文件名称：svm matlab版本
文件大小：86KB
文件格式：ZIP
更新时间：2020-10-16 08:16:07
svm
need to conduct installation. If you
have modified the sources and would like to re-build the package, type
'mex -setup' in MATLAB to choose a compiler for mex first. Then type
'make' to start the installation.
Starting from MATLAB 7.1 (R14SP3), the default MEX file extension is changed
from .dll to .mexw32 or .mexw64 (depends on 32-bit or 64-bit Windows). If your
MATLAB is older than 7.1, you have to build these files yourself.
Example:
matlab> mex -setup
(ps: MATLAB will show the following messages to setup default compiler.)
Please choose your compiler for building external interface (MEX) files:
Would you like mex to locate installed compilers [y]/n? y
Select a compiler:
[1] Microsoft Visual C/C++ version 7.1 in C:\Program Files\Microsoft Visual Studio
[0] None
Compiler: 1
Please verify your choices:
Compiler: Microsoft Visual C/C++ 7.1
Location: C:\Program Files\Microsoft Visual Studio
Are these correct?([y]/n): y
matlab> make
Under 64-bit Windows, Visual Studio 2005 user will need "X64 Compiler and Tools".
The package won't be installed by default, but you can find it in customized
installation options.
For list of supported/compatible compilers for MATLAB, please check the
following page:
http://www.mathworks.com/support/compilers/current_release/
Usage
=====
matlab> model = svmtrain(training_label_vector, training_instance_matrix [, 'libsvm_options']);
-training_label_vector:
An m by 1 vector of training labels (type must be double).
-training_instance_matrix:
An m by n matrix of m training instances with n features.
It can be dense or sparse (type must be double).
-libsvm_options:
A string of training options in the same format as that of LIBSVM.
matlab> [predicted_label, accuracy, decision_values/prob_estimates] = svmpredict(testing_label_vector, testing_instance_matrix, model [, 'libsvm_options']);
-testing_label_vector:
An m by 1 vector of prediction labels. If labels of test
data are unknown, simply use any random values. (type must be double)
-testing_instance_matrix:
An m by n matrix of m testing instances with n features.
It can be dense or sparse. (type must be double)
-model:
The output of svmtrain.
-libsvm_options:
A string of testing options in the same format as that of LIBSVM.
Returned Model Structure
========================
The 'svmtrain' function returns a model which can be used for future
prediction. It is a structure 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: -b of the decision function(s) wx+b
-Label: label of each class; empty for regression/one-class SVM
-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 not use the option '-b 1', ProbA and ProbB are empty
matrices. If the '-v' option is specified, cross validation is
conducted and the returned model is just a scalar: cross-validation
accuracy for classification and mean-squared error for regression.
More details about this model can be found in LIBSVM FAQ
(http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html) and LIBSVM
implementation document
(http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf).
Result of Prediction
====================
The function 'svmpredict' has three outputs. The first one,
predictd_label, is a vector of predicted labels. The second output,
accuracy, is a vector including accuracy (for classification), mean
squared error, and squared correlation coefficient (for regression).
The third is a matrix containing decision values or probability
estimates (if '-b 1' is specified). If k is the number of classes,
for decision values, each row includes results of predicting
k(k-1)/2 binary-class SVMs. For probabilities, each row contains k values
indicating the probability that the testing instance is in each class.
Note that the order of classes here is the same as 'Label' field
in the model structure.
Examples
========
Train and test on the provided data heart_scale:
matlab> load heart_scale.mat
matlab> model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 0.07');
matlab> [predict_label, accuracy, dec_values] = svmpredict(heart_scale_label, heart_scale_inst, model); % test the training data
For probability estimates, you need '-b 1' for training and testing:
matlab> load heart_scale.mat
matlab> model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 0.07 -b 1');
matlab> load heart_scale.mat
matlab> [predict_label, accuracy, prob_estimates] = svmpredict(heart_scale_label, heart_scale_inst, model, '-b 1');
To use precomputed kernel, you must include sample serial number as
the first column of the training and testing data (assume your kernel
matrix is K, # of instances is n):
matlab> K1 = [(1:n)', K]; % include sample serial number as first column
matlab> model = svmtrain(label_vector, K1, '-t 4');
matlab> [predict_label, accuracy, dec_values] = svmpredict(label_vector, K1, model); % test the training data
We give the following detailed example by splitting heart_scale into
150 training and 120 testing data. Constructing a linear kernel
matrix and then using the precomputed kernel gives exactly the same
testing error as using the LIBSVM built-in linear kernel.
matlab> load heart_scale.mat
matlab>
matlab> % Split Data
matlab> train_data = heart_scale_inst(1:150,:);
matlab> train_label = heart_scale_label(1:150,:);
matlab> test_data = heart_scale_inst(151:270,:);
matlab> test_label = heart_scale_label(151:270,:);
matlab>
matlab> % Linear Kernel
matlab> model_linear = svmtrain(train_label, train_data, '-t 0');
matlab> [predict_label_L, accuracy_L, dec_values_L] = svmpredict(test_label, test_data, model_linear);
matlab>
matlab> % Precomputed Kernel
matlab> model_precomputed = svmtrain(train_label, [(1:150)', train_data*train_data'], '-t 4');
matlab> [predict_label_P, accuracy_P, dec_values_P] = svmpredict(test_label, [(1:120)', test_data*train_data'], model_precomputed);
matlab>
matlab> accuracy_L % Display the accuracy using linear kernel
matlab> accuracy_P % Display the accuracy using precomputed kernel
Note that for testing, you can put anything in the
testing_label_vector. For more details of precomputed kernels, please
read the section ``Precomputed Kernels'' in the README of the LIBSVM
package.
Other Utilities
===============
A matlab function libsvmread reads files in LIBSVM format:
[label_vector, instance_matrix] = libsvmread('data.txt');
Two outputs are labels and instances, which can then be used as inputs
of svmtrain or svmpredict.
A matlab function libsvmwrite writes Matlab matrix to a file in LIBSVM format:
libsvmwrite('data.txt', label_vector, instance_matrix]
The instance_matrix must be a sparse matrix. (type must be double)
These codes are prepared by Rong-En Fan and Kai-Wei Chang from National
* University.
Additional Information
======================
This interface was initially written by Jun-Cheng Chen, Kuan-Jen Peng,
Chih-Yuan Yang and Chih-Huai Cheng from Department of Computer
Science, National * University. The current version was prepared
by Rong-En Fan and Ting-Fan Wu. If you find this tool useful, please
cite LIBSVM as follows
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
For any question, please contact Chih-Jen Lin
【文件预览】：
libsvm-mat-2.91-1
----COPYRIGHT(1KB)
----svmtrain.mexw32(49KB)
----svm_model_matlab.c(8KB)
----libsvmread.c(4KB)
----svm.h(3KB)
----svmpredict.mexw32(21KB)
----libsvmwrite.mexw32(8KB)
----svm_model_matlab.h(201B)
----make.m(273B)
----README(9KB)
----svmpredict.c(9KB)
----svm.cpp(61KB)
----heart_scale.mat(28KB)
----svmtrain.c(11KB)
----Makefile(1KB)
----libsvmwrite.c(2KB)
----libsvmread.mexw32(9KB)