1克等于多少毫克的翻譯是：什么意思

1克等于多少毫克的翻譯是：什么意思

1克等于多少毫克1 grams equals how many milligrams milligram 英[?m?ligr?m] 美[?m?l??ɡr?m] n. 毫克（千分之一克）; [例句]When injected or inhaled, as little as one-half milligram of ricin is lethal to humans.當(dāng)注射或吸入少量，僅半毫克的蓖麻毒素就會(huì)致*。

美國(guó)計(jì)量單位英文單詞

FRO

一 cup 是多少克？

不同的物質(zhì)重量是不同的：
黃油1cup=227g;
面粉1cup=120g;
細(xì)砂糖1cup=180~200g;
粗砂糖1cup=200~220g;
糖粉1cup=130g;
碎干果1cup=114g;
葡萄干1cup=170g;
蜂蜜1cup=340g。
cup是烘焙計(jì)量單位，美國(guó)一般用量杯量勺，而其他地方一般用秤。

【TODO】【scikit-learn翻譯】4.2.3Text feature extraction

Text Analysis is a major application field for machine learning algorithms. However the raw data, a sequence of symbols cannot be fed directly to the algorithms themselves as most of them expect numerical feature vectors with a fixed size rather than the raw text documents with variable length. 文本分析是機(jī)器學(xué)習(xí)算法的主要應(yīng)用領(lǐng)域。 然而，原始數(shù)據(jù)，符號(hào)文字序列不能直接傳遞給算法，因?yàn)樗鼈兇蠖鄶?shù)要求具有固定長(zhǎng)度的數(shù)字矩陣特征向量，而不是具有可變長(zhǎng)度的原始文本文檔。

In order to address this, scikit-learn provides utilities for the most common ways to extract numerical features from text content, namely: 為解決這個(gè)問(wèn)題，scikit-learn提供了從文本內(nèi)容中提取數(shù)字特征的最常見(jiàn)方法，即： In this scheme, features and samples are defined as follows: 在該方案中，特征和樣本定義如下： A corpus of documents can thus be represented by a matrix with one row per document and one column per token (e.g. word) occurring in the corpus. 因此，文本的**可被表示為矩陣形式，每行對(duì)應(yīng)一條文本，每列對(duì)應(yīng)每個(gè)文本中出現(xiàn)的詞令牌(如單個(gè)詞)。

We call vectorization the general process of turning a collection of text documents into numerical feature vectors. This specific strategy (tokenization, counting and normalization) is called the Bag of Words or “Bag of n-grams” representation. Documents are described by word occurrences while completely ignoring the relative position information of the words in the document. 我們稱 向量化 是將文本文檔**轉(zhuǎn)換為數(shù)字**特征向量的普通方法。 這種特殊思想（令牌化，計(jì)數(shù)和歸一化）被稱為 Bag of Words 或 “Bag of n-grams” 模型。 文檔由單詞出現(xiàn)來(lái)描述，同時(shí)完全忽略文檔中單詞的相對(duì)位置信息。 As most documents will typically use a very **all subset of the words used in the corpus, the resulting matrix will have many feature values that are zeros (typically more than 99% of them). 由于大多數(shù)文本文檔通常只使用文本詞向量全集中的一個(gè)小子集，所以得到的矩陣將具有許多特征值為零（通常大于99％）。

For instance a collection of 10,000 short text documents (such as emails) will use a vocabulary with a size in the order of 100,000 unique words in total while each document will use 100 to 1000 unique words individually. 例如，10,000 個(gè)短文本文檔（如電子郵件）的**將使用總共100,000個(gè)獨(dú)特詞的大小的詞匯，而每個(gè)文檔將單獨(dú)使用100到1000個(gè)獨(dú)特的單詞。 In order to be able to store such a matrix in memory but also to speed up algebraic operations matrix / vector, implementations will typically use a sparse representation such as the implementations available in the scipy.sparse package. 為了能夠?qū)⑦@樣的矩陣存儲(chǔ)在存儲(chǔ)器中，并且還可以加速代數(shù)的矩陣/向量運(yùn)算，實(shí)現(xiàn)通常將使用諸如 scipy.sparse 包中的稀疏實(shí)現(xiàn)。 CountVectorizer implements both tokenization and occurrence counting in a single class: 類 CountVectorizer 在單個(gè)類中實(shí)現(xiàn)了 tokenization （詞語(yǔ)切分）和 occurrence counting （出現(xiàn)頻數(shù)統(tǒng)計(jì)）: This model has many parameters, however the default values are quite reasonable (please see the reference documentation for the details): 這個(gè)模型有很多參數(shù)，但參數(shù)的默認(rèn)初始值是相當(dāng)合理的（請(qǐng)參閱 參考文檔 了解詳細(xì)信息）: Let’s use it to tokenize and count the word occurrences of a minimalistic corpus of text documents: 我們用它來(lái)對(duì)簡(jiǎn)約的文本語(yǔ)料庫(kù)進(jìn)行 tokenize（分詞）和統(tǒng)計(jì)單詞出現(xiàn)頻數(shù): The default configuration tokenizes the string by extracting words of at least 2 letters. The specific function that does this step can be requested explicitly: 默認(rèn)配置通過(guò)提取至少 2 個(gè)字母的單詞來(lái)對(duì) string 進(jìn)行分詞。

做這一步的函數(shù)可以顯式地被調(diào)用: Each term found by the ****yzer during the fit is assigned a unique integer index corresponding to a column in the resulting matrix. This interpretation of the columns can be retrieved as follows: ****yzer 在擬合過(guò)程中找到的每個(gè) term（項(xiàng)）都會(huì)被分配一個(gè)**的整數(shù)索引，對(duì)應(yīng)于 resulting matrix（結(jié)果矩陣）中的一列。此列的一些說(shuō)明可以被檢索如下: The converse mapping from feature name to column index is stored in the vocabulary_ attribute of the vectorizer: 從 feature 名稱到 column index（列索引） 的逆映射存儲(chǔ)在 vocabulary_ 屬性中: Hence words that were not seen in the training corpus will be completely ignored in future calls to the transform method: 因此，在未來(lái)對(duì) transform 方法的調(diào)用中，在 training corpus （訓(xùn)練語(yǔ)料庫(kù)）中沒(méi)有看到的單詞將被完全忽略: Note that in the previous corpus, the first and the last documents have exactly the same words hence are encoded in equal vectors. In particular we lose the information that the last document is an interrogative form. To preserve some of the local ordering information we can extract 2-grams of words in addition to the 1-grams (individual words): 請(qǐng)注意，在前面的 corpus（語(yǔ)料庫(kù)）中，**個(gè)和**一個(gè)文檔具有完全相同的詞，因?yàn)楸痪幋a成相同的向量。 特別是我們丟失了**一個(gè)文件是一個(gè)疑問(wèn)的形式的信息。

為了防止詞組順序顛倒，除了提取一元模型 1-grams（個(gè)別詞）之外，我們還可以提取 2-grams 的單詞: The vocabulary extracted by this vectorizer is hence much bigger and can now resolve ambiguities encoded in local positioning patterns: 由 vectorizer（向量化器）提取的 vocabulary（詞匯）因此會(huì)變得更大，同時(shí)可以在定位模式時(shí)消除歧義: In particular the interrogative form “Is this” is only present in the last document: 特別是 “Is this” 的疑問(wèn)形式只出現(xiàn)在**一個(gè)文檔中: In a large text corpus, some words will be very present (e.g. “the”, “a”, “is” in English) hence carrying very little meaningful information about the actual contents of the document. If we were to feed the direct count data directly to a classifier those very frequent terms would shadow the frequencies of rarer yet more interesting terms. 在一個(gè)大的文本語(yǔ)料庫(kù)中，一些單詞將出現(xiàn)很多次（例如 “the”, “a”, “is” 是英文），因此對(duì)文檔的實(shí)際內(nèi)容沒(méi)有什么有意義的信息。 如果我們將直接計(jì)數(shù)數(shù)據(jù)直接提供給分類器，那么這些頻繁詞組會(huì)掩蓋住那些我們關(guān)注但很少出現(xiàn)的詞。 In order to re-weight the count features into floating point values suitable for usage by a classifier it is very common to use the tf–idf transform. 為了為了重新計(jì)算特征權(quán)重，并將其轉(zhuǎn)化為適合分類器使用的浮點(diǎn)值，因此使用 tf-idf 變換是非常常見(jiàn)的。

Tf means term-frequency while tf–idf means term-frequency times inverse document-frequency : Using the TfidfTransformer ’s default settings, TfidfTransformer(norm=\’l2\’, use_idf=True, **ooth_idf=True, sublinear_tf=False) the term frequency, the number of times a term occurs in a given document, is multiplied with idf component, which is computed as , where is the total number of documents, and is the number of documents that contain term . The resulting tf-idf vectors are then normalized by the Euclidean norm: . Tf表示 詞頻 ，而 tf-idf 表示術(shù)語(yǔ)頻率乘以 逆文檔頻率 : 使用 TfidfTransformer 的默認(rèn)設(shè)置， TfidfTransformer(norm=\’l2\’, use_idf=True, **ooth_idf=True, sublinear_tf=False) 詞頻即一個(gè)詞在給定文檔中出現(xiàn)的次數(shù)，乘以 idf 即通過(guò) 計(jì)算, 其中 是文檔的總數(shù)， 是包含詞 的文檔數(shù)。 然后，所得到的tf-idf向量通過(guò)歐幾里得范數(shù)歸一化： . This was originally a term weighting scheme developed for information retrieval (as a ranking function for search engines results) that has also found good use in document classification and clustering. The following sections contain further explanations and examples that illustrate how the tf-idfs are computed exactly and how the tf-idfs computed in scikit-learn’s TfidfTransformer and TfidfVectorizer differ slightly from the standard textbook notation that defines the idf as In the TfidfTransformer and TfidfVectorizer with **ooth_idf=False , the “1” count is added to the idf instead of the idf’s denominator: 它源于一個(gè)詞權(quán)重的信息檢索方式(作為搜索引擎結(jié)果的評(píng)級(jí)函數(shù))，同時(shí)也在文檔分類和聚類中表現(xiàn)良好。 以下部分包含進(jìn)一步說(shuō)明和示例，說(shuō)明如何**計(jì)算 tf-idfs 以及如何在 scikit-learn 中計(jì)算 tf-idfs， TfidfTransformer 并 TfidfVectorizer 與定義 idf 的標(biāo)準(zhǔn)教科書(shū)符號(hào)略有不同 在 TfidfTransformer 和 TfidfVectorizer 中 **ooth_idf=False ，將 “1” 計(jì)數(shù)添加到 idf 而不是 idf 的分母: This normalization is implemented by the TfidfTransformer class: 該歸一化由類 TfidfTransformer 實(shí)現(xiàn): Again please see the reference documentation for the details on all the parameters. 有關(guān)所有參數(shù)的詳細(xì)信息，請(qǐng)參閱 參考文檔 。 Let’s take an example with the following counts. The first term is present **** of the time hence not very interesting. The two other features only in less than 50% of the time hence probably more representative of the content of the documents: 讓我們以下方的詞頻為例。

**個(gè)次在任何時(shí)間都是100％出現(xiàn)，因此不是很有重要。另外兩個(gè)特征只占不到50％的比例，因此可能更具有代表性: Each row is normalized to have unit Euclidean norm: For example, we can compute the tf-idf of the first term in the first document in the <cite style=\”font-style: normal;\”>counts</cite> array as follows: Now, if we repeat this computation for the remaining 2 terms in the document, we get and the vector of raw tf-idfs: Then, applying the Euclidean (L2) norm, we obtain the following tf-idfs for document 1: Furthermore, the default parameter **ooth_idf=True adds “1” to the numerator and denominator as if an extra document was seen containing every term in the collection exactly once, which prevents zero divisions: Using this modification, the tf-idf of the third term in document 1 changes to 1.8473: And the L2-normalized tf-idf changes to : 每行都被正則化，使其適應(yīng)歐幾里得標(biāo)準(zhǔn): 例如，我們可以計(jì)算 計(jì)數(shù) 數(shù)組中**個(gè)文檔中**個(gè)項(xiàng)的 tf-idf ，如下所示百科: 現(xiàn)在，如果我們對(duì)文檔中剩下的2個(gè)術(shù)語(yǔ)重復(fù)這個(gè)計(jì)算，我們得到: 和原始 tf-idfs 的向量: 然后，應(yīng)用歐幾里德（L2）規(guī)范，我們獲得文檔1的以下 tf-idfs: 此外，默認(rèn)參數(shù) **ooth_idf=True 將 “1” 添加到分子和分母，就好像一個(gè)額外的文檔被看到一樣包含**中的每個(gè)術(shù)語(yǔ)，這樣可以避免零分割: 使用此修改，文檔1中第三項(xiàng)的 tf-idf 更改為 1.8473: 而 L2 標(biāo)準(zhǔn)化的 tf-idf 變?yōu)?: The weights of each feature computed by the fit method call are stored in a model attribute: 通過(guò) fit 方法調(diào)用計(jì)算出的每個(gè)特征的權(quán)重存儲(chǔ)在模型屬性中: As tf–idf is very often used for text features, there is also another class called TfidfVectorizer that combines all the options of CountVectorizer and TfidfTransformer in a single model: 由于 tf-idf 經(jīng)常用于文本特征，所以還有一個(gè)類 TfidfVectorizer ，它將 CountVectorizer 和 TfidfTransformer 的所有選項(xiàng)組合在一個(gè)單例模型中: While the tf–idf normalization is often very useful, there might be cases where the binary occurrence markers might offer better features. This can be achieved by using the binary parameter of CountVectorizer . In particular, some estimators such as Bernoulli Naive Bayes explicitly model discrete boolean random variables. Also, very short texts are likely to have noisy tf–idf values while the binary occurrence info is more stable. 雖然tf-idf標(biāo)準(zhǔn)化通常非常有用，但是可能有一種情況是二元變量顯示會(huì)提供更好的特征。 這可以使用類 CountVectorizer 的 二進(jìn)制 參數(shù)來(lái)實(shí)現(xiàn)。 特別地，一些估計(jì)器，諸如 伯努利樸素貝葉斯 顯式的使用離散的布爾隨機(jī)變量。

而且，非常短的文本很可能影響 tf-idf 值，而二進(jìn)制出現(xiàn)信息更穩(wěn)定。

哪位大蝦幫忙說(shuō)一下英語(yǔ)中\(zhòng)”表語(yǔ),代詞,狀語(yǔ),系動(dòng)詞\”之類的語(yǔ)法啊?

分類: 教育/科學(xué) >> 外語(yǔ)學(xué)習(xí) 問(wèn)題描述: 好急!!!!!!!!!!!!!!!!!!!!!!!!! 解析: （一）、副詞及其基本用法 副詞主要用來(lái)修飾動(dòng)詞，形容詞，副詞或其他結(jié)構(gòu)。 一、副詞的位置： 1） 在動(dòng)詞之前。

2） 在be動(dòng)詞、助動(dòng)詞之后。

3） 多個(gè)助動(dòng)詞時(shí)，副詞一般放在**個(gè)助動(dòng)詞后。 注意： a. 大多數(shù)方式副詞位于句尾，但賓語(yǔ)過(guò)長(zhǎng)，副詞可以提前，以使句子平衡。 We could see very clearly a strange light ahead of us. b. 方式副詞well，badly糟、壞，hard等只放在句尾。 He speaks English well. 二、副詞的排列順序： 1） 時(shí)間，地點(diǎn)副詞，小單位的在前，大單位在后。

2） 方式副詞，短的在前，長(zhǎng)的在后，并用and或but等連詞連接。 Please write slowly and carefully. 3） 多個(gè)不同副詞排列：程度+地點(diǎn)+方式+時(shí)間副詞 注意：副詞very 可以修飾形容詞，但不能修飾動(dòng)詞。 改錯(cuò)：(錯(cuò)) I very like English. (對(duì)) I like English very much. 注意：副詞enough要放在形容詞的后面，形容詞enough放在名詞前后都可。

I don\’t know him well enough. There is enough food for everyone to eat. There is food enough for everyone to eat. （二）及物動(dòng)詞與不及物動(dòng)詞 英語(yǔ)中按動(dòng)詞后可否直接跟賓語(yǔ)，可把動(dòng)詞分成及物動(dòng)詞與和及物動(dòng)詞。 1.及物動(dòng)詞： 字典里詞后標(biāo)有vt. 的就是及物動(dòng)詞。及物動(dòng)詞后必須跟有動(dòng)作的對(duì)象（即賓語(yǔ)），可直接跟賓語(yǔ)。

see 看見(jiàn) (vt.) +賓語(yǔ) I can see a boy. 2.不及物動(dòng)詞：字典里詞后標(biāo)有vi. 的就是不及物動(dòng)詞。不及物動(dòng)詞后不能直接跟有動(dòng)作的對(duì)象（即賓語(yǔ)）。若要跟賓語(yǔ)，必須先在其后添加上某個(gè)介詞，如to,of ,at后方可跟上賓語(yǔ)。

具體每個(gè)動(dòng)詞后究竟加什么介詞就得背動(dòng)詞短語(yǔ)了，如listen to,look at….. 3. 賓語(yǔ)（動(dòng)作的對(duì)象）：是名詞或代詞，或相當(dāng)于名詞的詞或短語(yǔ)（如動(dòng)名詞）。其它詞不看作動(dòng)作的對(duì)象呢。 4.舉例：“看” (1)see 看見(jiàn) (vt.) +賓語(yǔ) I can see a boy. (2)look 看 (vi.) x賓語(yǔ)(即不能直接加賓語(yǔ)). Look! She is singing. Look carefully! (注意：carefully 是副詞，不是名詞，故不作賓語(yǔ)喲) (3)look at 看…….+賓語(yǔ) Look at me carefully! (me是代詞，作賓語(yǔ)了) （三）終止性動(dòng)詞 英語(yǔ)中，動(dòng)詞按其動(dòng)作發(fā)生的方式、動(dòng)作發(fā)生過(guò)程的長(zhǎng)短，可分為延續(xù)性動(dòng)詞和終止性動(dòng)詞。 終止性動(dòng)詞也稱非延續(xù)性動(dòng)詞、瞬間動(dòng)詞或短暫性動(dòng)詞，表示不能延續(xù)的動(dòng)作，這種動(dòng)作發(fā)生后立即結(jié)束。

如open, close, finish, begin, e, go, arrive, reach, get to, leave, move, borrow,buy等。 終止性動(dòng)詞的用法特征 1.終止性動(dòng)詞可用來(lái)表示某一動(dòng)作完成，因此可用于現(xiàn)在完成時(shí)。如： The train has arrived.火車到了。 Have you joined the puter group?你加入電腦小組了嗎？ 2.終止性動(dòng)詞表示的動(dòng)作極其短暫，不能持續(xù)。

因此，不可與表示一段時(shí)間的狀語(yǔ)連用(只限肯定式)。如： (1)他*了三年了。 誤：He has died for three years. 正：He has been dead for three years. 正：He died three years ago. 正：It is three years since he died. 正：Three years has passed since he died. (2)他來(lái)這兒五天了。 誤：He has e here for five days. 正：He has been here for five days. 正：He came here five days ago. 正：It is five days since he came here. 正：Five days has passed since he came here. (1)、(2)句中的die、e為終止性動(dòng)詞，不能與表示\”段時(shí)間\”的狀語(yǔ)連用。

那么，應(yīng)如何正確表達(dá)呢？可以采用下面的四種方法： (1)將句中終止性動(dòng)詞轉(zhuǎn)換為相應(yīng)的延續(xù)性動(dòng)詞，如上面兩例中的**種正確表達(dá)方式。下面列舉幾例：leave→be away, borrow→keep, buy→have, begin/start→be on, die→be dead, move to→live in, finish→be over, join→be in/be a member of, open sth.→keep sth. open, fall ill→be ill, get up→be up, catch a cold→have a cold。 (2)將句中表示\”段時(shí)間\”的狀語(yǔ)改為表示過(guò)去確定時(shí)間的狀語(yǔ)，如下面兩例中的第二種正確表達(dá)方式。

(3)用句型\”It is+段時(shí)間+since…\”表達(dá)原意，如上面兩例中的第三種正確表達(dá)方式。 (4)用句型\”時(shí)間+has passed+since…\”表達(dá)原意，如上面兩例中的第四種正確表達(dá)方式。 3.終止性動(dòng)詞可用于現(xiàn)在完成時(shí)否定式中，成為可以延續(xù)的狀態(tài)，因而可與表示一段時(shí)間的狀語(yǔ)連用。

如： He hasn\’t left here since 1986. I haven\’t heard from my father for o weeks. 4.終止性動(dòng)詞的否定式與until/till連用，構(gòu)成\”not+終止性動(dòng)詞+until/till …\”的句型，意為\”直到……才……\”。如： You can\’t leave here until I arrive.直到我到了，你才能離開(kāi)這里。 I will not go to bed until I finish drawing the picture tonight.今天晚上直到我畫(huà)完畫(huà)，我才上床睡覺(jué)。 5.終止性動(dòng)詞可以用于when引導(dǎo)的時(shí)間狀語(yǔ)從句中，但不可以用于while引導(dǎo)的時(shí)間狀語(yǔ)從句中。

when表示的時(shí)間是\”點(diǎn)時(shí)間\”(從句謂語(yǔ)動(dòng)詞用終止性動(dòng)詞)，也可以是\”段時(shí)間\”(從句謂語(yǔ)動(dòng)詞用延續(xù)性動(dòng)詞)。而while表示的是一個(gè)較長(zhǎng)的時(shí)間或過(guò)程，從句謂語(yǔ)動(dòng)詞用延續(xù)性動(dòng)詞。如： When we reached London, it was elve o\’clock. (reach為終止性動(dòng)詞) Please look after my daughter while/when we are away. (be away為延續(xù)性動(dòng)詞短語(yǔ)) 6.終止性動(dòng)詞完成時(shí)不可與how long連用(只限于肯定式)。

如： 誤：How long have you e here? 正：How long have you been here? 正：When did you e here? （四）復(fù)數(shù)名詞 英語(yǔ)上名詞按可數(shù)與否可分為可數(shù)名詞和不可數(shù)名詞。 可數(shù)名詞按數(shù)目又可分為單數(shù)名詞和復(fù)數(shù)名詞兩類。（注：不可數(shù)名詞沒(méi)有復(fù)數(shù)形式如water(水)。） 單數(shù)名詞主要用來(lái)表示“一個(gè)”東西的概念。

兩個(gè)及其以上就應(yīng)用復(fù)數(shù)名詞來(lái)描述。 怎樣把單數(shù)名詞變復(fù)數(shù)名詞呢？方法如下： 1.一般在名詞詞尾加-s。如：dog-dogs, house-houses, gram-grams. 2.以-o或-s,-sh, -ch及-x結(jié)尾的名詞加-es構(gòu)成其復(fù)數(shù)形式。

如： tomato-tomatoes, kiss-kisses, watch-watches, box-boxes, bush-bushes. 3.有些以-o結(jié)尾，是外來(lái)語(yǔ)或縮寫(xiě)名詞, 則加-s。如：piano-pianos, dynamo-dynamos, photo-photos, kimono-kimonos. 4.有些以-o結(jié)尾的名詞,其-o前是元音字母則加-s。如：。