def recoverData(Z, U, K): """ Recovers an approximation of the original data when using the projected data """ m,n = Z.shape[0],U.shape[0] X_rec = np.zeros((m,n)) U_reduced = U[:,:K] for i in range(m): X_rec[i,:] = Z[i,:] @ U_reduced.T return X_rec
时间: 2023-06-26 09:05:22 浏览: 46
这是一个 Python 函数,用于从经过投影的数据 Z 和降维后的特征向量 U 中恢复近似原始数据。K 是降维后使用的特征向量数量。具体来说,函数首先创建一个大小为 (m,n) 的零矩阵 X_rec,其中 m 是样本数,n 是特征数。然后,它从 U 中选择前 K 个特征向量,将其存储在 U_reduced 中。接下来,函数遍历每个样本,将其对应的投影数据 Z[i,:] 与 U_reduced.T 相乘,并将结果存储在 X_rec[i,:] 中。最后,函数返回 X_rec。
相关问题
myArray :+= Math.pow(2, -calAvgPathLength(value) / normFactor)代码执行到这里时会被提示这样的错误org.apache.spark.SparkException: This RDD lacks a SparkContext. It could happen in the following cases: (1) RDD transformations and actions are NOT invoked by the driver, but inside of other transformations; for example, rdd1.map(x => rdd2.values.count() * x) is invalid because the values transformation and count action cannot be performed inside of the rdd1.map transformation. For more information, see SPARK-5063. (2) When a Spark Streaming job recovers from checkpoint, this exception will be hit if a reference to an RDD not defined by the streaming job is used in DStream operations. For more information, See SPARK-13758.应该怎么修改
这个错误的意思是这个 RDD 缺少 SparkContext。这可能发生在以下两种情况:
1. RDD 转换和操作不是由驱动程序调用的,而是在其他转换内部调用的;例如,rdd1.map(x => rdd2.values.count() * x) 是无效的,因为 values 转换和 count 操作不能在 rdd1.map 转换内执行。有关更多信息,请参见 SPARK-5063。
2. 当 Spark Streaming 作业从检查点恢复时,如果在 DStream 操作中使用了由流式处理作业未定义的 RDD 的引用,则会出现此异常。有关更多信息,请参见 SPARK-13758。
要解决这个问题,您需要确保在执行 RDD 转换和操作时,有一个 SparkContext 可用。这通常意味着您需要在 Spark 程序的驱动程序中执行这些操作,而不是在其他 RDD 转换内部执行。
例如,假设您有以下代码:
```
val rdd1 = sc.parallelize(1 to 10)
val rdd2 = sc.parallelize(11 to 20)
val result = rdd1.map(x => rdd2.count() * x)
```
这是有效的,因为 rdd2.count() 操作在 rdd1.map 转换外部执行。但是,如果您将 rdd2.count() 移动到 rdd1.map 中,则会出现上述错误:
```
val result = rdd1.map(x => {
val count = rdd2.count()
count * x
})
```
为
Exception in thread "main" java.lang.OutOfMemoryError: GC overhead limit exceeded
This error occurs when the Java Virtual Machine (JVM) spends too much time executing garbage collection (GC) and recovers very little memory in each cycle. This can happen when the heap size is too small for the amount of data being processed or when the application creates too many objects that are not properly disposed of.
To fix this error, you can try increasing the heap size by adding the -Xmx flag when running the JVM. For example, java -Xmx2g MyClass can set the maximum heap size to 2 gigabytes. You can also try optimizing your code to reduce memory usage, such as reusing objects instead of creating new ones, or explicitly calling the System.gc() method to trigger garbage collection at appropriate times.