Introduction
============

This tool provides a Python interface to LIBSVM with instance weight support

Installation
============

Please check README for detail.

USAGE
=====

The usage is bascally the same as the version without supporting
instance weights. We only show differences below.

- Function: svm_train

    There are three ways to call svm_train()

    >>> model = svm_train(W, y, x [, 'training_options'])
    >>> model = svm_train(prob [, 'training_options'])
    >>> model = svm_train(prob, param)

    W: a list/tuple of l training weights (type must be double). 
       Use [] if no weights.

    y: a list/tuple of l training labels (type must be int/double).

    x: a list/tuple of l training instances. The feature vector of
       each training instance is an instance of list/tuple or dictionary.

    training_options: a string in the same form as that for LIBSVM command
                      mode.

    prob: an svm_problem instance generated by calling
          svm_problem(W, y, x).

    param: an svm_parameter instance generated by calling
           svm_parameter('training_options')

    model: the returned svm_model instance. See svm.h for details of this
           structure. If '-v' is specified, cross validation is
           conducted and the returned model is just a scalar: cross-validation
           accuracy for classification and mean-squared error for regression.

    To train the same data many times with different
    parameters, the second and the third ways should be faster..

    Examples:

    >>> y, x = svm_read_problem('../heart_scale')
    >>> W = [1] * len(y)
    >>> W[0] = 10
    >>> prob = svm_problem(W, y, x)
    >>> param = svm_parameter('-s 3 -c 5 -h 0')
    >>> m = svm_train([], y, x, '-c 5')
    >>> m = svm_train(W, y, x)
    >>> m = svm_train(prob, '-t 2 -c 5')
    >>> m = svm_train(prob, param)
    >>> CV_ACC = svm_train(W, y, x, '-v 3')