Metadata-Version: 1.0
Name: svmlight
Version: 0.2
Summary: Interface to Thorsten Joachims' SVM-Light
Home-page: http://bitbucket.org/wcauchois/pysvmlight
Author: William Cauchois
Author-email: wcauchois@gmail.com
License: UNKNOWN
Description: 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().
        
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
