at

你装的软件应该是有反越狱插件的软件，所以如果不是必须要用的话就不建议装了，试试找别的同类型软件。要不然就先卸载，装好再越狱。希望帮到您。

拜腾巴特恩德斯汀奥斯博恩奔腾宝丽兹泰德.贝斯

translate及物动词 vt. 1.翻译,转译[(+from/into)]2.(用另外的词语)解释,说明,表达[(+into)]I translate his silence as a refusal. 我把他的沉默解释为拒绝。3.使转化,使转变[(+into)]4.转移,调动[(+to)]5.自动转发(电报)不及物动词 vi. 1.翻译;被翻译Many foreign expressions do not translate well into English. 许多外国用语不容易译成英语。 2.从事翻译工作3.(用另外的词语)解释,说明,表达4.转化,转变interpret及物动词 vt. 1.解释,说明,诠释2.理解[(+as)]3.口译,翻译4.(根据自己的诠释)演奏,演Not everybody agreed with the way she interpreted the violin concerts, but it was still a technically perfect performance. 并非每人都同意她对那首小提琴协奏曲的诠释,但是她的演奏在技巧上仍是无懈可击。不及物动词 vi. 1.作解释2.作口译,当翻译Would you like me to interpret for you? 我给你当翻译好吗?construe及物动词 vt. 1.解释;理解为[(+as)][O][OZ]Her silence was construed as agreement. 她的沉默被看作是同意。2.推断3.分析(句子)4.习惯上连用[(+with)]5.翻译(尤指口译)不及物动词 vi. 1.作文法性的分析2.(句子)可作文法性分析This sentence does not construe. 这个句子文法上无法分析。3.【废】作推断

没有s

用 show ip nat statistics

sworn translations保证说实话的翻译 客观的翻译sworn[英][swɔ:n][美][swɔ:rn]adj.保证说实话的; 宣过誓的; （朋友）感情极深的; （敌人）不共戴天的; v.诅咒，咒骂( swear的过去分词 ); （使某人）就…宣誓; 郑重承诺; 发誓要;translations[英][træns"leɪʃnz][美][træns"leɪʃnz]n.翻译( translation的名词复数 ); 译本; 转变; 转化; .-----------------------------------如有疑问欢迎追问！满意请点击右上方【选为满意回答】按钮

part iv translation第四部分翻译translation[英][trænsˈleɪʃn][美][trænsˈleʃən, trænz-]n.翻译; 译本; 转变; 转化; 复数：translations例句:1.Machine translation can certainly help in these cases. 机器翻译当然可以帮助在这些情况下。.-----------------------------------如有疑问欢迎追问！满意请点击右上方【选为满意回答】按钮

解释双语对照词典结果：interpretation[英][ɪnˌtɜ:prɪˈteɪʃn][美][ɪnˌtɜ:rprɪˈteɪʃn]n.理解; 解释，说明; 翻译; 表演，演绎; 复数：interpretations以上结果来自金山词霸例句:1.Subtle interpretation is required. 微妙的解释是必要的。翻译双语对照词典结果：translation[英][trænsˈleɪʃn][美][trænsˈleʃən, trænz-]n.翻译; 译本; 转化; 转变; 复数：translations以上结果来自金山词霸例句:1.Might be worse than that.seems my translation was off. 可能会更糟糕。看来我的翻译是关闭的。

外汇/币换算

英语by translation requests翻译成中文是：“根据翻译请求”。重点词汇： translation一、单词音标 translation单词发音：英 [trænsˈleɪʃən]  美 [trænsˈleɪʃən] 。二、单词释义n. 翻译；译文；RNA的转录；平移三、词形变化复数: translations四、短语搭配compare the translations 比较译文do〔make〕 translation 翻译，进行翻译Chinese〔English〕 translation 汉〔英〕译本close〔faithful, good, rough〕 translation 精确〔忠实，很好，粗略〕的翻译free〔literal〕 translation 意〔直〕译word-for-word translation 逐字翻译五、词义辨析translation，version，paraphrase这些名词均含“翻译，译文”之意。translation普通用词，指从一种语言到另一种语言的翻译。version可与translation换用，尤指不拘泥于文字的意译，更常指某种作品的一种译文。paraphrase可指非常自由的解释，不拘泥遣词造句，重在传意。通常指用同样语言深入浅出地解释艰深的句子或段落。六、双语例句Poetry always loses something in translation. 诗歌一经翻译总会失去某些东西。The exam consists of an essay and an unseen translation. 考试包括一篇作文和一篇即席翻译。Most of the translation he did for me was complete nonsense. 他给我做的大多数译文完全不知所云。The book loses something in translation. 此书在翻译过程中丢失了一些原意。Publishers have commissioned a French translation of the book. 出版商已委托人把这本书译成法语。

translation翻译双语对照词典结果：translation[英][trænsˈleɪʃn][美][trænsˈleʃən, trænz-]n.翻译; 译本; 转变; 转化; 复数：translations以上结果来自金山词霸例句:1.Here, for example, is google"s translation of the first pages of the savage detectives. 例如，你可以点击此处查看用谷歌翻译的《荒野侦探》（The Savage Detectives）这部小说的前几页。

my translation 我的翻译

Mies van der Rohe-- 密斯.范.德.罗厄 个人觉得既然是品牌就不要太长，叫 凡德罗 比较顺口Regency - 叫 帝豪 或 王朝 如何？ regent 有帝王的意思，regency有被大量用在酒店名上，这里都有王朝，帝王的意思

Domestication and ForeignizationZhao Ni(School of Interpretation and Translation, Shandong University at Weihai, Weihai, Shandong,264209)Abstract: In field of translation, there has long been a hot debate over the proper translation strategy chosen for the transmission of cultural contents. The two major approaches are domesticationa and foreignization, which have been the focus of debate since their appearance. This thesis aims to analyse the chioce of domestication and foreignization from a new approach, namely, Skopostheorie.Keywords: domesticationa, foreignization, Skopostheorie1.Definitions of Domesticationa and Foreignization Domestication refers to the target-culture-oriented translation in which unusual expressions to the target culture are exploited and turned into some familiar ones so as to make the translated text intelligible and easy for the target readers. Foreignization is a source-culture-oriented translation which strives to preserve the foreign flavor as much as possible in order to transfer the source language and culture into the target one.2.Overview of the Debate over Domesticationa and Foreignization The debate on foreignization or domestication can be viewed as the extension of the debate on “literal translation” and “free translation”. A literal translation is a translation that follows closely not only the content but also the form of the source language, it is also known as word-for-word translation. And translators engaged in literalism have been willing to sacrifice the formal elements of the target language and even the intelligibility of the target language text for the sake of preserving what they regard as the integrity of the source text. While those who favor free translation have quite often chosen to sacrifice the form of the source language for the sake of elegance and intelligibility in the target language. But most scholars hold that literal and free translation are limited on the level of content and form, when two languages are very similar in their structures, the issue of literal versus free translating may not seen to be so acute. The two pairs of strategies share some similarities: literal translation and foreignization put emphasis on the linguistic and stylistic features of the source text, and the target text translated in these ways may not be very smooth in language and the content may not be familiar to the target readers, so they may feel foreign when reading the translation, while free translation and domestication pay more attention to the target audience, because of the smooth sentences, the familiar expressions and cultural phenomena, sometimes the target readers may not realize that they are actually reading a translated text from another culture. However, this does not mean the two pairs are just one. There are some diferences between them. When a translator resorts to either literal translating method or free translating method, he puts his attention mainly to the linguistic factors of the source text and tries his utmost to keep the original meaning in the target text. But with the development of the translation studies, plenty of translators and theorists have realized that translation is a far more complicated activity with various cultural, poetic, political as well as economic factors related to it. Therefore, foreignization and domestication are a pair of new translation strategies which are more complex and extensive than literal translation and free translation method.3. A New Approach: Skopostheorie Which strategy is more appropriate as far as specific literary work is concerned ? Which strategy can make the translated text have a better efect among its target audience, foreignization or domestication? So far no theory can give a definite answer to the question, nor can any theorist completely negate one of them. Personally speaking, I thinks both domestication and foreignization are just two different strategies of translation and should complement with each other, because in translation practice, both methods have their functions which cannot be substituted. To strictly insist upon one another is just bring the strategy to extremes. Both strategies are justified if used in suitable situations from the perspective of the functionalist theory.3.1 An Overview of the Skopostheorie Translation, as a form of translaitonal action, like any other forms of human action, must be oriented by certain purpose. In translation practice, which strategy should be chosen should not be determined by the text itself or the translator himself/herself, but should be mainly decided by the purpose of the translation. This purpose-oriented approach of translation is one of the central idea of functionalist theory. The functionalist theory put forward by some German scholars has made a new perspective for translation studies. The German scholars are referred to as the "German school": Katharina Reiss and her functionalist translation criticism, Hans. J. Vermeer"s Skopostheorie and its extensions, Justa Holz-Manttari"s theory of translational action and Christiane Nord"s loyalty plus Skopos.(Nord 2001:4) Funcitionalists focus on the function or functions of texts and translations, or in German language the skopos of the translation. According to functionalist approaches to translation, i.e. the skopotheorie as presented by Vermeer, there are three major rules in the skopotheorie, namely, the skopos rule, coherence rule and fidelity rule. In the functionalist theory, the top-ranking rule for any translation is the “Skopos rule”, which says that a translational action is determined by its Skopos; that is, "the end justifies the means" (Reiss and Vermeer 1984: 101). Vermeer explains the Skopos rule as follows: Translate/interpret/speak/write in a way that enables your text/translation to function in the situation in which it is used and with the people who want to use it and precisely in the way they want it to function (Vermeer 1989a: 20). We can distinguish between three possible kinds of purpose in the field of translation: the general purpose aimed at by the translator in the translational process, the communicative purpose aimed at by the TT in the target situation and the purpose aimed at by a particular translation strategy or procedure. Nevertheless, the term skopos usually refers to the purpose of the TT,(Nord, 2001: 27-28) which is decided by the initiator of the translational action. Though most translational actions have a variety of Skopoi to realize, or more than one purpose to achieve, they usually will follow a hierarchical order. The translator, as a decision-maker, should judge which particular Skopos should be the most important one for him to carry out in a translational process. It also gives the translator a new perspective to decide which strategy will be employed in the whole process. The translator"s task is to ascertain and then apply the suitable strategies to reach its purpose. As Vermeer puts it, What the Skopos states is that one must translate, consciously and consistently, in accordance with some principle respecting the target text The theory does not state what the principle is and this must be decided separately in each specific case (1989b: 182). The coherence rule is also called as the intratextual coherence by functionalists. It requires that the translated text should make sense in the communicative situation in which it is received. It specifies that a translation should be acceptable in a sense that it is coherent with the receivers" situation (Reiss and Vermeer 1984:113). Therefore, in the translating process, the translator should take the target culture into careful consideration and do some alterations in order to make the translation intelligible. Otherwise, the translated text may lose its significance and become meaningless in a target culture. Since translation is the offer of the information from the source text, the translated text must bear certain relation with the source one. Vermeer called this relationship "intertextual coherence" or "fidelity". This coherence exists between the source text and the target text and the form it takes depends both on the translator"s interpretation of the source text and on the translation Skopos.The core of Skopostheorie is that the translation purpose plays the most important role in a translational process, or “the translation purpose justifies the translation procedures”. But problems arise when the translation purpose is not in line with the communicative intentions of the original author. Another member of the “German School”---Christiane Nord proposes her loyalty principle,which commits the translator bilaterally to the source and the target sides. It refers to the responsibility the translator has toward the source text producer, the target receiver and other agents involved in a translational interaction. Nord emphasizes that the term cannot be mixed up with fidelity or faithfulness, concepts that usually refer to a relationship holding between the source text and the target text. Loyalty is an interpersonal category referring to a social relationship between people. Loyalty demands the translator should be responsible for the target readers, but this does not mean that the translator is always obliged to do exactly what the readers expect Yet at the same time, the translator should also have a sense of moral responsibility not to deceive his readers (Nord 2001: 125)4. Domesticationa and Foreignization in the Framework of Skopotheorie4.1 the Relationship between the Two Stategies Under the framework of skopotheorie, foreignization and domestication may not contradict each other judging from the new functionalist perspective. Since a translation, generally involves various purposes, diferent strategies have to be taken in order to achieve each of them. The functionalist theory can provide guidance for him to decide which strategy is more suitable to employ in a specific translational action. Within the framework of the functionalist theory, the commissioner or the initiator should inform the translator much detailed information concerning the action such as the intended functions and the addressees of the target text at the beginning. Taking all these factors into careful consideration, the translator can give preference to foreignization or to domestication.If a translation is intended to widen the target addressees" visions and to introduce the source culture into the target culture, the translator may choose foreignization. In this way, the source culture can be transferred into the target culture and further enrich the target culture as well as language. However, this method is not suitable to the texts under all circumstances. Cultural faithfulness should not be acquired at the expense of a vague broken language, resulting in miscomprehension or making little readability of the target text into sense. Therefore translator should also take demestication into consideration when employing the foreignizing strategy. On the contrary, if the Skopos is to provide a smoother translation without many difficulties for common readers, domesticating method should be taken. It will overcome the culture barriers as well as the language ones for providing an easy reading. Then the most important task for the translator is to eliminate the cultural conflicts, which may be the obstacles in communication and result in misunderstanding. It is also the translators" job to be aware of the connotations of some cultural elements in the source text. Thus a successful communication can be achieved. But the domesticating strategy has its limitations, too. Sometimes it may not be suitable since a translation should read like a translation, remaining something foreign and exotic. If it loses all the characteristics of a translation, it may also lose its atraction. Therefore, both strategies have their positive points as well as the negative ones. They are far from being all-purpose and can only be taken to reach different Skopoi designed by translation brief. The relationship between foreignization and domestication is in fact dialectical and complementary.Different participants play different roles, among which the intended TT receiver is of utmost importance. This is why in the framework of Vermeer"s theory, one of the most important factors determining the purpose of a translation is the addressee, who is the intended receiver or audience of the TT with their culture-specific world-knowledge, their expectations and their communicative needs. Every translation is directed at an intended audience, since to translate means “to produce a text in a target setting for a target purpose and target addressees in target circumstances”(1987a:29) A text is made meaningful by its receiver and for its receiver. Different receivers or even the same receiver at different times find different meanings in the same linguistic material offered by the text. What the translator can do, and should do, is to produce a text that is at least likely to be meaningful to target-culture receivers.4.2 Case Study For example, as for the two versions of Hong Lou Meng by Yang Xianyi couple and David Hawkes respectively, the two translators adopt different methods concerning the cultural factors in the story. Yang Xianyi and Gladys Yang address their translation to the foreign readers who have some, or at least a litle knowledge about China and Chinese culture. In a word, the addressees of Yangs" translation are some foreigners or experts who are learning Chinese and its culture. On the contrary, Hawkes" translation is for the common English-speaking readers who may or may not know much about Chi

ip nat pool xue 192.168.2.2 192.168.2.2 netmask 255.255.255.0此条命令不对，地址池的地址不应该和outside global地址相同（192.168.2.2）应该使用别的IP地址比如：192.168.2.100.路由器R0上少一条默认路由。0.0.0.0/0

赫赫~~

这句英文的含义是用英文字典结合出来的

一、PAT原理简介 PAT是英文"Performance Acceleration Technology"的缩写，中文含义即"性能加速技术"。实际测试表明，PAT技术依靠优化的内存访问路径带来了3%～5%的性能提升(如图所示)。一方面，通过设置在i875P芯片组MCH芯片内的"Bypass Patch(旁路)"，可以让处理器对内存的数据访问请求过程减少一个时钟周期；另一方面，通过构建"Optimized Patch(优化路径)"，可以让MCH芯片内核当中的内存控制器对内存芯片的选择时间减少1个时钟周期。 二、为何i865也能"PAT" i865也能实现"PAT"功能，其原因就在于i875P MCH和i865 MCH其实来自于同一条生产线，这就为i865留下了开启PAT的后门！ 刚开始只是i865PE实现类似PAT的功能，到后来连i865G也可以开启这一功能！实际测试表明，i865系列在开启类似PAT的功能后，性能同i875P已经不相上下了。 三、开启你的"PAT" 为了避免与Intel的名字发生冲突，目前各家主板厂商对PAT的称法各不相同，这给消费者在选购时带来了困惑。下面笔者就介绍一下能提供类似PAT功能的主板型号、相应的技术名称、开启方法。 1． ASUS(华硕)主板 华硕把类似于PAT的技术称之为"Hyper Path"，目前能实现该功能的型号有P4P800、P4P800 Deluxe。开启的方法很简单，进入BIOS后，打开"Advance BIOS Features"，将"Memory Acceleration Mode"设置为"Enabled"。 2． MSI(微星)主板 微星把类似于PAT的技术称之为"MAT"(Memory Acceleration Technology)，目前微星支持该技术的主板有865PE(MS-6728)和865G(MS-6728G)，两者都是在1.3版的BIOS开始支持MAT。相应的下载地址，MS-6728：http://www.microstar.com.cn/bios/bios.asp? model=865PE%20Neo2-FIS2R(MS-6728)，MS-6728G：http://www.microstar.com.cn/bios/bios.asp?model=865G%20Neo2- S(MS-6728G)。进入BIOS，依次打开"Frequency/Voltage Control→Performance Mode"，选择"Turbo"或"Ultra-Turbo"。 3． ABIT(升技)主板 升技把类似技术称之为"Game Accelerator"，其IS7主板可通过升级BIOS支持该功能，下载地址：http://fae.abit.com.tw/cht/download/bios/is7.htm，要求BIOS版在1.3或以上。开启方法：在BIOS中依次打开"SoftMenuⅢ→Advance Chipset Feature"，找到"Game Accelerator"选项。它一共提供了四种加速模式：Auto、Turbo、Street Racer、F1，其中F1模式能将整体效能至最高极限，但对整体硬件系统要求较高。 4． AOpen(建基)主板 类似的技术名称为"PBE"，目前支持该功能的主板型号是AX4SPE、AX4SPE-N、AX4SPE-L、AX4SPEMAX，BIOS版本要求1.02版或以上，不满足该条件的朋友请下载相应程序升级。 5． ESC(精英)主板 类似的技术名称为"Photon Acceleration Technology"，有意思的是，英文缩写也是"PAT"！目前能实现的主板型号为PF1 i865。 6． EPoX(磐正)主板 目前磐正能实现类似PAT功能的主板型号有EP-4PDAI、EP-4PDA2+，新的BIOS在内存选项中增加了"Turbo Mode、Intel Fast CS、Intel CPC Function、Selective CPC"4项功能。 四、稳定和性能可兼得吗 根据Intel的说法，虽然i875P和i865是出自同一生产线的产品，但为了保证稳定性，i875P是从中挑选出来品质优异的核心。而i865在开启PAT后，处于极限负载情况下，它的内存控制器可能就会出错，使系统出现不稳定甚至崩溃！由于i865系列上市的时间还不长，到底稳定和性能两者能不能兼得，相信不久之后广大消费者会给出一个定论的。 不可否认的是，由于不同批次及个体差异，不同主板的i865芯片组在核心品质上也不尽相同，而且各家主板在选材用料、研发实力上的差异，也影响了i865主板在开启"PAT"后的表现。 值得注意的是，为了在稳定和性能两者间求个平衡，部分主板厂商将PAT加速分成几个等级，如果你在使用中发现不稳定的现象，建议你只开启基本的PAT功能。

这个是在越狱时安装Cydia的时候Cydia自带安装的，不必搞清楚是干什么的。只需明确这个不要去卸载掉，否则会导致Cydia错误，完美越狱变成不完美甚至白苹果。 =========================如有不明白可追问或者直接百度搜索~=========================望采纳为满意回答！感谢~=========================

translation[英][trænsˈleɪʃn][美][trænsˈleʃən, trænz-]n.翻译; 译本; 转化; 转变; 复数：translations

更新没问题。。。。

在线翻译

WHAT IS TRANSLATION?Translation is the interpreting of the meaning of a text and the subsequent production of an equivalent text, likewise called a "translation," that communicates the same message in another language. The text to be translated is called the "source text," and the language that it is to be translated into is called the "target language"; the final product is sometimes called the "target text."WHAT ARE THE PRINCIPLES?Translation must take into account constraints that include context, the rules of grammar of the two languages, their writing conventions, and their idioms. A common misconception is that there exists a simple word-for-word correspondence between any two languages, and that translation is a straightforward mechanical process; such a word-for-word translation, however, cannot take into account context, grammar, conventions, and idioms.It is now pretty generally agreed, that translating the writings of the ancients is, if not the sole, at least the plainest, the shortest, and the surest means of becoming well acquainted with them and their language. It is also agreed, that a translation ought exactly to express the original; that it should neither be too free nor to servile; that it should neither deviate into long circumlocutions, which weaken the ideas, nor adhere to strictly to the letter, which debases the sentiment.TYPES OF TRANSLATION?- The Dynamic Equivalent TranslationThis type of translation takes into account the context and apparent intent of the original. When providing a dynamic equivalent translation, the translator considers the culture of the original document, understands the nuances of the original language, and takes into account idiomatic expressions. The result is a finished product that comes alive to the reader with the actual intent of the original.- Literal translationLiteral translation, also known as direct translation, is the rendering of text from one language to another "word-for-word" (Latin: "verbum pro verbo") rather than conveying the sense of the original. Literal translations thus commonly mis-translate idioms. Also, in the context of translating an analytic language to a synthetic language, it renders even the grammar unintelligible.希望回答对你有帮助。

您好！interpretation【音标】[英][ɪnˌtɜ:prɪˈteɪʃn] [ 美][ɪnˌtɜ:rprɪˈteɪʃn]【意思】n.理解; 解释，说明; 翻译; 表演，演绎; 【复数】interpretations-------------------------------------------------------------------------------------------translation【音标】[英][trænsˈleɪʃn] [美][trænsˈleʃən, trænz-]【意思】n.翻译; 译本; 转化; 转变; 【复数】translations采纳我吧，谢谢你啦！

是不是宣誓译本，仅供参考。

可以删 把 nvidia geforce experience卸载过程中会显示正在删除NvBackend应该是驱动备份

翻译采纳我哟！

translation意思是翻译。读音：英［trænzˈleɪʃn; trænsˈleɪʃn]，美［trænzˈleɪʃn,trænsˈleɪʃn]。释义：n.翻译；译文；转化；调任。例句：We should avoid errors in translation.我们应该避免翻译错误。复数：translations。短语：in translation翻译的。compare the translations比较译文。translation to译成…的文字。词义辨析translation，version，paraphrase这些名词均含“翻译，译文”之意。translation普通用词，指从一种语言到另一种语言的翻译。version可与translation换用，尤指不拘泥于文字的意译，更常指某种作品的一种译文。paraphrase可指非常自由的解释，不拘泥遣词造句，重在传意。通常指用同样语言深入浅出地解释艰深的句子或段落。

translation意思是：V.翻译;译;译文;译本;译作;转变;转化 复数： translations1.译文;译本A translation is a piece of writing or speech that has been translated from a different language.例句...MacNiece"s excellent English translation of "Faust"...麦克尼斯精彩的《浮士德》英文译本2. 翻译中意义受损(或流失)If you say that a quality of something has been lost in translation, you mean that it is not very good as a result of being translated into another language or told in a different way.例句Much of the wit is lost in translation…很多诙谐的成分都在翻译中丢失了。

深圳的万向城－1楼的超市就有。

care 原意为照顾 照看 care sb 可以表示照顾某人 也可以表示上述意思 还有care for sb

在网页搜索。通过游戏平台和网站可以进行下载。badguysatschool，70MB，251次安装，新的校园模拟玩法，成为校园恶霸吧，校园、剧情、互动、冒险、史上最骚杀手。

你是坏人。我讨厌你。

badguy。badguy是英国歌手BillieEilish所唱的Alternative派的歌，整首旋律燃，歌词是Whiteshirtnowredmybloodyno，whatifthebadguywasnotabadguy。

Look at the elephant! It has a big body.看那头大象啊！它的个头真大！百度嫌我字数不够

I"m a bad guy, I"ll smoke, drink, and I"m not good at all我是个坏家伙，我会抽烟，喝酒，我也不好

1.to advice 2.translator 3.ability 4.facial 5.lawyer

两种常用图类型：Lineandscatterplots(使用plot()命令),histogram(使用hist()命令)2.1折线图&散点图Lineandscatterplots2.1.1折线图Lineplots(关联一组x和y值的直线)importnumpyasnpimportpylabasplx=[1,2,3,4,5]#Makeanarrayofxvaluesy=[1,4,9,16,25]#Makeanarrayofyvaluesforeachxvaluepl.plot(x,y)#usepylabtoplotxandypl.show()#showtheplotonthescreen图例Figurelegendspl.legend((plot1,plot2),("label1,label2"),"best",numpoints=1)其中第三个参数表示图例放置的位置:"best"‘upperright",‘upperleft",‘center",‘lowerleft",‘lowerright".如果在当前figure里plot的时候已经指定了label，如plt.plot(x,z,label=cos(x2))，直接调用plt.legend()就可以了哦。importnumpyasnpimportpylabasplx1=[1,2,3,4,5]#Makex,yarraysforeachgraphy1=[1,4,9,16,25]x2=[1,2,4,6,8]y2=[2,4,8,12,16]plot1=pl.plot(x1,y1,"r")#usepylabtoplotxandy:Giveyourplotsnamesplot2=pl.plot(x2,y2,"go")pl.title("Plotofyvs.x")#giveplotatitlepl.xlabel("xaxis")#makeaxislabelspl.ylabel("yaxis")pl.xlim(0.0,9.0)#setaxislimitspl.ylim(0.0,30.)pl.legend([plot1,plot2],("redline","greencircles"),"best",numpoints=1)#makelegendpl.show()#showtheplotonthescreen

>> help errorbar errorbar Error bar plot. errorbar(X,Y,L,U) plots the graph of vector X vs. vector Y with error bars specified by the vectors L and U. L and U contain the lower and upper error ranges for each point in Y. Each error bar is L(i) + U(i) long and is drawn a distance of U(i) above and L(i) below the points in (X,Y). The vectors X,Y,L and U must all be the same length. If X,Y,L and U are matrices then each column produces a separate line. errorbar(X,Y,E) or errorbar(Y,E) plots Y with error bars [Y-E Y+E]. errorbar(...,"LineSpec") uses the color and linestyle specified by the string "LineSpec". The color is applied to the data line and error bars while the linestyle and marker are applied to the data line only. See PLOT for possibilities. errorbar(AX,...) plots into AX instead of GCA. H = errorbar(...) returns a vector of errorbarseries handles in H. For example, x = 1:10; y = sin(x); e = std(y)*ones(size(x)); errorbar(x,y,e) draws symmetric error bars of unit standard deviation.

为你解答。句意：一只猫的坠落存活能力是以事实为基础的。解释：这个句子就是个简单句，不包含从句。survive是动词，falls是名词，意思是在坠落之后存活下来。tosurvivefalls是ability的定语，相当于形容词，就是“……的能力”。你说的主干是对的。

set(get(ax(1),"Ylabel"),"String","左Y轴"); set(get(ax(2),"Ylabel"),"String","右Y轴‘）;记得给我加分哦

对滴，意思是 猫从高空中坠落后生还的能力

https://satijalab.org/seurat/articles/integration_introduction.html https://blog.csdn.net/zengwanqin/article/details/114969068 Integration goals The following tutorial is designed to give you an overview of the kinds of comparative analyses on complex cell types that are possible using the Seurat integration procedure. Here, we address a few key goals: 1Create an ‘integrated" data assay for downstream analysis 2Identify cell types that are present in both datasets 3Obtain cell type markers that are conserved in both control and stimulated cells 4Compare the datasets to find cell-type specific responses to stimulation 1Setup the Seurat objects library(Seurat) library(SeuratData) library(patchwork) InstallData("ifnb") LoadData("ifnb") ifnb str(ifnb) ifnb.list <- SplitObject(ifnb, split.by = "stim") ifnb.list <- lapply(X = ifnb.list, FUN = function(x) { x <- NormalizeData(x) x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000) }) features <- SelectIntegrationFeatures(object.list = ifnb.list) 2Perform integration We then identify anchors using the FindIntegrationAnchors() function, which takes a list of Seurat objects as input, and use these anchors to integrate the two datasets together with IntegrateData(). immune.anchors <- FindIntegrationAnchors(object.list = ifnb.list, anchor.features = features) immune.anchors str(immune.anchors) immune.combined <- IntegrateData(anchorset = immune.anchors) 3Perform an integrated analysis Now we can run a single integrated analysis on all cells! DefaultAssay(immune.combined) <- "integrated" immune.combined <- ScaleData(immune.combined, verbose = FALSE) immune.combined <- RunPCA(immune.combined, npcs = 30, verbose = FALSE) immune.combined <- RunUMAP(immune.combined, reduction = "pca", dims = 1:30) immune.combined <- FindNeighbors(immune.combined, reduction = "pca", dims = 1:30) immune.combined <- FindClusters(immune.combined, resolution = 0.5) p1 <- DimPlot(immune.combined, reduction = "umap", group.by = "stim") p2 <- DimPlot(immune.combined, reduction = "umap", label = TRUE, repel = TRUE) p1 + p2 To visualize the two conditions side-by-side, we can use the split.by argument to show each condition colored by cluster. DimPlot(immune.combined, reduction = "umap", split.by = "stim") 4Identify conserved cell type markers To identify canonical cell type marker genes that are conserved across conditions, we provide the FindConservedMarkers() function. This function performs differential gene expression testing for each dataset/group and combines the p-values using meta-analysis methods from the MetaDE R package. For example, we can calculated the genes that are conserved markers irrespective of stimulation condition in cluster 6 (NK cells). DefaultAssay(immune.combined) <- "RNA" """ BiocManager::install("multtest") install.packages("metap") library("multtest") """ nk.markers <- FindConservedMarkers(immune.combined, ident.1 = 6, grouping.var = "stim", verbose = FALSE) head(nk.markers) We can explore these marker genes for each cluster and use them to annotate our clusters as specific cell types. str(nk.markers) rownames(nk.markers) head(rownames(nk.markers)) FeaturePlot(immune.combined, features = head(rownames(nk.markers),5), min.cutoff = "q9") FeaturePlot(immune.combined, features = c("CD3D", "SELL", "CREM", "CD8A", "GNLY", "CD79A", "FCGR3A", "CCL2", "PPBP"), min.cutoff = "q9") immune.combined <- RenameIdents(immune.combined, 0 = "CD14 Mono", 1 = "CD4 Naive T", 2 = "CD4 Memory T", 3 = "CD16 Mono", 4 = "B", 5 = "CD8 T", 6 = "NK", 7 = "T activated", 8 = "DC", 9 = "B Activated", 10 = "Mk", 11 = "pDC", 12 = "Eryth", 13 = "Mono/Mk Doublets", 14 = "HSPC") DimPlot(immune.combined, label = TRUE) The DotPlot() function with the split.by parameter can be useful for viewing conserved cell type markers across conditions, showing both the expression level and the percentage of cells in a cluster expressing any given gene. Here we plot 2-3 strong marker genes for each of our 14 clusters. Idents(immune.combined) <- factor(Idents(immune.combined), levels = c("HSPC", "Mono/Mk Doublets", "pDC", "Eryth", "Mk", "DC", "CD14 Mono", "CD16 Mono", "B Activated", "B", "CD8 T", "NK", "T activated", "CD4 Naive T", "CD4 Memory T")) markers.to.plot <- c("CD3D", "CREM", "HSPH1", "SELL", "GIMAP5", "CACYBP", "GNLY", "NKG7", "CCL5", "CD8A", "MS4A1", "CD79A", "MIR155HG", "NME1", "FCGR3A", "VMO1", "CCL2", "S100A9", "HLA-DQA1", "GPR183", "PPBP", "GNG11", "HBA2", "HBB", "TSPAN13", "IL3RA", "IGJ", "PRSS57") DotPlot(immune.combined, features = markers.to.plot, cols = c("blue", "red"), dot.scale = 8, split.by = "stim") + RotatedAxis() 5Identify differential expressed genes across conditions Now that we"ve aligned the stimulated and control cells, we can start to do comparative analyses and look at the differences induced by stimulation. One way to look broadly at these changes is to plot the average expression of both the stimulated and control cells and look for genes that are visual outliers on a scatter plot. Here, we take the average expression of both the stimulated and control naive T cells and CD14 monocyte populations and generate the scatter plots, highlighting genes that exhibit dramatic responses to interferon stimulation. library(ggplot2) library(cowplot) theme_set(theme_cowplot()) Idents(immune.combined) t.cells <- subset(immune.combined, idents = "CD4 Naive T") head(t.cells) tail(t.cells) str(t.cells) """ my_tcells<-immune.combined@assays$RNA[,orig.ident=c("IMMUNE_CTRL","IMMUNE_STIM")] head(my_tcells) colnames(my_tcells) """ Idents(t.cells) ##此时的level为 CD4 Naive T Idents(t.cells) <- "stim" ##此时的level为 CONTROL 和STIM t.cells RNA t.cells@assays RNA)) avg.t.cells avg.t.cells$gene <- rownames(avg.t.cells) avg.t.cells cd14.mono <- subset(immune.combined, idents = "CD14 Mono") Idents(cd14.mono) <- "stim" avg.cd14.mono <- as.data.frame(log1p(AverageExpression(cd14.mono, verbose = FALSE) gene <- rownames(avg.cd14.mono) avg.cd14.mono genes.to.label = c("ISG15", "LY6E", "IFI6", "ISG20", "MX1", "IFIT2", "IFIT1", "CXCL10", "CCL8") p1 <- ggplot(avg.t.cells, aes(CTRL, STIM)) + geom_point() + ggtitle("CD4 Naive T Cells") p1 p1 <- LabelPoints(plot = p1, points = genes.to.label, repel = FALSE) p2 <- ggplot(avg.cd14.mono, aes(CTRL, STIM)) + geom_point() + ggtitle("CD14 Monocytes") p2 <- LabelPoints(plot = p2, points = genes.to.label, repel = TRUE) p1 + p2 """ library(dplyr) avg.t.cells avg.t.cells %>% select(CTRL,STIM) %>% unlist () %>% sd() """ As you can see, many of the same genes are upregulated in both of these cell types and likely represent a conserved interferon response pathway. Because we are confident in having identified common cell types across condition, we can ask what genes change in different conditions for cells of the same type. First, we create a column in the meta.data slot to hold both the cell type and stimulation information and switch the current ident to that column. Then we use FindMarkers() to find the genes that are different between stimulated and control B cells. Notice that many of the top genes that show up here are the same as the ones we plotted earlier as core interferon response genes. Additionally, genes like CXCL10 which we saw were specific to monocyte and B cell interferon response show up as highly significant in this list as well. head(immune.combined@meta.data,10) immune.combined stim, sep = "_") head(immune.combined@meta.data,10) immune.combined$celltype <- Idents(immune.combined) head(immune.combined@meta.data,10) Idents(immune.combined) Idents(immune.combined) <- "celltype.stim" Idents(immune.combined) b.interferon.response <- FindMarkers(immune.combined, ident.1 = "B_STIM", ident.2 = "B_CTRL", verbose = FALSE) head(b.interferon.response, n = 15) Another useful way to visualize these changes in gene expression is with the split.by option to the FeaturePlot() or VlnPlot() function. This will display FeaturePlots of the list of given genes, split by a grouping variable (stimulation condition here). Genes such as CD3D and GNLY are canonical cell type markers (for T cells and NK/CD8 T cells) that are virtually unaffected by interferon stimulation and display similar gene expression patterns in the control and stimulated group. IFI6 and ISG15, on the other hand, are core interferon response genes and are upregulated accordingly in all cell types. Finally, CD14 and CXCL10 are genes that show a cell type specific interferon response. CD14 expression decreases after stimulation in CD14 monocytes, which could lead to misclassification in a supervised analysis framework, underscoring the value of integrated analysis. CXCL10 shows a distinct upregulation in monocytes and B cells after interferon stimulation but not in other cell types. FeaturePlot(immune.combined, features = c("CD3D", "GNLY", "IFI6"), split.by = "stim", max.cutoff = 3, cols = c("grey", "red")) plots <- VlnPlot(immune.combined, features = c("LYZ", "ISG15", "CXCL10"), split.by = "stim", group.by = "celltype",split.plot = TRUE, pt.size = 0, combine = FALSE) wrap_plots(plots = plots, ncol = 1) https://satijalab.org/seurat/articles/integration_introduction.html https://blog.csdn.net/zengwanqin/article/details/114969068 Integration goals The following tutorial is designed to give you an overview of the kinds of comparative analyses on complex cell types that are possible using the Seurat integration procedure. Here, we address a few key goals: 1Create an ‘integrated" data assay for downstream analysis 2Identify cell types that are present in both datasets 3Obtain cell type markers that are conserved in both control and stimulated cells 4Compare the datasets to find cell-type specific responses to stimulation 1Setup the Seurat objects library(Seurat) library(SeuratData) library(patchwork) InstallData("ifnb") LoadData("ifnb") ifnb str(ifnb) ifnb.list <- SplitObject(ifnb, split.by = "stim") ifnb.list <- lapply(X = ifnb.list, FUN = function(x) { x <- NormalizeData(x) x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000) }) features <- SelectIntegrationFeatures(object.list = ifnb.list) 2Perform integration We then identify anchors using the FindIntegrationAnchors() function, which takes a list of Seurat objects as input, and use these anchors to integrate the two datasets together with IntegrateData(). immune.anchors <- FindIntegrationAnchors(object.list = ifnb.list, anchor.features = features) immune.anchors str(immune.anchors) immune.combined <- IntegrateData(anchorset = immune.anchors) 3Perform an integrated analysis Now we can run a single integrated analysis on all cells! DefaultAssay(immune.combined) <- "integrated" immune.combined <- ScaleData(immune.combined, verbose = FALSE) immune.combined <- RunPCA(immune.combined, npcs = 30, verbose = FALSE) immune.combined <- RunUMAP(immune.combined, reduction = "pca", dims = 1:30) immune.combined <- FindNeighbors(immune.combined, reduction = "pca", dims = 1:30) immune.combined <- FindClusters(immune.combined, resolution = 0.5) p1 <- DimPlot(immune.combined, reduction = "umap", group.by = "stim") p2 <- DimPlot(immune.combined, reduction = "umap", label = TRUE, repel = TRUE) p1 + p2 To visualize the two conditions side-by-side, we can use the split.by argument to show each condition colored by cluster. DimPlot(immune.combined, reduction = "umap", split.by = "stim") 4Identify conserved cell type markers To identify canonical cell type marker genes that are conserved across conditions, we provide the FindConservedMarkers() function. This function performs differential gene expression testing for each dataset/group and combines the p-values using meta-analysis methods from the MetaDE R package. For example, we can calculated the genes that are conserved markers

所有的m文件默认都保存在work目录下，除非你更改保持位置。画图命令一般包含在m文件的程序中，难道要另外保存画图文件？！没必要吧

This tutorial explains matplotlib"s way of making plots in simplified parts so you gain the knowledge and a clear understanding of how to build and modify full featured matplotlib plots. The goal of this tutorial is to make you understand ‘how plotting with matplotlib works" and make you comfortable to build full-featured plots with matplotlib. The following piece of code is found in pretty much any python code that has matplotlib plots. matplotlib.pyplot is usually imported as plt . It is the core object that contains the methods to create all sorts of charts and features in a plot. The %matplotlib inline is a jupyter notebook specific command that let"s you see the plots in the notbook itself. Suppose you want to draw a specific type of plot, say a scatterplot, the first thing you want to check out are the methods under plt (type plt and hit tab or type dir(plt) in python prompt). Let"s begin by making a simple but full-featured scatterplot and take it from there. Let"s see what plt.plot() creates if you an arbitrary sequence of numbers. I just gave a list of numbers to plt.plot() and it drew a line chart automatically. It assumed the values of the X-axis to start from zero going up to as many items in the data. Notice the line matplotlib.lines.Line2D in code output? That"s because Matplotlib returns the plot object itself besides drawing the plot. If you only want to see the plot, add plt.show() at the end and execute all the lines in one shot. Alright, notice instead of the intended scatter plot, plt.plot drew a line plot. That"s because of the default behaviour. So how to draw a scatterplot instead? Well to do that, let"s understand a bit more about what arguments plt.plot() expects. The plt.plot accepts 3 basic arguments in the following order: (x, y, format). This format is a short hand combination of {color}{marker}{line} . For example, the format "go-" has 3 characters standing for: ‘green colored dots with solid line". By omitting the line part (‘-‘) in the end, you will be left with only green dots (‘go"), which makes it draw a scatterplot. Few commonly used short hand format examples are: For a complete list of colors, markers and linestyles, check out the help(plt.plot) command. Let"s draw a scatterplot with greendots. Good. Now how to plot another set of 5 points of different color in the same figure? Simply call plt.plot() again, it will add those point to the same picture. You might wonder, why it does not draw these points in a new panel altogether? I will come to that in the next section. Looks good. Now let"s add the basic plot features: Title, Legend, X and Y axis labels. How to do that? The plt object has corresponding methods to add each of this. Good. Now, how to increase the size of the plot? (The above plot would actually look small on a jupyter notebook) The easy way to do it is by setting the figsize inside plt.figure() method. Ok, we have some new lines of code there. What does plt.figure do? Well, every plot that matplotlib makes is drawn on something called "figure" . You can think of the figure object as a canvas that holds all the subplots and other plot elements inside it. And a figure can have one or more subplots inside it called axes , arranged in rows and columns. Every figure has atleast one axes. (Don"t confuse this axes with X and Y axis, they are different.) Let"s understand figure and axes in little more detail. Suppose, I want to draw our two sets of points (green rounds and blue stars) in two separate plots side-by-side instead of the same plot. How would you do that? You can do that by creating two separate subplots, aka, axes using plt.subplots(1, 2) . This creates and returns two objects: Previously, I called plt.plot() to draw the points. Since there was only one axes by default, it drew the points on that axes itself. But now, since you want the points drawn on different subplots (axes), you have to call the plot function in the respective axes ( ax1 and ax2 in below code) instead of plt . Notice in below code, I call ax1.plot() and ax2.plot() instead of calling plt.plot() twice. Setting sharey=True in plt.subplots() shares the Y axis between the two subplots. And dpi=120 increased the number of dots per inch of the plot to make it look more sharp and clear. You will notice a distinct improvement in clarity on increasing the dpi especially in jupyter notebooks. Thats sounds like a lot of functions to learn. Well it"s quite easy to remember it actually. The ax1 and ax2 objects, like plt , has equivalent set_title , set_xlabel and set_ylabel functions. Infact, the plt.title() actually calls the current axes set_title() to do the job. Alternately, to save keystrokes, you can set multiple things in one go using the ax.set() . <iframe loading="lazy" data-ezsrc="https://18190a29.sibforms.com/serve/MUIEAHmY0EyBInhP7H5cwh_RCLOP_xkML6ZLF9xXWRj6P_N_UbjBZWur2FlORJKlWFF8GI2YTOci5FITiNxT-fWokYZlrtDU_9Q-xc-vEF6m_l6hoM0hgerRQLqqdKa4rWVDQJPxWyt_vx8_f0RRmYiDX62-g1hpSe8iB4-4_p7NO_lxJXXna9lFMAHEzstWXha3LEGHCTLl8wFW" width="540" height="680" frameborder="0" scrolling="auto" allowfullscreen="allowfullscreen" class=" ezlazyloaded" ezoid="0.20520201884265243" style="box-sizing: border-box; border: none; font-size: 16px; font-style: inherit; font-weight: inherit; margin: 0px auto; outline: 0px; padding: 0px; vertical-align: baseline; max-width: 100%; width: 688.188px; line-height: 1; display: block;"></iframe> A known ‘problem" with learning matplotlib is, it has two coding interfaces: This is partly the reason why matplotlib doesn"t have one consistent way of achieving the same given output, making it a bit difficult to understand for new comers. The syntax you"ve seen so far is the Object-oriented syntax, which I personally prefer and is more intuitive and pythonic to work with. However, since the original purpose of matplotlib was to recreate the plotting facilities of matlab in python, the matlab-like-syntax is retained and still works. The matlab syntax is ‘stateful". That means, the plt keeps track of what the current axes is. So whatever you draw with plt.{anything} will reflect only on the current subplot. Practically speaking, the main difference between the two syntaxes is, in matlab-like syntax, all plotting is done using plt methods instead of the respective axes ‘s method as in object oriented syntax. So, how to recreate the above multi-subplots figure (or any other figure for that matter) using matlab-like syntax? The general procedure is: You manually create one subplot at a time (using plt.subplot() or plt.add_subplot() ) and immediately call plt.plot() or plt.{anything} to modify that specific subplot (axes). Whatever method you call using plt will be drawn in the current axes . The code below shows this in practice. Let"s breakdown the above piece of code. In plt.subplot(1,2,1) , the first two values, that is (1,2) specifies the number of rows (1) and columns (2) and the third parameter (1) specifies the position of current subplot. The subsequent plt functions, will always draw on this current subplot. You can get a reference to the current (subplot) axes with plt.gca() and the current figure with plt.gcf() . Likewise, plt.cla() and plt.clf() will clear the current axes and figure respectively. Alright, compare the above code with the object oriented (OO) version. The OO version might look a but confusing because it has a mix of both ax1 and plt commands. However, there is a significant advantage with axes approach. That is, since plt.subplots returns all the axes as separate objects, you can avoid writing repetitive code by looping through the axes. Always remember: plt.plot() or plt.{anything} will always act on the plot in the current axes , whereas, ax.{anything} will modify the plot inside that specific ax . Did you notice in above plot, the Y-axis does not have ticks? That"s because I used ax.yaxis.set_ticks_position("none") to turn off the Y-axis ticks. This is another advantage of the object-oriented interface. You can actually get a reference to any specific element of the plot and use its methods to manipulate it. Can you guess how to turn off the X-axis ticks? The plt.suptitle() added a main title at figure level title. plt.title() would have done the same for the current subplot (axes). The verticalalignment="bottom" parameter denotes the hingepoint should be at the bottom of the title text, so that the main title is pushed slightly upwards. Alright, What you"ve learned so far is the core essence of how to create a plot and manipulate it using matplotlib. Next, let"s see how to get the reference to and modify the other components of the plot There are 3 basic things you will probably ever need in matplotlib when it comes to manipulating axis ticks: 1. How to control the position and tick labels? (using plt.xticks() or ax.set xticks() and ax.set xticklabels()) 2. How to control which axis"s ticks (top/bottom/left/right) should be displayed (using plt.tick_params()) 3. Functional formatting of tick labels If you are using ax syntax, you can use ax.set_xticks() and ax.set_xticklabels() to set the positions and label texts respectively. If you are using the plt syntax, you can set both the positions as well as the label text in one call using the plt.xticks() . Actually, if you look at the code of plt.xticks() method (by typing ??plt.xticks in jupyter notebook), it calls ax.set_xticks() and ax.set_xticklabels() to do the job. plt.xticks takes the ticks and labels as required parameters but you can also adjust the label"s fontsize , rotation , ‘horizontalalignment" and ‘verticalalignment" of the hinge points on the labels, like I"ve done in the below example. In above code, plt.tick_params() is used to determine which all axis of the plot (‘top" / ‘bottom" / ‘left" / ‘right") you want to draw the ticks and which direction (‘in" / ‘out") the tick should point to. the matplotlib.ticker module provides the FuncFormatter to determine how the final tick label should be shown. The look and feel of various components of a matplotlib plot can be set globally using rcParams . The complete list of rcParams can be viewed by typing: You can adjust the params you"d like to change by updating it. The below snippet adjusts the font by setting it to ‘stix", which looks great on plots by the way. After modifying a plot, you can rollback the rcParams to default setting using: Matplotlib comes with pre-built styles which you can look by typing: I"ve just shown few of the pre-built styles, the rest of the list is definitely worth a look. Matplotlib also comes with pre-built colors and palettes. Type the following in your jupyter/python console to check out the available colors. The most common way to make a legend is to define the label parameter for each of the plots and finally call plt.legend() . However, sometimes you might want to construct the legend on your own. In that case, you need to pass the plot items you want to draw the legend for and the legend text as parameters to plt.legend() in the following format: plt.legend((line1, line2, line3), ("label1", "label2", "label3")) plt.text and plt.annotate adds the texts and annotations respectively. If you have to plot multiple texts you need to call plt.text() as many times typically in a for-loop. Let"s annotate the peaks and troughs adding arrowprops and a bbox for the text. Notice, all the text we plotted above was in relation to the data. That is, the x and y position in the plt.text() corresponds to the values along the x and y axes. However, sometimes you might work with data of different scales on different subplots and you want to write the texts in the same position on all the subplots. In such case, instead of manually computing the x and y positions for each axes, you can specify the x and y values in relation to the axes (instead of x and y axis values). You can do this by setting tr

--是线型（双短线），r是颜色（红色），s是点型（正方形）

(a) 修改预训练模型的最后三层的步骤如下：加载 VGG-19 网络：net = vgg19();使用 VGG-19 网络的前几层作为转移层：layersTransfer = net.Layers(1:end-3);计算类别数量：numClasses = numel(categories(pictures.Labels));定义新的完整连接层，并将其添加到转移层之后：layers = [layersTransferfullyConnectedLayer(numClasses,"WeightLearnRateFactor", 20, ..."BiasLearnRateFactor", 20)softmaxLayerclassificationLayer];修改预训练模型的最后三层的原因是，预训练的模型是用来解决一般性问题的，而在这个例子中，我们希望模型能够识别花的种类。因此，我们需要修改最后三层，使它们能够将图像的特征与花的种类相关联。(b) 运行结果是分类器对输入图像的预测类别。要改进结果，可以考虑以下几点：增加训练数据的数量和多样性。增加训练迭代次数。调整超参数，如学习率和小批量大小。使用数据增强来扩展训练数据。尝试使用不同的模型结构。(c) 关于模型的行为，可以做出如下假设：模型可能会更倾向于将图像分类为其中的主要物体，例如如果图像中有花，模型可能会将其分类为花，而不是将其分类为背景或其他物体。模型可能会受到图像质量的影响，例如图像分辨率低、噪声较多等。模型可能会受到训练数据的影响，例如训练数据中缺乏某些类别或训练数据不够多样时。模型可能会受到超参数的影响，例如学习率过高或过低、小批量大小过大或过小等。这些假设的基础是模型的结构和训练过程。如果模型的结构不能很好地捕捉图像的特征，则可能会出现分类错误。如果训练数据不够丰富，则模型可能无法学习足够的信息来准确地分类图像。超参数也可能会对模型的行为产生影响，例如学习率过高可能会导致模型无法收敛，而过低则可能会使模型训练过慢。

你打开stackloss这个内置数据库>stackloss有4列数据，那么你plot他们就相当于两两做plot，一共16幅图。对角线的4幅是名称Air.Flow Water.Temp Acid.Conc. stack.loss那么比如第2行第3幅图就是Water.Temp Acid.Conc （水温和酸碱环境）的散点图。其实面对多列数据，plot运行的就是pairs的功能，所以结果和pairs是一样的如果你只需要其中一幅(还是第2行第3幅图)，就>plot(stackloss[2],stackloss[3]) 谁在横坐标，谁在前

动名词

correlation plot 相关图plot 英[plɒt]美[plɑ:t]n. 地基，基址图; （戏剧、小说等的） 情节; 一块地; 测算表;vt. 密谋; 以图表画出，制图; 把…分成小块; 为（文学作品）设计情节;vi. 设计作品情节; 标示于图表上; 密谋，暗中策划;[例句]Security forces have uncovered a plot to overthrow the government安全部队揭露了一起推翻政府的阴谋。[其他] 第三人称单数：plots 复数：plots 现在分词：plotting 过去式：plotted 过去分词：plotted

PSDPLOT Plot Power Spectral Density (PSD) data. PSDPLOT(Pxx,W) plots the PSD, Pxx, computed at the frequencies specified in W (in radians/sample). PSDPLOT(Pxx,W,UNITS) specifies the frequency (x-axis) units for the plot. UNITS can be either "RAD/SAMPLE" (default) or "Hz". PSDPLOT(Pxx,W,UNITS,YSCALE), where YSCALE can be either "LINEAR" or "DB", specifies the scaling of the Y-axis for the PSD plot. PSDPLOT(Pxx,W,UNITS,YSCALE,TITLESTRING) will use the specified string for the title of the plot. NOTE: The PSDPLOT function is OBSOLETE. To plot spectrum data use the new DSP data objects. With these objects you can plot Power Spectral Density or Power Spectrum data, and specify data units and frequency units. Type help dspdata/psd or dspdata/ps for details and an example. See also dspdata/psd, dspdata/ps, spectrum/periodogram, spectrum/welch, spectrum/mtm, spectrum/music, spectrum/eigenvector, spectrum/burg, spectrum/cov, spectrum/mcov, spectrum/yulear.

01首先，我们需要将我们要分析的数据文件整理为矩阵文件，即行列分明的数据文件。02我们打开matlab之后，点击菜单栏里的“import data”,准备加载我们需要统计分析的数据。03打开加载界面之后，我们找到我们要加载的数据文件，点击打开。04在加载的界面，我们将类型选择“matrix”矩阵列表，选择我们需要导入的列数据，然后点击右侧的“improt selection”进行导入。05导入之后，我们点击我们导入的矩阵列表，如图中的“S260”，注意不要打开，选中即可，然后点击菜单的“plots”,在绘图工具栏里，点击图标右侧的小三角准备打开更多图表类型。06在展开后的matlab图标里，我们找到“histfit”,并点击打开。07最后，我们需要的正态分布图及柱状分布图即绘制完成。如何用matlab计算正态分布的标准差方法/步骤01正态分布的数学表达若随机变量X服从一个数学期望为μ、方差为σ^2的高斯分布，记为N(μ,σ²)。其概率密度函数为正态分布的期望值μ决定了其位置，其标准差σ决定了分布的幅度。服从正态分布的N(μ,σ²)的连续性随机变量X的概率密度和累计概率密度函数分别如下图所示：02matlab提供的关于正态分布的三个常用指令的调用语法规则和功能，详见下图所示：03正态分布标准差的集合表示，这一步我们将计算指定区间的概率，标准差的含义和几何表示。具体的计算、实现代码、以及注释如下图所示：04下图是上一步计算代码执行的结果。05正态分布标准差的概率意义我们可以从上一步图中看出，观察值x落在[μ-σ,μ+σ]，[μ-2σ，μ+2σ]，[μ-3σ，μ+3σ]区间的概率，即P(μ-k·σ≤x≤μ+k·σ)分别是0.68269，0.9545，0.9973。因为P(μ-k·σ≤x≤μ+k·σ)=P(x-k·σ≤x≤x+k·σ)，所以这个概率意义又可以说成：测量数据两侧的一、二、三倍标准差区间包含该被测数据均值的概率分别是：0.68269，0.9545，0.9973。

waterfall plots是指瀑布图，有点类似波形图，一般在数据处理时用的。bode graphs是波特图，是种log(幅度)随log(频率)变化的曲线它描述了与频率有关的系统的输出响应,表示系统的传递函数.波特图使幅度的乘法简单地变成了曲线上距离地相加。

“影响统计学”和“举债经营计策”。我不懂计量经济学，仅从英语角度翻译，仅供参考吧。

水疗图

先hold off;然后再lsim（）

应该不能直接使用，就是不能像控件那样拖下来用。plot是代码编写生成才会出现，将会根据变量或数组画图。如果你没有生成变量，plot将不可用。

1.private与子函数：子函数只能在主函数内容中调用，在函数外无法使用function MainFunction %主函数内容 function SubFunction1 %子函数内容 end function SubFunction2 %子函数内容 end function SubFunction3 %子函数内容 endendprivate函数是matlab软件中广泛使用的一种技术，其作用是限定某一些函数（private文件夹内）只能被令一些函数（private文件夹所在文件夹中的函数）使用，其他函数不能使用，这样的话就可以避免一些无用（因为一般的matlab函数是全局可见的，而private函数只能被private文件夹所在文件夹中的函数调用。2.ezplot和fplotezplot是easy plot的简称，意思就是简化画图，只需给出函数体而无需变量范围，例如想画个圆，用ezplot就很方便：syms x yezplot("x^2+y^2=1",[-1.5 1.5],[-1 1])fplot的意思是function plot，就是画某一函数的曲线。顾名思义,若要画一条函数的曲线，只要给出函数名和自变量范围即可,例子如下：fplot("sin",[-2 2])

真诚地完全地逐渐地感激地

no longer在这做 状语成分 修饰 knew 的 翻译成 不再知道，，，，The happiest people don"t necessarily have the best of everything;they just make the best of everything they have.中第一分句的宾语是 have 第二分句的谓语是 make 祝你学习进步

got、played、carried、came、drove、speant、caught、spoke、wrote、thought、told、said、replied、skied

皮革的奖章

如果不用从句的话此句可转换为isentinvitationto60people.only20ofthemhavereplied.在这里whom是代替of后面的them。此句中间是逗号，明显，后面应为从句，如果硬要选B，则应该为：isentinvitationto60people.butonly20ofthesepeolehavereplied.若选B，那么后面的句子only20ofthese（whohavereplied）.就是定语从句，此句既是一个不完整的句子，只有主语20ofthese，和定语who从句，没有谓语了。

后面的是宾语从句的主语，前面的是主句的主语

respond to…对…有回应，翻译：他对药物已经有反应了，正在迅速的恢复

regarded the graduates关系毕业生regarded v. 关系( regard的过去式和过去分词 ); 注意; （尤指以某种方式） 注视; 尊敬;[网络] 看作; 认为; 视为;[例句]Endangering their lives will be regarded as a crime against humanity.危害他们的生命将被视为犯下了反人类罪。

应该用that,引导并与从句,which不能.

identification是“身份证明；鉴定，验明，认出；认同”之意思；badge是“徽章，证章；标记，标识；象征”之意。所以identification badge可以理解为“识别标志”。

hope指希望得到积极、有利的结果，所期待的可能实现，也可能不会实现。例：hope I haven"t said anything to upset you.但愿我没有说过让你不高兴的话。anticipate指对未来的期望，一般是对好事的期待。例：I anticipate deriving much instruction from the lecture. 我期望从这演讲中获得很多教益。

进入REC，清除所有数据，可以重启恢复出厂设置。如果有权限刷机，就需要多清除一项数据后，从sd卡刷入ROM。还有其他等等功能。

RC、Deployment、DaemonSet都是面向无状态的服务，它们所管理的Pod的IP、名字，启停顺序等都是随机的，而StatefulSet是什么？顾名思义，有状态的集合，管理所有有状态的服务，比如MySQL、MongoDB集群等。 StatefulSet本质上是Deployment的一种变体，在v1.9版本中已成为GA版本，它为了解决有状态服务的问题，它所管理的Pod拥有固定的Pod名称，启停顺序，在StatefulSet中，Pod名字称为网络标识(hostname)，还必须要用到共享存储。 在Deployment中，与之对应的服务是service，而在StatefulSet中与之对应的headless service，headless service，即无头服务，与service的区别就是它没有Cluster IP，解析它的名称时将返回该Headless Service对应的全部Pod的Endpoint列表。 除此之外，StatefulSet在Headless Service的基础上又为StatefulSet控制的每个Pod副本创建了一个DNS域名，这个域名的格式为： 接下来看一些示例，演示下上面所说的特性，以加深理解 通过该配置文件，可看出StatefulSet的三个组成部分： 创建： 看下这三个Pod创建过程： 根据volumeClaimTemplates自动创建的PVC Statefulset名称为web 三个Pod副本: web-0，web-1,web-2，volumeClaimTemplates名称为：www，那么自动创建出来的PVC名称为www-web[0-2]，为每个Pod创建一个PVC。 匹配Pod name(网络标识)的模式为： (序号)，比如上面的示例：web-0，web-1，web-2。 StatefulSet为每个Pod副本创建了一个DNS域名，这个域名的格式为： $(podname).(headless server name)，也就意味着服务间是通过Pod域名来通信而非Pod IP，因为当Pod所在Node发生故障时，Pod会被飘移到其它Node上，Pod IP会发生变化，但是Pod域名不会有变化。 StatefulSet使用Headless服务来控制Pod的域名，这个域名的FQDN为： (namespace).svc.cluster.local，其中，“cluster.local”指的是集群的域名。 根据volumeClaimTemplates，为每个Pod创建一个pvc，pvc的命名规则匹配模式：( volumeClaimTemplates.name)-(pod_name )，比如上面的 volumeMounts.name=www ， Pod name=web-[0-2]，因此创建出来的PVC是www-web-0、www-web-1、www-web-2。 删除Pod不会删除其pvc，手动删除pvc将自动释放pv。 关于Cluster Domain、headless service名称、StatefulSet 名称如何影响StatefulSet的Pod的DNS域名的示例： 在v1.7以后，通过允许修改Pod排序策略，同时通过.spec.podManagementPolicy字段确保其身份的唯一性。 在Kubernetes 1.7及更高版本中，通过.spec.updateStrategy字段允许配置或禁用Pod、labels、source request/limits、annotations自动滚动更新功能。 StatefulSet控制器将删除并重新创建StatefulSet中的每个Pod。它将以Pod终止（从最大序数到最小序数）的顺序进行，一次更新每个Pod。在更新下一个Pod之前，必须等待这个Pod Running and Ready。 参考： https://www.cnblogs.com/tylerzhou/p/11027559.html https://www.cnblogs.com/buyicoding/p/12591259.html

提醒您每挂载32次，或超过180天，下次挂载的时候就要对文件系统进行fsck检查

rsize和wsize，一般在网络文件系统里面有较大意义，尤其是NFS文件系统。 这2个值指的是文件系统的读写数据块大小，也就是文件系统每一次读写的最大数据块大小。注意和磁盘的blocksize不是一个概念。 在不同的linux发行版，这2个值的上限不一样。单位是字节。 比方rsize = 65535，那么读取655350字节的数据，文件系统底层必须读10次才能读完。虽然说这2个值对应用层来说是透明的，但是如果设置的不合理，会影响文件系统的性能。

我们来看看如下这组词汇的区别吧：endure  tolerate  abide 和  put up with这组动词都含有“忍受”的意思。Endure是指“（尤指勇敢或无怨言地）忍耐，忍受（身体或精神上的痛苦或磨难）”，如：1. Paula bravely endured the other children"s taunts.葆拉勇敢地忍受了其他孩子的奚落。Tolerate是指“接受，忍受，容忍（不喜欢或不赞同的人或事）”，如：2. I tolerate my sister"s husband for her sake, but I don"t like him.我看在姐姐的面子上接受了姐夫，但我并不喜欢他。3. They had put up with behaviour from their son which they would not have tolerated from anyone else.  他们（有耐心）容忍了儿子的行为，如果换作别人他们可做不到（无法忍受了）。Abide是指“默默忍受，容忍（极端令人厌烦的人或事）”，只用于否定句式，如：4. One thing he could never abide was rudeness.他绝不能容忍的一件事就是粗鲁无礼。Put up with是指“（耐心地）容忍，忍受（难相处的人、困难的处境或某种行为）”，如：5. I"m not going to put up with their smoking any longer.我再也不能容忍他们抽烟了。还有另一组 bear  stand  stomach  类似，更多的在“词不离句：英语常用词汇8000分级过关‘"（链接请百度“词不离句”）。●● 不要试图去记那些用法，是记不住的；而是把例句记下来，这样用法也就记住了！通过听录音，复述，默写，就轻松地把句子记住啦！在英语常用词汇里有哪些词汇是同义词，近义词及易混词呢？《词不离句：英语常用词汇8000分级过关》统计如下：从上可见，在英语常用的词汇8000多个里，有过半的词汇是同义词，近义词及易混词，这是很多人没想到的，我们都得区分清楚它们的用法。

great好的，巨大的sure肯定的

Sure. How many plates.

直接在华为应用市场就可以下载有道翻译官app

病菌、病原菌祝你好运

tolerate 主要指 公差，差别，容许

pathogenic bacteria[英][ˌpæθəˈdʒenɪk bækˈtiəriə][美][ˌpæθəˈdʒɛnɪk bækˈtɪriə]病菌，病原菌; 以上结果来自金山词霸例句:1.As a large number of pathogenic bacteria contain these copper-resistant genes, and asthere are very few copper-containing enzymes in bacteria, it is likely that an excess ratherthan a deficit of copper is a problem for bacteria inside the human body. 由于大量的病原菌含有抗铜基因，而且在细菌细胞内只有极少量的酶含有铜元素，因此过量的铜（而不是铜元素的缺乏）更可能对人体内的细菌构成威胁。