Python 程序的运行时间需要 0.1 秒，慢了 100 倍，但由于 网络更慢，需要等待 1 秒，你想，用户能感觉到 1.001 秒和 1.1 秒的区 别吗？这就好比 F1 赛车和普通的出租车在北京三环路上行驶的道理一 样，虽然 F1 赛车理论时速高达 400 公里，但由于三环路堵车的时速只 有 20 公里，因此，作为乘客，你感觉的时速永远是 20 公里。 Python3 基础教程【完整版】 种参数都可以组合使用，除了可变参数 无法和命名关键字参数混合。但是请注意，参数定义的顺序必须是：必 选参数、默认参数、可变参数/命名关键字参数和关键字参数。 比如定义一个函数，包含上述若干种参数： def f1(a, b, c=0, *args, **kw): print('a =', a, 'b =', b, 'c =', c, 'args =', args, 'kw =', kw) def 参数传进去。 >>> f1(1, 2) a = 1 b = 2 c = 0 args = () kw = {} >>> f1(1, 2, c=3) a = 1 b = 2 c = 3 args = () kw = {} >>> f1(1, 2, 3, 'a', 'b') a = 1 b = 2 c = 3 args = ('a', 'b') kw = {} >>> f1(1, 2, 3, 'a'

continue; } let result = minimax(opposite, &board_next); let current_score = match result { Some(x) => x.side, _ => Players::Blank, }; best_move, SinglePlayAction { position: pos, side: current_score, }, ) } best_move } //Promote only better or collate equally scored game as_mut().unwrap(); //New game action is scored from the current side and the current saved best score against the new game action. match (current_side, play_actions.side, appendee.side) {

server’s score being called first: No point – “Love” First point – “15” Second point – “30” Third point – “40” Fourth point – “Game” except that if each player/team has won three points, the score is “Deuce “Deuce.” After “Deuce,” the score is “Advantage” for the player/team who wins the next point. If that same player/team also wins the next point, that player/team wins the “Game”; if the opposing player/team the score is again “Deuce.” A player/team needs to win two consecutive points immediately after “Deuce” to win the “Game.” We first define an inductive type to represent scores: inductive score : Type

Query 做分组统计求平均，如下图 10 所示，纵轴代 表作为 Query 的入口、横轴代表作为 Key 的入口，图中每个点的值代表某一入口对 作为 Query-Key 情况下 Attention score 的平均值。例如，第二行代表着美食金刚 (D1) 作为 Query 时，对其它入口 Expert 的平均激活概率，发现模型可以学习到符 合认知的入口相似关系，例如，当下午茶样本 (D7) 作为 为精准反映模型迭代的离线效果，我们通过多轮人工标注方式构造了一批 Bench- mark，考虑到当前线上实际使用时主要目标为降低 BadCase 指标，即对不相关商户 的准确识别，我们采用负例的准确率、召回率、F1 值作为衡量指标。经过两阶段训 练、样本构造及模型迭代带来的收益如下表 1 所示： 表 1 点评搜索相关性模型迭代离线指标 初始方法（Base）采用 Query 拼接 POI 匹配字段摘要信息的 配字段摘要文本拼接方式。引入基于点击数据的两阶段训练后，负例 F1 指标相比 Base 方法提升 1.84%，通过引入对比样本、难例样本持续迭代训练样本并配合第二 阶段的模型输入构造，负例 F1 相比 Base 显著提升 10.35%，引入基于多相似矩阵 的深度交互方法后，负例 F1 相比 Base 提升 11.14%。模型在 Benchmark 上的整 体指标也达到了 AUC 为 0.96，F1 为 0.97 的高值。 4

的性能）。而 Augur 提供了表达式解析的 Benchmark 测试用例， 可以进行解析性能的验证。 _I(_I('xxx')) Benchmark Mode Cnt Score Error Units AbsBenchmarkTest.test avgt 25 1.644 ± 0.009 ms/op 一个两层嵌套的表达式解析 10W 次的性能是 1.6ms PRM[5]，结合美团搜索实践的情况，重排序模型相比 PRM 做了一 些调整。具体结构如图 6 所示，其中 D1，D2，…，Dn 是重排商户集合，最后根据 模型的输出 Score(D1)，Score(D2)，…，Score(Dn) 按照从大到小进行排序。 算法 < 33 图 6 Transformer 重排序 主要由以下几个部分构成： ● 特征向量生成：由原始特征（user、item、交叉等维度的稠密统计特征）经过 P 进行 concat 作为最终输入。 ● Transformer 编码层：一层 Multi-Head Attention 和 FFN 的。 ● 输出层：一层全连接网络得到打分输出 Score。 模型细节： ● 特征向量生成部分和重排序模型是一个整体，联合端到端训练。 ● 训练和预测阶段固定选择 TopK 进行重排，遇到某些请求曝光 item 集不够 TopK 的情况下，在末尾补零向量进行对齐。

5:96:65:b7:b7:1b:08:77: 43:de:cb:ad:3a:79:ec:cc:2a:bc:d7:93:68:ae:92: 1c:4b:d8:32:47:d6:3d:72:32:f1:f1:fb:26:e4:69: c2:eb:c9:45:69:99:78:d7:68:a9:77:09:88:c6:53: 01:2a:c1:f8:c0 ASN1 OID: prime256v1 NIST Needs Verified. • Reply: Click on this button after reviewing to add a final review message and a score, -1, 0 or +1. 306 Chapter 10. Contributions Welcome! hyperledger-fabricdocs Documentation, Release write some final comments, and submit your score for the patch set. Click Post to submit the review of each reviewed file, as well as your final comment and score. Gerrit sends an email to the change-submitter

43:de:cb:ad:3a:79:ec:cc:2a:bc:d7:93:68:ae:92: 1c:4b:d8:32:47:d6:3d:72:32:f1:f1:fb:26:e4:69: c2:eb:c9:45:69:99:78:d7:68:a9:77:09:88:c6:53: 01:2a:c1:f8:c0 reads: Needs Verified. Reply: Click on this button after reviewing to add a final review message and a score, -1, 0 or +1. Patch Sets: If multiple revisions of a patch exist, this button enables navigation among write some final comments, and submit your score for the patch set. Click Post to submit the review of each reviewed file, as well as your final comment and score. Gerrit sends an email to the change-submitter

43:de:cb:ad:3a:79:ec:cc:2a:bc:d7:93:68:ae:92: 1c:4b:d8:32:47:d6:3d:72:32:f1:f1:fb:26:e4:69: c2:eb:c9:45:69:99:78:d7:68:a9:77:09:88:c6:53: 01:2a:c1:f8:c0 reads: Needs Verified. Reply: Click on this button after reviewing to add a final review message and a score, -1, 0 or +1. Patch Sets: If multiple revisions of a patch exist, this button enables navigation among write some final comments, and submit your score for the patch set. Click Post to submit the review of each reviewed file, as well as your final comment and score. Gerrit sends an email to the change-submitter

5:96:65:b7:b7:1b:08:77: 43:de:cb:ad:3a:79:ec:cc:2a:bc:d7:93:68:ae:92: 1c:4b:d8:32:47:d6:3d:72:32:f1:f1:fb:26:e4:69: c2:eb:c9:45:69:99:78:d7:68:a9:77:09:88:c6:53: 01:2a:c1:f8:c0 ASN1 OID: prime256v1 NIST Needs Verified. • Reply: Click on this button after reviewing to add a final review message and a score, -1, 0 or +1. • Patch Sets: If multiple revisions of a patch exist, this button enables navigation write some final comments, and submit your score for the patch set. Click Post to submit the review of each reviewed file, as well as your final comment and score. Gerrit sends an email to the change-submitter

type. "Fast" arrays like NumPy arrays that store elements in-place (i.e., dtype is np.float64, [('f1', np.uint64), ('f2', np.int32)], etc.) can be rep- resented by Array{T} where T is a concrete, immutable Musical Keyboard U+1F3BA \:trumpet: Trumpet U+1F3BB \:violin: Violin U+1F3BC \:musical_score: Musical Score U+1F3BD \:run- ning_shirt_with_sash: Running Shirt With Sash U+1F3BE \:tennis: Tennis states that calling with certain argtypes is a part of public API. For example, the change between f1 and f2 in the example below is usually considered compatible because the change is invisible by the