matlab实现SVM算法进行分类
1.实验内容
（1）掌握支持向量机（SVM）的原理，核函数类型选择以及核参数选择原则等，并用malab的SVM函数求解各类分类问题实例。
（2）熟悉基于libsvm二分类的一般流程与方法，并对"bedroom,forest"两组数据进行分类（二分类），最后对试验分类的准确率进行分析。
2.实验过程
（1）线性分类
>> sp = [3,7;6,4;4,6;7.5,6.5]
sp =
3.0000 7.0000
6.0000 4.0000
4.0000 6.0000
7.5000 6.5000
>> nsp = size(sp)
nsp =
4 2
>> sn = [1,3;5,2;7,3;3,4;6,1]
sn =
1 3
5 2
7 3
3 4
6 1
>> nsn = size(sn)
nsn =
5 2
>> sd = [sp;sn]
sd =
3.0000 7.0000
6.0000 4.0000
4.0000 6.0000
7.5000 6.5000
1.0000 3.0000
5.0000 2.0000
7.0000 3.0000
3.0000 4.0000
6.0000 1.0000
>> lsd = [true true true true false false false false false]
lsd =
1×9 logical 数组
1 1 1 1 0 0 0 0 0
>> Y = nominal(lsd)
Y =
1×9 nominal 数组
true true true true false false false false false
>> figure(1);
>> subplot(1,2,1)
>> plot(sp(1:nsp,1),sp(1:nsp,2),'m+');
>> hold on
>> plot(sn(1:nsn,1),sn(1:nsn,2),'c*');
>> subplot(1,2,2)
>> svmStruct = svmtrain(sd,Y,'showplot',true);
警告: svmtrain will be removed in a future release. Use fitcsvm instead.
> In svmtrain (line 230)
>> 线性分类结果：
（2）非线性分类
>> sp = [3,7;6,6;4,6;5,6.5]
sp =
3.0000 7.0000
6.0000 6.0000
4.0000 6.0000
5.0000 6.5000
>> nsp = size(sp);
>> sn = [1,2;3,5;7,3;3,4;6,2.7;4,3;2,7];
>> nsn = size(sn)
nsn =
7 2
>> sd = [sp;sn]
sd =
3.0000 7.0000
6.0000 6.0000
4.0000 6.0000
5.0000 6.5000
1.0000 2.0000
3.0000 5.0000
7.0000 3.0000
3.0000 4.0000
6.0000 2.7000
4.0000 3.0000
2.0000 7.0000
>> lsd = [true true true true false false false false false false false]
lsd =
1×11 logical 数组
1 1 1 1 0 0 0 0 0 0 0
>> Y = nominal(lsd)
Y =
1×11 nominal 数组
true true true true false false false false false false false
>> figure(1);
>> subplot(1,2,1)
>> plot(sp(1:nsp,1),sp(1:nsp,2),'m+');
>> hold on
>> plot(sn(1:nsn,1),sn(1:nsn,2),'c*');
>> subplot(1,2,2)
>> svmStruct = svmtrain(sd,Y,'kernel_Function','quadratic','showplot',true);
警告: svmtrain will be removed in a future release. Use fitcsvm instead.
> In svmtrain (line 230)
>> %use the trained svm (svmStruct) to classify the data
>> RD = svmclassify(svmStruct,sd,'showplot',true)
警告: svmclassify will be removed in a future release. Use the predict method of an object returned by fitcsvm instead.
> In svmclassify (line 47)
RD =
11×1 nominal 数组
true
true
true
true
false
false
false
false
false
false
false
>> % RD is the classification result vector非线性分类结果
（3）高斯分类
>> sp = [5,4.5;3,7;6,6;4,6;5,6.5]%positive sample points
sp =
5.0000 4.5000
3.0000 7.0000
6.0000 6.0000
4.0000 6.0000
5.0000 6.5000
>> nsp = size(sp);
>> sn = [1,2;3,5;7,3;3,4;6,2.7;4,3;2,7]
sn =
1.0000 2.0000
3.0000 5.0000
7.0000 3.0000
3.0000 4.0000
6.0000 2.7000
4.0000 3.0000
2.0000 7.0000
>> nsn = size(sn)
nsn =
7 2
>> sd = [sp,sn]
错误使用 horzcat
串联的矩阵的维度不一致。
>> sd = [sp;sn]
sd =
5.0000 4.5000
3.0000 7.0000
6.0000 6.0000
4.0000 6.0000
5.0000 6.5000
1.0000 2.0000
3.0000 5.0000
7.0000 3.0000
3.0000 4.0000
6.0000 2.7000
4.0000 3.0000
2.0000 7.0000
>> lsd = [true true true true true false false false false false false false]
lsd =
1×12 logical 数组
1 1 1 1 1 0 0 0 0 0 0 0
>> Y = nominal(lsd)
Y =
1×12 nominal 数组
true true true true true false false false false false false false
>> figure(1);
>> subplot(1,2,1)
>> plot(sp(1:nsp,1),sp(1:nsp,2),'m+');
>> hold on
>> plot(sn(1:nsn,1),sn(1:nsn,2),'c*');
>> subplot(1,2,2)
>> svmStruct = svmtrain(sd,Y,'Kernel_Function','rbf','rbf_sigma',0.6,'method','SMO','showplot',true);
警告: svmtrain will be removed in a future release. Use fitcsvm instead.
> In svmtrain (line 230)
>> % use the trained svm (svmStruct) to classify the data
>> RD = svmclassify(svmStruct,sd,'showplot',true)
警告: svmclassify will be removed in a future release. Use the predict method
of an object returned by fitcsvm instead.
> In svmclassify (line 47)
RD =
12×1 nominal 数组
true
true
true
true
true
false
false
false
false
false
false
false
>> %RD is the classification result vector高斯分类结果图
（4）对两组图片数据进行分类
>> load('bedroom.mat')
>> load('forest.mat')
>> load('labelset.mat')
>> dataset = [bedroom;MITforest];
>> save dataset.mat dataset
>> %选定训练集和测试集，将第一类的1-5，第二类的11-15作为训练集
>> train_set = [dataset(1:5,:);dataset(11:15,:)];
>> %相应的训练集的标签也要分离出来
>> train_set_labels = [lableset(1:5);lableset(11:15)];
>> %将第一类的6-10，,第二类的16-20作为测试集
>> test_set = [dataset(6:10,:);dataset(16:20,:)];
>> %相应的测试集的标签也要分离出来
>> test_set_labels = [lableset(6:10);lableset(16:20)];
>> %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_set);
>> [mtest,ntest] = size(test_set);
>> test_dataset = [train_set;test_set];
>> %mapminmax 为matlab自带的归一化函数
>> [dataset_scale,ps] = mapminmax (test_dataset,0,1);
>> dataset_scale = dataset_scale;
>> train_set = dataset_scale(1:mtrain,:);
>> test_set = dataset_scale((mtrain+1):(mtrain+mtest),:);
>> %SVM网络训练
>> model = svmtrain(train_set_labels,train_set,'-s 2-c 1 -g 0.07');
警告: svmtrain will be removed in a future release. Use fitcsvm instead.
> In svmtrain (line 230)
错误使用 svmtrain (line 236)
Y must be a vector or a character array.
>> %svm网络训练
>> [predict_label] = svmpredict(test_set_labels,test_set,model);
未定义函数或变量 'model'。
>> model = svmtrain(train_set_labels,train_set,'-s 2-c 1 -g 0.07');
警告: svmtrain will be removed in a future release. Use fitcsvm instead.
> In svmtrain (line 230)
错误使用 svmtrain (line 236)
Y must be a vector or a character array.
>> model = svmtrain(train_set_labels,train_set,'-s 2 -c 1 -g 0.07');
警告: svmtrain will be removed in a future release. Use fitcsvm instead.
> In svmtrain (line 230)
错误使用 svmtrain (line 236)
Y must be a vector or a character array.
>> [dataset_scale,ps] = mapminmax(test_dataset',0,1);
>> dataset_scale = dataset_scale';
>> train_set = dataset_scale(1:mtrain,:);
>> test_set = dataset_scale((mtrain+1):(mtrain+mtest),:);
>> %svm网络训练
>> model = svmtrain(train_set_labels,train_set, '-s 2 -c 1 -g 0.07');
警告: svmtrain will be removed in a future release. Use fitcsvm instead.
> In svmtrain (line 230)
错误使用 svmtrain (line 236)
Y must be a vector or a character array.
>> model = fitcsvm(train_set_labels,train_set, '-s 2 -c 1 -g 0.07');
错误使用 internal.stats.parseArgs (line 42)
Wrong number of arguments.
出错 classreg.learning.paramoptim.parseOptimizationArgs (line 5)
[OptimizeHyperparameters,~,~,RemainingArgs] = internal.stats.parseArgs(...
出错 fitcsvm (line 302)
[IsOptimizing, RemainingArgs] =
classreg.learning.paramoptim.parseOptimizationArgs(varargin);
>> model = fitcsvm(train_set_labels,train_set, '-s 2 -c 1 -g 0.07');
错误使用 internal.stats.parseArgs (line 42)
Wrong number of arguments.
出错 classreg.learning.paramoptim.parseOptimizationArgs (line 5)
[OptimizeHyperparameters,~,~,RemainingArgs] = internal.stats.parseArgs(...
出错 fitcsvm (line 302)
[IsOptimizing, RemainingArgs] =
classreg.learning.paramoptim.parseOptimizationArgs(varargin);
>> model = svmtrain(train_set_labels,train_set, '-s 2 -c 1 -g 0.07');
*
optimization finished, #iter = 3
obj = 9.208867, rho = 3.781010
nSV = 6, nBSV = 4
>> [predict_label] = svmpredict(test_set_labels,test_set,model);
Accuracy = 30% (3/10) (classification)
>> %结果
>> figure;
>> hold on;
>> plot(test_set_labels,'o');
>> plot(predict_labels,'r*');
未定义函数或变量 'predict_labels'。
是不是想输入:
>> plot(predict_label,'r*');
>> xlabel('测试集样本‘，’FontSize‘,12);
xlabel('测试集样本‘，’FontSize‘,12);
↑
错误: 字符向量未正常终止。
>> 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;
>>
**出现问题**
> model = svmtrain(train_set_labels,train_set, '-s 2 -c 1 -g 0.07');
警告: svmtrain will be removed in a future release. Use fitcsvm instead.
> In svmtrain (line 230)
错误使用 svmtrain (line 236)
Y must be a vector or a character array.出现问题
> model = svmtrain(train_set_labels,train_set, '-s 2 -c 1 -g 0.07');
警告: svmtrain will be removed in a future release. Use fitcsvm instead.
> In svmtrain (line 230)
错误使用 svmtrain (line 236)
Y must be a vector or a character array.
解决办法
出现这个问题是因为libsvm的路径不对。解决办法是，将数据加载到matlab的工作区，然后将文件路径指到libsvm的路径即可。我这里是C:\Program Files\MATLAB\R2017b\toolbox\libsvm-3.23
分类准确率只有30%
图片数据分类结果图
