上次的帖子没有人回复，我顶了一下，结果变成“已答复”了。所以重新发一遍。

【问题来源】本论坛出版的《MATLAB神经网络30个案例分析》中的第12个案例 SVM神经网络的数据分类预测-葡萄酒种类识别

【程序代码】%使用了林教授最新版SVM工具箱，且已正确安装和编译。程序代码我自己未作什么改动。

close all;

clear;

clc;

format compact;

% 载入测试数据wine,其中包含的数据为classnumber = 3,wine:178*13的矩阵,wine_labes:178*1的列向量

load chapter12_wine.mat;

% 画出测试数据的box可视化图

figure;

boxplot(wine,'orientation','horizontal','labels',categories);

title('wine数据的box可视化图','FontSize',12);

xlabel('属性值','FontSize',12);

grid on;

% 画出测试数据的分维可视化图

figure

subplot(3,5,1);

hold on

for run = 1:178

plot(run,wine_labels(run),'*');

end

xlabel('样本','FontSize',10);

ylabel('类别标签','FontSize',10);

title('class','FontSize',10);

for run = 2:14

subplot(3,5,run);

hold on;

str = ['attrib ',num2str(run-1)];

for i = 1:178

plot(i,wine(i,run-1),'*');

end

xlabel('样本','FontSize',10);

ylabel('属性值','FontSize',10);

title(str,'FontSize',10);

end

% 选定训练集和测试集

% 将第一类的1-30,第二类的60-95,第三类的131-153做为训练集

train_wine = [wine(1:30,:);wine(60:95,:);wine(131:153,:)];

% 相应的训练集的标签也要分离出来

train_wine_labels = [wine_labels(1:30);wine_labels(60:95);wine_labels(131:153)];

% 将第一类的31-59,第二类的96-130,第三类的154-178做为测试集

test_wine = [wine(31:59,:);wine(96:130,:);wine(154:178,:)];

% 相应的测试集的标签也要分离出来

test_wine_labels = [wine_labels(31:59);wine_labels(96:130);wine_labels(154:178)];

[mtrain,ntrain] = size(train_wine);

[mtest,ntest] = size(test_wine);

dataset = [train_wine;test_wine];

% mapminmax为MATLAB自带的归一化函数

[dataset_scale,ps] = mapminmax(dataset',0,1);

dataset_scale = dataset_scale';

train_wine = dataset_scale(1:mtrain,:);

test_wine = dataset_scale( (mtrain+1):(mtrain+mtest),: );

%SVM网络训练

model = svmtrain(train_wine_labels, train_wine, '-c 2 -g 1');

%SVM网络预测

%此句输入出现问题，输出为空。图可以正常输出，只是predict_label, accuracy输出为空。

[predict_label, accuracy] = svmpredict(test_wine_labels, test_wine, model);

% 测试集的实际分类和预测分类图% 通过图可以看出只有一个测试样本是被错分的figure;hold on;plot(test_wine_labels,'o');

plot(predict_label,'r*');xlabel('测试集样本','FontSize',12);

ylabel('类别标签','FontSize',12);legend('实际测试集分类','预测测试集分类');

title('测试集的实际分类和预测分类图','FontSize',12);grid on;

【运行出现的问题】

Usage: [predicted_label, accuracy, decision_values/prob_estimates] = svmpredict(testing_label_vector, testing_instance_matrix, model, 'libsvm_options')

[predicted_label] = svmpredict(testing_label_vector, testing_instance_matrix, model, 'libsvm_options')

Parameters:

model: SVM model structure from svmtrain.

libsvm_options:

-b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); one-class SVM not supported yet

-q : quiet mode (no outputs)

Returns:

predicted_label: SVM prediction output vector.

accuracy: a vector with accuracy, mean squared error, squared correlation coefficient.

prob_estimates: If selected, probability estimate vector.

>> predict_label

predict_label =

[]

>> accuracy

accuracy =

[]

我希望看到预测和精度的输出结果，但不知为什么输出为空。