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--- MATLAB/OCTAVE interface of LIBSVM ---
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-----------------------------------------
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Table of Contents
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=================
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- Introduction
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- Installation
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- Usage
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- Returned Model Structure
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- Other Utilities
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- Examples
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- Additional Information
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Introduction
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============
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This tool provides a simple interface to LIBSVM, a library for support vector
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machines (http://www.csie.ntu.edu.tw/~cjlin/libsvm). It is very easy to use as
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the usage and the way of specifying parameters are the same as that of LIBSVM.
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Installation
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============
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On Windows systems, pre-built mex files are already in the
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directory '..\windows', so please just copy them to the matlab
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directory. Now we provide binary files only for 64bit MATLAB on
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Windows. If you would like to re-build the package, please rely on the
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following steps.
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We recommend using make.m on both MATLAB and OCTAVE. Just type 'make'
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to build 'libsvmread.mex', 'libsvmwrite.mex', 'svmtrain.mex', and
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'svmpredict.mex'.
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On MATLAB or Octave:
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>> make
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If make.m does not work on MATLAB (especially for Windows), try 'mex
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-setup' to choose a suitable compiler for mex. Make sure your compiler
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is accessible and workable. Then type 'make' to do the installation.
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Example:
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matlab>> mex -setup
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MATLAB will choose the default compiler. If you have multiple compliers,
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a list is given and you can choose one from the list. For more details,
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please check the following page:
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https://www.mathworks.com/help/matlab/matlab_external/choose-c-or-c-compilers.html
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On Windows, make.m has been tested via using Visual C++.
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On Unix systems, if neither make.m nor 'mex -setup' works, please use
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Makefile and type 'make' in a command window. Note that we assume
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your MATLAB is installed in '/usr/local/matlab'. If not, please change
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MATLABDIR in Makefile.
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Example:
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linux> make
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To use octave, type 'make octave':
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Example:
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linux> make octave
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For a list of supported/compatible compilers for MATLAB, please check
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the following page:
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http://www.mathworks.com/support/compilers/current_release/
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Usage
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=====
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matlab> model = svmtrain(training_label_vector, training_instance_matrix [, 'libsvm_options']);
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-training_label_vector:
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An m by 1 vector of training labels (type must be double).
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-training_instance_matrix:
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An m by n matrix of m training instances with n features.
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It can be dense or sparse (type must be double).
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-libsvm_options:
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A string of training options in the same format as that of LIBSVM.
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matlab> [predicted_label, accuracy, decision_values/prob_estimates] = svmpredict(testing_label_vector, testing_instance_matrix, model [, 'libsvm_options']);
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matlab> [predicted_label] = svmpredict(testing_label_vector, testing_instance_matrix, model [, 'libsvm_options']);
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-testing_label_vector:
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An m by 1 vector of prediction labels. If labels of test
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data are unknown, simply use any random values. (type must be double)
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-testing_instance_matrix:
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An m by n matrix of m testing instances with n features.
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It can be dense or sparse. (type must be double)
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-model:
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The output of svmtrain.
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-libsvm_options:
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A string of testing options in the same format as that of LIBSVM.
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Returned Model Structure
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========================
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The 'svmtrain' function returns a model which can be used for future
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prediction. It is a structure and is organized as [Parameters, nr_class,
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totalSV, rho, Label, ProbA, ProbB, Prob_density_marks, nSV, sv_coef, SVs]:
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-Parameters: parameters
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-nr_class: number of classes; = 2 for regression/one-class svm
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-totalSV: total #SV
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-rho: -b of the decision function(s) wx+b
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-Label: label of each class; empty for regression/one-class SVM
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-sv_indices: values in [1,...,num_traning_data] to indicate SVs in the training set
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-ProbA: pairwise probability information; empty if -b 0 or in one-class SVM
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-ProbB: pairwise probability information; empty if -b 0 or in one-class SVM
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-Prob_density_marks: probability information for one-class SVM; empty if -b 0 or not in one-class SVM
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-nSV: number of SVs for each class; empty for regression/one-class SVM
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-sv_coef: coefficients for SVs in decision functions
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-SVs: support vectors
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If you do not use the option '-b 1', ProbA and ProbB are empty
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matrices. If the '-v' option is specified, cross validation is
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conducted and the returned model is just a scalar: cross-validation
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accuracy for classification and mean-squared error for regression.
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More details about this model can be found in LIBSVM FAQ
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(http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html) and LIBSVM
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implementation document
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(http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf).
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Result of Prediction
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====================
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The function 'svmpredict' has three outputs. The first one,
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predictd_label, is a vector of predicted labels. The second output,
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accuracy, is a vector including accuracy (for classification), mean
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squared error, and squared correlation coefficient (for regression).
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The third is a matrix containing decision values or probability
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estimates (if '-b 1' is specified). If k is the number of classes
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in training data, for decision values, each row includes results of
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predicting k(k-1)/2 binary-class SVMs. For classification, k = 1 is a
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special case. Decision value +1 is returned for each testing instance,
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instead of an empty vector. For probabilities, each row contains k values
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indicating the probability that the testing instance is in each class.
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Note that the order of classes here is the same as 'Label' field
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in the model structure.
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For one-class SVM, each row contains two elements for probabilities
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of normal instance/outlier.
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Other Utilities
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===============
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A matlab function libsvmread reads files in LIBSVM format:
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[label_vector, instance_matrix] = libsvmread('data.txt');
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Two outputs are labels and instances, which can then be used as inputs
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of svmtrain or svmpredict.
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A matlab function libsvmwrite writes Matlab matrix to a file in LIBSVM format:
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libsvmwrite('data.txt', label_vector, instance_matrix)
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The instance_matrix must be a sparse matrix. (type must be double)
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For 32bit and 64bit MATLAB on Windows, pre-built binary files are ready
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in the directory `..\windows', but in future releases, we will only
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include 64bit MATLAB binary files.
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These codes are prepared by Rong-En Fan and Kai-Wei Chang from National
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Taiwan University.
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Examples
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========
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Train and test on the provided data heart_scale:
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matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
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matlab> model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 0.07');
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matlab> [predict_label, accuracy, dec_values] = svmpredict(heart_scale_label, heart_scale_inst, model); % test the training data
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For probability estimates, you need '-b 1' for training and testing:
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matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
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matlab> model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 0.07 -b 1');
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matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
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matlab> [predict_label, accuracy, prob_estimates] = svmpredict(heart_scale_label, heart_scale_inst, model, '-b 1');
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To use precomputed kernel, you must include sample serial number as
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the first column of the training and testing data (assume your kernel
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matrix is K, # of instances is n):
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matlab> K1 = [(1:n)', K]; % include sample serial number as first column
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matlab> model = svmtrain(label_vector, K1, '-t 4');
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matlab> [predict_label, accuracy, dec_values] = svmpredict(label_vector, K1, model); % test the training data
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We give the following detailed example by splitting heart_scale into
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150 training and 120 testing data. Constructing a linear kernel
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matrix and then using the precomputed kernel gives exactly the same
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testing error as using the LIBSVM built-in linear kernel.
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matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
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matlab>
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matlab> % Split Data
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matlab> train_data = heart_scale_inst(1:150,:);
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matlab> train_label = heart_scale_label(1:150,:);
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matlab> test_data = heart_scale_inst(151:270,:);
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matlab> test_label = heart_scale_label(151:270,:);
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matlab>
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matlab> % Linear Kernel
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matlab> model_linear = svmtrain(train_label, train_data, '-t 0');
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matlab> [predict_label_L, accuracy_L, dec_values_L] = svmpredict(test_label, test_data, model_linear);
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matlab>
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matlab> % Precomputed Kernel
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matlab> model_precomputed = svmtrain(train_label, [(1:150)', train_data*train_data'], '-t 4');
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matlab> [predict_label_P, accuracy_P, dec_values_P] = svmpredict(test_label, [(1:120)', test_data*train_data'], model_precomputed);
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matlab>
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matlab> accuracy_L % Display the accuracy using linear kernel
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matlab> accuracy_P % Display the accuracy using precomputed kernel
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Note that for testing, you can put anything in the
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testing_label_vector. For more details of precomputed kernels, please
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read the section ``Precomputed Kernels'' in the README of the LIBSVM
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package.
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Additional Information
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======================
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This interface was initially written by Jun-Cheng Chen, Kuan-Jen Peng,
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Chih-Yuan Yang and Chih-Huai Cheng from Department of Computer
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Science, National Taiwan University. The current version was prepared
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by Rong-En Fan and Ting-Fan Wu. If you find this tool useful, please
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cite LIBSVM as follows
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Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support
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vector machines. ACM Transactions on Intelligent Systems and
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Technology, 2:27:1--27:27, 2011. Software available at
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http://www.csie.ntu.edu.tw/~cjlin/libsvm
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For any question, please contact Chih-Jen Lin <cjlin@csie.ntu.edu.tw>,
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or check the FAQ page:
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http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html#/Q10:_MATLAB_interface
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