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refactoring, ability to run svm in different configurations; + modifi…

…ed pysvmlight (with most recent version of svmlight)
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1 parent 28a9b35 commit 718dfffa3c8ed45f35a94b496525ff7b06a31caa @cathywu committed Jan 11, 2012
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+repo: ca168779ff303e9b1e4d234628ccce62aec13c58
+node: ebd93ccda8c4159f9af0db6e758a9c26b580203d
+branch: default
+latesttag: null
+latesttagdistance: 19
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+syntax: glob
+build
+*.patch
+*.pyc
+*.log
+*.so
+*.so.old
+*.swp
+*.dat
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+PySVMLight
+==========
+
+A Python binding to the [SVM-Light](http://svmlight.joachims.org/) support vector machine library by Thorsten Joachims.
+
+Written by Bill Cauchois (<wcauchois@gmail.com>), with thanks to Lucas Beyer and n0mad for their contributions.
+
+Installation
+------------
+PySVMLight uses distutils for setup. Installation is as simple as
+
+ $ chmod +x setup.py
+ $ ./setup.py --help
+ $ ./setup.py build
+
+If you want to install SVMLight to your PYTHONPATH, type:
+
+ $ ./setup.py install
+
+(You may need to execute this command as the superuser.) Otherwise, look in the build/ directory to find svmlight.so and copy that file to the directory of your project. You should now be able to `import svmlight`.
+
+Getting Started
+---------------
+See examples/simple.py for example usage.
+
+Reference
+---------
+
+If you type `help(svmlight)`, you will see that there are currently three functions.
+
+ learn(training_data, **options) -> model
+
+Train a model based on a set of training data. The training data should be in the following format:
+
+ >> (<label>, [(<feature>, <value>), ...])
+
+or
+
+ >> (<label>, [(<feature>, <value>), ...], <queryid>)
+
+See examples/data.py for an example of some training data. Available options include (corresponding roughly to the command-line options for `svmlight` detailed on [this page](http://svmlight.joachims.org/) under the section titled "How to use"):
+
+ - `type`: select between 'classification', 'regression', 'ranking' (preference ranking), and 'optimization'.
+ - `kernel`: select between 'linear', 'polynomial', 'rbf', and 'sigmoid'.
+ - `verbosity`: set the verbosity level (default 0).
+ - `C`: trade-off between training error and margin.
+ - `poly_degree`: parameter d in polynomial kernel.
+ - `rbf_gamma`: parameter gamma in rbf kernel.
+ - `coef_lin`
+ - `coef_const`
+
+The result of this call is a model that you can pass to classify().
+
+ classify(model, test_data, **options) -> predictions
+
+Classify a set of test data using the provided model. The test data should be in the same format as training data (see above). The result will be a list of floats, corresponding to predicted labels for each of the test instances.
+
+ write_model(model, filename) -> None
+
+Write the provided model to the specified file. The file format used is the same format as that used by the command-line `svmlight` program.
+
+ read_model(filename) -> model
+
+Read a model that was saved using write_model().
+
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