shapelet算法代码matlab

以下是一个简单的 MATLAB 实现 Shapelet 算法的示例代码：

function [shapelet, idx] = shapelet_algorithm(X, Y, min_len, max_len)
% X: 训练数据，每行是一个时间序列
% Y: 训练数据的类别标签
% min_len: 最短的 shapelet 长度
% max_len: 最长的 shapelet 长度

% 初始化
num_classes = length(unique(Y)); % 类别数量
num_ts = size(X,1); % 时间序列数量
num_shapelets = num_ts * (max_len - min_len + 1); % shapelet 数量
shapelets = zeros(num_shapelets, max_len); % 所有 shapelets 的矩阵
indices = zeros(num_shapelets, 2); % shapelet 的起始和结束索引
distances = zeros(num_ts, num_shapelets); % 所有时间序列和 shapelet 之间的距离
labels = unique(Y); % 所有类别标签
best_gain = -Inf; % 最大信息增益
best_shapelet = []; % 最佳 shapelet
idx = -1; % 最佳 shapelet 的索引

% 生成所有可能的 shapelet
idx = 1;
for L = min_len:max_len
    for i = 1:num_ts
        for j = 1:(size(X,2)-L+1)
            shapelets(idx,:) = X(i,j:j+L-1);
            indices(idx,:) = [j j+L-1];
            idx = idx + 1;
        end
    end
end

% 计算所有时间序列和 shapelet 之间的距离
for i = 1:num_shapelets
    for j = 1:num_ts
        distances(j,i) = min_dist(shapelets(i,:), X(j,:));
    end
end

% 计算每个类别的数量
count = zeros(num_classes, 1);
for i = 1:num_classes
    count(i) = sum(Y == labels(i));
end

% 计算初始的熵
entropy = calc_entropy(count);

% 寻找最佳的 shapelet
for i = 1:num_shapelets
    info_gain = calc_info_gain(distances(:,i), Y, count, entropy);
    if info_gain > best_gain
        best_gain = info_gain;
        best_shapelet = shapelets(i,:);
        idx = i;
    end
end

% 返回最佳 shapelet 和其索引
shapelet = best_shapelet;
end

function d = min_dist(p, X)
% 计算 p 和 X 之间的最小距离
d = Inf;
for i = 1:(size(X,2)-length(p)+1)
    d = min(d, norm(p - X(i:i+length(p)-1)));
end
end

function entropy = calc_entropy(count)
% 计算熵
total = sum(count);
p = count / total;
entropy = -sum(p .* log2(p));
end

function info_gain = calc_info_gain(dist, Y, count, entropy)
% 计算信息增益
num_ts = length(Y);
left_count = zeros(length(count), 1);
left_entropy = zeros(length(count), 1);
right_count = count;
right_entropy = entropy;
sort_dist = sort(dist);
for i = 1:(num_ts-1)
    j = find(dist == sort_dist(i));
    class = Y(j);
    left_count(class) = left_count(class) + 1;
    right_count(class) = right_count(class) - 1;
    left_entropy(class) = calc_entropy(left_count);
    right_entropy(class) = calc_entropy(right_count);
    left_size = i;
    right_size = num_ts - i;
    info_gain(i) = entropy - (left_size/num_ts*left_entropy(class) + right_size/num_ts*right_entropy(class));
end
info_gain(isnan(info_gain)) = 0;
end

请注意，这只是一个简单的示例实现，仅供参考。在实际应用中，您可能需要进行更多的优化和调整，以获得更好的性能和结果。