model = svmtrain(train_class, train_features, '-s 0 -t 0');
% 预测训练集和测试集
[predicted_train, train_accuracy, train_decision_values] = svmpredict(train_class, train_features, model);
[predicted_test, test_accuracy, test_decision_values] = svmpredict(test_class, test_features, model);
% 存储准确度
accuracy_list(fold, :) = [train_accuracy(1); test_accuracy(1)];
end
% 绘制箱线图
boxplot(accuracy_list, 'Labels', {'训练集', '测试集'});
xlabel('数据集');
ylabel('Accuracy');
title('训练集和测试集的分类准确度');
% 计算平均识别率
average_accuracy = mean(accuracy_list, 1);
fprintf('平均识别率:\n');
fprintf('训练集:%.2f%%\n', average_accuracy(1));
fprintf('测试集:%.2f%%\n', average_accuracy(2));
```
请确保你已经将libsvm的MATLAB接口添加到MATLAB搜索路径中,并且替换`DATA_PCA_2445M_100.mat`为你的数据文件路径。这段代码将会将数据分为不同的训练集和测试集,使用SVM模型对训练集和测试集进行预测,并绘制箱线图展示准确度的分布情况,并计算平均识别率。