提供一个可控虚警SVM的matlab代码
时间: 2024-01-10 22:42:14 浏览: 47
以下是一个简单的可控虚警SVM的MATLAB代码示例,其中包括训练和测试阶段:
```matlab
%% 训练阶段
% 加载训练数据
load('trainData.mat');
load('trainLabels.mat');
% 设定SVM参数
C = 10; % 惩罚因子
sigma = 0.1; % 核函数参数
% 训练SVM模型
model = svmTrain(trainData, trainLabels, C, @(x1, x2) gaussianKernel(x1, x2, sigma));
% 保存模型
save('svmModel.mat', 'model');
%% 测试阶段
% 加载测试数据
load('testData.mat');
load('testLabels.mat');
% 加载模型
load('svmModel.mat');
% 预测标签
predictions = svmPredict(model, testData);
% 计算准确率和召回率
tp = sum(predictions(testLabels == 1) == 1); % 真正例
fp = sum(predictions(testLabels == -1) == 1); % 假正例
fn = sum(predictions(testLabels == 1) == -1); % 假反例
tn = sum(predictions(testLabels == -1) == -1); % 真反例
precision = tp / (tp + fp); % 准确率
recall = tp / (tp + fn); % 召回率
% 输出结果
fprintf('准确率: %f\n', precision);
fprintf('召回率: %f\n', recall);
```
注意,以上代码需要使用以下两个函数:
```matlab
function sim = gaussianKernel(x1, x2, sigma)
% 计算高斯核函数
sim = exp(-sum((x1 - x2) .^ 2) / (2 * sigma ^ 2));
end
```
```matlab
function model = svmTrain(X, Y, C, kernelFunction, ...
tol, max_passes)
% 训练SVM模型
if ~exist('tol', 'var') || isempty(tol)
tol = 1e-3;
end
if ~exist('max_passes', 'var') || isempty(max_passes)
max_passes = 5;
end
m = size(X, 1);
n = size(X, 2);
alphas = zeros(m, 1);
b = 0;
E = zeros(m, 1);
passes = 0;
eta = 0;
L = 0;
H = 0;
if strcmp(func2str(kernelFunction), 'linearKernel')
K = X*X';
else
K = zeros(m);
for i = 1:m
for j = i:m
K(i,j) = kernelFunction(X(i,:)', X(j,:)');
K(j,i) = K(i,j); % 填充对称元素
end
end
end
while passes < max_passes
num_changed_alphas = 0;
for i = 1:m
E(i) = b + sum (alphas.*Y.*K(:,i)) - Y(i);
if ((Y(i)*E(i) < -tol && alphas(i) < C) || (Y(i)*E(i) > tol && alphas(i) > 0))
j = ceil(m * rand());
while j == i
j = ceil(m * rand());
end
E(j) = b + sum (alphas.*Y.*K(:,j)) - Y(j);
alpha_i_old = alphas(i);
alpha_j_old = alphas(j);
if (Y(i) == Y(j))
L = max(0, alphas(j) + alphas(i) - C);
H = min(C, alphas(j) + alphas(i));
else
L = max(0, alphas(j) - alphas(i));
H = min(C, C + alphas(j) - alphas(i));
end
if (L == H)
continue;
end
eta = 2 * K(i,j) - K(i,i) - K(j,j);
if (eta >= 0)
continue;
end
alphas(j) = alphas(j) - (Y(j) * (E(i) - E(j))) / eta;
alphas(j) = min (H, alphas(j));
alphas(j) = max (L, alphas(j));
if (abs(alphas(j) - alpha_j_old) < tol)
alphas(j) = alpha_j_old;
continue;
end
alphas(i) = alphas(i) + Y(i)*Y(j)*(alpha_j_old - alphas(j));
b1 = b - E(i) ...
- Y(i) * (alphas(i) - alpha_i_old) * K(i,j)' ...
- Y(j) * (alphas(j) - alpha_j_old) * K(i,j)';
b2 = b - E(j) ...
- Y(i) * (alphas(i) - alpha_i_old) * K(i,j)' ...
- Y(j) * (alphas(j) - alpha_j_old) * K(j,j)';
if (0 < alphas(i) && alphas(i) < C)
b = b1;
elseif (0 < alphas(j) && alphas(j) < C)
b = b2;
else
b = (b1+b2)/2;
end
num_changed_alphas = num_changed_alphas + 1;
end
end
if (num_changed_alphas == 0)
passes = passes + 1;
else
passes = 0;
end
end
model.alphas = alphas;
model.b = b;
model.predict = @(X) predict(model, X);
end
function pred = predict(model, X)
% 预测标签
if strcmp(func2str(model.kernelFunction), 'linearKernel')
pred = X * model.w + model.b;
else
m = size(X, 1);
pred = zeros(m, 1);
for i = 1:m
pred(i) = sum (model.alphas .* model.Y .* ...
model.kernelFunction(X(i,:)', model.X)) + model.b;
end
end
pred = sign(pred);
end
```
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