# Applying functions to lists ---------------- x = list(a = 1:10, b=c(5,8,1,7)) y = lapply(x,median); y y = sapply(x,median); y sapply(x,sort) lapply(x,sort) x = list(a=1:5,b=5:1) sapply(x,sort)
时间: 2024-01-11 20:03:35 浏览: 36
这段代码介绍了 R 语言中如何对列表中的元素应用函数。
首先,创建了一个名为 x 的列表,其中包含了两个元素:a 和 b。
使用 lapply() 函数可以对列表中的每个元素应用一个函数,并返回一个新的列表,其中每个元素为应用函数后的结果。使用 sapply() 函数与 lapply() 函数类似,但会尝试将返回值转换为简单的向量或矩阵。
使用 sapply(x,sort) 可以对列表中的每个元素进行排序,并返回一个矩阵,其中每列为排序后的元素。
使用 lapply(x,sort) 也可以对列表中的每个元素进行排序,并返回一个新的列表,其中每个元素为排序后的结果。
创建了一个新的名为 x 的列表,其中包含了两个元素:a 和 b。使用 sapply(x,sort) 可以对列表中的每个元素进行排序,并返回一个矩阵,其中每列为排序后的元素。
相关问题
iptables: Applying firewall rules: iptables-restore: line 1 failed [FAILED]
这个错误信息提示iptables防火墙规则的应用失败,可能是由于规则文件中的语法错误或不正确的规则所致。
您可以使用以下命令检查规则文件的语法是否正确:
```
iptables-restore -t <规则文件>
```
如果没有任何输出,则说明规则文件的语法正确。如果存在语法错误,则会输出错误信息。
如果规则文件的语法正确,您可以尝试手动应用规则文件:
```
iptables-restore <规则文件>
```
如果仍然失败,请检查规则文件中是否有不正确的规则,并进行修复。您还可以尝试使用较小的规则文件进行测试,以确定问题的根本原因。
Transformer-Based Visual Segmentation: A Survey
Visual segmentation is one of the most important tasks in computer vision, which involves dividing an image into multiple segments, each of which corresponds to a different object or region of interest in the image. In recent years, transformer-based methods have emerged as a promising approach for visual segmentation, leveraging the self-attention mechanism to capture long-range dependencies in the image.
This survey paper provides a comprehensive overview of transformer-based visual segmentation methods, covering their underlying principles, architecture, training strategies, and applications. The paper starts by introducing the basic concepts of visual segmentation and transformer-based models, followed by a discussion of the key challenges and opportunities in applying transformers to visual segmentation.
The paper then reviews the state-of-the-art transformer-based segmentation methods, including both fully transformer-based approaches and hybrid approaches that combine transformers with other techniques such as convolutional neural networks (CNNs). For each method, the paper provides a detailed description of its architecture and training strategy, as well as its performance on benchmark datasets.
Finally, the paper concludes with a discussion of the future directions of transformer-based visual segmentation, including potential improvements in model design, training methods, and applications. Overall, this survey paper provides a valuable resource for researchers and practitioners interested in the field of transformer-based visual segmentation.
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