train_svms.py 文件源码

def append_neg_and_retrain(self, feat=None, force=False):
        if feat is not None:
            num = feat.shape[0]
            self.neg = np.vstack((self.neg, feat))
            self.num_neg_added += num
        if self.num_neg_added > self.retrain_limit or force:
            self.num_neg_added = 0
            new_w_b, pos_scores, neg_scores = self.train()
            # scores = np.dot(self.neg, new_w_b[0].T) + new_w_b[1]
            # easy_inds = np.where(neg_scores < self.evict_thresh)[0]
            not_easy_inds = np.where(neg_scores >= self.evict_thresh)[0]
            if len(not_easy_inds) > 0:
                self.neg = self.neg[not_easy_inds, :]
                # self.neg = np.delete(self.neg, easy_inds)
            print('    Pruning easy negatives')
            print('    Cache holds {} pos examples and {} neg examples'.
                  format(self.pos.shape[0], self.neg.shape[0]))
            print('    {} pos support vectors'.format((pos_scores <= 1).sum()))
            print('    {} neg support vectors'.format((neg_scores >= -1).sum()))
            return new_w_b
        else:
            return None