pysvmlight / svmlight / svmlight.py

from ctypes import *

VERSION = "V6.02"
VERSION_DATE = "14.08.08"

MAXFEATNUM = 99999999

LINEAR  = 0
POLY    = 1
RBF     = 2
SIGMOID = 3

CLASSIFICATION = 1
REGRESSION     = 2
RANKING        = 3
OPTIMIZATION   = 4

MAXSHRINK = 50000
svm = CDLL("./svmlight.so")

class WORD(Structure):
	_fields_ = [("wnum", c_long),
			    ("weight", c_float)]

class SVECTOR(Structure):
	_fields_ = [("words", POINTER(WORD)),
			    ("twonorm_sq", c_double),
				("userdefined", c_char),
				("kernel_id", c_long),
				("next", POINTER(SVECTOR),
				("factor", c_double)]


class DOC(Structure):
	_fields_ = [("docnum", c_long),
			    ("queryid", c_long),
				("costfactor", c_double),
				("slackid", c_long),
				("fvec", POINTER(SVECTOR))]


if __name__ == "__main__":
	svec = SVECTOR()
	for i, word in enumerate(svec.words):
		word.wnum = i

	print svec.words

	resize(svec.words, 64)

	for word in svec.words:
		print word.wnum

def svm_learn2(*args, **kwds):
    docs = POINTER(POINTER(DOC()))
    target = POINTER(c_double)
    totwords = c_int
	totdoc = c_int
    kernel_cache = POINTER(KERNEL_CACHE)
    learn_parm = LEARN_PARM
    kernel_parm = KERNEL_PARM
    verbosity = c_long
    #doclist = POINTER(PyObject) #FIXME
    model = POINTER(MODEL)
    result = POINTER(MODEL_AND_DOCS)
	# DO THIS ONE!

	# NULL: alpha in
    if learn_parm.type == CLASSIFICATION {
        svm.svm_learn_classification(docs, target, totdoc, totwords,
			addressof(learn_parm), addressof(kernel_parm), kernel_cache,
			model, None)
    }


'''

static PyObject *svm_learn(PyObject *self, PyObject *args, PyObject *kwds)
{
    #DOC **docs;
    double* target;
    int totwords, totdoc;
    KERNEL_CACHE *kernel_cache;
    LEARN_PARM learn_parm;
    KERNEL_PARM kernel_parm;
    long verbosity;
    PyObject *doclist;
    MODEL *model;
    MODEL_AND_DOCS *result;

    if(!PyArg_ParseTuple(args, "O", &doclist))
        return NULL;
    read_learning_parameters(kwds, &verbosity, &learn_parm, &kernel_parm);
    if(!unpack_doclist(doclist, &docs, &target, &totwords, &totdoc))
        return NULL;

    model = malloc(sizeof(MODEL));
    if(kernel_parm.kernel_type == LINEAR)
        kernel_cache = NULL;
    else
        kernel_cache = kernel_cache_init(totdoc, learn_parm.kernel_cache_size);

	# DO THIS ONE!
    if(learn_parm.type == CLASSIFICATION) {
        svm_learn_classification(docs, target, totdoc, totwords, &learn_parm,
                                 &kernel_parm, kernel_cache, model, NULL /* alpha_in */);
    }
    else if(learn_parm.type == REGRESSION) {
        svm_learn_regression(docs, target, totdoc, totwords, &learn_parm,
                             &kernel_parm, &kernel_cache, model);
    }
    else if(learn_parm.type == RANKING) {
        svm_learn_ranking(docs, target, totdoc, totwords, &learn_parm,
                          &kernel_parm, &kernel_cache, model);
    }
    else if(learn_parm.type == OPTIMIZATION) {
        svm_learn_optimization(docs, target, totdoc, totwords, &learn_parm,
                               &kernel_parm, kernel_cache, model, NULL /* alpha_in */);
    }
'''
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