def bridge_prob_neg_log()

in ais.py [0:0]

• 12 lines of code
• 5 McCabe index (conditional complexity)

def bridge_prob_neg_log(alpha, x, e_func, weights, temp):

    if FLAGS.dataset == "gauss":
        norm_prob =  (1-alpha) * uniform_prob_log(x) + alpha * gauss_prob_log(x, prec=FLAGS.temperature)
    else:
        norm_prob =  (1-alpha) * uniform_prob_log(x) + alpha * model_prob_log(x, e_func, weights, temp)
    # Add an additional log likelihood penalty so that points outside of (0, 1) box are *highly* unlikely


    if FLAGS.dataset == '2d' or FLAGS.dataset == 'gauss':
        oob_prob = tf.reduce_sum(tf.square(100 * (x - tf.clip_by_value(x, 0, FLAGS.rescale))), axis = [1])
    elif FLAGS.dataset == 'mnist':
        oob_prob = tf.reduce_sum(tf.square(100 * (x - tf.clip_by_value(x, 0, FLAGS.rescale))), axis = [1, 2])
    else:
        oob_prob = tf.reduce_sum(tf.square(100 * (x - tf.clip_by_value(x, 0., FLAGS.rescale))), axis = [1, 2, 3])

    return -norm_prob + oob_prob