你可以写一个shapelet的时序分类的matlab代码嘛

当然可以！以下是一个简单的时序分类器示例，该分类器使用shapelet作为特征提取器。该示例使用UCR时间序列分类数据集中的TwoLeadECG数据集进行演示。请注意，这只是一个示例代码，您可能需要根据自己的数据集进行更改和调整。

% load the TwoLeadECG dataset
load('TwoLeadECG.mat');

% define the number of shapelets to extract
num_shapelets = 5;

% randomly select the shapelets from the training set
rand_idx = randperm(length(train_labels));
selected_shapelets = zeros(num_shapelets, length(train_data(1,:)));
for i = 1:num_shapelets
    selected_shapelets(i,:) = train_data(rand_idx(i),:);
end

% extract shapelet features from the training and testing sets
train_features = zeros(length(train_labels), num_shapelets);
for i = 1:length(train_labels)
    train_features(i,:) = extract_shapelet_features(train_data(i,:), selected_shapelets);
end
test_features = zeros(length(test_labels), num_shapelets);
for i = 1:length(test_labels)
    test_features(i,:) = extract_shapelet_features(test_data(i,:), selected_shapelets);
end

% train a k-nearest neighbors (k-NN) classifier using the shapelet features
mdl = fitcknn(train_features, train_labels, 'NumNeighbors', 3);

% test the classifier and calculate the accuracy
pred_labels = predict(mdl, test_features);
acc = sum(pred_labels == test_labels) / length(test_labels);
fprintf('Accuracy: %.2f%%\n', acc * 100);

% function to extract shapelet features from a time series
function features = extract_shapelet_features(ts, shapelets)
    distances = zeros(length(shapelets(:,1)), length(ts) - length(shapelets(1,:)) + 1);
    for i = 1:length(shapelets(:,1))
        for j = 1:length(ts) - length(shapelets(i,:)) + 1
            distances(i,j) = norm(ts(j:j+length(shapelets(i,:))-1) - shapelets(i,:));
        end
    end
    features = min(distances,[],2)';
end

希望这个代码对您有用！