gwr模型的matlab代码

GWR（Growing Neural Gas）模型是一种基于竞争学习的神经网络模型，用于数据聚类和可视化。以下是一个简单的MATLAB实现：

function [w, c] = gwr(data, params)
% GWR algorithm implementation
% Inputs:
%   data: input data (n x d matrix)
%   params: algorithm parameters (struct)
% Outputs:
%   w: weight vectors (m x d matrix)
%   c: cluster labels (m x 1 vector)

% Initialize weight vectors randomly
[m, d] = size(data);
w = rand(m, d);

% Initialize age matrix
age = zeros(m, m);

% Initialize distance matrix
dist = pdist2(w, w);

% Algorithm parameters
params_initial = 0.1; % initial learning rate
params_decay = 0.95; % learning rate decay
params_neighbor = 0.5; % neighbor learning rate
params_threshold = 0.1; % node threshold
params_max_nodes = 100; % maximum number of nodes

% Main loop
for t = 1:params_max_nodes
    % Select random data point
    i = randi(m);
    x = data(i,:);
    
    % Compute distances and find winning node
    d = sqrt(sum((w - x).^2, 2));
    [~, j] = min(d);
    
    % Update winner's weight vector
    w(j,:) = w(j,:) + params_initial*(x - w(j,:));
    
    % Update age matrix
    age(j,:) = age(j,:) + 1;
    age(:,j) = age(:,j) + 1;
    
    % Remove nodes with age greater than threshold
    [r,c] = find(age > params_threshold);
    for k = 1:length(r)
        age(r(k),c(k)) = 0;
        age(c(k),r(k)) = 0;
        w(r(k),:) = [];
        age(r(k),:) = [];
        age(:,r(k)) = [];
    end
    
    % Add new node if necessary
    if size(w,1) < params_max_nodes && mod(t,10) == 0
        [~,j] = max(d);
        w(end+1,:) = (x + w(j,:))/2;
        age(end+1,:) = 0;
        age(:,end+1) = 0;
        m = m + 1;
    end
    
    % Update distances
    dist = pdist2(w, w);
    
    % Update learning rate
    params_initial = params_initial*params_decay;
    
    % Update neighbor learning rate
    params_neighbor = params_neighbor*params_decay;
    
    % Update weight vectors of neighbors
    neighbors = find(dist(j,:) < params_neighbor);
    for k = 1:length(neighbors)
        w(neighbors(k),:) = w(neighbors(k),:) + params_neighbor*(x - w(neighbors(k),:));
    end
end

% Assign data points to clusters
c = dsearchn(w, data);

end

参数结构体`params`应该包含上述算法参数的值。`data`是输入数据，每行是一个数据点，`w`是输出的权重向量矩阵，每行对应一个节点，`c`是每个数据点的聚类标签。

请注意，这只是一个简单的实现，可能需要进行优化和调整以适应不同的数据集。