基于SVM的故障模式识别

function chapter_GridSearch
clear all
clc
format compact;
load shuju.mat
load shujulabel.mat
load categories 
% 画出测试数据的box可视化图
figure;
boxplot(shuju,'orientation','horizontal','labels',categories);
title('gear fault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(3,3,1);
hold on
for run = 1:30
    plot(run,shujulabel(run),'*');
end
xlabel('样本','FontSize',10);
ylabel('类别标签','FontSize',10);
title('class','FontSize',10);
for run = 2:9
    subplot(3,3,run);
    hold on;
    str = ['attrib ',num2str(run-1)];
    for i = 1:30
        plot(i,shuju(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1-5,第二类的11-15,第三类的21-25做为训练集
train_matrix = [shuju(1:5,:);shuju(11:15,:);shuju(21:25,:)];
% 相应的训练集的标签也要分离出来
train_label = [shujulabel(1:5);shujulabel(11:15);shujulabel(21:25)];
% 将第一类的6-10,第二类的16-20,第三类的26-30做为测试集
test_matrix = [shuju(6:10,:);shuju(16:20,:);shuju(26:30,:)];
% 相应的测试集的标签也要分离出来
test_label = [shujulabel(6:10);shujulabel(16:20);shujulabel(26:30)];

%% 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_matrix);
[mtest,ntest] = size(test_matrix);

dataset = [train_matrix;test_matrix];
% mapminmax为MATLAB自带的归一化函数
[dataset_scale,ps] = mapminmax(dataset',0,1);
dataset_scale = dataset_scale';

train_matrix = dataset_scale(1:mtrain,:);
test_matrix = 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_label,train_matrix,-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_label,train_matrix,-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_label,train_matrix,cmd);
%% SVM网络预测
[predict_label,accuracy,dec_values] = svmpredict(test_label,test_matrix,model);

% 测试集分类准确率
total = length(test_label);
right = sum(predict_label == test_label);
disp('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

figure;
plot(test_label,'o');hold on;
plot(predict_label,'r*');hold on;
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

%
% by faruto
%Email:[email protected] QQ:516667408 http://blog.sina.com.cn/faruto BNU
%last modified 2010.01.17
%Super Moderator @ www.ilovematlab.cn

% 若转载请注明：
% faruto and liyang , LIBSVM-farutoUltimateVersion 
% a toolbox with implements for support vector machines based on libsvm, 2009. 
% Software available at http://www.ilovematlab.cn
% 
% Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for
% support vector machines, 2001. Software available at
% http://www.csie.ntu.edu.tw/~cjlin/libsvm

% 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 = 'c和g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 = 'c和g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);