classifier_tf.py 文件源码

def add_noise(self, labels, seed_str):
        if self.false_positive_rate > 0 or self.false_negative_rate > 0:
            r = np.random.RandomState()
            b = bytes(",".join(
                [seed_str, str(self.false_positive_rate),
                 str(self.false_negative_rate)]), 'ascii')
            r.seed(list(hashlib.md5(b).digest()))
            p = r.rand(len(labels))
            original_labels = np.copy(labels)
            labels[(~original_labels) & (p < self.false_positive_rate)] = True
            labels[(original_labels) & (p < self.false_negative_rate)] = False
        return labels

    ## currently unused, randomizes *noise_prob* of the labels