libsvm3.21在MATLAB2014a的安装应用之一：安装

转自：关于SVM的那点破事 http://www.matlabsky.com/thread-10966-1-1.html
MATLAB技术论坛：MATLABsky.com；视频教学下载交流地址：http://www.matlabsky.com/forum-5-1.html

安装方法：http://v.youku.com/v_showMini/id_XMjc2NTY3MzYw_ft_131.html

1.设置路径：用Add with Subfolders添加目录（将工具箱所在文件夹的子目录也添加到MATLAB工作搜索目录）

2.选择编译器：mex -setup（mex后面有空格）

3.编译：make（要把MATLAB当前目录调整到libsvm工具箱所在文件夹）双击make.m文件

PS：运行help train得到的是MATLAB自带的svmtrain函数的帮助文件

         运行help svmpredict会有报错：svmpredict not found

         工具箱中的README稳健可以算是帮助文件

         table键对函数进行补全

测试数据：

1.libsvm官方提供测试数据（原始数据格式是给libsvm C++版本使用，MATLAB平台下需要使用libsvmread进行格式转换）
http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/
ps：数据文件所在必须是当前文件夹，不能为当前文件夹的子文件

    数据文件是在C++平台下写的，MATLAB中不能直接load，用libsvmread

    [label_vector, instance_matrix] = libsvmread('filename')（后面不能有；）

例：[label_vector, instance_matrix] = libsvmread('breast_cancer.txt')
ps：在工作区可查看当前数据集的数据规模与属性个数
模型训练：model = svmtrain(label_vector,instance_matrix)
2.UCI数据库
http://archive.ics.uci.edu/ml/
导入MATLAB并转换为.mat文件：

1）导入：file--import file（2014a导入数据），其分隔符为comma（逗号）

2）load wine.data / load('wine.data')

3）抽取标签，抽取属性矩阵，保存为MATLAB可用的.mat文件

   wine_label = wine(:,1);

   wine_data = wine(:,2:end);

   save winedat.mat

4）模型应用

load winedat;

modelw = svmtrain(wine_label,wine_data);

[plabelw,accuracyw] = svmpredict(wine_label,wine_data,modelw);

ps：File--import data：导入外部数据文件进入MATLAB文件（非自带）/uiimport命令

         数据保存：36min

        F9：右侧选中行运行（脚本函数）

LibSVM_README（部分翻译）

Returned Model Structure

========================

The 'svmtrain' function returns a model which canbe used for future prediction.  It is astructure and is organized as [Parameters, nr_class, totalSV, rho,
Label,ProbA, ProbB, nSV, sv_coef, SVs]:

       -Parameters: parameters

       -nr_class: number of classes; = 2 for regression/one-class svm

       -totalSV: total #SV

        -rho: -bof the decision function(s) wx+b

        -Label:label of each class; empty for regression/one-class SVM

       -sv_indices: values in [1,...,num_traning_data] to indicate SVs in thetraining set

        -ProbA:pairwise probability information; empty if -b 0 or in one-class SVM

        -ProbB:pairwise probability information; empty if -b 0 or in one-class SVM

        -nSV:number of SVs for each class; empty for regression/one-class SVM

       -sv_coef: coefficients for SVs in decision functions

        -SVs:support vectors

If  you do notuse the option '-b 1', ProbA and ProbB are empty matrices. If the '-v' optionis specified, cross validation
is conducted and the returned model is just ascalar: cross-validation accuracy for classification and mean-squared error forregression.

More details about this model can be found in LIBSVMFAQ

(http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html)and LIBSVM

implementation document

(http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf).

‘svmtrain’返回一个用于预测的模型，该模型为[Parameters, nr_class, totalSV,rho, Label, ProbA, ProbB, nSV, sv_coef, SVs]

-Parameters: parameters

-nr_class: 类别个数;回归SVM和1分类SVM为2

-totalSV: total #SV

-rho: -b of the decision function(s) wx+b

-Label: 标签; 回归SVM和1分类SVM 为空

-sv_indices: 在[1,...,num_traning_data]范围内的值，表示在训练集中的SVs

-ProbA: 成对概率信息;-b 0或1分类SVM 中为空

-ProbB: 同上

-nSV: 每个类的SVs; 回归SVM和1分类SVM 为空

-sv_coef: 决策函数中SVs的系数

-SVs: support vectors

如果不使用选项'-b1'，ProbA和ProbB为空矩阵；如果设定‘-v’选项，执行交叉验证，返回模型是标量（scalar）

分类—交叉验证准确率；回归—均方误差

Result of Prediction

====================

The function 'svmpredict' has three outputs. The firstone, predictd_label, is a vector of predicted labels. The second output, accuracy,is a vector including accuracy (for classification), mean squared error, andsquared correlation coefficient (for regression).
The third is a matrixcontaining decision values or probability estimates (if '-b 1' is specified).If k is the number of classes in training data, for decision values, each rowincludes results of predicting k(k-1)/2 binary-class SVMs.For[S3] classification,
k = 1 is a special case. Decision value +1 is returned for eachtesting instance, instead of an empty vector. For probabilities, each rowcontains k values indicating the probability that the testing instance is ineach class. Note that the order of classes here
is the same as 'Label' field inthe model structure.

‘svmpredict’有三个输出：

1.  预测标签向量

2.  准确率。向量，包括：准确率（分类），均方误差，平方相关系数（回归）

3.  包含决策值/概率预测的矩阵（当设定’-b 1’时）

若k为训练集中类别个数，决策值每一列包含对k(k-1)/2binary-class SVMs的预测结果。分类问题中，k=1是特殊情况，此时对每个测试实例返回Decisionvalue +1，而非空向量。每列包含的k个值分别表示测试实例在每个类别中的概率。注意，此处类别的顺序和模型结构中’Label’的一样。

Other Utilities

===============

A matlab function libsvmread reads files in LIBSVMformat:

[label_vector, instance_matrix] = libsvmread('data.txt');

Two outputs are labels and instances, which can thenbe used as inputs of svmtrain or svmpredict.

A matlab function libsvmwrite writes Matlab matrix toa file in LIBSVM format:

libsvmwrite('data.txt', label_vector, instance_matrix)

The instance_matrix must be a sparse matrix. (typemust be double) For 32bit and 64bit MATLAB on Windows, pre-built binary filesare ready in the directory `..\windows', but in future releases, we will onlyinclude 64bit MATLAB binary files.

Libsvmread’读取LIBSVM格式的数据，输出标签和实例，可用于svmtrain和svmpredict的输入。

‘Libsvmwrite’将MATLAB矩阵写入一个LIBSVM格式的文件中。Instance_matrix必须是稀疏矩阵。对Windows32位和64位的MATLAB，预编译二进制文件在目录’..\windows'下，但以后的版本将只包含64位二进制文件。