classifier.py 文件源码

def _calculate_new_weights(self, instance_probabilites, bag_probabilities):
        weights = []
        for p_ij, p_i, Y_i in zip(self._bag_split(instance_probabilites),
                                  bag_probabilities,
                                  self._bag_labels):
            if Y_i > 0:
                if p_i == 0.0:
                    p_i = np.finfo(float).resolution
                term_1 = (2 * p_ij * (1 - p_ij)) / p_i
            else:
                if p_i == 1.0:
                    p_i = 1 - np.finfo(float).resolution
                term_1 = -((2 * p_ij * (1 - p_ij)) / (1 - p_i))
            weights += (term_1 * self.softmax_fcn.d_dt(p_ij)).tolist()

        return np.array(weights) / np.sum(np.abs(weights))