gwr模型的matlab代码
时间: 2023-10-01 09:11:50 浏览: 240
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:
```matlab
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。
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