写一个基于布谷鸟算法优化支持向量机参数c和g的matlab 代码

我可以回答这个问题。以下是基于布谷鸟算法优化支持向量机参数c和g的matlab代码：

% 基于布谷鸟算法优化支持向量机参数c和g的matlab代码
% 作者：CSDN开发的C知道

% 导入数据
load iris_dataset.mat
X = meas(:,1:2);
Y = (strcmp('versicolor',species) | strcmp('virginica',species)) + ;

% 定义适应度函数
function f = fitness(X,Y,c,g)
model = fitcsvm(X,Y,'KernelFunction','rbf','BoxConstraint',c,'KernelScale',g);
f = 1 - model.kfoldPredict;
end

% 定义布谷鸟算法
function [best_c,best_g] = cuckoo_search(X,Y)
% 初始化参数
N = 20; % 种群大小
pa = .25; % 父代保留率
alpha = 1; % 步长因子
beta = 1; % 收缩因子
c_min = 1e-3; % c的最小值
c_max = 1e3; % c的最大值
g_min = 1e-3; % g的最小值
g_max = 1e3; % g的最大值
max_iter = 100; % 最大迭代次数
tol = 1e-6; % 收敛精度

% 初始化种群
c = c_min + (c_max - c_min) * rand(N,1);
g = g_min + (g_max - g_min) * rand(N,1);
f = zeros(N,1);
for i = 1:N
f(i) = fitness(X,Y,c(i),g(i));
end

% 迭代搜索
iter = 1;
while iter <= max_iter
% 选择父代
[~,idx] = sort(f);
idx = idx(1:round(pa*N));
c_p = c(idx);
g_p = g(idx);

% 生成新解
c_new = c_p + alpha * randn(size(c_p));
g_new = g_p + alpha * randn(size(g_p));

% 边界处理
c_new = max(c_new,c_min);
c_new = min(c_new,c_max);
g_new = max(g_new,g_min);
g_new = min(g_new,g_max);

% 选择最优解
f_new = zeros(size(c_new));
for i = 1:length(c_new)
f_new(i) = fitness(X,Y,c_new(i),g_new(i));
end
[~,idx] = sort(f_new);
idx = idx(1);
c_best = c_new(idx);
g_best = g_new(idx);
f_best = f_new(idx);

% 更新种群
for i = 1:N
if rand < pa
c(i) = c_p(i);
g(i) = g_p(i);
else
c(i) = c(i) + beta * (c_best - c(i)) + alpha * randn;
g(i) = g(i) + beta * (g_best - g(i)) + alpha * randn;
end
c(i) = max(c(i),c_min);
c(i) = min(c(i),c_max);
g(i) = max(g(i),g_min);
g(i) = min(g(i),g_max);
f(i) = fitness(X,Y,c(i),g(i));
end

% 判断是否收敛
if abs(f_best - f(idx(1))) < tol
break;
end

iter = iter + 1;
end

% 返回最优解
best_c = c_best;
best_g = g_best;
end

% 调用布谷鸟算法优化支持向量机参数
[best_c,best_g] = cuckoo_search(X,Y);

% 训练支持向量机模型
model = fitcsvm(X,Y,'KernelFunction','rbf','BoxConstraint',best_c,'KernelScale',best_g);

% 绘制决策边界
x1 = linspace(min(X(:,1)),max(X(:,1)),100);
x2 = linspace(min(X(:,2)),max(X(:,2)),100);
[X1,X2] = meshgrid(x1,x2);
X_test = [X1(:),X2(:)];
Y_test = predict(model,X_test);
Y_test = reshape(Y_test,size(X1));
figure;
gscatter(X(:,1),X(:,2),Y);
hold on;
contour(X1,X2,Y_test,[.5,.5],'k-');
title(sprintf('c = %g, g = %g',best_c,best_g));