| ----------------------------------------- | |
| --- MATLAB/OCTAVE interface of LIBSVM --- | |
| ----------------------------------------- | |
| Table of Contents | |
| ================= | |
| - Introduction | |
| - Installation | |
| - Usage | |
| - Returned Model Structure | |
| - Examples | |
| - Other Utilities | |
| - Additional Information | |
| Introduction | |
| ============ | |
| This tool provides a simple interface to LIBSVM, a library for support vector | |
| machines (http://www.csie.ntu.edu.tw/~cjlin/libsvm). It is very easy to use as | |
| the usage and the way of specifying parameters are the same as that of LIBSVM. | |
| Installation | |
| ============ | |
| On Unix systems, we recommend using GNU g++ as your | |
| compiler and type 'make' to build 'svmtrain.mexglx' and 'svmpredict.mexglx'. | |
| Note that we assume your MATLAB is installed in '/usr/local/matlab', | |
| if not, please change MATLABDIR in Makefile. | |
| Example: | |
| linux> make | |
| To use Octave, type 'make octave': | |
| Example: | |
| linux> make octave | |
| On Windows systems, pre-built 'svmtrain.mexw32' and 'svmpredict.mexw32' are | |
| included in this package, so no 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 | |
| Taiwan 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 Taiwan 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 <cjlin@csie.ntu.edu.tw>, | |
| or check the FAQ page: | |
| http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html#/Q9:_MATLAB_interface | |