def __init__(self,arg1,arg2=None): if arg2 == None: # create model from file filename = arg1 self.model = svmc.svm_load_model(filename) else: # create model from problem and parameter prob,param = arg1,arg2 self.prob = prob if param.gamma == 0: param.gamma = 1.0/prob.maxlen msg = svmc.svm_check_parameter(prob.prob,param.param) if msg: raise ValueError, msg self.model = svmc.svm_train(prob.prob,param.param) #setup some classwide variables self.nr_class = svmc.svm_get_nr_class(self.model) self.svm_type = svmc.svm_get_svm_type(self.model) #create labels(classes) intarr = svmc.new_int(self.nr_class) svmc.svm_get_labels(self.model,intarr) self.labels = _int_array_to_list(intarr, self.nr_class) svmc.delete_int(intarr) #check if valid probability model self.probability = svmc.svm_check_probability_model(self.model)
def _int_array(seq): size = len(seq) array = svmc.new_int(size) i = 0 for item in seq: svmc.int_setitem(array,i,item) i = i + 1 return array
