Vector Element Names Name can be assigned to each element of vector using names() function. Indexing by name is possible. Name can be removed by assigning NULL. > x <- 1:3 > names(x) <- c(’a’,’b’,’c’) >x abc 123 > x[c(’a’,’c’)] ac 13 > names(x) <- NULL >x [1] 1 2 3

这段代码介绍了 R 语言中向量元素名称的使用。

可以使用 names() 函数为向量的每个元素指定名称。例如，在代码中，首先定义了一个包含 1 到 3 的整数向量 x，然后使用 names() 函数将其元素命名为 "a"、"b"、"c"。可以通过向量元素名称来索引向量，例如，使用 x["a"] 或 x["c"] 来获取向量中对应的元素值。

如果想要移除向量元素的名称，可以将 names(x) 赋值为 NULL。在代码中，使用 names(x) <- NULL 将向量 x 的元素名称移除，此时再次打印向量 x，会发现它没有元素名称，只有元素值。

相关问题

It looks like you are adding an Embedding layer to your model using Keras. This layer will be used to convert your input text data into a dense vector representation that can be processed by your model. Here's what each of the arguments you've provided to the Embedding layer mean: input_dim: This specifies the size of the vocabulary, or the number of unique words in your input data. It should be set to MAX_WORDS_NUM+1, which ensures that all words in your data are assigned a unique index. output_dim: This specifies the size of the embedding vector for each word. Typically, this is set to a value between 50 and 300, depending on the size of your dataset and complexity of the task. input_length: This specifies the length of each input sequence, or the number of words in each document. It should be set to MAX_SEQUENCE_LEN, which ensures that all input sequences have the same length and can be processed by your model. By adding the Embedding layer to your model, you are essentially creating a lookup table that maps each word in your vocabulary to a dense vector representation. This allows your model to learn meaningful relationships between words and make predictions based on the context of the input data.翻译

看起来你正在使用Keras向你的模型添加一个Embedding层。这个层将被用来将你的输入文本数据转换成一个密集的向量表示，可以被你的模型处理。下面是你提供给Embedding层的每个参数的含义：

- input_dim: 这个参数指定了词汇表的大小，或者说是输入数据中唯一单词的数量。它应该设置为MAX_WORDS_NUM+1，这样确保所有单词都被分配了一个唯一的索引。

- output_dim: 这个参数指定了每个单词的嵌入向量的大小。通常，这个值设置为50到300之间的一个值，取决于数据集的大小和任务的复杂性。

- input_length: 这个参数指定了每个输入序列的长度，或者说是每个文档中单词的数量。它应该设置为MAX_SEQUENCE_LEN，这样确保所有输入序列都具有相同的长度，可以被你的模型处理。

通过向你的模型添加Embedding层，你实际上创建了一个查找表，将词汇表中的每个单词映射到一个密集的向量表示。这样，你的模型可以学习单词之间的有意义的关系，并根据输入数据的上下文进行预测。

Description Consider the following 5 picture frames placed on an 9 x 8 array. ........ ........ ........ ........ .CCC.... EEEEEE.. ........ ........ ..BBBB.. .C.C.... E....E.. DDDDDD.. ........ ..B..B.. .C.C.... E....E.. D....D.. ........ ..B..B.. .CCC.... E....E.. D....D.. ....AAAA ..B..B.. ........ E....E.. D....D.. ....A..A ..BBBB.. ........ E....E.. DDDDDD.. ....A..A ........ ........ E....E.. ........ ....AAAA ........ ........ EEEEEE.. ........ ........ ........ ........ 1 2 3 4 5 Now place them on top of one another starting with 1 at the bottom and ending up with 5 on top. If any part of a frame covers another it hides that part of the frame below. Viewing the stack of 5 frames we see the following. .CCC.... ECBCBB.. DCBCDB.. DCCC.B.. D.B.ABAA D.BBBB.A DDDDAD.A E...AAAA EEEEEE.. In what order are the frames stacked from bottom to top? The answer is EDABC. Your problem is to determine the order in which the frames are stacked from bottom to top given a picture of the stacked frames. Here are the rules: 1. The width of the frame is always exactly 1 character and the sides are never shorter than 3 characters. 2. It is possible to see at least one part of each of the four sides of a frame. A corner shows two sides. 3. The frames will be lettered with capital letters, and no two frames will be assigned the same letter. Input Each input block contains the height, h (h<=30) on the first line and the width w (w<=30) on the second. A picture of the stacked frames is then given as h strings with w characters each. Your input may contain multiple blocks of the format described above, without any blank lines in between. All blocks in the input must be processed sequentially. Output Write the solution to the standard output. Give the letters of the frames in the order they were stacked from bottom to top. If there are multiple possibilities for an ordering, list all such possibilities in alphabetical order, each one on a separate line. There will always be at least one legal ordering for each input block. List the output for all blocks in the input sequentially, without any blank lines (not even between blocks). Sample Input 9 8 .CCC.... ECBCBB.. DCBCDB.. DCCC.B.. D.B.ABAA D.BBBB.A DDDDAD.A E...AAAA EEEEEE.. Sample Output EDABC

这是一道经典的拓扑排序问题。我们可以将每个图形看作一个节点，如果一个图形可以覆盖另一个图形，那就从覆盖的图形指向被覆盖的图形。最后得到的图是一个 DAG，我们可以对其进行拓扑排序。拓扑排序的结果就是图形的叠放顺序。

代码实现可以使用邻接表存储 DAG，然后使用 Kahn 算法进行拓扑排序。对于存在多个拓扑序列的情况，我们可以使用字典序最小的方式输出所有的序列。

以下是 Python 代码实现：