def add_multiclass_classifier(self, classifier): """ Add information about an additional weak learner to the queue of classifiers to be blended. """ formatted = classifier if(formatted.__class__ == numpy.ndarray): formatted = formatted.tolist() # TODO: check the size of the input LPBoostMulticlassClassifier_wrap.add_multiclass_classifier(self, formatted)
def __init__(self, number_of_classes, nu, **kwargs): self.number_of_classes = number_of_classes self.nu = nu self.weight_sharing = kwargs.get("weight_sharing", True) self.labels = kwargs.get("labels", range(0, self.number_of_classes)) if(self.labels.__class__ == numpy.ndarray): self.labels = self.labels.tolist() self.interior_point = kwargs.get("interior_point", False) self.solver = kwargs.get("solver", "clp") LPBoostMulticlassClassifier_wrap.__init__(self, self.number_of_classes, self.nu, self.weight_sharing) self.initialize_boosting(self.labels, self.interior_point, self.solver)