下面这个是测试的M文件

%% Matlab神经网络43个案例分析

%% 清空环境变量

function chapter_GridSearch

close all;

clear;

clc;

format compact;

%% 数据提取

% 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 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)];

%% 数据预处理

% 数据预处理,将训练集和测试集归一化到[0,1]区间

[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),: );

%% 选择最佳的SVM参数c&g

% 首先进行粗略选择: c&g 的变化范围是 2^(-10),2^(-9),...,2^(10)

[bestacc,bestc,bestg] = SVMcgForClass(train_wine_labels,train_wine,-10,10,-10,10);

% 打印粗略选择结果

disp('打印粗略选择结果');

str = sprintf( 'Best Cross Validation Accuracy = %g%% Best c = %g Best g = %g',bestacc,bestc,bestg);

disp(str);

% 根据粗略选择的结果图再进行精细选择: c 的变化范围是 2^(-2),2^(-1.5),...,2^(4), g 的变化范围是 2^(-4),2^(-3.5),...,2^(4),

[bestacc,bestc,bestg] = SVMcgForClass(train_wine_labels,train_wine,-2,4,-4,4,3,0.5,0.5,0.9);

% 打印精细选择结果

disp('打印精细选择结果');

str = sprintf( 'Best Cross Validation Accuracy = %g%% Best c = %g Best g = %g',bestacc,bestc,bestg);

disp(str);

%% 利用最佳的参数进行SVM网络训练

cmd = ['-c ',num2str(bestc),' -g ',num2str(bestg)];

model = svmtrain(train_wine_labels,train_wine,cmd);

%% SVM网络预测

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

% 打印测试集分类准确率

total = length(test_wine_labels);

right = sum(predict_label == test_wine_labels);

disp('打印测试集分类准确率');

str = sprintf( 'Accuracy = %g%% (%d/%d)',accuracy(1),right,total);

disp(str);

%% 结果分析

% 测试集的实际分类和预测分类图

% 通过图可以看出只有三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;

snapnow;

%% 子函数 SVMcgForClass.m

function [bestacc,bestc,bestg] = SVMcgForClass(train_label,train,cmin,cmax,gmin,gmax,v,cstep,gstep,accstep)

%SVMcg cross validation by faruto

% about the parameters of SVMcg

if nargin < 10

accstep = 4.5;

end

if nargin < 8

cstep = 0.8;

gstep = 0.8;

end

if nargin < 7

v = 5;

end

if nargin < 5

gmax = 8;

gmin = -8;

end

if nargin < 3

cmax = 8;

cmin = -8;

end

% X:c Y:g cg:CVaccuracy

[X,Y] = meshgrid(cmin:cstep:cmax,gmin:gstep:gmax);

[m,n] = size(X);

cg = zeros(m,n);

eps = 10^(-4);

% record acc with different c & g,and find the bestacc with the smallest c

bestc = 1;

bestg = 0.1;

bestacc = 0;

basenum = 2;

for i = 1:m

for j = 1:n

cmd = ['-v ',num2str(v),' -c ',num2str( basenum^X(i,j) ),' -g ',num2str( basenum^Y(i,j) )];

cg(i,j) = svmtrain(train_label, train, cmd);

if cg(i,j) <= 55

continue;

end

if cg(i,j) > bestacc

bestacc = cg(i,j);

bestc = basenum^X(i,j);

bestg = basenum^Y(i,j);

end

if abs( cg(i,j)-bestacc )<=eps && bestc > basenum^X(i,j)

bestacc = cg(i,j);

bestc = basenum^X(i,j);

bestg = basenum^Y(i,j);

end

end

end

% to draw the acc with different c & g

figure;

[C,h] = contour(X,Y,cg,70:accstep:100);

clabel(C,h,'Color','r');

xlabel('log2c','FontSize',12);

ylabel('log2g','FontSize',12);

firstline = 'SVC参数选择结果图(等高线图)[GridSearchMethod]';

secondline = ['Best c=',num2str(bestc),' g=',num2str(bestg), ...

' CVAccuracy=',num2str(bestacc),'%'];

title({firstline;secondline},'Fontsize',12);

grid on;

figure;

meshc(X,Y,cg);

% mesh(X,Y,cg);

% surf(X,Y,cg);

axis([cmin,cmax,gmin,gmax,30,100]);

xlabel('log2c','FontSize',12);

ylabel('log2g','FontSize',12);

zlabel('Accuracy(%)','FontSize',12);

firstline = 'SVC参数选择结果图(3D视图)[GridSearchMethod]';

secondline = ['Best c=',num2str(bestc),' g=',num2str(bestg), ...

' CVAccuracy=',num2str(bestacc),'%'];

title({firstline;secondline},'Fontsize',12);