Aggregate Graph

Aggregate Graph是用于以表格形式和图形形式显示测试结果的监听器。\[1\]它可以显示每个采样器请求的响应时间图，包括平均值、中位数、偏差和吞吐量。\[2\]同时，它还可以提供响应时间随测试运行时间的图形表示。\[3\]
#### 引用[.reference_title]
- *1* *2* *3* [[12] JMeter-结果分析之图形图表](https://blog.csdn.net/lijing742180/article/details/81183084)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insert_down28v1,239^v3^insert_chatgpt"}} ] [.reference_item]
[ .reference_list ]

相关问题

Aggregate Report Generator怎么看

要使用JMeter自带的Aggregate Report生成器来查看和分析JTL文件，您可以按照以下步骤进行操作：

1. 打开JMeter：确保您已经安装了Apache JMeter，并将其启动。

2. 添加Aggregate Report生成器：在JMeter的主界面上，右键单击"Test Plan"（测试计划），然后选择"Add"（添加） -> "Listener"（监听器） -> "Aggregate Report"（聚合报告）。

3. 配置Aggregate Report生成器：在Aggregate Report生成器的属性窗口中，您可以设置以下参数：
- Filename（文件名）：选择要分析的JTL文件。
- Configure（配置）：可以选择要显示的列和排序方式。
- Graph（图表）：可以选择要绘制的图表类型。

4. 运行测试并生成报告：在JMeter中运行测试计划，完成测试后，选择Aggregate Report生成器，并点击"Start"（开始）按钮来生成报告。

5. 查看和分析报告：一旦报告生成完成，您将看到一个包含有关测试结果的表格和图表。您可以查看各个请求的响应时间、吞吐量、错误率等指标，并使用图表来可视化这些数据。

请注意，Aggregate Report生成器提供了一种基本的分析方式，如果您需要更复杂和高级的分析功能，可以考虑使用第三方工具或自定义报告生成方式。

Semi-Supervised Classification with Graph Convolutional Networks

Semi-supervised classification with graph convolutional networks (GCNs) is a method for predicting labels for nodes in a graph. GCNs are a type of neural network that operates on graph-structured data, where each node in the graph represents an entity (such as a person, a product, or a webpage) and edges represent relationships between entities.

The semi-supervised classification problem arises when we have a graph where only a small subset of nodes have labels, and we want to predict the labels of the remaining nodes. GCNs can be used to solve this problem by learning to propagate information through the graph, using the labeled nodes as anchors.

The key idea behind GCNs is to use a graph convolution operation to aggregate information from a node's neighbors, and then use this aggregated information to update the node's representation. This operation is then repeated over multiple layers, allowing the network to capture increasingly complex relationships between nodes.

To train a GCN for semi-supervised classification, we use a combination of labeled and unlabeled nodes as input, and optimize a loss function that encourages the network to correctly predict the labels of the labeled nodes while also encouraging the network to produce smooth predictions across the graph.

Overall, semi-supervised classification with GCNs is a powerful and flexible method for predicting labels on graph-structured data, and has been successfully applied to a wide range of applications including social network analysis, drug discovery, and recommendation systems.