Two things are provided in this directory: an automatic model selectiontool and a python binding for libsvm.		Part I: Model Selection ToolsIntroduction===============grid.py is a model selection tool for C-SVM classification using rbf(radial basis function) kernel. It uses cross validation (CV) techniqueto estimate the accuracy of each parameter combination in the specified range and helps you to decide the best parameters for your problem.grid.py directly executes libsvm binaries (so no python binding is needed)for cross validation and then draw contour of CV accuracy using gnuplot.You must have libsvm and gnuplot installed before using it. The package gnuplot is available at http://www.gnuplot.info/Usage: grid.py [-c begin,end,step] [-g begin,end,step] [-v fold] [additional parameters for svm-train] datasetThe program conducts v-fold cross valiation using parameter C (and gamma) = 2^begin, 2^(begin+step), ..., 2^end. In grid.py you can specify where the libsvm executable and gnuplot are:svmtrain_exe = "../svm-train"gnuplot_exe = "/usr/bin/gnuplot"# example for windows# svmtrain_exe = r"c:\tmp\libsvm-2.36\windows\svmtrain.exe"# gnuplot_exe = r"c:\tmp\gp371w32\pgnuplot.exe"For windows users, if you are using gnuplot 3.7.1, in addition to editingthe path of binaries, you also have to replace pgnuplot.exe in the gnuplotpackage by the one included in this directory. The original one has a bug.Example=======> python grid.py -c -5,5,1 -g -4,0,1 -v 5 -m 300 heart_scale(Windows users might also use "start grid.py" instead of "python grid.py")Output: two filesdataset.png: the contour plot of the CV accuracy (generated by gnuplot)dataset.out: the log of accuracy at each (C,gamma)Parallel grid search (experimental)===================================You can conduct a parallel grid search by dispatching jobs to a cluster of computers which share the same file system. First, you add machine names in grid.py:remote_workers = ["linux1", "linux5", "linux5"]The same machine (e.g., linux5 here) can be listed more than once ifit has multiple CPUs or has more RAM. If the local machine is thebest, you can also enlarge the nr_local_worker. For example:nr_local_worker = 2Example:> python grid.py heart_scalePassword: ********login ok linux1login ok linux5login ok linux5...The password is the one used for entering your system. If -c, -g, or-v is not specified, defaule values are used.		Part II: Python-to-libsvm interfaceIntroduction============Python (http://www.python.org/) is a programming language suitable forrapid development. This python-to-libsvm interface is developed so users can easily experiment with libsvm using python. The interface is developed with SWIG, The original idea and the SWIG interface file was provided by Carl Staelin(staelin@hpl.hp.com) from HP Labs. The interface was integrated into thelibsvm package by Li-lun Wang (llwang@infor.org) from National TaiwanUniversity. Chih-Chung Chang (b4506055@csie.ntu.edu.tw) from NationalTaiwan University also contributed a lot of useful suggestions and help.Installation============The build process for the various Unix systems is as follows:Before you build the module, you need to find out the python includedirectory, which is typically located at /usr/local/include/python2.2 or/usr/include/python. You can set the variable PYTHON_INCLUDEDIR inMakefile manually or use something like the following:	make PYTHON_INCLUDEDIR=/usr/include/python allAlthough the interface is generated by SWIG, it is not necessary to haveSWIG installed because the generated svmc_wrap.c is included in this package(It was generated using SWIG 1.1p5). If you prefer to generate the interfacewith SWIG on your own, you can simply remove the generated files with	make morecleanbefore building the module.When the build process completes, a shared object called svmc.so will becreated.For win32 systems, the shared library svmc.dll is ready in this package.Usage=====To use the module, the files svm.py and the shared library (namely svmc.soor svmc.dll) must be placed in the current directory, the python librarydirectory, or the directory where the environment variable PYTHONHOMEpoints to. The user then imports everything in svm.py to use libsvm inpython:	from svm import *There are three classes in svm.py, namely svm_parameter, svm_problem, andsvm_model.svm_parameter is used to set the parameters of the training process. Theattributes in svm_parameter include svm_type, kernel_type, degree, gamma,coef0, nu, cache_size, C, eps, p, shrinking, and nr_weight. Available svmtypes include C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, and NU_SVR. Availablekernel types include LINEAR, POLY, RBF, and SIGMOID. The user can setup theparameters with the constructor and keyword arguments:	param = svm_parameter(kernel_type = LINEAR, C = 10)The user can also modify the parameters later:	param.kernel_type = RBFsvm_problem is used to hold the training data for the problem. Theconstructor takes two arguments; the first of them is the list of labels,and the other is the list of samples. For example	prob = svm_problem([1,-1],[[1,0,1],[-1,0,-1]])or equivalently	prob = svm_problem([1,-1],[{1:1,3:1},{1:-1,3:-1}])Once the parameter and problem are ready, we can construct the model:	m = svm_model(prob, param)To predict a new sample with the model:	r = m.predict([1, 1, 1])To save the model to a file:	m.save('test.model')and to load the model from a file:	m = svm_model('test.model')Examples========There are two examples in this package. The one is svm_test.py, and theother is test_cross_validation.py.svm_test.py tests four kernels, i.e., linear, polynomial, RBF, andsigmoid, on the XOR problem with C-SVM.test_cross_validation.py makes use of cross_validation.py which is animplementation of cross validation in python. This example demonstratesloading data from file and does a ten-fold cross validation on theheart_scale dataset.