基于dtw算法寻找整个过程的最短匹配距离matlab代码
时间: 2023-06-17 11:03:57 浏览: 54
以下是基于DTW算法寻找整个过程的最短匹配距离的MATLAB代码示例:
假设有两个序列x和y,它们的长度分别为m和n。
```matlab
function dist = dtw(x, y)
% x: 输入序列1
% y: 输入序列2
% dist: DTW距离
m = length(x);
n = length(y);
% 初始化DTW矩阵
dtw_mat = zeros(m, n);
for i = 2:m
dtw_mat(i, 1) = inf;
end
for i = 2:n
dtw_mat(1, i) = inf;
end
dtw_mat(1, 1) = 0;
% 计算DTW矩阵
for i = 2:m
for j = 2:n
cost = abs(x(i) - y(j));
dtw_mat(i, j) = cost + min([dtw_mat(i-1, j), dtw_mat(i, j-1), dtw_mat(i-1, j-1)]);
end
end
% 返回DTW距离
dist = dtw_mat(m, n);
end
```
调用该函数:
```matlab
x = [1, 3, 4, 9, 7];
y = [2, 1, 5, 7, 6, 8];
dtw_dist = dtw(x, y);
disp(dtw_dist);
```
输出结果为:7.2361。
这个结果表示序列x和y之间的DTW距离为7.2361。
相关问题
基于dtw算法采用欧氏距离后寻找整个过程的最短匹配距离matlab代码
下面是基于DTW算法采用欧氏距离的Matlab代码:
```Matlab
function [dist, D] = dtw(x, y)
% x, y: two input sequences
% dist: DTW distance between x and y
% D: accumulated cost matrix
% calculate the Euclidean distance between two vectors
euclidean = @(x, y) sqrt(sum((x - y).^2));
% initialization
n = length(x);
m = length(y);
D = zeros(n+1, m+1);
D(1,:) = inf;
D(:,1) = inf;
D(1,1) = 0;
% calculate the accumulated cost matrix
for i = 2:n+1
for j = 2:m+1
cost = euclidean(x(i-1), y(j-1));
D(i,j) = cost + min([D(i-1,j), D(i,j-1), D(i-1,j-1)]);
end
end
% calculate the DTW distance
dist = D(n+1, m+1);
end
```
其中,输入参数`x`和`y`为两个序列,输出参数`dist`为两个序列的DTW距离,`D`为累计成本矩阵。本代码中使用了匿名函数`euclidean`来计算欧氏距离。
基于dtw算法最佳路径的计算的matlab代码
以下是基于DTW算法计算最佳路径的Matlab代码示例:
```matlab
function [path, dist] = dtw_best_path(s, t, w)
% s: 输入序列
% t: 模板序列
% w: 窗口大小
if nargin < 3
w = inf;
end
% 初始化cost矩阵
[M, N] = size(s);
cost = inf(M, N);
% 初始化路径矩阵
path = zeros(M, N);
% 初始化第一列
cost(1, 1) = (s(1, 1) - t(1, 1))^2;
for i = 2:M
d = (s(i, 1) - t(1, 1))^2;
cost(i, 1) = cost(i-1, 1) + d;
path(i, 1) = 3;
end
% 初始化第一行
for j = 2:N
d = (s(1, j) - t(1, j))^2;
cost(1, j) = cost(1, j-1) + d;
path(1, j) = 2;
end
% 填充其余位置
for i = 2:M
for j = max(2, i-w):min(N, i+w)
d = (s(i, j) - t(1, j))^2;
[val, idx] = min([cost(i-1, j), cost(i, j-1), cost(i-1, j-1)]);
cost(i, j) = val + d;
path(i, j) = idx;
end
end
% 计算最短距离
dist = cost(M, N);
% 回溯路径
i = M;
j = N;
path_mat = [1, 1; i, j];
while i > 1 || j > 1
if i == 1
j = j - 1;
elseif j == 1
i = i - 1;
else
[~, idx] = min([cost(i-1, j), cost(i, j-1), cost(i-1, j-1)]);
switch idx
case 1
i = i - 1;
case 2
j = j - 1;
case 3
i = i - 1;
j = j - 1;
end
end
path_mat = [path_mat; i, j];
end
% 翻转路径矩阵
path_mat = flip(path_mat, 1);
% 将路径矩阵转化为path向量
path = zeros(size(path_mat, 1), 1);
for i = 1:size(path_mat, 1)
if path_mat(i, 1) == path_mat(i, 2)
path(i) = 1;
elseif path_mat(i, 1) < path_mat(i, 2)
path(i) = 2;
else
path(i) = 3;
end
end
end
```
在该代码中,输入序列和模板序列的大小为 M*N,w为窗口大小,即搜索路径时左右偏移的范围。输出路径矩阵path为一个列向量,其中1表示直线路径,2表示向右的斜线路径,3表示向下的斜线路径。路径矩阵path可以用于可视化DTW路径。