用MATLAB写一段用训练集和测试集的plsr模型的代码
时间: 2024-03-14 21:46:56 浏览: 22
好的,这是一个简单的用训练集和测试集的PLSR模型的MATLAB代码示例:
首先,加载数据集,并将其分成训练集和测试集:
```matlab
load('dataset.mat'); % 加载数据集
num_samples = size(dataset,1);
num_train = floor(0.8*num_samples); % 80%的数据作为训练集
indices = randperm(num_samples);
train_indices = indices(1:num_train);
test_indices = indices(num_train+1:end);
train_data = dataset(train_indices, 1:end-1);
train_labels = dataset(train_indices, end);
test_data = dataset(test_indices, 1:end-1);
test_labels = dataset(test_indices, end);
```
接下来,使用PLSR函数训练模型:
```matlab
num_components = 5; % 设定PLSR模型的成分数
[XL,YL,XS,YS,beta,PCTVAR] = plsregress(train_data, train_labels, num_components);
```
这里使用plsregress函数训练PLSR模型,设定成分数为5。
然后,使用测试集来评估PLSR模型的性能:
```matlab
predicted_labels = [ones(size(test_data,1),1) test_data]*beta;
rmse = sqrt(mean((test_labels - predicted_labels).^2));
disp("测试集RMSE: " + rmse);
```
这里使用训练好的beta参数,对测试集进行预测,然后计算RMSE指标。
完整的代码如下:
```matlab
load('dataset.mat'); % 加载数据集
num_samples = size(dataset,1);
num_train = floor(0.8*num_samples); % 80%的数据作为训练集
indices = randperm(num_samples);
train_indices = indices(1:num_train);
test_indices = indices(num_train+1:end);
train_data = dataset(train_indices, 1:end-1);
train_labels = dataset(train_indices, end);
test_data = dataset(test_indices, 1:end-1);
test_labels = dataset(test_indices, end);
num_components = 5; % 设定PLSR模型的成分数
[XL,YL,XS,YS,beta,PCTVAR] = plsregress(train_data, train_labels, num_components);
predicted_labels = [ones(size(test_data,1),1) test_data]*beta;
rmse = sqrt(mean((test_labels - predicted_labels).^2));
disp("测试集RMSE: " + rmse);
```