我需要在SVM中调整C＆Sigma时跟踪F1分数，例如以下代码跟踪准确度，我需要将其更改为F1-Score但我无法做到......

%# read some training data

[labels,data] = libsvmread('./heart_scale');

%# grid of parameters

folds = 5;

[C,gamma] = meshgrid(-5:2:15, -15:2:3);

%# grid search, and cross-validation

cv_acc = zeros(numel(C),1);

for i=1:numel(C)

cv_acc(i) = svmtrain(labels, data, ...

sprintf('-c %f -g %f -v %d', 2^C(i), 2^gamma(i), folds));

end

%# pair (C,gamma) with best accuracy

[~,idx] = max(cv_acc);

%# now you can train you model using best_C and best_gamma

best_C = 2^C(idx);

best_gamma = 2^gamma(idx);

%# ...

我看过以下两个链接

我知道我必须首先在训练数据中找到最佳的C和gamma / sigma参数，然后使用这两个值进行LEAVE-ONE-OUT交叉验证分类实验，所以我现在想要的是先做一个网格 - 调整C＆sigma的研究 . 请我更喜欢使用MATLAB-SVM而不是LIBSVM . 以下是我的LEAVE-ONE-OUT交叉验证分类代码 .

... clc

clear all

close all

a = load('V1.csv');

X = double(a(:,1:12));

Y = double(a(:,13));

% train data

datall=[X,Y];

A=datall;

n = 40;

ordering = randperm(n);

B = A(ordering, :);

good=B;

input=good(:,1:12);

target=good(:,13);

CVO = cvpartition(target,'leaveout',1);

cp = classperf(target); %# init performance tracker

svmModel=[];

for i = 1:CVO.NumTestSets %# for each fold

trIdx = CVO.training(i);

teIdx = CVO.test(i);

%# train an SVM model over training instances

svmModel = svmtrain(input(trIdx,:), target(trIdx), ...

'Autoscale',true, 'Showplot',false, 'Method','ls', ...

'BoxConstraint',0.1, 'Kernel_Function','rbf', 'RBF_Sigma',0.1);

%# test using test instances

pred = svmclassify(svmModel, input(teIdx,:), 'Showplot',false);

%# evaluate and update performance object

cp = classperf(cp, pred, teIdx);

end

%# get accuracy

accuracy=cp.CorrectRate*100

sensitivity=cp.Sensitivity*100

specificity=cp.Specificity*100

PPV=cp.PositivePredictiveValue*100

NPV=cp.NegativePredictiveValue*100

%# get confusion matrix

%# columns:actual, rows:predicted, last-row: unclassified instances

cp.CountingMatrix

recallP = sensitivity;

recallN = specificity;

precisionP = PPV;

precisionN = NPV;

f1P = 2*((precisionP*recallP)/(precisionP + recallP));

f1N = 2*((precisionN*recallN)/(precisionN + recallN));

aF1 = ((f1P+f1N)/2);

我已经改变了代码，但我犯了一些错误，我收到了错误，

a = load('V1.csv');

X = double(a(:,1:12));

Y = double(a(:,13));

% train data

datall=[X,Y];

A=datall;

n = 40;

ordering = randperm(n);

B = A(ordering, :);

good=B;

inpt=good(:,1:12);

target=good(:,13);

k=10;

cvFolds = crossvalind('Kfold', target, k); %# get indices of 10-fold CV

cp = classperf(target); %# init performance tracker

svmModel=[];

for i = 1:k

testIdx = (cvFolds == i); %# get indices of test instances

trainIdx = ~testIdx;

C = 0.1:0.1:1;

S = 0.1:0.1:1;

fscores = zeros(numel(C), numel(S)); %// Pre-allocation

for c = 1:numel(C)

for s = 1:numel(S)

vals = crossval(@(XTRAIN, YTRAIN, XVAL, YVAL)(fun(XTRAIN, YTRAIN, XVAL, YVAL, C(c), S(c))),inpt(trainIdx,:),target(trainIdx));

fscores(c,s) = mean(vals);

end

end

end

[cbest, sbest] = find(fscores == max(fscores(:)));

C_final = C(cbest);

S_final = S(sbest);

.......

和功能.....

.....

function fscore = fun(XTRAIN, YTRAIN, XVAL, YVAL, C, S)

svmModel = svmtrain(XTRAIN, YTRAIN, ...

'Autoscale',true, 'Showplot',false, 'Method','ls', ...

'BoxConstraint', C, 'Kernel_Function','rbf', 'RBF_Sigma', S);

pred = svmclassify(svmModel, XVAL, 'Showplot',false);

cp = classperf(YVAL, pred)

%# get accuracy

accuracy=cp.CorrectRate*100

sensitivity=cp.Sensitivity*100

specificity=cp.Specificity*100

PPV=cp.PositivePredictiveValue*100

NPV=cp.NegativePredictiveValue*100

%# get confusion matrix

%# columns:actual, rows:predicted, last-row: unclassified instances

cp.CountingMatrix

recallP = sensitivity;

recallN = specificity;

precisionP = PPV;

precisionN = NPV;

f1P = 2*((precisionP*recallP)/(precisionP + recallP));

f1N = 2*((precisionN*recallN)/(precisionN + recallN));

fscore = ((f1P+f1N)/2);

end