python svm 源码实现

Python-to-libsvm interface Introduction ============ Python (http://www.python.org/) is a programming language suitable for rapid 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 the libsvm package by Li-lun Wang (llwang@infor.org) from National Taiwan University. Chih-Chung Chang (b4506055@csie.ntu.edu.tw) from National Taiwan University also contributed a lot of useful suggestions and help. Installation ============ We first show the instructions for Unix and then those for MS Windows. The build process for the various Unix systems is as follows: Before you build the module, you need to find out the python include directory, which is typically located at /usr/local/include/python2.4 or /usr/include/python. You can set the variable PYTHON_INCLUDEDIR in Makefile manually or use something like the following: make PYTHON_INCLUDEDIR=/usr/include/python all Although the interface is generated by SWIG, it is not necessary to have SWIG installed because the generated svmc_wrap.c is included in this package (It was generated using SWIG 1.3.31). If you prefer generating the interface with SWIG on your own, you can simply remove the generated files with make moreclean before building the module. Note that SWIG version > 1.3.7 should be used. When the build process completes, a shared object called svmc.so will be created. For win32 systems, the shared library svmc.pyd is ready in the directory windows/python. You need to copy it to this directory. The .pyd file depends on different versions of python, so you may have to re-make it by following the instruction of building windows binaries in libsvm README. Usage ===== To use the module, the files svm.py and the shared library (namely svmc.so or svmc.pyd) must be placed in the current directory, the python library directory, or the directory where the environment variable PYTHONPATH points to. The user then imports everything in svm.py to use libsvm in python: from svm import * There are three classes in svm.py, namely svm_parameter, svm_problem, and svm_model. svm_parameter is used to set the parameters of the training process. The attributes in svm_parameter include svm_type, kernel_type, degree, gamma, coef0, nu, cache_size, C, eps, p, shrinking, nr_weight, weight_label, and weight. Available svm types include C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, and NU_SVR. Available kernel types include LINEAR, POLY, RBF, and SIGMOID. The user can setup the parameters 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 = RBF svm_problem is used to hold the training data for the problem. The constructor 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}]) For precomputed kernels, the first element of each instance must be the ID. For example, samples = [[1, 0, 0, 0, 0], [2, 0, 1, 0, 1], [3, 0, 0, 1, 1], [4, 0, 1, 1, 2]] problem = svm_problem(labels, samples); For more details of precomputed kernels, please check README of the parent directory. Once the parameter and problem are ready, we can construct the model: m = svm_model(prob, param) To conduct n-fold cross validation; predicted labels in the validation process are returned. target = cross_validation(prob, param, n) To predict a new sample with the model: r = m.predict([1, 1, 1]) To obtain decision values of predicting a sample: d = m.predict_values([1, 1, 1]) To predict a new sample and obtain probability estimates; return value is a dict that maps labels to probabilities. prd, prb = m.predict_probability([1, 1, 1]) sample of prd : 1.0 sample of prb : {1:0.6, -1:0.4} To obtain sigma of the probability density function for regression; see ../README for the definition of the function. sigma = m.get_svr_probability() To obtain the probability density function for regression; see ../README for the definition of the function. pdf = m.get_svr_pdf() probability = pdf(z) 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 the other is test_cross_validation.py. svm_test.py tests various kernels on a three-class problem with C-SVM. It also demonstrates how to obtain decision values and probability estimates. test_cross_validation.py demonstrates loading data from a file and does a ten-fold cross validation on the heart_scale dataset. It makes use of cross_validation.py which calls the C++ cross validation subroutine.