import org.apache.spark.graphx._val graph: Graph[Double, Int] = ...// 初始化每个顶点的属性val initialGraph = graph.mapVertices((id, _) => Map(id -> 0.0))// 第一次迭代val firstIterGraph = initialGraph.mapTriplets(triplet => { // 将dst顶点属性中跳数字段标记为1,发给src顶点 Iterator((triplet.srcId, Map(triplet.dstId -> 1.0)))})// 第二次迭代val secondIterGraph = firstIterGraph.aggregateMessages[Map[VertexId, Double]]( triplet => { // 筛选出dstAttr里跳数为1的Key-Value,发给对应的src顶点 if (triplet.attr.getOrElse(triplet.dstId, Double.MaxValue) == 1.0) { Iterator((triplet.srcId, Map(triplet.dstId -> 2.0)), (triplet.dstId, Map(triplet.srcId -> 2.0))) } else { Iterator.empty } }, (a, b) => { // 聚合这些消息得到所有2跳邻居 a ++ b })// 输出结果secondIterGraph.vertices.foreach(println)你的代码出现的问题
时间: 2024-03-05 20:54:37 浏览: 19
这段代码出现问题的原因是在使用getOrElse方法时,返回值的类型不正确。在第二次迭代中,你使用了以下代码:
```
if (triplet.attr.getOrElse(triplet.dstId, Double.MaxValue) == 1.0)
```
这里的默认值 `Double.MaxValue` 返回的是一个 Double 类型的值,而不是一个 Map[VertexId, Double] 类型的值。因此,你需要将默认值改为一个空的 Map,例如:
```
if (triplet.attr.getOrElse(triplet.dstId, Map.empty[VertexId, Double]).getOrElse(triplet.dstId, 0.0) == 1.0)
```
这里默认值是一个空的 Map,如果在这个 Map 中没有找到对应的键,则返回 0.0。这个问题的根本原因是getOrElse方法的返回值类型与你的代码中使用的类型不匹配,因此需要进行类型转换或更改默认值。
相关问题
plt.plot(range(12),error_val,label="Cross Validation",color="r"
This code is used to plot a line graph with the x-axis ranging from 0 to 11 and the y-axis representing the error values. The label "Cross Validation" is added to the legend of the graph, and the color of the line is set to red. However, this code snippet is incomplete and requires additional code to display the graph. Here's an example of how to complete the code:
```
import matplotlib.pyplot as plt
# assuming 'error_val' is a list of error values
plt.plot(range(12), error_val, label="Cross Validation", color="r")
plt.xlabel("X-axis label")
plt.ylabel("Y-axis label")
plt.title("Title of the graph")
plt.legend()
plt.show()
```
This code will display a line graph with the x-axis labeled as "X-axis label", the y-axis labeled as "Y-axis label", and the title of the graph set to "Title of the graph". The legend will display the label "Cross Validation" for the red line.
spark graphx中图查询方法代码
在 Spark GraphX 中,可以使用 `Graph` 类提供的方法进行图查询。以下是几个常用的方法及示例代码:
1. 获取所有顶点:
```scala
val graph: Graph[Int, Double] = ...
val vertices = graph.vertices.collect()
```
2. 获取所有边:
```scala
val graph: Graph[Int, Double] = ...
val edges = graph.edges.collect()
```
3. 获取所有入度为 0 的顶点:
```scala
val graph: Graph[Int, Double] = ...
val zeroInDegreeVertices = graph.inDegrees.filter(_._2 == 0).map(_._1).collect()
```
4. 获取所有出度为 0 的顶点:
```scala
val graph: Graph[Int, Double] = ...
val zeroOutDegreeVertices = graph.outDegrees.filter(_._2 == 0).map(_._1).collect()
```
5. 获取指定顶点的所有出边:
```scala
val graph: Graph[Int, Double] = ...
val vertexId = 1L
val outEdges = graph.edges.filter(_.srcId == vertexId).collect()
```
6. 获取指定顶点的所有入边:
```scala
val graph: Graph[Int, Double] = ...
val vertexId = 1L
val inEdges = graph.edges.filter(_.dstId == vertexId).collect()
```
7. 获取指定顶点的所有邻居:
```scala
val graph: Graph[Int, Double] = ...
val vertexId = 1L
val neighbors = graph.collectNeighborIds(EdgeDirection.Either).lookup(vertexId).head
```
以上是常用的几个图查询方法,你可以根据自己的需求来选择合适的方法。